week1_session2_numpy_pandas.ipynb

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   "source": [
    "# AI in Medicine\n",
    "From vectors and matrices to artificial intelligence\n",
    "\n",
    "## Data Science - Basics I\n",
    "### Python Programming: *numpy* and *pandas*\n",
    "\n",
    "Instructors:\n",
    "- Dominique Sydow, AG Volkamer, Charité (dominique.sydow@charite.de)\n",
    "- Moritz Seiler, AG Ritter, Charité (moritz.seiler@charite.de)\n",
    "\n",
    "January 2020"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Aims of this session\n",
    "\n",
    "In this talktorial, you will get in touch with **data science**. Using the **Python packages *numpy* and *pandas***, you will load, describe, and manipulate a dataset on the early detection and tracking of **Alzheimer’s disease** for further use in visualization, machine learning, and artifical intelligence later this week."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Learning goals\n",
    "\n",
    "### Theory\n",
    "\n",
    "* Data science\n",
    "* The *numpy* library\n",
    "* The *pandas* library\n",
    "\n",
    "### Practical\n",
    "\n",
    "* Dataset\n",
    "* Read data with *pandas* as DataFrame\n",
    "* Look at data\n",
    "* Look at individual DataFrame components\n",
    "* Sort data\n",
    "* Group data\n",
    "* Subset data\n",
    "* Drop columns\n",
    "* Write data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. References\n",
    "\n",
    "- Data science, machine learning, artificial intelligence\n",
    "  - http://varianceexplained.org/r/ds-ml-ai/\n",
    "- Vectors, matrices, tensors\n",
    "  - https://www.quantstart.com/articles/scalars-vectors-matrices-and-tensors-linear-algebra-for-deep-learning-part-1/\n",
    "  - https://dev.to/mmithrakumar/scalars-vectors-matrices-and-tensors-with-tensorflow-2-0-1f66\n",
    "- Numpy\n",
    "  - https://scipy-lectures.org/intro/numpy/array_object.html\n",
    "- Pandas\n",
    "  - https://medium.com/dunder-data/how-to-learn-pandas-108905ab4955\n",
    "  - https://www.shanelynn.ie/select-pandas-dataframe-rows-and-columns-using-iloc-loc-and-ix/#iloc-selection\n",
    "  - https://medium.com/dunder-data/selecting-subsets-of-data-in-pandas-6fcd0170be9c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Theory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Science"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What's the difference between data science, machine learning, and artificial intelligence? \n",
    "\n",
    "Taken from David Robinson's blog post: http://varianceexplained.org/r/ds-ml-ai/\n",
    "\n",
    "The fields do have a great deal of **overlap**, but they are **not interchangeable**.\n",
    "\n",
    "- **Data science** produces **insights**\n",
    "- **Machine learning** (ML) produces **predictions**\n",
    "- **Artificial intelligence** (AI) produces **actions**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data science produces insights\n",
    "  - “The average patient has a 70% chance of survival” (descriptive: describe a dataset)\n",
    "  - “Different patients have different chances of survival” (exploratory: find relationships you did not know about)  \n",
    "  - “A randomized experiment shows that patients assigned to Alice are more likely to survive than those assigned to Bob” (causal: find out what happens to one variable when you make another variable change)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Machine learning produces predictions\n",
    "  - \"Predict whether this patient will go into sepsis”\n",
    "  - “Predict whether this image has a bird in it\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Artificial intelligence produces actions\n",
    "  - Game-playing algorithms (Deep Blue, AlphaGo)\n",
    "  - Robotics and control theory (motion planning, walking a bipedal robot)\n",
    "  - Optimization (Google Maps choosing a route)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The *numpy* library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Overview\n",
    "\n",
    "* Role: Scientific computing (with arrays)\n",
    "* Website: https://numpy.org/\n",
    "> NumPy is the fundamental package for scientific computing with Python. It contains among other things:\n",
    "  * a powerful N-dimensional array object\n",
    "  * sophisticated (broadcasting) functions\n",
    "  * tools for integrating C/C++ and Fortran code\n",
    "  * useful linear algebra, Fourier transform, and random number capabilities\n",
    "* Documentation: https://numpy.org/devdocs/"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Applications\n",
    "\n",
    "- Create vectors (1D), matrices (2D), tensors (>= 3D) in the form of arrays\n",
    "- Use a large collection of high-level mathematical functions to operate on these arrays\n",
    "\n",
    "![](https://res.cloudinary.com/practicaldev/image/fetch/s--oTgfo1EL--/c_limit%2Cf_auto%2Cfl_progressive%2Cq_auto%2Cw_880/https://raw.githubusercontent.com/adhiraiyan/DeepLearningWithTF2.0/master/notebooks/figures/fig0201a.png)\n",
    "Figure source: https://dev.to/mmithrakumar/scalars-vectors-matrices-and-tensors-with-tensorflow-2-0-1f66"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The *pandas* library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Overview\n",
    "\n",
    "* Role: Data manipulation and analysis\n",
    "* Website: https://pandas.pydata.org/\n",
    "> *pandas* is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.\n",
    "* Documentation: https://pandas.pydata.org/pandas-docs/stable/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Applications\n",
    "\n",
    "Taken from: https://medium.com/dunder-data/how-to-learn-pandas-108905ab4955\n",
    "\n",
    "*pandas* is capable of many tasks including:\n",
    "\n",
    "* Reading/writing many different data formats\n",
    "* Selecting subsets of data\n",
    "* Calculating across rows and down columns\n",
    "* Finding and filling missing data\n",
    "* Applying operations to independent groups within the data\n",
    "* Reshaping data into different forms\n",
    "* Visualization through matplotlib and seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### DataFrame and Series\n",
    "\n",
    "The *pandas* library has two main containers of data, the DataFrame and the Series. The DataFrame is used more than the Series, so let’s take a look at its components.\n",
    "\n",
    "![DataFrame anatomy](https://cdn-images-1.medium.com/max/1600/1*ZSehcrMtBWN7_qCWq_HiSg.png)\n",
    "Figure source: https://medium.com/dunder-data/selecting-subsets-of-data-in-pandas-6fcd0170be9c"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "## 5. Practical"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1. Dataset\n",
    "\n",
    "The dataset cotains observations from a longitudinal multi-site study for the early detection and tracking of **Alzheimer’s disease**. Alzheimer’s disease is an irreversible neurodegenerative disease resulting in a loss of mental function caused by the deterioration of brain tissue. \n",
    "\n",
    "For this study, patients\n",
    "\n",
    "- with Alzheimer’s disease (AD)\n",
    "- with mild cognitive impairment (MCI) as well as \n",
    "- with no signs of cognitive impairment / healthy controls (CN) \n",
    "\n",
    "were recruited at multiple sites with the overall goal to track the progression of the disease using biomarkers, together with clinical measures, for an assessment of the brain‘s structure and function over the course of the disease states.\n",
    "\n",
    "We use two files during our course:\n",
    "\n",
    "* File with raw data containing personal and health-related patient data: `alzheimers_disease_rand.csv`\n",
    "* File containing descriptions for column names: `dictionary.csv`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2. Read data with *pandas* as DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The *pandas* library provides the function `read_csv` to read a comma-separated values (csv) file into a DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nbuser/anaconda3_420/lib/python3.5/site-packages/IPython/core/interactiveshell.py:2728: DtypeWarning: Columns (16,17,18,99,100) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('./data/alzheimers_disease_rand.csv', delimiter=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the number of rows displayed in this Jupyter notebook (default 60)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', 110)  # Set the number of rows shown in the notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read in the csv file containing the description of column names to check what kind of data we are working with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
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       "      <td>CDRSB</td>\n",
       "      <td>CDR-SB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ADAS11</td>\n",
       "      <td>ADAS 11</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ADAS13</td>\n",
       "      <td>ADAS 13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ADASQ4</td>\n",
       "      <td>ADAS Delayed Word Recall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>MMSE</td>\n",
       "      <td>MMSE</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>RAVLT_immediate</td>\n",
       "      <td>RAVLT Immediate (sum of 5 trials)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>RAVLT_learning</td>\n",
       "      <td>RAVLT Learning (trial 5 - trial 1)</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>RAVLT_forgetting</td>\n",
       "      <td>RAVLT Forgetting (trial 5 - delayed)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>RAVLT_perc_forgetting</td>\n",
       "      <td>RAVLT Percent Forgetting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>LDELTOTAL</td>\n",
       "      <td>Logical Memory - Delayed Recall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>DIGITSCOR</td>\n",
       "      <td>Digit Symbol Substitution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>IMAGEUID</td>\n",
       "      <td>Image ID</td>\n",
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       "      <td>FAQ</td>\n",
       "      <td>FAQ</td>\n",
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       "      <td>MOCA</td>\n",
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       "      <th>33</th>\n",
       "      <td>EcogPtMem</td>\n",
       "      <td>Pt ECog - Mem</td>\n",
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       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>EcogPtLang</td>\n",
       "      <td>Pt ECog - Lang</td>\n",
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       "      <td>EcogPtVisspat</td>\n",
       "      <td>Pt ECog - Vis/Spat</td>\n",
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       "      <td>EcogPtPlan</td>\n",
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       "      <td>EcogPtOrgan</td>\n",
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       "      <th>38</th>\n",
       "      <td>EcogPtDivatt</td>\n",
       "      <td>Pt ECog - Div atten</td>\n",
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       "      <th>39</th>\n",
       "      <td>EcogPtTotal</td>\n",
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       "      <td>EcogSPMem</td>\n",
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       "      <th>41</th>\n",
       "      <td>EcogSPLang</td>\n",
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       "    <tr>\n",
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       "      <td>EcogSPVisspat</td>\n",
       "      <td>SP ECog - Vis/Spat</td>\n",
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       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>EcogSPPlan</td>\n",
       "      <td>SP ECog - Plan</td>\n",
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       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>EcogSPOrgan</td>\n",
       "      <td>SP ECog - Organ</td>\n",
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       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>EcogSPDivatt</td>\n",
       "      <td>SP ECog - Div atten</td>\n",
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       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>EcogSPTotal</td>\n",
       "      <td>SP ECog - Total</td>\n",
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       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>FLDSTRENG</td>\n",
       "      <td>MRI Field Strength</td>\n",
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       "      <th>48</th>\n",
       "      <td>Ventricles</td>\n",
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       "      <td>Hippocampus</td>\n",
       "      <td>UCSF Hippocampus</td>\n",
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       "      <td>WholeBrain</td>\n",
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       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Entorhinal</td>\n",
       "      <td>UCSF Entorhinal</td>\n",
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       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Fusiform</td>\n",
       "      <td>UCSF Fusiform</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>MidTemp</td>\n",
       "      <td>UCSF Med Temp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>ICV</td>\n",
       "      <td>UCSF ICV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>DX</td>\n",
       "      <td>Diagnosis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>mPACCdigit</td>\n",
       "      <td>modified Preclinical Alzheimer's Cognitive Com...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>EXAMDATE_bl</td>\n",
       "      <td>Date</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>CDRSB_bl</td>\n",
       "      <td>CDR-SB at baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>ADAS11_bl</td>\n",
       "      <td>ADAS 11 at baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>ADAS13_bl</td>\n",
       "      <td>ADAS 13 at baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>ADASQ4_bl</td>\n",
       "      <td>ADAS Delayed Word Recall at baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>MMSE_bl</td>\n",
       "      <td>MMSE at baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>RAVLT_immediate_bl</td>\n",
       "      <td>RAVLT Immediate (sum of 5 trials) at baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>RAVLT_learning_bl</td>\n",
       "      <td>RAVLT Learning (trial 5 - trial 1) at baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>RAVLT_forgetting_bl</td>\n",
       "      <td>RAVLT Forgetting (trial 5 - delayed) at baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>RAVLT_perc_forgetting_bl</td>\n",
       "      <td>RAVLT Percent Forgetting at baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>LDELTOTAL_bl</td>\n",
       "      <td>Logical Memory - Delayed Recall at baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>DIGITSCOR_bl</td>\n",
       "      <td>Digit Symbol Substitution at baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>TRABSCOR_bl</td>\n",
       "      <td>Trails B at baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>FAQ_bl</td>\n",
       "      <td>FAQ at baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>mPACCdigit_bl</td>\n",
       "      <td>modified Preclinical Alzheimer's Cognitive Com...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>mPACCtrailsB_bl</td>\n",
       "      <td>modified Preclinical Alzheimer's Cognitive Com...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>FLDSTRENG_bl</td>\n",
       "      <td>MRI Field Strength at baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Ventricles_bl</td>\n",
       "      <td>UCSF Ventricles at baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Hippocampus_bl</td>\n",
       "      <td>UCSF Hippocampus at baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>WholeBrain_bl</td>\n",
       "      <td>UCSF WholeBrain at baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Entorhinal_bl</td>\n",
       "      <td>UCSF Entorhinal at baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Fusiform_bl</td>\n",
       "      <td>UCSF Fusiform at baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>MidTemp_bl</td>\n",
       "      <td>UCSF Med Temp at baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>ICV_bl</td>\n",
       "      <td>UCSF ICV at baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>MOCA_bl</td>\n",
       "      <td>MOCA at baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>EcogPtMem_bl</td>\n",
       "      <td>Pt ECog - Mem at baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>EcogPtLang_bl</td>\n",
       "      <td>Pt ECog - Lang at baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>EcogPtVisspat_bl</td>\n",
       "      <td>Pt ECog - Vis/Spat at baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>EcogPtPlan_bl</td>\n",
       "      <td>Pt ECog - Plan at baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>EcogPtOrgan_bl</td>\n",
       "      <td>Pt ECog - Organ at baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>EcogPtDivatt_bl</td>\n",
       "      <td>Pt ECog - Div atten at baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>EcogPtTotal_bl</td>\n",
       "      <td>Pt ECog - Total at baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>EcogSPMem_bl</td>\n",
       "      <td>SP ECog - Mem at baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>EcogSPLang_bl</td>\n",
       "      <td>SP ECog - Lang at baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>EcogSPVisspat_bl</td>\n",
       "      <td>SP ECog - Vis/Spat at baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>EcogSPPlan_bl</td>\n",
       "      <td>SP ECog - Plan at baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>EcogSPOrgan_bl</td>\n",
       "      <td>SP ECog - Organ at baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>EcogSPDivatt_bl</td>\n",
       "      <td>SP ECog - Div atten at baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>EcogSPTotal_bl</td>\n",
       "      <td>SP ECog - Total at baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>ABETA_bl</td>\n",
       "      <td>CSF ABETA at baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>PTAU_bl</td>\n",
       "      <td>CSF PTAU at baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>TAU_bl</td>\n",
       "      <td>CSF TAU at baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>FDG_bl</td>\n",
       "      <td>Average FDG-PET of angular, temporal, and post...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>PIB_bl</td>\n",
       "      <td>Average PIB SUVR of frontal cortex, anterior c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>AV45_bl</td>\n",
       "      <td>Reference region - florbetapir mean of whole c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Years_bl</td>\n",
       "      <td>Years since baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Month_bl</td>\n",
       "      <td>Month since baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Month</td>\n",
       "      <td>Months since baseline (integer)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>M</td>\n",
       "      <td>Month since baseline (integer)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>update_stamp</td>\n",
       "      <td>Date of last update</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 column names  \\\n",
       "0                         RID   \n",
       "1                     VISCODE   \n",
       "2                        SITE   \n",
       "3                    EXAMDATE   \n",
       "4                       DX_bl   \n",
       "5                         AGE   \n",
       "6                    PTGENDER   \n",
       "7                    PTEDUCAT   \n",
       "8                        WORK   \n",
       "9                    PTETHCAT   \n",
       "10                   PTRACCAT   \n",
       "11                    PTMARRY   \n",
       "12                      APOE4   \n",
       "13                        FDG   \n",
       "14                        PIB   \n",
       "15                       AV45   \n",
       "16                      ABETA   \n",
       "17                        TAU   \n",
       "18                       PTAU   \n",
       "19                      CDRSB   \n",
       "20                     ADAS11   \n",
       "21                     ADAS13   \n",
       "22                     ADASQ4   \n",
       "23                       MMSE   \n",
       "24            RAVLT_immediate   \n",
       "25             RAVLT_learning   \n",
       "26           RAVLT_forgetting   \n",
       "27      RAVLT_perc_forgetting   \n",
       "28                  LDELTOTAL   \n",
       "29                  DIGITSCOR   \n",
       "30                   IMAGEUID   \n",
       "31                        FAQ   \n",
       "32                       MOCA   \n",
       "33                  EcogPtMem   \n",
       "34                 EcogPtLang   \n",
       "35              EcogPtVisspat   \n",
       "36                 EcogPtPlan   \n",
       "37                EcogPtOrgan   \n",
       "38               EcogPtDivatt   \n",
       "39                EcogPtTotal   \n",
       "40                  EcogSPMem   \n",
       "41                 EcogSPLang   \n",
       "42              EcogSPVisspat   \n",
       "43                 EcogSPPlan   \n",
       "44                EcogSPOrgan   \n",
       "45               EcogSPDivatt   \n",
       "46                EcogSPTotal   \n",
       "47                  FLDSTRENG   \n",
       "48                 Ventricles   \n",
       "49                Hippocampus   \n",
       "50                 WholeBrain   \n",
       "51                 Entorhinal   \n",
       "52                   Fusiform   \n",
       "53                    MidTemp   \n",
       "54                        ICV   \n",
       "55                         DX   \n",
       "56                 mPACCdigit   \n",
       "57                EXAMDATE_bl   \n",
       "58                   CDRSB_bl   \n",
       "59                  ADAS11_bl   \n",
       "60                  ADAS13_bl   \n",
       "61                  ADASQ4_bl   \n",
       "62                    MMSE_bl   \n",
       "63         RAVLT_immediate_bl   \n",
       "64          RAVLT_learning_bl   \n",
       "65        RAVLT_forgetting_bl   \n",
       "66   RAVLT_perc_forgetting_bl   \n",
       "67               LDELTOTAL_bl   \n",
       "68               DIGITSCOR_bl   \n",
       "69                TRABSCOR_bl   \n",
       "70                     FAQ_bl   \n",
       "71              mPACCdigit_bl   \n",
       "72            mPACCtrailsB_bl   \n",
       "73               FLDSTRENG_bl   \n",
       "74              Ventricles_bl   \n",
       "75             Hippocampus_bl   \n",
       "76              WholeBrain_bl   \n",
       "77              Entorhinal_bl   \n",
       "78                Fusiform_bl   \n",
       "79                 MidTemp_bl   \n",
       "80                     ICV_bl   \n",
       "81                    MOCA_bl   \n",
       "82               EcogPtMem_bl   \n",
       "83              EcogPtLang_bl   \n",
       "84           EcogPtVisspat_bl   \n",
       "85              EcogPtPlan_bl   \n",
       "86             EcogPtOrgan_bl   \n",
       "87            EcogPtDivatt_bl   \n",
       "88             EcogPtTotal_bl   \n",
       "89               EcogSPMem_bl   \n",
       "90              EcogSPLang_bl   \n",
       "91           EcogSPVisspat_bl   \n",
       "92              EcogSPPlan_bl   \n",
       "93             EcogSPOrgan_bl   \n",
       "94            EcogSPDivatt_bl   \n",
       "95             EcogSPTotal_bl   \n",
       "96                   ABETA_bl   \n",
       "97                    PTAU_bl   \n",
       "98                     TAU_bl   \n",
       "99                     FDG_bl   \n",
       "100                    PIB_bl   \n",
       "101                   AV45_bl   \n",
       "102                  Years_bl   \n",
       "103                  Month_bl   \n",
       "104                     Month   \n",
       "105                         M   \n",
       "106              update_stamp   \n",
       "\n",
       "                                                  text  \n",
       "0                                Participant roster ID  \n",
       "1                                           Visit code  \n",
       "2                                                 Site  \n",
       "3                                                 Date  \n",
       "4                                          Baseline Dx  \n",
       "5                                                  Age  \n",
       "6                                                  Sex  \n",
       "7                                            Education  \n",
       "8                                           Profession  \n",
       "9                                            Ethnicity  \n",
       "10                                                Race  \n",
       "11                                             Marital  \n",
       "12                                               ApoE4  \n",
       "13   Average FDG-PET of angular, temporal, and post...  \n",
       "14   Average PIB SUVR of frontal cortex, anterior c...  \n",
       "15   Reference region - florbetapir mean of whole c...  \n",
       "16                                           CSF ABETA  \n",
       "17                                             CSF TAU  \n",
       "18                                            CSF PTAU  \n",
       "19                                              CDR-SB  \n",
       "20                                             ADAS 11  \n",
       "21                                             ADAS 13  \n",
       "22                            ADAS Delayed Word Recall  \n",
       "23                                                MMSE  \n",
       "24                   RAVLT Immediate (sum of 5 trials)  \n",
       "25                  RAVLT Learning (trial 5 - trial 1)  \n",
       "26                RAVLT Forgetting (trial 5 - delayed)  \n",
       "27                            RAVLT Percent Forgetting  \n",
       "28                     Logical Memory - Delayed Recall  \n",
       "29                           Digit Symbol Substitution  \n",
       "30                                            Image ID  \n",
       "31                                                 FAQ  \n",
       "32                                                MOCA  \n",
       "33                                       Pt ECog - Mem  \n",
       "34                                      Pt ECog - Lang  \n",
       "35                                  Pt ECog - Vis/Spat  \n",
       "36                                      Pt ECog - Plan  \n",
       "37                                     Pt ECog - Organ  \n",
       "38                                 Pt ECog - Div atten  \n",
       "39                                     Pt ECog - Total  \n",
       "40                                       SP ECog - Mem  \n",
       "41                                      SP ECog - Lang  \n",
       "42                                  SP ECog - Vis/Spat  \n",
       "43                                      SP ECog - Plan  \n",
       "44                                     SP ECog - Organ  \n",
       "45                                 SP ECog - Div atten  \n",
       "46                                     SP ECog - Total  \n",
       "47                                  MRI Field Strength  \n",
       "48                                     UCSF Ventricles  \n",
       "49                                    UCSF Hippocampus  \n",
       "50                                     UCSF WholeBrain  \n",
       "51                                     UCSF Entorhinal  \n",
       "52                                       UCSF Fusiform  \n",
       "53                                       UCSF Med Temp  \n",
       "54                                            UCSF ICV  \n",
       "55                                           Diagnosis  \n",
       "56   modified Preclinical Alzheimer's Cognitive Com...  \n",
       "57                                                Date  \n",
       "58                                  CDR-SB at baseline  \n",
       "59                                 ADAS 11 at baseline  \n",
       "60                                 ADAS 13 at baseline  \n",
       "61                ADAS Delayed Word Recall at baseline  \n",
       "62                                    MMSE at baseline  \n",
       "63       RAVLT Immediate (sum of 5 trials) at baseline  \n",
       "64      RAVLT Learning (trial 5 - trial 1) at baseline  \n",
       "65    RAVLT Forgetting (trial 5 - delayed) at baseline  \n",
       "66                RAVLT Percent Forgetting at baseline  \n",
       "67         Logical Memory - Delayed Recall at baseline  \n",
       "68               Digit Symbol Substitution at baseline  \n",
       "69                                Trails B at baseline  \n",
       "70                                     FAQ at baseline  \n",
       "71   modified Preclinical Alzheimer's Cognitive Com...  \n",
       "72   modified Preclinical Alzheimer's Cognitive Com...  \n",
       "73                      MRI Field Strength at baseline  \n",
       "74                         UCSF Ventricles at baseline  \n",
       "75                        UCSF Hippocampus at baseline  \n",
       "76                         UCSF WholeBrain at baseline  \n",
       "77                         UCSF Entorhinal at baseline  \n",
       "78                           UCSF Fusiform at baseline  \n",
       "79                           UCSF Med Temp at baseline  \n",
       "80                                UCSF ICV at baseline  \n",
       "81                                    MOCA at baseline  \n",
       "82                           Pt ECog - Mem at baseline  \n",
       "83                          Pt ECog - Lang at baseline  \n",
       "84                      Pt ECog - Vis/Spat at baseline  \n",
       "85                          Pt ECog - Plan at baseline  \n",
       "86                         Pt ECog - Organ at baseline  \n",
       "87                     Pt ECog - Div atten at baseline  \n",
       "88                         Pt ECog - Total at baseline  \n",
       "89                           SP ECog - Mem at baseline  \n",
       "90                          SP ECog - Lang at baseline  \n",
       "91                      SP ECog - Vis/Spat at baseline  \n",
       "92                          SP ECog - Plan at baseline  \n",
       "93                         SP ECog - Organ at baseline  \n",
       "94                     SP ECog - Div atten at baseline  \n",
       "95                         SP ECog - Total at baseline  \n",
       "96                               CSF ABETA at baseline  \n",
       "97                                CSF PTAU at baseline  \n",
       "98                                 CSF TAU at baseline  \n",
       "99   Average FDG-PET of angular, temporal, and post...  \n",
       "100  Average PIB SUVR of frontal cortex, anterior c...  \n",
       "101  Reference region - florbetapir mean of whole c...  \n",
       "102                               Years since baseline  \n",
       "103                               Month since baseline  \n",
       "104                    Months since baseline (integer)  \n",
       "105                     Month since baseline (integer)  \n",
       "106                                Date of last update  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('./data/dictionary.csv', delimiter=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: The first time, we read in the csv file and passed it in the variable `data`, thus no output is shown in this notebook. The second time, we read in the csv file without passing it to a variable, thus the output is shown directly in this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reduce the number of rows displayed in this Jupyter notebook for further use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3. Look at data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First of all, let's set pandas to display all columns in our DataFrame by increasing the `'display.max_columns'` option."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', 110)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### DataFrame dimensionality\n",
    "\n",
    "Show the number of columns and rows (= dimensionality/shape) of the table in the form of `(number of rows, number of columns)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14532, 110)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### DataFrame head/tail\n",
    "\n",
    "Have a look at the first few rows of the table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RID</th>\n",
       "      <th>VISCODE</th>\n",
       "      <th>SITE</th>\n",
       "      <th>EXAMDATE</th>\n",
       "      <th>DX_bl</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PTGENDER</th>\n",
       "      <th>PTEDUCAT</th>\n",
       "      <th>WORK</th>\n",
       "      <th>PTETHCAT</th>\n",
       "      <th>PTRACCAT</th>\n",
       "      <th>PTMARRY</th>\n",
       "      <th>APOE4</th>\n",
       "      <th>FDG</th>\n",
       "      <th>PIB</th>\n",
       "      <th>AV45</th>\n",
       "      <th>ABETA</th>\n",
       "      <th>TAU</th>\n",
       "      <th>PTAU</th>\n",
       "      <th>CDRSB</th>\n",
       "      <th>ADAS11</th>\n",
       "      <th>ADAS13</th>\n",
       "      <th>ADASQ4</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>RAVLT_immediate</th>\n",
       "      <th>RAVLT_learning</th>\n",
       "      <th>RAVLT_forgetting</th>\n",
       "      <th>RAVLT_perc_forgetting</th>\n",
       "      <th>LDELTOTAL</th>\n",
       "      <th>DIGITSCOR</th>\n",
       "      <th>TRABSCOR</th>\n",
       "      <th>FAQ</th>\n",
       "      <th>MOCA</th>\n",
       "      <th>EcogPtMem</th>\n",
       "      <th>EcogPtLang</th>\n",
       "      <th>EcogPtVisspat</th>\n",
       "      <th>EcogPtPlan</th>\n",
       "      <th>EcogPtOrgan</th>\n",
       "      <th>EcogPtDivatt</th>\n",
       "      <th>EcogPtTotal</th>\n",
       "      <th>EcogSPMem</th>\n",
       "      <th>EcogSPLang</th>\n",
       "      <th>EcogSPVisspat</th>\n",
       "      <th>EcogSPPlan</th>\n",
       "      <th>EcogSPOrgan</th>\n",
       "      <th>EcogSPDivatt</th>\n",
       "      <th>EcogSPTotal</th>\n",
       "      <th>FLDSTRENG</th>\n",
       "      <th>IMAGEUID</th>\n",
       "      <th>Ventricles</th>\n",
       "      <th>Hippocampus</th>\n",
       "      <th>WholeBrain</th>\n",
       "      <th>Entorhinal</th>\n",
       "      <th>Fusiform</th>\n",
       "      <th>MidTemp</th>\n",
       "      <th>ICV</th>\n",
       "      <th>DX</th>\n",
       "      <th>mPACCdigit</th>\n",
       "      <th>mPACCtrailsB</th>\n",
       "      <th>EXAMDATE_bl</th>\n",
       "      <th>CDRSB_bl</th>\n",
       "      <th>ADAS11_bl</th>\n",
       "      <th>ADAS13_bl</th>\n",
       "      <th>ADASQ4_bl</th>\n",
       "      <th>MMSE_bl</th>\n",
       "      <th>RAVLT_immediate_bl</th>\n",
       "      <th>RAVLT_learning_bl</th>\n",
       "      <th>RAVLT_forgetting_bl</th>\n",
       "      <th>RAVLT_perc_forgetting_bl</th>\n",
       "      <th>LDELTOTAL_BL</th>\n",
       "      <th>DIGITSCOR_bl</th>\n",
       "      <th>TRABSCOR_bl</th>\n",
       "      <th>FAQ_bl</th>\n",
       "      <th>mPACCdigit_bl</th>\n",
       "      <th>mPACCtrailsB_bl</th>\n",
       "      <th>FLDSTRENG_bl</th>\n",
       "      <th>Ventricles_bl</th>\n",
       "      <th>Hippocampus_bl</th>\n",
       "      <th>WholeBrain_bl</th>\n",
       "      <th>Entorhinal_bl</th>\n",
       "      <th>Fusiform_bl</th>\n",
       "      <th>MidTemp_bl</th>\n",
       "      <th>ICV_bl</th>\n",
       "      <th>MOCA_bl</th>\n",
       "      <th>EcogPtMem_bl</th>\n",
       "      <th>EcogPtLang_bl</th>\n",
       "      <th>EcogPtVisspat_bl</th>\n",
       "      <th>EcogPtPlan_bl</th>\n",
       "      <th>EcogPtOrgan_bl</th>\n",
       "      <th>EcogPtDivatt_bl</th>\n",
       "      <th>EcogPtTotal_bl</th>\n",
       "      <th>EcogSPMem_bl</th>\n",
       "      <th>EcogSPLang_bl</th>\n",
       "      <th>EcogSPVisspat_bl</th>\n",
       "      <th>EcogSPPlan_bl</th>\n",
       "      <th>EcogSPOrgan_bl</th>\n",
       "      <th>EcogSPDivatt_bl</th>\n",
       "      <th>EcogSPTotal_bl</th>\n",
       "      <th>ABETA_bl</th>\n",
       "      <th>TAU_bl</th>\n",
       "      <th>PTAU_bl</th>\n",
       "      <th>FDG_bl</th>\n",
       "      <th>PIB_bl</th>\n",
       "      <th>AV45_bl</th>\n",
       "      <th>Years_bl</th>\n",
       "      <th>Month_bl</th>\n",
       "      <th>Month</th>\n",
       "      <th>M</th>\n",
       "      <th>update_stamp</th>\n",
       "      <th>Unnamed: 109</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>128</td>\n",
       "      <td>bl</td>\n",
       "      <td>164</td>\n",
       "      <td>2005-09-08</td>\n",
       "      <td>CN</td>\n",
       "      <td>74.2</td>\n",
       "      <td>Male</td>\n",
       "      <td>16</td>\n",
       "      <td>technical writer and editor</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>1.36665</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.67</td>\n",
       "      <td>18.67</td>\n",
       "      <td>5.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>54.5455</td>\n",
       "      <td>10.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35479.0</td>\n",
       "      <td>118226.0</td>\n",
       "      <td>8340.0</td>\n",
       "      <td>1229736.0</td>\n",
       "      <td>4182.0</td>\n",
       "      <td>16562.0</td>\n",
       "      <td>27930.0</td>\n",
       "      <td>1984658.0</td>\n",
       "      <td>CN</td>\n",
       "      <td>-4.41005</td>\n",
       "      <td>-4.23545</td>\n",
       "      <td>2005-09-08</td>\n",
       "      <td>0</td>\n",
       "      <td>10.67</td>\n",
       "      <td>18.67</td>\n",
       "      <td>5.0</td>\n",
       "      <td>28</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>54.5455</td>\n",
       "      <td>10.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-4.41005</td>\n",
       "      <td>-4.23545</td>\n",
       "      <td>NaN</td>\n",
       "      <td>118226.0</td>\n",
       "      <td>8340.0</td>\n",
       "      <td>1229736.0</td>\n",
       "      <td>4182.0</td>\n",
       "      <td>16562.0</td>\n",
       "      <td>27930.0</td>\n",
       "      <td>1984658.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.36665</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>129</td>\n",
       "      <td>bl</td>\n",
       "      <td>164</td>\n",
       "      <td>2005-09-12</td>\n",
       "      <td>AD</td>\n",
       "      <td>82.4</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>Secretary</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>1.08355</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>741.5</td>\n",
       "      <td>239.7</td>\n",
       "      <td>22.83</td>\n",
       "      <td>4.5</td>\n",
       "      <td>22.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32241.0</td>\n",
       "      <td>84599.0</td>\n",
       "      <td>5318.0</td>\n",
       "      <td>1129825.0</td>\n",
       "      <td>1790.0</td>\n",
       "      <td>15507.0</td>\n",
       "      <td>18425.0</td>\n",
       "      <td>1920689.0</td>\n",
       "      <td>Dementia</td>\n",
       "      <td>-16.6244</td>\n",
       "      <td>-16.23320</td>\n",
       "      <td>2005-09-12</td>\n",
       "      <td>4.5</td>\n",
       "      <td>22.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-16.62440</td>\n",
       "      <td>-16.23320</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84599.0</td>\n",
       "      <td>5318.0</td>\n",
       "      <td>1129825.0</td>\n",
       "      <td>1790.0</td>\n",
       "      <td>15507.0</td>\n",
       "      <td>18425.0</td>\n",
       "      <td>1920689.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>741.5</td>\n",
       "      <td>239.7</td>\n",
       "      <td>22.83</td>\n",
       "      <td>1.08355</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>129</td>\n",
       "      <td>m06</td>\n",
       "      <td>164</td>\n",
       "      <td>2006-03-13</td>\n",
       "      <td>AD</td>\n",
       "      <td>81.4</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>Elementary school teacher</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>1.05803</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>19.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31851.0</td>\n",
       "      <td>88580.0</td>\n",
       "      <td>5445.0</td>\n",
       "      <td>1100055.0</td>\n",
       "      <td>2426.0</td>\n",
       "      <td>14401.0</td>\n",
       "      <td>16975.0</td>\n",
       "      <td>1906429.0</td>\n",
       "      <td>Dementia</td>\n",
       "      <td>-15.092</td>\n",
       "      <td>-13.49320</td>\n",
       "      <td>2005-09-12</td>\n",
       "      <td>4.5</td>\n",
       "      <td>22.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-16.62440</td>\n",
       "      <td>-16.23320</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84599.0</td>\n",
       "      <td>5318.0</td>\n",
       "      <td>1129825.0</td>\n",
       "      <td>1790.0</td>\n",
       "      <td>15507.0</td>\n",
       "      <td>18425.0</td>\n",
       "      <td>1920689.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>741.5</td>\n",
       "      <td>239.7</td>\n",
       "      <td>22.83</td>\n",
       "      <td>1.08355</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.498289</td>\n",
       "      <td>5.96721</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>129</td>\n",
       "      <td>m12</td>\n",
       "      <td>164</td>\n",
       "      <td>2006-09-12</td>\n",
       "      <td>AD</td>\n",
       "      <td>81.3</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>Communication</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>1.09690</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>601.4</td>\n",
       "      <td>251.7</td>\n",
       "      <td>24.18</td>\n",
       "      <td>3.5</td>\n",
       "      <td>24.00</td>\n",
       "      <td>35.00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35572.0</td>\n",
       "      <td>90099.0</td>\n",
       "      <td>5156.0</td>\n",
       "      <td>1095635.0</td>\n",
       "      <td>1595.0</td>\n",
       "      <td>14618.0</td>\n",
       "      <td>17333.0</td>\n",
       "      <td>1903819.0</td>\n",
       "      <td>Dementia</td>\n",
       "      <td>-21.4587</td>\n",
       "      <td>-20.29090</td>\n",
       "      <td>2005-09-12</td>\n",
       "      <td>4.5</td>\n",
       "      <td>22.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-16.62440</td>\n",
       "      <td>-16.23320</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84599.0</td>\n",
       "      <td>5318.0</td>\n",
       "      <td>1129825.0</td>\n",
       "      <td>1790.0</td>\n",
       "      <td>15507.0</td>\n",
       "      <td>18425.0</td>\n",
       "      <td>1920689.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>741.5</td>\n",
       "      <td>239.7</td>\n",
       "      <td>22.83</td>\n",
       "      <td>1.08355</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.999316</td>\n",
       "      <td>11.96720</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>129</td>\n",
       "      <td>m24</td>\n",
       "      <td>164</td>\n",
       "      <td>2007-09-12</td>\n",
       "      <td>AD</td>\n",
       "      <td>80.5</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>1.03258</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>25.67</td>\n",
       "      <td>37.67</td>\n",
       "      <td>10.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88263.0</td>\n",
       "      <td>97420.0</td>\n",
       "      <td>5138.0</td>\n",
       "      <td>1088555.0</td>\n",
       "      <td>1174.0</td>\n",
       "      <td>14034.0</td>\n",
       "      <td>16401.0</td>\n",
       "      <td>1903419.0</td>\n",
       "      <td>Dementia</td>\n",
       "      <td>-20.1324</td>\n",
       "      <td>-20.34260</td>\n",
       "      <td>2005-09-12</td>\n",
       "      <td>4.5</td>\n",
       "      <td>22.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-16.62440</td>\n",
       "      <td>-16.23320</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84599.0</td>\n",
       "      <td>5318.0</td>\n",
       "      <td>1129825.0</td>\n",
       "      <td>1790.0</td>\n",
       "      <td>15507.0</td>\n",
       "      <td>18425.0</td>\n",
       "      <td>1920689.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>741.5</td>\n",
       "      <td>239.7</td>\n",
       "      <td>22.83</td>\n",
       "      <td>1.08355</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.998630</td>\n",
       "      <td>23.93440</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RID VISCODE  SITE    EXAMDATE DX_bl   AGE PTGENDER  PTEDUCAT  \\\n",
       "0  128      bl   164  2005-09-08    CN  74.2     Male        16   \n",
       "1  129      bl   164  2005-09-12    AD  82.4     Male        18   \n",
       "2  129     m06   164  2006-03-13    AD  81.4     Male        18   \n",
       "3  129     m12   164  2006-09-12    AD  81.3     Male        18   \n",
       "4  129     m24   164  2007-09-12    AD  80.5     Male        18   \n",
       "\n",
       "                          WORK         PTETHCAT PTRACCAT  PTMARRY APOE4  \\\n",
       "0  technical writer and editor  Not Hisp/Latino    White  Married     0   \n",
       "1                    Secretary  Not Hisp/Latino    White  Married     1   \n",
       "2    Elementary school teacher  Not Hisp/Latino    White  Married     1   \n",
       "3                Communication  Not Hisp/Latino    White  Married     1   \n",
       "4                   Accounting  Not Hisp/Latino    White  Married     1   \n",
       "\n",
       "       FDG  PIB  AV45  ABETA    TAU   PTAU  CDRSB  ADAS11  ADAS13  ADASQ4  \\\n",
       "0  1.36665  NaN   NaN    NaN    NaN    NaN    0.0   10.67   18.67     5.0   \n",
       "1  1.08355  NaN   NaN  741.5  239.7  22.83    4.5   22.00   31.00     8.0   \n",
       "2  1.05803  NaN   NaN    NaN    NaN    NaN    6.0   19.00   30.00    10.0   \n",
       "3  1.09690  NaN   NaN  601.4  251.7  24.18    3.5   24.00   35.00    10.0   \n",
       "4  1.03258  NaN   NaN    NaN    NaN    NaN    8.0   25.67   37.67    10.0   \n",
       "\n",
       "   MMSE  RAVLT_immediate  RAVLT_learning  RAVLT_forgetting  \\\n",
       "0  28.0             44.0             4.0               6.0   \n",
       "1  20.0             22.0             1.0               4.0   \n",
       "2  24.0             19.0             2.0               6.0   \n",
       "3  17.0             31.0             2.0               7.0   \n",
       "4  19.0             23.0             1.0               5.0   \n",
       "\n",
       "   RAVLT_perc_forgetting  LDELTOTAL  DIGITSCOR  TRABSCOR   FAQ  MOCA  \\\n",
       "0                54.5455       10.0       34.0     112.0   0.0   NaN   \n",
       "1               100.0000        2.0       25.0     148.0  10.0   NaN   \n",
       "2               100.0000        NaN       19.0     135.0  12.0   NaN   \n",
       "3               100.0000        0.0       21.0     126.0  17.0   NaN   \n",
       "4               100.0000        0.0       16.0     275.0  14.0   NaN   \n",
       "\n",
       "   EcogPtMem  EcogPtLang  EcogPtVisspat  EcogPtPlan  EcogPtOrgan  \\\n",
       "0        NaN         NaN            NaN         NaN          NaN   \n",
       "1        NaN         NaN            NaN         NaN          NaN   \n",
       "2        NaN         NaN            NaN         NaN          NaN   \n",
       "3        NaN         NaN            NaN         NaN          NaN   \n",
       "4        NaN         NaN            NaN         NaN          NaN   \n",
       "\n",
       "   EcogPtDivatt  EcogPtTotal  EcogSPMem  EcogSPLang  EcogSPVisspat  \\\n",
       "0           NaN          NaN        NaN         NaN            NaN   \n",
       "1           NaN          NaN        NaN         NaN            NaN   \n",
       "2           NaN          NaN        NaN         NaN            NaN   \n",
       "3           NaN          NaN        NaN         NaN            NaN   \n",
       "4           NaN          NaN        NaN         NaN            NaN   \n",
       "\n",
       "   EcogSPPlan  EcogSPOrgan  EcogSPDivatt  EcogSPTotal  FLDSTRENG  IMAGEUID  \\\n",
       "0         NaN          NaN           NaN          NaN        NaN   35479.0   \n",
       "1         NaN          NaN           NaN          NaN        NaN   32241.0   \n",
       "2         NaN          NaN           NaN          NaN        NaN   31851.0   \n",
       "3         NaN          NaN           NaN          NaN        NaN   35572.0   \n",
       "4         NaN          NaN           NaN          NaN        NaN   88263.0   \n",
       "\n",
       "   Ventricles  Hippocampus  WholeBrain  Entorhinal  Fusiform  MidTemp  \\\n",
       "0    118226.0       8340.0   1229736.0      4182.0   16562.0  27930.0   \n",
       "1     84599.0       5318.0   1129825.0      1790.0   15507.0  18425.0   \n",
       "2     88580.0       5445.0   1100055.0      2426.0   14401.0  16975.0   \n",
       "3     90099.0       5156.0   1095635.0      1595.0   14618.0  17333.0   \n",
       "4     97420.0       5138.0   1088555.0      1174.0   14034.0  16401.0   \n",
       "\n",
       "         ICV        DX mPACCdigit  mPACCtrailsB EXAMDATE_bl CDRSB_bl  \\\n",
       "0  1984658.0        CN   -4.41005      -4.23545  2005-09-08        0   \n",
       "1  1920689.0  Dementia   -16.6244     -16.23320  2005-09-12      4.5   \n",
       "2  1906429.0  Dementia    -15.092     -13.49320  2005-09-12      4.5   \n",
       "3  1903819.0  Dementia   -21.4587     -20.29090  2005-09-12      4.5   \n",
       "4  1903419.0  Dementia   -20.1324     -20.34260  2005-09-12      4.5   \n",
       "\n",
       "   ADAS11_bl  ADAS13_bl  ADASQ4_bl  MMSE_bl  RAVLT_immediate_bl  \\\n",
       "0      10.67      18.67        5.0       28                44.0   \n",
       "1      22.00      31.00        8.0       20                22.0   \n",
       "2      22.00      31.00        8.0       20                22.0   \n",
       "3      22.00      31.00        8.0       20                22.0   \n",
       "4      22.00      31.00        8.0       20                22.0   \n",
       "\n",
       "   RAVLT_learning_bl  RAVLT_forgetting_bl  RAVLT_perc_forgetting_bl  \\\n",
       "0                4.0                  6.0                   54.5455   \n",
       "1                1.0                  4.0                  100.0000   \n",
       "2                1.0                  4.0                  100.0000   \n",
       "3                1.0                  4.0                  100.0000   \n",
       "4                1.0                  4.0                  100.0000   \n",
       "\n",
       "   LDELTOTAL_BL  DIGITSCOR_bl  TRABSCOR_bl  FAQ_bl  mPACCdigit_bl  \\\n",
       "0          10.0          34.0        112.0     0.0       -4.41005   \n",
       "1           2.0          25.0        148.0    10.0      -16.62440   \n",
       "2           2.0          25.0        148.0    10.0      -16.62440   \n",
       "3           2.0          25.0        148.0    10.0      -16.62440   \n",
       "4           2.0          25.0        148.0    10.0      -16.62440   \n",
       "\n",
       "   mPACCtrailsB_bl  FLDSTRENG_bl  Ventricles_bl  Hippocampus_bl  \\\n",
       "0         -4.23545           NaN       118226.0          8340.0   \n",
       "1        -16.23320           NaN        84599.0          5318.0   \n",
       "2        -16.23320           NaN        84599.0          5318.0   \n",
       "3        -16.23320           NaN        84599.0          5318.0   \n",
       "4        -16.23320           NaN        84599.0          5318.0   \n",
       "\n",
       "   WholeBrain_bl  Entorhinal_bl  Fusiform_bl  MidTemp_bl     ICV_bl  MOCA_bl  \\\n",
       "0      1229736.0         4182.0      16562.0     27930.0  1984658.0      NaN   \n",
       "1      1129825.0         1790.0      15507.0     18425.0  1920689.0      NaN   \n",
       "2      1129825.0         1790.0      15507.0     18425.0  1920689.0      NaN   \n",
       "3      1129825.0         1790.0      15507.0     18425.0  1920689.0      NaN   \n",
       "4      1129825.0         1790.0      15507.0     18425.0  1920689.0      NaN   \n",
       "\n",
       "   EcogPtMem_bl  EcogPtLang_bl  EcogPtVisspat_bl  EcogPtPlan_bl  \\\n",
       "0           NaN            NaN               NaN            NaN   \n",
       "1           NaN            NaN               NaN            NaN   \n",
       "2           NaN            NaN               NaN            NaN   \n",
       "3           NaN            NaN               NaN            NaN   \n",
       "4           NaN            NaN               NaN            NaN   \n",
       "\n",
       "   EcogPtOrgan_bl  EcogPtDivatt_bl  EcogPtTotal_bl  EcogSPMem_bl  \\\n",
       "0             NaN              NaN             NaN           NaN   \n",
       "1             NaN              NaN             NaN           NaN   \n",
       "2             NaN              NaN             NaN           NaN   \n",
       "3             NaN              NaN             NaN           NaN   \n",
       "4             NaN              NaN             NaN           NaN   \n",
       "\n",
       "   EcogSPLang_bl  EcogSPVisspat_bl  EcogSPPlan_bl  EcogSPOrgan_bl  \\\n",
       "0            NaN               NaN            NaN             NaN   \n",
       "1            NaN               NaN            NaN             NaN   \n",
       "2            NaN               NaN            NaN             NaN   \n",
       "3            NaN               NaN            NaN             NaN   \n",
       "4            NaN               NaN            NaN             NaN   \n",
       "\n",
       "   EcogSPDivatt_bl  EcogSPTotal_bl ABETA_bl TAU_bl PTAU_bl   FDG_bl  PIB_bl  \\\n",
       "0              NaN             NaN      NaN    NaN     NaN  1.36665     NaN   \n",
       "1              NaN             NaN    741.5  239.7   22.83  1.08355     NaN   \n",
       "2              NaN             NaN    741.5  239.7   22.83  1.08355     NaN   \n",
       "3              NaN             NaN    741.5  239.7   22.83  1.08355     NaN   \n",
       "4              NaN             NaN    741.5  239.7   22.83  1.08355     NaN   \n",
       "\n",
       "   AV45_bl  Years_bl  Month_bl  Month     M           update_stamp  \\\n",
       "0      NaN  0.000000   0.00000    0.0   0.0  2019-12-04 04:19:56.0   \n",
       "1      NaN  0.000000   0.00000    0.0   0.0  2019-12-04 04:19:56.0   \n",
       "2      NaN  0.498289   5.96721    6.0   6.0  2019-12-04 04:19:56.0   \n",
       "3      NaN  0.999316  11.96720   12.0  12.0  2019-12-04 04:19:56.0   \n",
       "4      NaN  1.998630  23.93440   24.0  24.0  2019-12-04 04:19:56.0   \n",
       "\n",
       "  Unnamed: 109  \n",
       "0          NaN  \n",
       "1          NaN  \n",
       "2          NaN  \n",
       "3          NaN  \n",
       "4          NaN  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()  # data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Have a look at the last few rows of the table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RID</th>\n",
       "      <th>VISCODE</th>\n",
       "      <th>SITE</th>\n",
       "      <th>EXAMDATE</th>\n",
       "      <th>DX_bl</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PTGENDER</th>\n",
       "      <th>PTEDUCAT</th>\n",
       "      <th>WORK</th>\n",
       "      <th>PTETHCAT</th>\n",
       "      <th>PTRACCAT</th>\n",
       "      <th>PTMARRY</th>\n",
       "      <th>APOE4</th>\n",
       "      <th>FDG</th>\n",
       "      <th>PIB</th>\n",
       "      <th>AV45</th>\n",
       "      <th>ABETA</th>\n",
       "      <th>TAU</th>\n",
       "      <th>PTAU</th>\n",
       "      <th>CDRSB</th>\n",
       "      <th>ADAS11</th>\n",
       "      <th>ADAS13</th>\n",
       "      <th>ADASQ4</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>RAVLT_immediate</th>\n",
       "      <th>RAVLT_learning</th>\n",
       "      <th>RAVLT_forgetting</th>\n",
       "      <th>RAVLT_perc_forgetting</th>\n",
       "      <th>LDELTOTAL</th>\n",
       "      <th>DIGITSCOR</th>\n",
       "      <th>TRABSCOR</th>\n",
       "      <th>FAQ</th>\n",
       "      <th>MOCA</th>\n",
       "      <th>EcogPtMem</th>\n",
       "      <th>EcogPtLang</th>\n",
       "      <th>EcogPtVisspat</th>\n",
       "      <th>EcogPtPlan</th>\n",
       "      <th>EcogPtOrgan</th>\n",
       "      <th>EcogPtDivatt</th>\n",
       "      <th>EcogPtTotal</th>\n",
       "      <th>EcogSPMem</th>\n",
       "      <th>EcogSPLang</th>\n",
       "      <th>EcogSPVisspat</th>\n",
       "      <th>EcogSPPlan</th>\n",
       "      <th>EcogSPOrgan</th>\n",
       "      <th>EcogSPDivatt</th>\n",
       "      <th>EcogSPTotal</th>\n",
       "      <th>FLDSTRENG</th>\n",
       "      <th>IMAGEUID</th>\n",
       "      <th>Ventricles</th>\n",
       "      <th>Hippocampus</th>\n",
       "      <th>WholeBrain</th>\n",
       "      <th>Entorhinal</th>\n",
       "      <th>Fusiform</th>\n",
       "      <th>MidTemp</th>\n",
       "      <th>ICV</th>\n",
       "      <th>DX</th>\n",
       "      <th>mPACCdigit</th>\n",
       "      <th>mPACCtrailsB</th>\n",
       "      <th>EXAMDATE_bl</th>\n",
       "      <th>CDRSB_bl</th>\n",
       "      <th>ADAS11_bl</th>\n",
       "      <th>ADAS13_bl</th>\n",
       "      <th>ADASQ4_bl</th>\n",
       "      <th>MMSE_bl</th>\n",
       "      <th>RAVLT_immediate_bl</th>\n",
       "      <th>RAVLT_learning_bl</th>\n",
       "      <th>RAVLT_forgetting_bl</th>\n",
       "      <th>RAVLT_perc_forgetting_bl</th>\n",
       "      <th>LDELTOTAL_BL</th>\n",
       "      <th>DIGITSCOR_bl</th>\n",
       "      <th>TRABSCOR_bl</th>\n",
       "      <th>FAQ_bl</th>\n",
       "      <th>mPACCdigit_bl</th>\n",
       "      <th>mPACCtrailsB_bl</th>\n",
       "      <th>FLDSTRENG_bl</th>\n",
       "      <th>Ventricles_bl</th>\n",
       "      <th>Hippocampus_bl</th>\n",
       "      <th>WholeBrain_bl</th>\n",
       "      <th>Entorhinal_bl</th>\n",
       "      <th>Fusiform_bl</th>\n",
       "      <th>MidTemp_bl</th>\n",
       "      <th>ICV_bl</th>\n",
       "      <th>MOCA_bl</th>\n",
       "      <th>EcogPtMem_bl</th>\n",
       "      <th>EcogPtLang_bl</th>\n",
       "      <th>EcogPtVisspat_bl</th>\n",
       "      <th>EcogPtPlan_bl</th>\n",
       "      <th>EcogPtOrgan_bl</th>\n",
       "      <th>EcogPtDivatt_bl</th>\n",
       "      <th>EcogPtTotal_bl</th>\n",
       "      <th>EcogSPMem_bl</th>\n",
       "      <th>EcogSPLang_bl</th>\n",
       "      <th>EcogSPVisspat_bl</th>\n",
       "      <th>EcogSPPlan_bl</th>\n",
       "      <th>EcogSPOrgan_bl</th>\n",
       "      <th>EcogSPDivatt_bl</th>\n",
       "      <th>EcogSPTotal_bl</th>\n",
       "      <th>ABETA_bl</th>\n",
       "      <th>TAU_bl</th>\n",
       "      <th>PTAU_bl</th>\n",
       "      <th>FDG_bl</th>\n",
       "      <th>PIB_bl</th>\n",
       "      <th>AV45_bl</th>\n",
       "      <th>Years_bl</th>\n",
       "      <th>Month_bl</th>\n",
       "      <th>Month</th>\n",
       "      <th>M</th>\n",
       "      <th>update_stamp</th>\n",
       "      <th>Unnamed: 109</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14527</th>\n",
       "      <td>6223</td>\n",
       "      <td>m24</td>\n",
       "      <td>252</td>\n",
       "      <td>2019-12-12</td>\n",
       "      <td>SMC</td>\n",
       "      <td>74.6</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>RN</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>27.2727</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.33333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.26316</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.11111</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.16667</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.07895</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.56502</td>\n",
       "      <td>-1.67829</td>\n",
       "      <td>2017-12-04</td>\n",
       "      <td>0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29</td>\n",
       "      <td>45.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.2857</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.843630</td>\n",
       "      <td>1.736790</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.750</td>\n",
       "      <td>1.33333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.26316</td>\n",
       "      <td>1.250</td>\n",
       "      <td>1.11111</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.33333</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.23684</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.004900</td>\n",
       "      <td>2.02053</td>\n",
       "      <td>24.1967</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2019-12-14 04:20:30.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14528</th>\n",
       "      <td>4832</td>\n",
       "      <td>m96</td>\n",
       "      <td>190</td>\n",
       "      <td>2019-12-16</td>\n",
       "      <td>EMCI</td>\n",
       "      <td>60.9</td>\n",
       "      <td>Male</td>\n",
       "      <td>14</td>\n",
       "      <td>painting contractor</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-05-16</td>\n",
       "      <td>2</td>\n",
       "      <td>8.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>30</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>44.4444</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-1.969820</td>\n",
       "      <td>-1.562110</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18008.0</td>\n",
       "      <td>7733.0</td>\n",
       "      <td>1091811.0</td>\n",
       "      <td>4085.0</td>\n",
       "      <td>19721.0</td>\n",
       "      <td>22308.0</td>\n",
       "      <td>1479077.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3.125</td>\n",
       "      <td>2.44444</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.16667</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.94872</td>\n",
       "      <td>2.125</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>1.14286</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.50000</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.76923</td>\n",
       "      <td>&gt;1700</td>\n",
       "      <td>261.3</td>\n",
       "      <td>23.94</td>\n",
       "      <td>1.28876</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.946004</td>\n",
       "      <td>7.58385</td>\n",
       "      <td>90.8197</td>\n",
       "      <td>90.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2019-12-17 04:20:22.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14529</th>\n",
       "      <td>6262</td>\n",
       "      <td>m24</td>\n",
       "      <td>194</td>\n",
       "      <td>2019-12-03</td>\n",
       "      <td>CN</td>\n",
       "      <td>60.7</td>\n",
       "      <td>Male</td>\n",
       "      <td>12</td>\n",
       "      <td>Surgeon</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>Black</td>\n",
       "      <td>Never married</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-12-14</td>\n",
       "      <td>0</td>\n",
       "      <td>7.33</td>\n",
       "      <td>8.33</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29</td>\n",
       "      <td>55.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>46.6667</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.937768</td>\n",
       "      <td>0.187803</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.500</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.16667</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.13158</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.96851</td>\n",
       "      <td>23.5738</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2019-12-17 04:20:25.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14530</th>\n",
       "      <td>6736</td>\n",
       "      <td>m12</td>\n",
       "      <td>159</td>\n",
       "      <td>2019-11-26</td>\n",
       "      <td>LMCI</td>\n",
       "      <td>60.7</td>\n",
       "      <td>Male</td>\n",
       "      <td>10</td>\n",
       "      <td>Insurance Broker</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-11-20</td>\n",
       "      <td>2.5</td>\n",
       "      <td>15.67</td>\n",
       "      <td>21.67</td>\n",
       "      <td>5.0</td>\n",
       "      <td>28</td>\n",
       "      <td>31.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>57.1429</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>199.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-6.728800</td>\n",
       "      <td>-7.397610</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.250</td>\n",
       "      <td>1.11111</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.33333</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.18919</td>\n",
       "      <td>2.625</td>\n",
       "      <td>1.44444</td>\n",
       "      <td>1.50000</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.33333</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2.15789</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.19248</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.01574</td>\n",
       "      <td>12.1639</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2019-12-17 04:20:26.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14531</th>\n",
       "      <td>6960</td>\n",
       "      <td>bl</td>\n",
       "      <td>169</td>\n",
       "      <td>2019-12-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81.3</td>\n",
       "      <td>Female</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>Black</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>69.2308</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1.55556</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.33333</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2.05263</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.22222</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.26316</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.92281</td>\n",
       "      <td>-1.78967</td>\n",
       "      <td>2019-12-11</td>\n",
       "      <td>1</td>\n",
       "      <td>10.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>30</td>\n",
       "      <td>46.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>69.2308</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.922810</td>\n",
       "      <td>-1.789670</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2.250</td>\n",
       "      <td>1.55556</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.33333</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2.05263</td>\n",
       "      <td>1.500</td>\n",
       "      <td>1.22222</td>\n",
       "      <td>1.50000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.26316</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-17 04:20:26.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        RID VISCODE  SITE    EXAMDATE DX_bl   AGE PTGENDER  PTEDUCAT  \\\n",
       "14527  6223     m24   252  2019-12-12   SMC  74.6     Male        18   \n",
       "14528  4832     m96   190  2019-12-16  EMCI  60.9     Male        14   \n",
       "14529  6262     m24   194  2019-12-03    CN  60.7     Male        12   \n",
       "14530  6736     m12   159  2019-11-26  LMCI  60.7     Male        10   \n",
       "14531  6960      bl   169  2019-12-11   NaN  81.3   Female        16   \n",
       "\n",
       "                      WORK         PTETHCAT PTRACCAT        PTMARRY APOE4  \\\n",
       "14527                   RN  Not Hisp/Latino    White        Married     1   \n",
       "14528  painting contractor  Not Hisp/Latino    White        Married     1   \n",
       "14529              Surgeon  Not Hisp/Latino    Black  Never married     1   \n",
       "14530     Insurance Broker  Not Hisp/Latino    White        Married   NaN   \n",
       "14531                  NaN  Not Hisp/Latino    Black       Divorced   NaN   \n",
       "\n",
       "       FDG  PIB  AV45 ABETA  TAU PTAU  CDRSB  ADAS11  ADAS13  ADASQ4  MMSE  \\\n",
       "14527  NaN  NaN   NaN   NaN  NaN  NaN    0.0    10.0    12.0     1.0  27.0   \n",
       "14528  NaN  NaN   NaN   NaN  NaN  NaN    NaN     NaN     NaN     NaN   NaN   \n",
       "14529  NaN  NaN   NaN   NaN  NaN  NaN    NaN     NaN     NaN     NaN   NaN   \n",
       "14530  NaN  NaN   NaN   NaN  NaN  NaN    NaN     NaN     NaN     NaN   NaN   \n",
       "14531  NaN  NaN   NaN   NaN  NaN  NaN    1.0    10.0    16.0     5.0  30.0   \n",
       "\n",
       "       RAVLT_immediate  RAVLT_learning  RAVLT_forgetting  \\\n",
       "14527             40.0             7.0               3.0   \n",
       "14528              NaN             NaN               NaN   \n",
       "14529              NaN             NaN               NaN   \n",
       "14530              NaN             NaN               NaN   \n",
       "14531             46.0             9.0               9.0   \n",
       "\n",
       "       RAVLT_perc_forgetting  LDELTOTAL  DIGITSCOR  TRABSCOR  FAQ  MOCA  \\\n",
       "14527                27.2727       10.0        NaN      67.0  0.0  26.0   \n",
       "14528                    NaN        NaN        NaN       NaN  NaN   NaN   \n",
       "14529                    NaN        NaN        NaN       NaN  NaN   NaN   \n",
       "14530                    NaN        NaN        NaN       NaN  NaN   NaN   \n",
       "14531                69.2308       10.0        NaN      86.0  0.0  24.0   \n",
       "\n",
       "       EcogPtMem  EcogPtLang  EcogPtVisspat  EcogPtPlan  EcogPtOrgan  \\\n",
       "14527       1.75     1.33333            1.0         1.0      1.00000   \n",
       "14528        NaN         NaN            NaN         NaN          NaN   \n",
       "14529        NaN         NaN            NaN         NaN          NaN   \n",
       "14530        NaN         NaN            NaN         NaN          NaN   \n",
       "14531       2.25     1.55556            2.0         2.2      2.33333   \n",
       "\n",
       "       EcogPtDivatt  EcogPtTotal  EcogSPMem  EcogSPLang  EcogSPVisspat  \\\n",
       "14527          1.25      1.26316        1.0     1.11111            1.0   \n",
       "14528           NaN          NaN        NaN         NaN            NaN   \n",
       "14529           NaN          NaN        NaN         NaN            NaN   \n",
       "14530           NaN          NaN        NaN         NaN            NaN   \n",
       "14531          2.25      2.05263        1.5     1.22222            1.5   \n",
       "\n",
       "       EcogSPPlan  EcogSPOrgan  EcogSPDivatt  EcogSPTotal  FLDSTRENG  \\\n",
       "14527         1.0      1.16667          1.25      1.07895        NaN   \n",
       "14528         NaN          NaN           NaN          NaN        NaN   \n",
       "14529         NaN          NaN           NaN          NaN        NaN   \n",
       "14530         NaN          NaN           NaN          NaN        NaN   \n",
       "14531         1.0      1.00000          1.25      1.26316        NaN   \n",
       "\n",
       "       IMAGEUID  Ventricles  Hippocampus  WholeBrain  Entorhinal  Fusiform  \\\n",
       "14527       NaN         NaN          NaN         NaN         NaN       NaN   \n",
       "14528       NaN         NaN          NaN         NaN         NaN       NaN   \n",
       "14529       NaN         NaN          NaN         NaN         NaN       NaN   \n",
       "14530       NaN         NaN          NaN         NaN         NaN       NaN   \n",
       "14531       NaN         NaN          NaN         NaN         NaN       NaN   \n",
       "\n",
       "       MidTemp  ICV   DX mPACCdigit  mPACCtrailsB EXAMDATE_bl CDRSB_bl  \\\n",
       "14527      NaN  NaN  NaN   -2.56502      -1.67829  2017-12-04        0   \n",
       "14528      NaN  NaN  NaN        NaN           NaN  2012-05-16        2   \n",
       "14529      NaN  NaN  NaN        NaN           NaN  2017-12-14        0   \n",
       "14530      NaN  NaN  NaN        NaN           NaN  2018-11-20      2.5   \n",
       "14531      NaN  NaN  NaN   -1.92281      -1.78967  2019-12-11        1   \n",
       "\n",
       "       ADAS11_bl  ADAS13_bl  ADASQ4_bl  MMSE_bl  RAVLT_immediate_bl  \\\n",
       "14527       8.00       9.00        1.0       29                45.0   \n",
       "14528       8.00      12.00        4.0       30                30.0   \n",
       "14529       7.33       8.33        1.0       29                55.0   \n",
       "14530      15.67      21.67        5.0       28                31.0   \n",
       "14531      10.00      16.00        5.0       30                46.0   \n",
       "\n",
       "       RAVLT_learning_bl  RAVLT_forgetting_bl  RAVLT_perc_forgetting_bl  \\\n",
       "14527               11.0                  2.0                   14.2857   \n",
       "14528                6.0                  4.0                   44.4444   \n",
       "14529                9.0                  7.0                   46.6667   \n",
       "14530                4.0                  4.0                   57.1429   \n",
       "14531                9.0                  9.0                   69.2308   \n",
       "\n",
       "       LDELTOTAL_BL  DIGITSCOR_bl  TRABSCOR_bl  FAQ_bl  mPACCdigit_bl  \\\n",
       "14527          15.0           NaN         64.0     0.0       1.843630   \n",
       "14528           8.0           NaN         77.0     5.0      -1.969820   \n",
       "14529           8.0           NaN         51.0     0.0      -0.937768   \n",
       "14530           4.0           NaN        199.0     5.0      -6.728800   \n",
       "14531          10.0           NaN         86.0     0.0      -1.922810   \n",
       "\n",
       "       mPACCtrailsB_bl  FLDSTRENG_bl  Ventricles_bl  Hippocampus_bl  \\\n",
       "14527         1.736790           NaN            NaN             NaN   \n",
       "14528        -1.562110           NaN        18008.0          7733.0   \n",
       "14529         0.187803           NaN            NaN             NaN   \n",
       "14530        -7.397610           NaN            NaN             NaN   \n",
       "14531        -1.789670           NaN            NaN             NaN   \n",
       "\n",
       "       WholeBrain_bl  Entorhinal_bl  Fusiform_bl  MidTemp_bl     ICV_bl  \\\n",
       "14527            NaN            NaN          NaN         NaN        NaN   \n",
       "14528      1091811.0         4085.0      19721.0     22308.0  1479077.0   \n",
       "14529            NaN            NaN          NaN         NaN        NaN   \n",
       "14530            NaN            NaN          NaN         NaN        NaN   \n",
       "14531            NaN            NaN          NaN         NaN        NaN   \n",
       "\n",
       "       MOCA_bl  EcogPtMem_bl  EcogPtLang_bl  EcogPtVisspat_bl  EcogPtPlan_bl  \\\n",
       "14527     30.0         1.750        1.33333               1.0            1.0   \n",
       "14528     19.0         3.125        2.44444               1.0            1.4   \n",
       "14529     26.0         1.500        1.00000               1.0            1.0   \n",
       "14530     25.0         1.250        1.11111               1.2            1.0   \n",
       "14531     24.0         2.250        1.55556               2.0            2.2   \n",
       "\n",
       "       EcogPtOrgan_bl  EcogPtDivatt_bl  EcogPtTotal_bl  EcogSPMem_bl  \\\n",
       "14527         1.00000             1.25         1.26316         1.250   \n",
       "14528         1.16667             2.00         1.94872         2.125   \n",
       "14529         1.16667             1.00         1.13158         1.000   \n",
       "14530         1.33333             1.25         1.18919         2.625   \n",
       "14531         2.33333             2.25         2.05263         1.500   \n",
       "\n",
       "       EcogSPLang_bl  EcogSPVisspat_bl  EcogSPPlan_bl  EcogSPOrgan_bl  \\\n",
       "14527        1.11111           1.00000            1.4         1.33333   \n",
       "14528        2.00000           1.14286            1.8         1.50000   \n",
       "14529        1.00000           1.00000            1.0         1.00000   \n",
       "14530        1.44444           1.50000            2.8         2.33333   \n",
       "14531        1.22222           1.50000            1.0         1.00000   \n",
       "\n",
       "       EcogSPDivatt_bl  EcogSPTotal_bl ABETA_bl TAU_bl PTAU_bl   FDG_bl  \\\n",
       "14527             1.50         1.23684      NaN    NaN     NaN      NaN   \n",
       "14528             2.00         1.76923    >1700  261.3   23.94  1.28876   \n",
       "14529             1.00         1.00000      NaN    NaN     NaN      NaN   \n",
       "14530             2.75         2.15789      NaN    NaN     NaN  1.19248   \n",
       "14531             1.25         1.26316      NaN    NaN     NaN      NaN   \n",
       "\n",
       "       PIB_bl   AV45_bl  Years_bl  Month_bl  Month     M  \\\n",
       "14527     NaN  1.004900   2.02053   24.1967   24.0  24.0   \n",
       "14528     NaN  0.946004   7.58385   90.8197   90.0  96.0   \n",
       "14529     NaN       NaN   1.96851   23.5738   24.0  24.0   \n",
       "14530     NaN       NaN   1.01574   12.1639   12.0  12.0   \n",
       "14531     NaN       NaN   0.00000    0.0000    0.0   0.0   \n",
       "\n",
       "                update_stamp Unnamed: 109  \n",
       "14527  2019-12-14 04:20:30.0          NaN  \n",
       "14528  2019-12-17 04:20:22.0          NaN  \n",
       "14529  2019-12-17 04:20:25.0          NaN  \n",
       "14530  2019-12-17 04:20:26.0          NaN  \n",
       "14531  2019-12-17 04:20:26.0          NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()  # data.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise**\n",
    "\n",
    "Why is there a column named \"Unnamed: 109\" with `NaN` values? Check the csv file for answers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### DataFrame information\n",
    "\n",
    "Get information about a DataFrame including:\n",
    "- Index dtype\n",
    "- Column dtypes\n",
    "- Non-null values\n",
    "- Memory usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14532 entries, 0 to 14531\n",
      "Columns: 110 entries, RID to Unnamed: 109\n",
      "dtypes: float64(85), int64(4), object(21)\n",
      "memory usage: 12.2+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![bla](https://pbpython.com/images/pandas_dtypes.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### DataFrame column names\n",
    "\n",
    "Get a all columns names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['RID', 'VISCODE', 'SITE', 'EXAMDATE', 'DX_bl', 'AGE', 'PTGENDER',\n",
       "       'PTEDUCAT', 'WORK', 'PTETHCAT',\n",
       "       ...\n",
       "       'PTAU_bl', 'FDG_bl', 'PIB_bl', 'AV45_bl', 'Years_bl', 'Month_bl',\n",
       "       'Month', 'M', 'update_stamp', 'Unnamed: 109'],\n",
       "      dtype='object', length=110)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Descriptive statistics on DataFrame data\n",
    "\n",
    "Use the describe method to see how the data is distributed (numerical features only!)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RID</th>\n",
       "      <th>SITE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PTEDUCAT</th>\n",
       "      <th>FDG</th>\n",
       "      <th>PIB</th>\n",
       "      <th>AV45</th>\n",
       "      <th>CDRSB</th>\n",
       "      <th>ADAS11</th>\n",
       "      <th>ADAS13</th>\n",
       "      <th>ADASQ4</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>RAVLT_immediate</th>\n",
       "      <th>RAVLT_learning</th>\n",
       "      <th>RAVLT_forgetting</th>\n",
       "      <th>RAVLT_perc_forgetting</th>\n",
       "      <th>LDELTOTAL</th>\n",
       "      <th>DIGITSCOR</th>\n",
       "      <th>TRABSCOR</th>\n",
       "      <th>FAQ</th>\n",
       "      <th>MOCA</th>\n",
       "      <th>EcogPtMem</th>\n",
       "      <th>EcogPtLang</th>\n",
       "      <th>EcogPtVisspat</th>\n",
       "      <th>EcogPtPlan</th>\n",
       "      <th>EcogPtOrgan</th>\n",
       "      <th>EcogPtDivatt</th>\n",
       "      <th>EcogPtTotal</th>\n",
       "      <th>EcogSPMem</th>\n",
       "      <th>EcogSPLang</th>\n",
       "      <th>EcogSPVisspat</th>\n",
       "      <th>EcogSPPlan</th>\n",
       "      <th>EcogSPOrgan</th>\n",
       "      <th>EcogSPDivatt</th>\n",
       "      <th>EcogSPTotal</th>\n",
       "      <th>FLDSTRENG</th>\n",
       "      <th>IMAGEUID</th>\n",
       "      <th>Ventricles</th>\n",
       "      <th>Hippocampus</th>\n",
       "      <th>WholeBrain</th>\n",
       "      <th>Entorhinal</th>\n",
       "      <th>Fusiform</th>\n",
       "      <th>MidTemp</th>\n",
       "      <th>ICV</th>\n",
       "      <th>mPACCtrailsB</th>\n",
       "      <th>ADAS11_bl</th>\n",
       "      <th>ADAS13_bl</th>\n",
       "      <th>ADASQ4_bl</th>\n",
       "      <th>MMSE_bl</th>\n",
       "      <th>RAVLT_immediate_bl</th>\n",
       "      <th>RAVLT_learning_bl</th>\n",
       "      <th>RAVLT_forgetting_bl</th>\n",
       "      <th>RAVLT_perc_forgetting_bl</th>\n",
       "      <th>LDELTOTAL_BL</th>\n",
       "      <th>DIGITSCOR_bl</th>\n",
       "      <th>TRABSCOR_bl</th>\n",
       "      <th>FAQ_bl</th>\n",
       "      <th>mPACCdigit_bl</th>\n",
       "      <th>mPACCtrailsB_bl</th>\n",
       "      <th>FLDSTRENG_bl</th>\n",
       "      <th>Ventricles_bl</th>\n",
       "      <th>Hippocampus_bl</th>\n",
       "      <th>WholeBrain_bl</th>\n",
       "      <th>Entorhinal_bl</th>\n",
       "      <th>Fusiform_bl</th>\n",
       "      <th>MidTemp_bl</th>\n",
       "      <th>ICV_bl</th>\n",
       "      <th>MOCA_bl</th>\n",
       "      <th>EcogPtMem_bl</th>\n",
       "      <th>EcogPtLang_bl</th>\n",
       "      <th>EcogPtVisspat_bl</th>\n",
       "      <th>EcogPtPlan_bl</th>\n",
       "      <th>EcogPtOrgan_bl</th>\n",
       "      <th>EcogPtDivatt_bl</th>\n",
       "      <th>EcogPtTotal_bl</th>\n",
       "      <th>EcogSPMem_bl</th>\n",
       "      <th>EcogSPLang_bl</th>\n",
       "      <th>EcogSPVisspat_bl</th>\n",
       "      <th>EcogSPPlan_bl</th>\n",
       "      <th>EcogSPOrgan_bl</th>\n",
       "      <th>EcogSPDivatt_bl</th>\n",
       "      <th>EcogSPTotal_bl</th>\n",
       "      <th>FDG_bl</th>\n",
       "      <th>PIB_bl</th>\n",
       "      <th>AV45_bl</th>\n",
       "      <th>Years_bl</th>\n",
       "      <th>Month_bl</th>\n",
       "      <th>Month</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>14532.000000</td>\n",
       "      <td>14532.000000</td>\n",
       "      <td>14529.000000</td>\n",
       "      <td>14532.000000</td>\n",
       "      <td>3631.000000</td>\n",
       "      <td>223.000000</td>\n",
       "      <td>2669.000000</td>\n",
       "      <td>10431.000000</td>\n",
       "      <td>10298.000000</td>\n",
       "      <td>10203.000000</td>\n",
       "      <td>10324.000000</td>\n",
       "      <td>10341.000000</td>\n",
       "      <td>10215.000000</td>\n",
       "      <td>10216.000000</td>\n",
       "      <td>10188.000000</td>\n",
       "      <td>10116.000000</td>\n",
       "      <td>8309.000000</td>\n",
       "      <td>3801.000000</td>\n",
       "      <td>9899.000000</td>\n",
       "      <td>10440.000000</td>\n",
       "      <td>6329.000000</td>\n",
       "      <td>6463.000000</td>\n",
       "      <td>6443.000000</td>\n",
       "      <td>6392.000000</td>\n",
       "      <td>6433.000000</td>\n",
       "      <td>6307.000000</td>\n",
       "      <td>6396.000000</td>\n",
       "      <td>6451.000000</td>\n",
       "      <td>6514.000000</td>\n",
       "      <td>6519.000000</td>\n",
       "      <td>6383.000000</td>\n",
       "      <td>6437.000000</td>\n",
       "      <td>6230.000000</td>\n",
       "      <td>6345.000000</td>\n",
       "      <td>6512.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>7952.000000</td>\n",
       "      <td>7446.000000</td>\n",
       "      <td>6803.000000</td>\n",
       "      <td>7.690000e+03</td>\n",
       "      <td>6.470000e+03</td>\n",
       "      <td>6470.000000</td>\n",
       "      <td>6470.000000</td>\n",
       "      <td>7.954000e+03</td>\n",
       "      <td>10332.000000</td>\n",
       "      <td>14502.000000</td>\n",
       "      <td>14436.000000</td>\n",
       "      <td>14524.000000</td>\n",
       "      <td>14532.000000</td>\n",
       "      <td>14489.000000</td>\n",
       "      <td>14489.000000</td>\n",
       "      <td>14488.000000</td>\n",
       "      <td>14466.000000</td>\n",
       "      <td>14523.000000</td>\n",
       "      <td>7126.000000</td>\n",
       "      <td>14282.000000</td>\n",
       "      <td>14465.000000</td>\n",
       "      <td>14526.000000</td>\n",
       "      <td>14526.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>13146.000000</td>\n",
       "      <td>11824.000000</td>\n",
       "      <td>1.341700e+04</td>\n",
       "      <td>1.169300e+04</td>\n",
       "      <td>11693.000000</td>\n",
       "      <td>11693.000000</td>\n",
       "      <td>1.358100e+04</td>\n",
       "      <td>7.278000e+03</td>\n",
       "      <td>7359.000000</td>\n",
       "      <td>7353.000000</td>\n",
       "      <td>7328.000000</td>\n",
       "      <td>7356.000000</td>\n",
       "      <td>7200.00000</td>\n",
       "      <td>7310.000000</td>\n",
       "      <td>7357.000000</td>\n",
       "      <td>7334.000000</td>\n",
       "      <td>7341.000000</td>\n",
       "      <td>7239.000000</td>\n",
       "      <td>7286.000000</td>\n",
       "      <td>7003.000000</td>\n",
       "      <td>7179.000000</td>\n",
       "      <td>7332.000000</td>\n",
       "      <td>10204.000000</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>6749.000000</td>\n",
       "      <td>14529.000000</td>\n",
       "      <td>14532.000000</td>\n",
       "      <td>14532.000000</td>\n",
       "      <td>14531.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2689.323218</td>\n",
       "      <td>230.022296</td>\n",
       "      <td>73.522541</td>\n",
       "      <td>16.052092</td>\n",
       "      <td>1.205512</td>\n",
       "      <td>1.782337</td>\n",
       "      <td>1.189418</td>\n",
       "      <td>2.087528</td>\n",
       "      <td>11.494013</td>\n",
       "      <td>17.530563</td>\n",
       "      <td>5.102867</td>\n",
       "      <td>26.721400</td>\n",
       "      <td>35.260010</td>\n",
       "      <td>4.155149</td>\n",
       "      <td>4.079309</td>\n",
       "      <td>56.712539</td>\n",
       "      <td>8.614541</td>\n",
       "      <td>37.364904</td>\n",
       "      <td>117.866249</td>\n",
       "      <td>5.368103</td>\n",
       "      <td>23.276347</td>\n",
       "      <td>2.057865</td>\n",
       "      <td>1.718384</td>\n",
       "      <td>1.388757</td>\n",
       "      <td>1.408397</td>\n",
       "      <td>1.527153</td>\n",
       "      <td>1.823171</td>\n",
       "      <td>1.674671</td>\n",
       "      <td>2.110136</td>\n",
       "      <td>1.698362</td>\n",
       "      <td>1.582680</td>\n",
       "      <td>1.681200</td>\n",
       "      <td>1.761065</td>\n",
       "      <td>1.968348</td>\n",
       "      <td>1.803508</td>\n",
       "      <td>1.76316</td>\n",
       "      <td>238351.771127</td>\n",
       "      <td>42158.511818</td>\n",
       "      <td>6690.837719</td>\n",
       "      <td>1.011558e+06</td>\n",
       "      <td>3.780983e+03</td>\n",
       "      <td>17141.194590</td>\n",
       "      <td>19202.884853</td>\n",
       "      <td>1.533709e+06</td>\n",
       "      <td>-5.810488</td>\n",
       "      <td>9.704883</td>\n",
       "      <td>15.253412</td>\n",
       "      <td>4.809877</td>\n",
       "      <td>27.668318</td>\n",
       "      <td>37.150459</td>\n",
       "      <td>4.548623</td>\n",
       "      <td>4.258490</td>\n",
       "      <td>53.672283</td>\n",
       "      <td>7.977207</td>\n",
       "      <td>40.198569</td>\n",
       "      <td>109.817883</td>\n",
       "      <td>3.163844</td>\n",
       "      <td>-4.912862</td>\n",
       "      <td>-4.682232</td>\n",
       "      <td>-5.805320</td>\n",
       "      <td>38593.604138</td>\n",
       "      <td>6908.691644</td>\n",
       "      <td>1.027019e+06</td>\n",
       "      <td>3.758083e+03</td>\n",
       "      <td>17500.660737</td>\n",
       "      <td>19751.744120</td>\n",
       "      <td>1.535276e+06</td>\n",
       "      <td>4.979593e+02</td>\n",
       "      <td>2.090030</td>\n",
       "      <td>1.717113</td>\n",
       "      <td>1.365063</td>\n",
       "      <td>1.388882</td>\n",
       "      <td>1.49491</td>\n",
       "      <td>1.792442</td>\n",
       "      <td>1.663902</td>\n",
       "      <td>1.986185</td>\n",
       "      <td>1.552675</td>\n",
       "      <td>1.393904</td>\n",
       "      <td>1.505861</td>\n",
       "      <td>1.594714</td>\n",
       "      <td>1.780169</td>\n",
       "      <td>1.639943</td>\n",
       "      <td>1.250349</td>\n",
       "      <td>1.581759</td>\n",
       "      <td>1.193165</td>\n",
       "      <td>2.422421</td>\n",
       "      <td>29.003632</td>\n",
       "      <td>28.925473</td>\n",
       "      <td>28.746198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2050.550766</td>\n",
       "      <td>120.005597</td>\n",
       "      <td>7.092087</td>\n",
       "      <td>2.796145</td>\n",
       "      <td>0.159062</td>\n",
       "      <td>0.423372</td>\n",
       "      <td>0.227379</td>\n",
       "      <td>2.852369</td>\n",
       "      <td>8.364001</td>\n",
       "      <td>11.371985</td>\n",
       "      <td>3.108959</td>\n",
       "      <td>3.931502</td>\n",
       "      <td>13.744249</td>\n",
       "      <td>2.879895</td>\n",
       "      <td>10.634914</td>\n",
       "      <td>103.915605</td>\n",
       "      <td>6.345335</td>\n",
       "      <td>14.446934</td>\n",
       "      <td>77.273785</td>\n",
       "      <td>8.061208</td>\n",
       "      <td>4.735876</td>\n",
       "      <td>0.795149</td>\n",
       "      <td>0.640174</td>\n",
       "      <td>0.545718</td>\n",
       "      <td>0.559745</td>\n",
       "      <td>0.631117</td>\n",
       "      <td>0.757081</td>\n",
       "      <td>0.551183</td>\n",
       "      <td>0.996786</td>\n",
       "      <td>0.829791</td>\n",
       "      <td>0.850826</td>\n",
       "      <td>0.913555</td>\n",
       "      <td>0.974632</td>\n",
       "      <td>1.006557</td>\n",
       "      <td>0.853222</td>\n",
       "      <td>NaN</td>\n",
       "      <td>138006.638622</td>\n",
       "      <td>23291.051914</td>\n",
       "      <td>1396.681459</td>\n",
       "      <td>1.131636e+05</td>\n",
       "      <td>1.919770e+04</td>\n",
       "      <td>2830.513114</td>\n",
       "      <td>3138.488244</td>\n",
       "      <td>1.680023e+05</td>\n",
       "      <td>7.830616</td>\n",
       "      <td>5.818237</td>\n",
       "      <td>8.594518</td>\n",
       "      <td>2.844647</td>\n",
       "      <td>2.393435</td>\n",
       "      <td>12.167704</td>\n",
       "      <td>2.754395</td>\n",
       "      <td>2.595281</td>\n",
       "      <td>34.852861</td>\n",
       "      <td>5.369075</td>\n",
       "      <td>12.416141</td>\n",
       "      <td>67.177912</td>\n",
       "      <td>5.697284</td>\n",
       "      <td>5.755696</td>\n",
       "      <td>5.486149</td>\n",
       "      <td>4.651175</td>\n",
       "      <td>21693.852450</td>\n",
       "      <td>1237.887877</td>\n",
       "      <td>1.098149e+05</td>\n",
       "      <td>1.443252e+04</td>\n",
       "      <td>2660.002223</td>\n",
       "      <td>2964.206076</td>\n",
       "      <td>1.636493e+05</td>\n",
       "      <td>2.868337e+04</td>\n",
       "      <td>0.774728</td>\n",
       "      <td>0.621605</td>\n",
       "      <td>0.524857</td>\n",
       "      <td>0.541176</td>\n",
       "      <td>0.60674</td>\n",
       "      <td>0.733149</td>\n",
       "      <td>0.529170</td>\n",
       "      <td>0.887123</td>\n",
       "      <td>0.694449</td>\n",
       "      <td>0.640527</td>\n",
       "      <td>0.727897</td>\n",
       "      <td>0.813544</td>\n",
       "      <td>0.876843</td>\n",
       "      <td>0.685036</td>\n",
       "      <td>0.224457</td>\n",
       "      <td>0.305296</td>\n",
       "      <td>0.219321</td>\n",
       "      <td>2.559634</td>\n",
       "      <td>30.652204</td>\n",
       "      <td>30.620796</td>\n",
       "      <td>30.468485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>128.000000</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.636743</td>\n",
       "      <td>1.095000</td>\n",
       "      <td>0.805364</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-1035.000000</td>\n",
       "      <td>-9409.090000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.76316</td>\n",
       "      <td>31851.000000</td>\n",
       "      <td>5652.000000</td>\n",
       "      <td>2216.000000</td>\n",
       "      <td>6.597000e+03</td>\n",
       "      <td>1.040000e+03</td>\n",
       "      <td>2707.000000</td>\n",
       "      <td>8044.000000</td>\n",
       "      <td>2.968840e+02</td>\n",
       "      <td>-60.989100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-28.000000</td>\n",
       "      <td>-400.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-23.418500</td>\n",
       "      <td>-23.418500</td>\n",
       "      <td>-10.696400</td>\n",
       "      <td>5652.000000</td>\n",
       "      <td>2995.000000</td>\n",
       "      <td>6.792000e+03</td>\n",
       "      <td>1.425000e+03</td>\n",
       "      <td>3274.000000</td>\n",
       "      <td>9375.000000</td>\n",
       "      <td>1.807700e+04</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.693671</td>\n",
       "      <td>1.155000</td>\n",
       "      <td>0.805364</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>806.000000</td>\n",
       "      <td>174.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.108200</td>\n",
       "      <td>1.357500</td>\n",
       "      <td>1.007440</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>27.272700</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.222220</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>1.256410</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.127407</td>\n",
       "      <td>1.76316</td>\n",
       "      <td>107924.000000</td>\n",
       "      <td>25130.750000</td>\n",
       "      <td>5828.500000</td>\n",
       "      <td>9.336520e+05</td>\n",
       "      <td>2.905000e+03</td>\n",
       "      <td>15267.250000</td>\n",
       "      <td>17181.750000</td>\n",
       "      <td>1.417092e+06</td>\n",
       "      <td>-10.345900</td>\n",
       "      <td>5.670000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-8.925790</td>\n",
       "      <td>-8.480420</td>\n",
       "      <td>-7.988745</td>\n",
       "      <td>22997.000000</td>\n",
       "      <td>6106.000000</td>\n",
       "      <td>9.500680e+05</td>\n",
       "      <td>3.061000e+03</td>\n",
       "      <td>15753.000000</td>\n",
       "      <td>17802.000000</td>\n",
       "      <td>1.421003e+06</td>\n",
       "      <td>2.200000e+01</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.222220</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>1.270270</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.108110</td>\n",
       "      <td>1.160830</td>\n",
       "      <td>1.360000</td>\n",
       "      <td>1.015360</td>\n",
       "      <td>0.495551</td>\n",
       "      <td>5.934430</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2171.000000</td>\n",
       "      <td>194.000000</td>\n",
       "      <td>73.500000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>1.217810</td>\n",
       "      <td>1.850000</td>\n",
       "      <td>1.106450</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.330000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>1.875000</td>\n",
       "      <td>1.555560</td>\n",
       "      <td>1.142860</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>1.333330</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>1.538460</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>1.333330</td>\n",
       "      <td>1.142860</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>1.333330</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>1.459460</td>\n",
       "      <td>1.76316</td>\n",
       "      <td>259473.500000</td>\n",
       "      <td>37452.000000</td>\n",
       "      <td>6760.000000</td>\n",
       "      <td>1.009262e+06</td>\n",
       "      <td>3.482000e+03</td>\n",
       "      <td>17180.000000</td>\n",
       "      <td>19241.000000</td>\n",
       "      <td>1.522356e+06</td>\n",
       "      <td>-3.743110</td>\n",
       "      <td>8.670000</td>\n",
       "      <td>13.670000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.882295</td>\n",
       "      <td>-3.590360</td>\n",
       "      <td>-5.281090</td>\n",
       "      <td>33594.000000</td>\n",
       "      <td>6987.000000</td>\n",
       "      <td>1.025128e+06</td>\n",
       "      <td>3.594000e+03</td>\n",
       "      <td>17428.000000</td>\n",
       "      <td>19683.000000</td>\n",
       "      <td>1.522877e+06</td>\n",
       "      <td>2.400000e+01</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.555560</td>\n",
       "      <td>1.142860</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>1.33333</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>1.538460</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>1.142860</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>1.166670</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.378380</td>\n",
       "      <td>1.256040</td>\n",
       "      <td>1.490000</td>\n",
       "      <td>1.108140</td>\n",
       "      <td>1.524980</td>\n",
       "      <td>18.262300</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4615.000000</td>\n",
       "      <td>269.000000</td>\n",
       "      <td>78.500000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.310275</td>\n",
       "      <td>2.127500</td>\n",
       "      <td>1.353690</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>23.330000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.111110</td>\n",
       "      <td>1.571430</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>1.833330</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>1.948720</td>\n",
       "      <td>2.875000</td>\n",
       "      <td>2.118055</td>\n",
       "      <td>1.857140</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>2.282050</td>\n",
       "      <td>1.76316</td>\n",
       "      <td>350677.500000</td>\n",
       "      <td>53423.500000</td>\n",
       "      <td>7581.000000</td>\n",
       "      <td>1.088223e+06</td>\n",
       "      <td>4.013000e+03</td>\n",
       "      <td>19024.000000</td>\n",
       "      <td>21393.750000</td>\n",
       "      <td>1.641200e+06</td>\n",
       "      <td>0.066128</td>\n",
       "      <td>12.330000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>85.714300</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>124.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>-0.493413</td>\n",
       "      <td>-0.509204</td>\n",
       "      <td>-3.359780</td>\n",
       "      <td>48932.000000</td>\n",
       "      <td>7713.000000</td>\n",
       "      <td>1.099823e+06</td>\n",
       "      <td>4.114000e+03</td>\n",
       "      <td>19216.000000</td>\n",
       "      <td>21755.000000</td>\n",
       "      <td>1.641093e+06</td>\n",
       "      <td>2.600000e+01</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.111110</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>1.80000</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>1.921050</td>\n",
       "      <td>2.625000</td>\n",
       "      <td>1.777780</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>1.964290</td>\n",
       "      <td>1.339960</td>\n",
       "      <td>1.835000</td>\n",
       "      <td>1.360150</td>\n",
       "      <td>3.542780</td>\n",
       "      <td>42.426200</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>42.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6963.000000</td>\n",
       "      <td>1094.000000</td>\n",
       "      <td>93.100000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.751210</td>\n",
       "      <td>2.927500</td>\n",
       "      <td>2.669210</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>29.330000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>996.000000</td>\n",
       "      <td>161.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.76316</td>\n",
       "      <td>722839.000000</td>\n",
       "      <td>162729.000000</td>\n",
       "      <td>56830.000000</td>\n",
       "      <td>1.486041e+06</td>\n",
       "      <td>1.141372e+06</td>\n",
       "      <td>29952.000000</td>\n",
       "      <td>32193.000000</td>\n",
       "      <td>2.110293e+06</td>\n",
       "      <td>12.520800</td>\n",
       "      <td>42.670000</td>\n",
       "      <td>54.670000</td>\n",
       "      <td>28.330000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>6.264330</td>\n",
       "      <td>7.412500</td>\n",
       "      <td>-1.438470</td>\n",
       "      <td>145113.000000</td>\n",
       "      <td>50745.000000</td>\n",
       "      <td>1.486041e+06</td>\n",
       "      <td>1.163222e+06</td>\n",
       "      <td>29952.000000</td>\n",
       "      <td>32193.000000</td>\n",
       "      <td>2.110293e+06</td>\n",
       "      <td>1.850330e+06</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.948720</td>\n",
       "      <td>18.620000</td>\n",
       "      <td>2.282500</td>\n",
       "      <td>2.025560</td>\n",
       "      <td>14.086200</td>\n",
       "      <td>168.689000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>168.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                RID          SITE           AGE      PTEDUCAT          FDG  \\\n",
       "count  14532.000000  14532.000000  14529.000000  14532.000000  3631.000000   \n",
       "mean    2689.323218    230.022296     73.522541     16.052092     1.205512   \n",
       "std     2050.550766    120.005597      7.092087      2.796145     0.159062   \n",
       "min      128.000000    155.000000      0.000000      4.000000     0.636743   \n",
       "25%      806.000000    174.000000     69.000000     14.000000     1.108200   \n",
       "50%     2171.000000    194.000000     73.500000     16.000000     1.217810   \n",
       "75%     4615.000000    269.000000     78.500000     18.000000     1.310275   \n",
       "max     6963.000000   1094.000000     93.100000     20.000000     1.751210   \n",
       "\n",
       "              PIB         AV45         CDRSB        ADAS11        ADAS13  \\\n",
       "count  223.000000  2669.000000  10431.000000  10298.000000  10203.000000   \n",
       "mean     1.782337     1.189418      2.087528     11.494013     17.530563   \n",
       "std      0.423372     0.227379      2.852369      8.364001     11.371985   \n",
       "min      1.095000     0.805364      0.000000      0.000000      0.000000   \n",
       "25%      1.357500     1.007440      0.000000      6.000000      9.000000   \n",
       "50%      1.850000     1.106450      1.000000      9.330000     15.000000   \n",
       "75%      2.127500     1.353690      3.000000     15.000000     23.330000   \n",
       "max      2.927500     2.669210     18.000000     70.000000     85.000000   \n",
       "\n",
       "             ADASQ4          MMSE  RAVLT_immediate  RAVLT_learning  \\\n",
       "count  10324.000000  10341.000000     10215.000000    10216.000000   \n",
       "mean       5.102867     26.721400        35.260010        4.155149   \n",
       "std        3.108959      3.931502        13.744249        2.879895   \n",
       "min        0.000000      0.000000         0.000000       -8.000000   \n",
       "25%        3.000000     25.000000        25.000000        2.000000   \n",
       "50%        5.000000     28.000000        34.000000        4.000000   \n",
       "75%        8.000000     29.000000        45.000000        6.000000   \n",
       "max       29.330000     30.000000        75.000000       42.000000   \n",
       "\n",
       "       RAVLT_forgetting  RAVLT_perc_forgetting    LDELTOTAL    DIGITSCOR  \\\n",
       "count      10188.000000           10116.000000  8309.000000  3801.000000   \n",
       "mean           4.079309              56.712539     8.614541    37.364904   \n",
       "std           10.634914             103.915605     6.345335    14.446934   \n",
       "min        -1035.000000           -9409.090000     0.000000     0.000000   \n",
       "25%            3.000000              27.272700     3.000000    29.000000   \n",
       "50%            4.000000              60.000000     9.000000    38.000000   \n",
       "75%            6.000000             100.000000    14.000000    47.000000   \n",
       "max           15.000000             100.000000   100.000000    83.000000   \n",
       "\n",
       "          TRABSCOR           FAQ         MOCA    EcogPtMem   EcogPtLang  \\\n",
       "count  9899.000000  10440.000000  6329.000000  6463.000000  6443.000000   \n",
       "mean    117.866249      5.368103    23.276347     2.057865     1.718384   \n",
       "std      77.273785      8.061208     4.735876     0.795149     0.640174   \n",
       "min       0.000000      0.000000     0.000000     1.000000     1.000000   \n",
       "25%      65.000000      0.000000    21.000000     1.500000     1.222220   \n",
       "50%      89.000000      1.000000    24.000000     1.875000     1.555560   \n",
       "75%     141.000000      8.000000    27.000000     2.500000     2.111110   \n",
       "max     996.000000    161.000000    30.000000    26.000000     4.000000   \n",
       "\n",
       "       EcogPtVisspat   EcogPtPlan  EcogPtOrgan  EcogPtDivatt  EcogPtTotal  \\\n",
       "count    6392.000000  6433.000000  6307.000000   6396.000000  6451.000000   \n",
       "mean        1.388757     1.408397     1.527153      1.823171     1.674671   \n",
       "std         0.545718     0.559745     0.631117      0.757081     0.551183   \n",
       "min         1.000000     1.000000     1.000000      1.000000     1.000000   \n",
       "25%         1.000000     1.000000     1.000000      1.250000     1.256410   \n",
       "50%         1.142860     1.200000     1.333330      1.750000     1.538460   \n",
       "75%         1.571430     1.600000     1.833330      2.250000     1.948720   \n",
       "max         4.000000     4.000000     4.000000      4.000000     4.000000   \n",
       "\n",
       "         EcogSPMem   EcogSPLang  EcogSPVisspat   EcogSPPlan  EcogSPOrgan  \\\n",
       "count  6514.000000  6519.000000    6383.000000  6437.000000  6230.000000   \n",
       "mean      2.110136     1.698362       1.582680     1.681200     1.761065   \n",
       "std       0.996786     0.829791       0.850826     0.913555     0.974632   \n",
       "min       1.000000     1.000000       1.000000     1.000000     1.000000   \n",
       "25%       1.250000     1.000000       1.000000     1.000000     1.000000   \n",
       "50%       1.750000     1.333330       1.142860     1.200000     1.333330   \n",
       "75%       2.875000     2.118055       1.857140     2.000000     2.250000   \n",
       "max       4.000000     4.000000       4.000000     4.000000     4.000000   \n",
       "\n",
       "       EcogSPDivatt  EcogSPTotal  FLDSTRENG       IMAGEUID     Ventricles  \\\n",
       "count   6345.000000  6512.000000    1.00000    7952.000000    7446.000000   \n",
       "mean       1.968348     1.803508    1.76316  238351.771127   42158.511818   \n",
       "std        1.006557     0.853222        NaN  138006.638622   23291.051914   \n",
       "min        1.000000     1.000000    1.76316   31851.000000    5652.000000   \n",
       "25%        1.000000     1.127407    1.76316  107924.000000   25130.750000   \n",
       "50%        1.750000     1.459460    1.76316  259473.500000   37452.000000   \n",
       "75%        2.750000     2.282050    1.76316  350677.500000   53423.500000   \n",
       "max        4.000000     4.000000    1.76316  722839.000000  162729.000000   \n",
       "\n",
       "        Hippocampus    WholeBrain    Entorhinal      Fusiform       MidTemp  \\\n",
       "count   6803.000000  7.690000e+03  6.470000e+03   6470.000000   6470.000000   \n",
       "mean    6690.837719  1.011558e+06  3.780983e+03  17141.194590  19202.884853   \n",
       "std     1396.681459  1.131636e+05  1.919770e+04   2830.513114   3138.488244   \n",
       "min     2216.000000  6.597000e+03  1.040000e+03   2707.000000   8044.000000   \n",
       "25%     5828.500000  9.336520e+05  2.905000e+03  15267.250000  17181.750000   \n",
       "50%     6760.000000  1.009262e+06  3.482000e+03  17180.000000  19241.000000   \n",
       "75%     7581.000000  1.088223e+06  4.013000e+03  19024.000000  21393.750000   \n",
       "max    56830.000000  1.486041e+06  1.141372e+06  29952.000000  32193.000000   \n",
       "\n",
       "                ICV  mPACCtrailsB     ADAS11_bl     ADAS13_bl     ADASQ4_bl  \\\n",
       "count  7.954000e+03  10332.000000  14502.000000  14436.000000  14524.000000   \n",
       "mean   1.533709e+06     -5.810488      9.704883     15.253412      4.809877   \n",
       "std    1.680023e+05      7.830616      5.818237      8.594518      2.844647   \n",
       "min    2.968840e+02    -60.989100      0.000000      0.000000      0.000000   \n",
       "25%    1.417092e+06    -10.345900      5.670000      9.000000      2.000000   \n",
       "50%    1.522356e+06     -3.743110      8.670000     13.670000      5.000000   \n",
       "75%    1.641200e+06      0.066128     12.330000     20.000000      7.000000   \n",
       "max    2.110293e+06     12.520800     42.670000     54.670000     28.330000   \n",
       "\n",
       "            MMSE_bl  RAVLT_immediate_bl  RAVLT_learning_bl  \\\n",
       "count  14532.000000        14489.000000       14489.000000   \n",
       "mean      27.668318           37.150459           4.548623   \n",
       "std        2.393435           12.167704           2.754395   \n",
       "min        2.000000            0.000000          -8.000000   \n",
       "25%       26.000000           28.000000           3.000000   \n",
       "50%       28.000000           36.000000           4.000000   \n",
       "75%       30.000000           46.000000           7.000000   \n",
       "max       30.000000           71.000000          42.000000   \n",
       "\n",
       "       RAVLT_forgetting_bl  RAVLT_perc_forgetting_bl  LDELTOTAL_BL  \\\n",
       "count         14488.000000              14466.000000  14523.000000   \n",
       "mean              4.258490                 53.672283      7.977207   \n",
       "std               2.595281                 34.852861      5.369075   \n",
       "min             -28.000000               -400.000000      0.000000   \n",
       "25%               3.000000                 25.000000      4.000000   \n",
       "50%               4.000000                 50.000000      8.000000   \n",
       "75%               6.000000                 85.714300     12.000000   \n",
       "max              15.000000                100.000000    100.000000   \n",
       "\n",
       "       DIGITSCOR_bl   TRABSCOR_bl        FAQ_bl  mPACCdigit_bl  \\\n",
       "count   7126.000000  14282.000000  14465.000000   14526.000000   \n",
       "mean      40.198569    109.817883      3.163844      -4.912862   \n",
       "std       12.416141     67.177912      5.697284       5.755696   \n",
       "min        0.000000      0.000000      0.000000     -23.418500   \n",
       "25%       33.000000     66.000000      0.000000      -8.925790   \n",
       "50%       41.000000     87.000000      0.000000      -3.882295   \n",
       "75%       48.000000    124.000000      4.000000      -0.493413   \n",
       "max       80.000000    300.000000    165.000000       6.264330   \n",
       "\n",
       "       mPACCtrailsB_bl  FLDSTRENG_bl  Ventricles_bl  Hippocampus_bl  \\\n",
       "count     14526.000000      3.000000   13146.000000    11824.000000   \n",
       "mean         -4.682232     -5.805320   38593.604138     6908.691644   \n",
       "std           5.486149      4.651175   21693.852450     1237.887877   \n",
       "min         -23.418500    -10.696400    5652.000000     2995.000000   \n",
       "25%          -8.480420     -7.988745   22997.000000     6106.000000   \n",
       "50%          -3.590360     -5.281090   33594.000000     6987.000000   \n",
       "75%          -0.509204     -3.359780   48932.000000     7713.000000   \n",
       "max           7.412500     -1.438470  145113.000000    50745.000000   \n",
       "\n",
       "       WholeBrain_bl  Entorhinal_bl   Fusiform_bl    MidTemp_bl        ICV_bl  \\\n",
       "count   1.341700e+04   1.169300e+04  11693.000000  11693.000000  1.358100e+04   \n",
       "mean    1.027019e+06   3.758083e+03  17500.660737  19751.744120  1.535276e+06   \n",
       "std     1.098149e+05   1.443252e+04   2660.002223   2964.206076  1.636493e+05   \n",
       "min     6.792000e+03   1.425000e+03   3274.000000   9375.000000  1.807700e+04   \n",
       "25%     9.500680e+05   3.061000e+03  15753.000000  17802.000000  1.421003e+06   \n",
       "50%     1.025128e+06   3.594000e+03  17428.000000  19683.000000  1.522877e+06   \n",
       "75%     1.099823e+06   4.114000e+03  19216.000000  21755.000000  1.641093e+06   \n",
       "max     1.486041e+06   1.163222e+06  29952.000000  32193.000000  2.110293e+06   \n",
       "\n",
       "            MOCA_bl  EcogPtMem_bl  EcogPtLang_bl  EcogPtVisspat_bl  \\\n",
       "count  7.278000e+03   7359.000000    7353.000000       7328.000000   \n",
       "mean   4.979593e+02      2.090030       1.717113          1.365063   \n",
       "std    2.868337e+04      0.774728       0.621605          0.524857   \n",
       "min    4.000000e+00      1.000000       1.000000          1.000000   \n",
       "25%    2.200000e+01      1.500000       1.222220          1.000000   \n",
       "50%    2.400000e+01      2.000000       1.555560          1.142860   \n",
       "75%    2.600000e+01      2.500000       2.111110          1.500000   \n",
       "max    1.850330e+06     26.000000       4.000000          4.000000   \n",
       "\n",
       "       EcogPtPlan_bl  EcogPtOrgan_bl  EcogPtDivatt_bl  EcogPtTotal_bl  \\\n",
       "count    7356.000000      7200.00000      7310.000000     7357.000000   \n",
       "mean        1.388882         1.49491         1.792442        1.663902   \n",
       "std         0.541176         0.60674         0.733149        0.529170   \n",
       "min         1.000000         1.00000         1.000000        1.000000   \n",
       "25%         1.000000         1.00000         1.250000        1.270270   \n",
       "50%         1.200000         1.33333         1.750000        1.538460   \n",
       "75%         1.600000         1.80000         2.250000        1.921050   \n",
       "max         4.000000         4.00000         4.000000        4.000000   \n",
       "\n",
       "       EcogSPMem_bl  EcogSPLang_bl  EcogSPVisspat_bl  EcogSPPlan_bl  \\\n",
       "count   7334.000000    7341.000000       7239.000000    7286.000000   \n",
       "mean       1.986185       1.552675          1.393904       1.505861   \n",
       "std        0.887123       0.694449          0.640527       0.727897   \n",
       "min        1.000000       1.000000          1.000000       1.000000   \n",
       "25%        1.250000       1.000000          1.000000       1.000000   \n",
       "50%        1.750000       1.250000          1.142860       1.200000   \n",
       "75%        2.625000       1.777780          1.500000       1.800000   \n",
       "max        4.000000       4.000000          4.000000       4.000000   \n",
       "\n",
       "       EcogSPOrgan_bl  EcogSPDivatt_bl  EcogSPTotal_bl        FDG_bl  \\\n",
       "count     7003.000000      7179.000000     7332.000000  10204.000000   \n",
       "mean         1.594714         1.780169        1.639943      1.250349   \n",
       "std          0.813544         0.876843        0.685036      0.224457   \n",
       "min          1.000000         1.000000        1.000000      0.693671   \n",
       "25%          1.000000         1.000000        1.108110      1.160830   \n",
       "50%          1.166670         1.500000        1.378380      1.256040   \n",
       "75%          2.000000         2.250000        1.964290      1.339960   \n",
       "max          4.000000         4.000000        3.948720     18.620000   \n",
       "\n",
       "           PIB_bl      AV45_bl      Years_bl      Month_bl         Month  \\\n",
       "count  152.000000  6749.000000  14529.000000  14532.000000  14532.000000   \n",
       "mean     1.581759     1.193165      2.422421     29.003632     28.925473   \n",
       "std      0.305296     0.219321      2.559634     30.652204     30.620796   \n",
       "min      1.155000     0.805364      0.000000      0.000000      0.000000   \n",
       "25%      1.360000     1.015360      0.495551      5.934430      6.000000   \n",
       "50%      1.490000     1.108140      1.524980     18.262300     18.000000   \n",
       "75%      1.835000     1.360150      3.542780     42.426200     42.000000   \n",
       "max      2.282500     2.025560     14.086200    168.689000    168.000000   \n",
       "\n",
       "                  M  \n",
       "count  14531.000000  \n",
       "mean      28.746198  \n",
       "std       30.468485  \n",
       "min        0.000000  \n",
       "25%        6.000000  \n",
       "50%       18.000000  \n",
       "75%       42.000000  \n",
       "max      168.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise\n",
    "\n",
    "Note: You can click on \"> Solution\" to get the programmatic answer to the exercise questions (copy-paste it in the blank cell above each solution and execute the cell to get the answer). In this first exercise however, you can also simply check the outputs above for answers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the number of rows of this DataFrame? Why are some counts lower than that value?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary> > Solution</summary>\n",
    "    \n",
    "```python\n",
    "data.shape[0]  # Number of rows in DataFrame\n",
    "```\n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the mean patients' age?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary> > Solution</summary>\n",
    "    \n",
    "```python\n",
    "data.describe().loc[\"mean\", \"AGE\"]  # Patient's mean age\n",
    "```\n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. What is the minimum and maximum number of years that a patient (or multiple patients) spent on his/her/their education?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary> > Solution</summary>\n",
    "    \n",
    "```python\n",
    "data.describe().loc[\"min\", \"PTEDUCAT\"]  # Minimum number of years patients spent on education\n",
    "data.describe().loc[\"max\", \"PTEDUCAT\"]  # Maximum number of years patients spent on education\n",
    "```\n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.4. Look at individual DataFrame components\n",
    "\n",
    "Taken from: https://medium.com/dunder-data/selecting-subsets-of-data-in-pandas-6fcd0170be9c\n",
    "\n",
    "*pandas* DataFrame are composed of three components: \n",
    "- **index**\n",
    "- **columns** \n",
    "- data (**values**)\n",
    "\n",
    "Let's extract each of these components into their own variables and inspect them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=14532, step=1)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = data.index\n",
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['RID', 'VISCODE', 'SITE', 'EXAMDATE', 'DX_bl', 'AGE', 'PTGENDER',\n",
       "       'PTEDUCAT', 'WORK', 'PTETHCAT',\n",
       "       ...\n",
       "       'PTAU_bl', 'FDG_bl', 'PIB_bl', 'AV45_bl', 'Years_bl', 'Month_bl',\n",
       "       'Month', 'M', 'update_stamp', 'Unnamed: 109'],\n",
       "      dtype='object', length=110)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = data.columns\n",
    "columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[128, 'bl', 164, ..., 0.0, '2019-12-04 04:19:56.0', nan],\n",
       "       [129, 'bl', 164, ..., 0.0, '2019-12-04 04:19:56.0', nan],\n",
       "       [129, 'm06', 164, ..., 6.0, '2019-12-04 04:19:56.0', nan],\n",
       "       ...,\n",
       "       [6262, 'm24', 194, ..., 24.0, '2019-12-17 04:20:25.0', nan],\n",
       "       [6736, 'm12', 159, ..., 12.0, '2019-12-17 04:20:26.0', nan],\n",
       "       [6960, 'bl', 169, ..., 0.0, '2019-12-17 04:20:26.0', nan]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values = data.values\n",
    "values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.indexes.range.RangeIndex"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.indexes.base.Index"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(values)  # Important: Get numpy array from pandas DataFrame/Series!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interestingly, both the `index` and the `columns` are the same type. They are both a *pandas* `Index` objects. This object is quite powerful in itself, but for now you can just think of it as a sequence of labels for either the rows or the columns.\n",
    "\n",
    "The `values`'s type is a *numpy* `ndarray`, which stands for n-dimensional array, and is the primary container of data in the *numpy* library. *pandas* is built directly on top of *numpy* and it's this array that is responsible for the bulk of the workload."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.5. Sort data\n",
    "\n",
    "We can sort data by the values along one or multiple rows or columns using the `sort_values` function.\n",
    "\n",
    "For simplicity, we will only sort our data by one column, e.g. by the patients' examination date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RID</th>\n",
       "      <th>VISCODE</th>\n",
       "      <th>SITE</th>\n",
       "      <th>EXAMDATE</th>\n",
       "      <th>DX_bl</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PTGENDER</th>\n",
       "      <th>PTEDUCAT</th>\n",
       "      <th>WORK</th>\n",
       "      <th>PTETHCAT</th>\n",
       "      <th>PTRACCAT</th>\n",
       "      <th>PTMARRY</th>\n",
       "      <th>APOE4</th>\n",
       "      <th>FDG</th>\n",
       "      <th>PIB</th>\n",
       "      <th>AV45</th>\n",
       "      <th>ABETA</th>\n",
       "      <th>TAU</th>\n",
       "      <th>PTAU</th>\n",
       "      <th>CDRSB</th>\n",
       "      <th>ADAS11</th>\n",
       "      <th>ADAS13</th>\n",
       "      <th>ADASQ4</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>RAVLT_immediate</th>\n",
       "      <th>RAVLT_learning</th>\n",
       "      <th>RAVLT_forgetting</th>\n",
       "      <th>RAVLT_perc_forgetting</th>\n",
       "      <th>LDELTOTAL</th>\n",
       "      <th>DIGITSCOR</th>\n",
       "      <th>TRABSCOR</th>\n",
       "      <th>FAQ</th>\n",
       "      <th>MOCA</th>\n",
       "      <th>EcogPtMem</th>\n",
       "      <th>EcogPtLang</th>\n",
       "      <th>EcogPtVisspat</th>\n",
       "      <th>EcogPtPlan</th>\n",
       "      <th>EcogPtOrgan</th>\n",
       "      <th>EcogPtDivatt</th>\n",
       "      <th>EcogPtTotal</th>\n",
       "      <th>EcogSPMem</th>\n",
       "      <th>EcogSPLang</th>\n",
       "      <th>EcogSPVisspat</th>\n",
       "      <th>EcogSPPlan</th>\n",
       "      <th>EcogSPOrgan</th>\n",
       "      <th>EcogSPDivatt</th>\n",
       "      <th>EcogSPTotal</th>\n",
       "      <th>FLDSTRENG</th>\n",
       "      <th>IMAGEUID</th>\n",
       "      <th>Ventricles</th>\n",
       "      <th>Hippocampus</th>\n",
       "      <th>WholeBrain</th>\n",
       "      <th>Entorhinal</th>\n",
       "      <th>Fusiform</th>\n",
       "      <th>MidTemp</th>\n",
       "      <th>ICV</th>\n",
       "      <th>DX</th>\n",
       "      <th>mPACCdigit</th>\n",
       "      <th>mPACCtrailsB</th>\n",
       "      <th>EXAMDATE_bl</th>\n",
       "      <th>CDRSB_bl</th>\n",
       "      <th>ADAS11_bl</th>\n",
       "      <th>ADAS13_bl</th>\n",
       "      <th>ADASQ4_bl</th>\n",
       "      <th>MMSE_bl</th>\n",
       "      <th>RAVLT_immediate_bl</th>\n",
       "      <th>RAVLT_learning_bl</th>\n",
       "      <th>RAVLT_forgetting_bl</th>\n",
       "      <th>RAVLT_perc_forgetting_bl</th>\n",
       "      <th>LDELTOTAL_BL</th>\n",
       "      <th>DIGITSCOR_bl</th>\n",
       "      <th>TRABSCOR_bl</th>\n",
       "      <th>FAQ_bl</th>\n",
       "      <th>mPACCdigit_bl</th>\n",
       "      <th>mPACCtrailsB_bl</th>\n",
       "      <th>FLDSTRENG_bl</th>\n",
       "      <th>Ventricles_bl</th>\n",
       "      <th>Hippocampus_bl</th>\n",
       "      <th>WholeBrain_bl</th>\n",
       "      <th>Entorhinal_bl</th>\n",
       "      <th>Fusiform_bl</th>\n",
       "      <th>MidTemp_bl</th>\n",
       "      <th>ICV_bl</th>\n",
       "      <th>MOCA_bl</th>\n",
       "      <th>EcogPtMem_bl</th>\n",
       "      <th>EcogPtLang_bl</th>\n",
       "      <th>EcogPtVisspat_bl</th>\n",
       "      <th>EcogPtPlan_bl</th>\n",
       "      <th>EcogPtOrgan_bl</th>\n",
       "      <th>EcogPtDivatt_bl</th>\n",
       "      <th>EcogPtTotal_bl</th>\n",
       "      <th>EcogSPMem_bl</th>\n",
       "      <th>EcogSPLang_bl</th>\n",
       "      <th>EcogSPVisspat_bl</th>\n",
       "      <th>EcogSPPlan_bl</th>\n",
       "      <th>EcogSPOrgan_bl</th>\n",
       "      <th>EcogSPDivatt_bl</th>\n",
       "      <th>EcogSPTotal_bl</th>\n",
       "      <th>ABETA_bl</th>\n",
       "      <th>TAU_bl</th>\n",
       "      <th>PTAU_bl</th>\n",
       "      <th>FDG_bl</th>\n",
       "      <th>PIB_bl</th>\n",
       "      <th>AV45_bl</th>\n",
       "      <th>Years_bl</th>\n",
       "      <th>Month_bl</th>\n",
       "      <th>Month</th>\n",
       "      <th>M</th>\n",
       "      <th>update_stamp</th>\n",
       "      <th>Unnamed: 109</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14528</th>\n",
       "      <td>4832</td>\n",
       "      <td>m96</td>\n",
       "      <td>190</td>\n",
       "      <td>2019-12-16</td>\n",
       "      <td>EMCI</td>\n",
       "      <td>60.9</td>\n",
       "      <td>Male</td>\n",
       "      <td>14</td>\n",
       "      <td>painting contractor</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-05-16</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>30</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>44.4444</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-1.96982</td>\n",
       "      <td>-1.56211</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18008.0</td>\n",
       "      <td>7733.0</td>\n",
       "      <td>1091811.0</td>\n",
       "      <td>4085.0</td>\n",
       "      <td>19721.0</td>\n",
       "      <td>22308.0</td>\n",
       "      <td>1479077.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3.125</td>\n",
       "      <td>2.44444</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.16667</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.94872</td>\n",
       "      <td>2.125</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>1.14286</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.50000</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.76923</td>\n",
       "      <td>&gt;1700</td>\n",
       "      <td>261.3</td>\n",
       "      <td>23.94</td>\n",
       "      <td>1.28876</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.946004</td>\n",
       "      <td>7.58385</td>\n",
       "      <td>90.8197</td>\n",
       "      <td>90.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2019-12-17 04:20:22.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14522</th>\n",
       "      <td>5261</td>\n",
       "      <td>m84</td>\n",
       "      <td>182</td>\n",
       "      <td>2019-12-12</td>\n",
       "      <td>EMCI</td>\n",
       "      <td>76.5</td>\n",
       "      <td>Male</td>\n",
       "      <td>16</td>\n",
       "      <td>engineer</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.125</td>\n",
       "      <td>1.44444</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.16667</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.39474</td>\n",
       "      <td>2.000</td>\n",
       "      <td>1.33333</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.16667</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>1.64865</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-05-15</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>28</td>\n",
       "      <td>32.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-4.99839</td>\n",
       "      <td>-3.60723</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43152.0</td>\n",
       "      <td>6351.0</td>\n",
       "      <td>944750.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1213242.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.875</td>\n",
       "      <td>1.44444</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.35897</td>\n",
       "      <td>1.625</td>\n",
       "      <td>1.11111</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>2.75</td>\n",
       "      <td>1.38462</td>\n",
       "      <td>746.8</td>\n",
       "      <td>174.8</td>\n",
       "      <td>16.13</td>\n",
       "      <td>1.26256</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.186100</td>\n",
       "      <td>6.57632</td>\n",
       "      <td>78.7541</td>\n",
       "      <td>78.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>2019-12-17 04:20:24.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14527</th>\n",
       "      <td>6223</td>\n",
       "      <td>m24</td>\n",
       "      <td>252</td>\n",
       "      <td>2019-12-12</td>\n",
       "      <td>SMC</td>\n",
       "      <td>74.6</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>RN</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>27.2727</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.750</td>\n",
       "      <td>1.33333</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.26316</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.11111</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.16667</td>\n",
       "      <td>1.25000</td>\n",
       "      <td>1.07895</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.56502</td>\n",
       "      <td>-1.67829</td>\n",
       "      <td>2017-12-04</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29</td>\n",
       "      <td>45.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.2857</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.84363</td>\n",
       "      <td>1.73679</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.750</td>\n",
       "      <td>1.33333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.26316</td>\n",
       "      <td>1.250</td>\n",
       "      <td>1.11111</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.33333</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.23684</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.004900</td>\n",
       "      <td>2.02053</td>\n",
       "      <td>24.1967</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2019-12-14 04:20:30.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14521</th>\n",
       "      <td>4351</td>\n",
       "      <td>m96</td>\n",
       "      <td>155</td>\n",
       "      <td>2019-12-12</td>\n",
       "      <td>CN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>20</td>\n",
       "      <td>secretary</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>159.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.625</td>\n",
       "      <td>1.12500</td>\n",
       "      <td>1.33333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.22222</td>\n",
       "      <td>2.125</td>\n",
       "      <td>1.11111</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.40000</td>\n",
       "      <td>1.66667</td>\n",
       "      <td>1.42857</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-15.358</td>\n",
       "      <td>-13.33250</td>\n",
       "      <td>2011-10-04</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>28</td>\n",
       "      <td>44.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>30.7692</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.80238</td>\n",
       "      <td>-1.74772</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39314.0</td>\n",
       "      <td>8754.0</td>\n",
       "      <td>1280619.0</td>\n",
       "      <td>4097.0</td>\n",
       "      <td>23174.0</td>\n",
       "      <td>29432.0</td>\n",
       "      <td>1906709.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.250</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.05128</td>\n",
       "      <td>713.7</td>\n",
       "      <td>371.6</td>\n",
       "      <td>38.07</td>\n",
       "      <td>1.43957</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.496110</td>\n",
       "      <td>8.18891</td>\n",
       "      <td>98.0656</td>\n",
       "      <td>96.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2019-12-13 04:20:22.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14531</th>\n",
       "      <td>6960</td>\n",
       "      <td>bl</td>\n",
       "      <td>169</td>\n",
       "      <td>2019-12-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81.3</td>\n",
       "      <td>Female</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>Black</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>69.2308</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2.250</td>\n",
       "      <td>1.55556</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.33333</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2.05263</td>\n",
       "      <td>1.500</td>\n",
       "      <td>1.22222</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.25000</td>\n",
       "      <td>1.26316</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.92281</td>\n",
       "      <td>-1.78967</td>\n",
       "      <td>2019-12-11</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>30</td>\n",
       "      <td>46.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>69.2308</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.92281</td>\n",
       "      <td>-1.78967</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2.250</td>\n",
       "      <td>1.55556</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.33333</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2.05263</td>\n",
       "      <td>1.500</td>\n",
       "      <td>1.22222</td>\n",
       "      <td>1.50000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.26316</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-17 04:20:26.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        RID VISCODE  SITE    EXAMDATE DX_bl   AGE PTGENDER  PTEDUCAT  \\\n",
       "14528  4832     m96   190  2019-12-16  EMCI  60.9     Male        14   \n",
       "14522  5261     m84   182  2019-12-12  EMCI  76.5     Male        16   \n",
       "14527  6223     m24   252  2019-12-12   SMC  74.6     Male        18   \n",
       "14521  4351     m96   155  2019-12-12    CN  70.0     Male        20   \n",
       "14531  6960      bl   169  2019-12-11   NaN  81.3   Female        16   \n",
       "\n",
       "                      WORK         PTETHCAT PTRACCAT   PTMARRY APOE4  FDG  \\\n",
       "14528  painting contractor  Not Hisp/Latino    White   Married     1  NaN   \n",
       "14522             engineer  Not Hisp/Latino    White   Married     1  NaN   \n",
       "14527                   RN  Not Hisp/Latino    White   Married     1  NaN   \n",
       "14521            secretary  Not Hisp/Latino    White   Married     1  NaN   \n",
       "14531                  NaN  Not Hisp/Latino    Black  Divorced   NaN  NaN   \n",
       "\n",
       "       PIB  AV45 ABETA  TAU PTAU  CDRSB  ADAS11  ADAS13  ADASQ4  MMSE  \\\n",
       "14528  NaN   NaN   NaN  NaN  NaN    NaN     NaN     NaN     NaN   NaN   \n",
       "14522  NaN   NaN   NaN  NaN  NaN    NaN     NaN     NaN     NaN   NaN   \n",
       "14527  NaN   NaN   NaN  NaN  NaN    0.0    10.0    12.0     1.0  27.0   \n",
       "14521  NaN   NaN   NaN  NaN  NaN    NaN    22.0    34.0     9.0  24.0   \n",
       "14531  NaN   NaN   NaN  NaN  NaN    1.0    10.0    16.0     5.0  30.0   \n",
       "\n",
       "       RAVLT_immediate  RAVLT_learning  RAVLT_forgetting  \\\n",
       "14528              NaN             NaN               NaN   \n",
       "14522              NaN             NaN               NaN   \n",
       "14527             40.0             7.0               3.0   \n",
       "14521             25.0            -1.0               5.0   \n",
       "14531             46.0             9.0               9.0   \n",
       "\n",
       "       RAVLT_perc_forgetting  LDELTOTAL  DIGITSCOR  TRABSCOR  FAQ  MOCA  \\\n",
       "14528                    NaN        NaN        NaN       NaN  NaN   NaN   \n",
       "14522                    NaN        NaN        NaN       NaN  1.0   NaN   \n",
       "14527                27.2727       10.0        NaN      67.0  0.0  26.0   \n",
       "14521               100.0000        2.0        NaN     159.0  2.0  15.0   \n",
       "14531                69.2308       10.0        NaN      86.0  0.0  24.0   \n",
       "\n",
       "       EcogPtMem  EcogPtLang  EcogPtVisspat  EcogPtPlan  EcogPtOrgan  \\\n",
       "14528        NaN         NaN            NaN         NaN          NaN   \n",
       "14522      2.125     1.44444        1.00000         1.0      1.16667   \n",
       "14527      1.750     1.33333        1.00000         1.0      1.00000   \n",
       "14521      1.625     1.12500        1.33333         1.0      1.00000   \n",
       "14531      2.250     1.55556        2.00000         2.2      2.33333   \n",
       "\n",
       "       EcogPtDivatt  EcogPtTotal  EcogSPMem  EcogSPLang  EcogSPVisspat  \\\n",
       "14528           NaN          NaN        NaN         NaN            NaN   \n",
       "14522          1.25      1.39474      2.000     1.33333            1.2   \n",
       "14527          1.25      1.26316      1.000     1.11111            1.0   \n",
       "14521          1.00      1.22222      2.125     1.11111            1.0   \n",
       "14531          2.25      2.05263      1.500     1.22222            1.5   \n",
       "\n",
       "       EcogSPPlan  EcogSPOrgan  EcogSPDivatt  EcogSPTotal  FLDSTRENG  \\\n",
       "14528         NaN          NaN           NaN          NaN        NaN   \n",
       "14522         1.6      1.16667       3.00000      1.64865        NaN   \n",
       "14527         1.0      1.16667       1.25000      1.07895        NaN   \n",
       "14521         1.2      1.40000       1.66667      1.42857        NaN   \n",
       "14531         1.0      1.00000       1.25000      1.26316        NaN   \n",
       "\n",
       "       IMAGEUID  Ventricles  Hippocampus  WholeBrain  Entorhinal  Fusiform  \\\n",
       "14528       NaN         NaN          NaN         NaN         NaN       NaN   \n",
       "14522       NaN         NaN          NaN         NaN         NaN       NaN   \n",
       "14527       NaN         NaN          NaN         NaN         NaN       NaN   \n",
       "14521       NaN         NaN          NaN         NaN         NaN       NaN   \n",
       "14531       NaN         NaN          NaN         NaN         NaN       NaN   \n",
       "\n",
       "       MidTemp  ICV   DX mPACCdigit  mPACCtrailsB EXAMDATE_bl CDRSB_bl  \\\n",
       "14528      NaN  NaN  NaN        NaN           NaN  2012-05-16        2   \n",
       "14522      NaN  NaN  NaN        NaN           NaN  2013-05-15      0.5   \n",
       "14527      NaN  NaN  NaN   -2.56502      -1.67829  2017-12-04        0   \n",
       "14521      NaN  NaN  NaN    -15.358     -13.33250  2011-10-04        0   \n",
       "14531      NaN  NaN  NaN   -1.92281      -1.78967  2019-12-11        1   \n",
       "\n",
       "       ADAS11_bl  ADAS13_bl  ADASQ4_bl  MMSE_bl  RAVLT_immediate_bl  \\\n",
       "14528        8.0       12.0        4.0       30                30.0   \n",
       "14522       10.0       18.0        8.0       28                32.0   \n",
       "14527        8.0        9.0        1.0       29                45.0   \n",
       "14521        6.0       11.0        4.0       28                44.0   \n",
       "14531       10.0       16.0        5.0       30                46.0   \n",
       "\n",
       "       RAVLT_learning_bl  RAVLT_forgetting_bl  RAVLT_perc_forgetting_bl  \\\n",
       "14528                6.0                  4.0                   44.4444   \n",
       "14522                3.0                  7.0                  100.0000   \n",
       "14527               11.0                  2.0                   14.2857   \n",
       "14521               10.0                  4.0                   30.7692   \n",
       "14531                9.0                  9.0                   69.2308   \n",
       "\n",
       "       LDELTOTAL_BL  DIGITSCOR_bl  TRABSCOR_bl  FAQ_bl  mPACCdigit_bl  \\\n",
       "14528           8.0           NaN         77.0     5.0       -1.96982   \n",
       "14522          14.0           NaN         70.0     0.0       -4.99839   \n",
       "14527          15.0           NaN         64.0     0.0        1.84363   \n",
       "14521          12.0           NaN         64.0     0.0       -2.80238   \n",
       "14531          10.0           NaN         86.0     0.0       -1.92281   \n",
       "\n",
       "       mPACCtrailsB_bl  FLDSTRENG_bl  Ventricles_bl  Hippocampus_bl  \\\n",
       "14528         -1.56211           NaN        18008.0          7733.0   \n",
       "14522         -3.60723           NaN        43152.0          6351.0   \n",
       "14527          1.73679           NaN            NaN             NaN   \n",
       "14521         -1.74772           NaN        39314.0          8754.0   \n",
       "14531         -1.78967           NaN            NaN             NaN   \n",
       "\n",
       "       WholeBrain_bl  Entorhinal_bl  Fusiform_bl  MidTemp_bl     ICV_bl  \\\n",
       "14528      1091811.0         4085.0      19721.0     22308.0  1479077.0   \n",
       "14522       944750.0            NaN          NaN         NaN  1213242.0   \n",
       "14527            NaN            NaN          NaN         NaN        NaN   \n",
       "14521      1280619.0         4097.0      23174.0     29432.0  1906709.0   \n",
       "14531            NaN            NaN          NaN         NaN        NaN   \n",
       "\n",
       "       MOCA_bl  EcogPtMem_bl  EcogPtLang_bl  EcogPtVisspat_bl  EcogPtPlan_bl  \\\n",
       "14528     19.0         3.125        2.44444               1.0            1.4   \n",
       "14522     25.0         1.875        1.44444               1.0            1.0   \n",
       "14527     30.0         1.750        1.33333               1.0            1.0   \n",
       "14521     25.0         1.000        1.00000               1.0            1.0   \n",
       "14531     24.0         2.250        1.55556               2.0            2.2   \n",
       "\n",
       "       EcogPtOrgan_bl  EcogPtDivatt_bl  EcogPtTotal_bl  EcogSPMem_bl  \\\n",
       "14528         1.16667             2.00         1.94872         2.125   \n",
       "14522         1.00000             1.75         1.35897         1.625   \n",
       "14527         1.00000             1.25         1.26316         1.250   \n",
       "14521         1.00000             1.00         1.00000         1.250   \n",
       "14531         2.33333             2.25         2.05263         1.500   \n",
       "\n",
       "       EcogSPLang_bl  EcogSPVisspat_bl  EcogSPPlan_bl  EcogSPOrgan_bl  \\\n",
       "14528        2.00000           1.14286            1.8         1.50000   \n",
       "14522        1.11111           1.00000            1.4         1.00000   \n",
       "14527        1.11111           1.00000            1.4         1.33333   \n",
       "14521        1.00000           1.00000            1.0         1.00000   \n",
       "14531        1.22222           1.50000            1.0         1.00000   \n",
       "\n",
       "       EcogSPDivatt_bl  EcogSPTotal_bl ABETA_bl TAU_bl PTAU_bl   FDG_bl  \\\n",
       "14528             2.00         1.76923    >1700  261.3   23.94  1.28876   \n",
       "14522             2.75         1.38462    746.8  174.8   16.13  1.26256   \n",
       "14527             1.50         1.23684      NaN    NaN     NaN      NaN   \n",
       "14521             1.00         1.05128    713.7  371.6   38.07  1.43957   \n",
       "14531             1.25         1.26316      NaN    NaN     NaN      NaN   \n",
       "\n",
       "       PIB_bl   AV45_bl  Years_bl  Month_bl  Month     M  \\\n",
       "14528     NaN  0.946004   7.58385   90.8197   90.0  96.0   \n",
       "14522     NaN  1.186100   6.57632   78.7541   78.0  84.0   \n",
       "14527     NaN  1.004900   2.02053   24.1967   24.0  24.0   \n",
       "14521     NaN  1.496110   8.18891   98.0656   96.0  96.0   \n",
       "14531     NaN       NaN   0.00000    0.0000    0.0   0.0   \n",
       "\n",
       "                update_stamp Unnamed: 109  \n",
       "14528  2019-12-17 04:20:22.0          NaN  \n",
       "14522  2019-12-17 04:20:24.0          NaN  \n",
       "14527  2019-12-14 04:20:30.0          NaN  \n",
       "14521  2019-12-13 04:20:22.0          NaN  \n",
       "14531  2019-12-17 04:20:26.0          NaN  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sort_values(by='EXAMDATE', ascending=False).head()  # Sorted by latest examination date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RID</th>\n",
       "      <th>VISCODE</th>\n",
       "      <th>SITE</th>\n",
       "      <th>EXAMDATE</th>\n",
       "      <th>DX_bl</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PTGENDER</th>\n",
       "      <th>PTEDUCAT</th>\n",
       "      <th>WORK</th>\n",
       "      <th>PTETHCAT</th>\n",
       "      <th>PTRACCAT</th>\n",
       "      <th>PTMARRY</th>\n",
       "      <th>APOE4</th>\n",
       "      <th>FDG</th>\n",
       "      <th>PIB</th>\n",
       "      <th>AV45</th>\n",
       "      <th>ABETA</th>\n",
       "      <th>TAU</th>\n",
       "      <th>PTAU</th>\n",
       "      <th>CDRSB</th>\n",
       "      <th>ADAS11</th>\n",
       "      <th>ADAS13</th>\n",
       "      <th>ADASQ4</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>RAVLT_immediate</th>\n",
       "      <th>RAVLT_learning</th>\n",
       "      <th>RAVLT_forgetting</th>\n",
       "      <th>RAVLT_perc_forgetting</th>\n",
       "      <th>LDELTOTAL</th>\n",
       "      <th>DIGITSCOR</th>\n",
       "      <th>TRABSCOR</th>\n",
       "      <th>FAQ</th>\n",
       "      <th>MOCA</th>\n",
       "      <th>EcogPtMem</th>\n",
       "      <th>EcogPtLang</th>\n",
       "      <th>EcogPtVisspat</th>\n",
       "      <th>EcogPtPlan</th>\n",
       "      <th>EcogPtOrgan</th>\n",
       "      <th>EcogPtDivatt</th>\n",
       "      <th>EcogPtTotal</th>\n",
       "      <th>EcogSPMem</th>\n",
       "      <th>EcogSPLang</th>\n",
       "      <th>EcogSPVisspat</th>\n",
       "      <th>EcogSPPlan</th>\n",
       "      <th>EcogSPOrgan</th>\n",
       "      <th>EcogSPDivatt</th>\n",
       "      <th>EcogSPTotal</th>\n",
       "      <th>FLDSTRENG</th>\n",
       "      <th>IMAGEUID</th>\n",
       "      <th>Ventricles</th>\n",
       "      <th>Hippocampus</th>\n",
       "      <th>WholeBrain</th>\n",
       "      <th>Entorhinal</th>\n",
       "      <th>Fusiform</th>\n",
       "      <th>MidTemp</th>\n",
       "      <th>ICV</th>\n",
       "      <th>DX</th>\n",
       "      <th>mPACCdigit</th>\n",
       "      <th>mPACCtrailsB</th>\n",
       "      <th>EXAMDATE_bl</th>\n",
       "      <th>CDRSB_bl</th>\n",
       "      <th>ADAS11_bl</th>\n",
       "      <th>ADAS13_bl</th>\n",
       "      <th>ADASQ4_bl</th>\n",
       "      <th>MMSE_bl</th>\n",
       "      <th>RAVLT_immediate_bl</th>\n",
       "      <th>RAVLT_learning_bl</th>\n",
       "      <th>RAVLT_forgetting_bl</th>\n",
       "      <th>RAVLT_perc_forgetting_bl</th>\n",
       "      <th>LDELTOTAL_BL</th>\n",
       "      <th>DIGITSCOR_bl</th>\n",
       "      <th>TRABSCOR_bl</th>\n",
       "      <th>FAQ_bl</th>\n",
       "      <th>mPACCdigit_bl</th>\n",
       "      <th>mPACCtrailsB_bl</th>\n",
       "      <th>FLDSTRENG_bl</th>\n",
       "      <th>Ventricles_bl</th>\n",
       "      <th>Hippocampus_bl</th>\n",
       "      <th>WholeBrain_bl</th>\n",
       "      <th>Entorhinal_bl</th>\n",
       "      <th>Fusiform_bl</th>\n",
       "      <th>MidTemp_bl</th>\n",
       "      <th>ICV_bl</th>\n",
       "      <th>MOCA_bl</th>\n",
       "      <th>EcogPtMem_bl</th>\n",
       "      <th>EcogPtLang_bl</th>\n",
       "      <th>EcogPtVisspat_bl</th>\n",
       "      <th>EcogPtPlan_bl</th>\n",
       "      <th>EcogPtOrgan_bl</th>\n",
       "      <th>EcogPtDivatt_bl</th>\n",
       "      <th>EcogPtTotal_bl</th>\n",
       "      <th>EcogSPMem_bl</th>\n",
       "      <th>EcogSPLang_bl</th>\n",
       "      <th>EcogSPVisspat_bl</th>\n",
       "      <th>EcogSPPlan_bl</th>\n",
       "      <th>EcogSPOrgan_bl</th>\n",
       "      <th>EcogSPDivatt_bl</th>\n",
       "      <th>EcogSPTotal_bl</th>\n",
       "      <th>ABETA_bl</th>\n",
       "      <th>TAU_bl</th>\n",
       "      <th>PTAU_bl</th>\n",
       "      <th>FDG_bl</th>\n",
       "      <th>PIB_bl</th>\n",
       "      <th>AV45_bl</th>\n",
       "      <th>Years_bl</th>\n",
       "      <th>Month_bl</th>\n",
       "      <th>Month</th>\n",
       "      <th>M</th>\n",
       "      <th>update_stamp</th>\n",
       "      <th>Unnamed: 109</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>131</td>\n",
       "      <td>bl</td>\n",
       "      <td>164</td>\n",
       "      <td>2005-09-07</td>\n",
       "      <td>CN</td>\n",
       "      <td>75.7</td>\n",
       "      <td>Male</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>1.29343</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>547.3</td>\n",
       "      <td>337</td>\n",
       "      <td>33.43</td>\n",
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       "      <td>12.0</td>\n",
       "      <td>38.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32253.0</td>\n",
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       "      <td>24789.0</td>\n",
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       "      <td>1640772.0</td>\n",
       "      <td>CN</td>\n",
       "      <td>-1.95295</td>\n",
       "      <td>-1.64932</td>\n",
       "      <td>2005-09-07</td>\n",
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       "      <td>29</td>\n",
       "      <td>37.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>44.4444</td>\n",
       "      <td>12.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.952950</td>\n",
       "      <td>-1.64932</td>\n",
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       "      <td>34061.0</td>\n",
       "      <td>7068.0</td>\n",
       "      <td>1116634.0</td>\n",
       "      <td>4428.0</td>\n",
       "      <td>24789.0</td>\n",
       "      <td>21616.0</td>\n",
       "      <td>1640772.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>128</td>\n",
       "      <td>bl</td>\n",
       "      <td>164</td>\n",
       "      <td>2005-09-08</td>\n",
       "      <td>CN</td>\n",
       "      <td>74.2</td>\n",
       "      <td>Male</td>\n",
       "      <td>16</td>\n",
       "      <td>technical writer and editor</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>18.67</td>\n",
       "      <td>5.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>54.5455</td>\n",
       "      <td>10.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35479.0</td>\n",
       "      <td>118226.0</td>\n",
       "      <td>8340.0</td>\n",
       "      <td>1229736.0</td>\n",
       "      <td>4182.0</td>\n",
       "      <td>16562.0</td>\n",
       "      <td>27930.0</td>\n",
       "      <td>1984658.0</td>\n",
       "      <td>CN</td>\n",
       "      <td>-4.41005</td>\n",
       "      <td>-4.23545</td>\n",
       "      <td>2005-09-08</td>\n",
       "      <td>0</td>\n",
       "      <td>10.67</td>\n",
       "      <td>18.67</td>\n",
       "      <td>5.0</td>\n",
       "      <td>28</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>54.5455</td>\n",
       "      <td>10.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-4.410050</td>\n",
       "      <td>-4.23545</td>\n",
       "      <td>NaN</td>\n",
       "      <td>118226.0</td>\n",
       "      <td>8340.0</td>\n",
       "      <td>1229736.0</td>\n",
       "      <td>4182.0</td>\n",
       "      <td>16562.0</td>\n",
       "      <td>27930.0</td>\n",
       "      <td>1984658.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.36665</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>129</td>\n",
       "      <td>bl</td>\n",
       "      <td>164</td>\n",
       "      <td>2005-09-12</td>\n",
       "      <td>AD</td>\n",
       "      <td>82.4</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>Secretary</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>1.08355</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>741.5</td>\n",
       "      <td>239.7</td>\n",
       "      <td>22.83</td>\n",
       "      <td>4.5</td>\n",
       "      <td>22.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32241.0</td>\n",
       "      <td>84599.0</td>\n",
       "      <td>5318.0</td>\n",
       "      <td>1129825.0</td>\n",
       "      <td>1790.0</td>\n",
       "      <td>15507.0</td>\n",
       "      <td>18425.0</td>\n",
       "      <td>1920689.0</td>\n",
       "      <td>Dementia</td>\n",
       "      <td>-16.6244</td>\n",
       "      <td>-16.23320</td>\n",
       "      <td>2005-09-12</td>\n",
       "      <td>4.5</td>\n",
       "      <td>22.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-16.624400</td>\n",
       "      <td>-16.23320</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84599.0</td>\n",
       "      <td>5318.0</td>\n",
       "      <td>1129825.0</td>\n",
       "      <td>1790.0</td>\n",
       "      <td>15507.0</td>\n",
       "      <td>18425.0</td>\n",
       "      <td>1920689.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>741.5</td>\n",
       "      <td>239.7</td>\n",
       "      <td>22.83</td>\n",
       "      <td>1.08355</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11507</th>\n",
       "      <td>134</td>\n",
       "      <td>bl</td>\n",
       "      <td>164</td>\n",
       "      <td>2005-09-19</td>\n",
       "      <td>CN</td>\n",
       "      <td>85.8</td>\n",
       "      <td>Female</td>\n",
       "      <td>18</td>\n",
       "      <td>pharmaceutical salesperson</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>0</td>\n",
       "      <td>1.27450</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&gt;1700</td>\n",
       "      <td>440.2</td>\n",
       "      <td>37.51</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>16.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32267.0</td>\n",
       "      <td>18751.0</td>\n",
       "      <td>6072.0</td>\n",
       "      <td>948687.0</td>\n",
       "      <td>4194.0</td>\n",
       "      <td>14043.0</td>\n",
       "      <td>20073.0</td>\n",
       "      <td>1396073.0</td>\n",
       "      <td>CN</td>\n",
       "      <td>0.530658</td>\n",
       "      <td>1.10288</td>\n",
       "      <td>2005-09-19</td>\n",
       "      <td>0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28</td>\n",
       "      <td>51.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>16.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.530658</td>\n",
       "      <td>1.10288</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18751.0</td>\n",
       "      <td>6072.0</td>\n",
       "      <td>948687.0</td>\n",
       "      <td>4194.0</td>\n",
       "      <td>14043.0</td>\n",
       "      <td>20073.0</td>\n",
       "      <td>1396073.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&gt;1700</td>\n",
       "      <td>440.2</td>\n",
       "      <td>37.51</td>\n",
       "      <td>1.27450</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>133</td>\n",
       "      <td>bl</td>\n",
       "      <td>175</td>\n",
       "      <td>2005-10-06</td>\n",
       "      <td>AD</td>\n",
       "      <td>75.3</td>\n",
       "      <td>Male</td>\n",
       "      <td>10</td>\n",
       "      <td>chartered accountant</td>\n",
       "      <td>Hisp/Latino</td>\n",
       "      <td>More than one</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>27.33</td>\n",
       "      <td>40.33</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>75.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59346.0</td>\n",
       "      <td>25699.0</td>\n",
       "      <td>6725.0</td>\n",
       "      <td>875793.0</td>\n",
       "      <td>2046.0</td>\n",
       "      <td>12067.0</td>\n",
       "      <td>15379.0</td>\n",
       "      <td>1353521.0</td>\n",
       "      <td>Dementia</td>\n",
       "      <td>-19.9104</td>\n",
       "      <td>-19.64310</td>\n",
       "      <td>2005-10-06</td>\n",
       "      <td>6</td>\n",
       "      <td>27.33</td>\n",
       "      <td>40.33</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>75.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>-19.910400</td>\n",
       "      <td>-19.64310</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25699.0</td>\n",
       "      <td>6725.0</td>\n",
       "      <td>875793.0</td>\n",
       "      <td>2046.0</td>\n",
       "      <td>12067.0</td>\n",
       "      <td>15379.0</td>\n",
       "      <td>1353521.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       RID VISCODE  SITE    EXAMDATE DX_bl   AGE PTGENDER  PTEDUCAT  \\\n",
       "10     131      bl   164  2005-09-07    CN  75.7     Male        16   \n",
       "0      128      bl   164  2005-09-08    CN  74.2     Male        16   \n",
       "1      129      bl   164  2005-09-12    AD  82.4     Male        18   \n",
       "11507  134      bl   164  2005-09-19    CN  85.8   Female        18   \n",
       "20     133      bl   175  2005-10-06    AD  75.3     Male        10   \n",
       "\n",
       "                              WORK         PTETHCAT       PTRACCAT  PTMARRY  \\\n",
       "10                             NaN  Not Hisp/Latino          White  Married   \n",
       "0      technical writer and editor  Not Hisp/Latino          White  Married   \n",
       "1                        Secretary  Not Hisp/Latino          White  Married   \n",
       "11507   pharmaceutical salesperson  Not Hisp/Latino          White  Widowed   \n",
       "20            chartered accountant      Hisp/Latino  More than one  Married   \n",
       "\n",
       "      APOE4      FDG  PIB  AV45  ABETA    TAU   PTAU  CDRSB  ADAS11  ADAS13  \\\n",
       "10        0  1.29343  NaN   NaN  547.3    337  33.43    0.0    8.67   14.67   \n",
       "0         0  1.36665  NaN   NaN    NaN    NaN    NaN    0.0   10.67   18.67   \n",
       "1         1  1.08355  NaN   NaN  741.5  239.7  22.83    4.5   22.00   31.00   \n",
       "11507     0  1.27450  NaN   NaN  >1700  440.2  37.51    0.0    5.00    7.00   \n",
       "20        1      NaN  NaN   NaN    NaN    NaN    NaN    6.0   27.33   40.33   \n",
       "\n",
       "       ADASQ4  MMSE  RAVLT_immediate  RAVLT_learning  RAVLT_forgetting  \\\n",
       "10        4.0  29.0             37.0             4.0               4.0   \n",
       "0         5.0  28.0             44.0             4.0               6.0   \n",
       "1         8.0  20.0             22.0             1.0               4.0   \n",
       "11507     2.0  28.0             51.0             7.0               3.0   \n",
       "20       10.0  20.0             17.0             2.0               3.0   \n",
       "\n",
       "       RAVLT_perc_forgetting  LDELTOTAL  DIGITSCOR  TRABSCOR   FAQ  MOCA  \\\n",
       "10                   44.4444       12.0       38.0      90.0   0.0   NaN   \n",
       "0                    54.5455       10.0       34.0     112.0   0.0   NaN   \n",
       "1                   100.0000        2.0       25.0     148.0  10.0   NaN   \n",
       "11507                25.0000       16.0       49.0      51.0   0.0   NaN   \n",
       "20                   75.0000        0.0        9.0     300.0  17.0   NaN   \n",
       "\n",
       "       EcogPtMem  EcogPtLang  EcogPtVisspat  EcogPtPlan  EcogPtOrgan  \\\n",
       "10           NaN         NaN            NaN         NaN          NaN   \n",
       "0            NaN         NaN            NaN         NaN          NaN   \n",
       "1            NaN         NaN            NaN         NaN          NaN   \n",
       "11507        NaN         NaN            NaN         NaN          NaN   \n",
       "20           NaN         NaN            NaN         NaN          NaN   \n",
       "\n",
       "       EcogPtDivatt  EcogPtTotal  EcogSPMem  EcogSPLang  EcogSPVisspat  \\\n",
       "10              NaN          NaN        NaN         NaN            NaN   \n",
       "0               NaN          NaN        NaN         NaN            NaN   \n",
       "1               NaN          NaN        NaN         NaN            NaN   \n",
       "11507           NaN          NaN        NaN         NaN            NaN   \n",
       "20              NaN          NaN        NaN         NaN            NaN   \n",
       "\n",
       "       EcogSPPlan  EcogSPOrgan  EcogSPDivatt  EcogSPTotal  FLDSTRENG  \\\n",
       "10            NaN          NaN           NaN          NaN        NaN   \n",
       "0             NaN          NaN           NaN          NaN        NaN   \n",
       "1             NaN          NaN           NaN          NaN        NaN   \n",
       "11507         NaN          NaN           NaN          NaN        NaN   \n",
       "20            NaN          NaN           NaN          NaN        NaN   \n",
       "\n",
       "       IMAGEUID  Ventricles  Hippocampus  WholeBrain  Entorhinal  Fusiform  \\\n",
       "10      32253.0     34061.0       7068.0   1116634.0      4428.0   24789.0   \n",
       "0       35479.0    118226.0       8340.0   1229736.0      4182.0   16562.0   \n",
       "1       32241.0     84599.0       5318.0   1129825.0      1790.0   15507.0   \n",
       "11507   32267.0     18751.0       6072.0    948687.0      4194.0   14043.0   \n",
       "20      59346.0     25699.0       6725.0    875793.0      2046.0   12067.0   \n",
       "\n",
       "       MidTemp        ICV        DX mPACCdigit  mPACCtrailsB EXAMDATE_bl  \\\n",
       "10     21616.0  1640772.0        CN   -1.95295      -1.64932  2005-09-07   \n",
       "0      27930.0  1984658.0        CN   -4.41005      -4.23545  2005-09-08   \n",
       "1      18425.0  1920689.0  Dementia   -16.6244     -16.23320  2005-09-12   \n",
       "11507  20073.0  1396073.0        CN   0.530658       1.10288  2005-09-19   \n",
       "20     15379.0  1353521.0  Dementia   -19.9104     -19.64310  2005-10-06   \n",
       "\n",
       "      CDRSB_bl  ADAS11_bl  ADAS13_bl  ADASQ4_bl  MMSE_bl  RAVLT_immediate_bl  \\\n",
       "10           0       8.67      14.67        4.0       29                37.0   \n",
       "0            0      10.67      18.67        5.0       28                44.0   \n",
       "1          4.5      22.00      31.00        8.0       20                22.0   \n",
       "11507        0       5.00       7.00        2.0       28                51.0   \n",
       "20           6      27.33      40.33       10.0       20                17.0   \n",
       "\n",
       "       RAVLT_learning_bl  RAVLT_forgetting_bl  RAVLT_perc_forgetting_bl  \\\n",
       "10                   4.0                  4.0                   44.4444   \n",
       "0                    4.0                  6.0                   54.5455   \n",
       "1                    1.0                  4.0                  100.0000   \n",
       "11507                7.0                  3.0                   25.0000   \n",
       "20                   2.0                  3.0                   75.0000   \n",
       "\n",
       "       LDELTOTAL_BL  DIGITSCOR_bl  TRABSCOR_bl  FAQ_bl  mPACCdigit_bl  \\\n",
       "10             12.0          38.0         90.0     0.0      -1.952950   \n",
       "0              10.0          34.0        112.0     0.0      -4.410050   \n",
       "1               2.0          25.0        148.0    10.0     -16.624400   \n",
       "11507          16.0          49.0         51.0     0.0       0.530658   \n",
       "20              0.0           9.0        300.0    17.0     -19.910400   \n",
       "\n",
       "       mPACCtrailsB_bl  FLDSTRENG_bl  Ventricles_bl  Hippocampus_bl  \\\n",
       "10            -1.64932           NaN        34061.0          7068.0   \n",
       "0             -4.23545           NaN       118226.0          8340.0   \n",
       "1            -16.23320           NaN        84599.0          5318.0   \n",
       "11507          1.10288           NaN        18751.0          6072.0   \n",
       "20           -19.64310           NaN        25699.0          6725.0   \n",
       "\n",
       "       WholeBrain_bl  Entorhinal_bl  Fusiform_bl  MidTemp_bl     ICV_bl  \\\n",
       "10         1116634.0         4428.0      24789.0     21616.0  1640772.0   \n",
       "0          1229736.0         4182.0      16562.0     27930.0  1984658.0   \n",
       "1          1129825.0         1790.0      15507.0     18425.0  1920689.0   \n",
       "11507       948687.0         4194.0      14043.0     20073.0  1396073.0   \n",
       "20          875793.0         2046.0      12067.0     15379.0  1353521.0   \n",
       "\n",
       "       MOCA_bl  EcogPtMem_bl  EcogPtLang_bl  EcogPtVisspat_bl  EcogPtPlan_bl  \\\n",
       "10         NaN           NaN            NaN               NaN            NaN   \n",
       "0          NaN           NaN            NaN               NaN            NaN   \n",
       "1          NaN           NaN            NaN               NaN            NaN   \n",
       "11507      NaN           NaN            NaN               NaN            NaN   \n",
       "20         NaN           NaN            NaN               NaN            NaN   \n",
       "\n",
       "       EcogPtOrgan_bl  EcogPtDivatt_bl  EcogPtTotal_bl  EcogSPMem_bl  \\\n",
       "10                NaN              NaN             NaN           NaN   \n",
       "0                 NaN              NaN             NaN           NaN   \n",
       "1                 NaN              NaN             NaN           NaN   \n",
       "11507             NaN              NaN             NaN           NaN   \n",
       "20                NaN              NaN             NaN           NaN   \n",
       "\n",
       "       EcogSPLang_bl  EcogSPVisspat_bl  EcogSPPlan_bl  EcogSPOrgan_bl  \\\n",
       "10               NaN               NaN            NaN             NaN   \n",
       "0                NaN               NaN            NaN             NaN   \n",
       "1                NaN               NaN            NaN             NaN   \n",
       "11507            NaN               NaN            NaN             NaN   \n",
       "20               NaN               NaN            NaN             NaN   \n",
       "\n",
       "       EcogSPDivatt_bl  EcogSPTotal_bl ABETA_bl TAU_bl PTAU_bl   FDG_bl  \\\n",
       "10                 NaN             NaN    547.3    337   33.43  1.29343   \n",
       "0                  NaN             NaN      NaN    NaN     NaN  1.36665   \n",
       "1                  NaN             NaN    741.5  239.7   22.83  1.08355   \n",
       "11507              NaN             NaN    >1700  440.2   37.51  1.27450   \n",
       "20                 NaN             NaN      NaN    NaN     NaN      NaN   \n",
       "\n",
       "       PIB_bl  AV45_bl  Years_bl  Month_bl  Month    M           update_stamp  \\\n",
       "10        NaN      NaN       0.0       0.0    0.0  0.0  2019-12-04 04:19:56.0   \n",
       "0         NaN      NaN       0.0       0.0    0.0  0.0  2019-12-04 04:19:56.0   \n",
       "1         NaN      NaN       0.0       0.0    0.0  0.0  2019-12-04 04:19:56.0   \n",
       "11507     NaN      NaN       0.0       0.0    0.0  0.0  2019-12-04 04:19:56.0   \n",
       "20        NaN      NaN       0.0       0.0    0.0  0.0  2019-12-04 04:19:56.0   \n",
       "\n",
       "      Unnamed: 109  \n",
       "10             NaN  \n",
       "0              NaN  \n",
       "1              NaN  \n",
       "11507          NaN  \n",
       "20             NaN  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sort_values(by='EXAMDATE', ascending=True).head()  # Sorted by earliest examination date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise\n",
    "\n",
    "Sort data by youngest patients and show only top 3 patients. How old is the youngest patient?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary> > Solution</summary>\n",
    "    \n",
    "```python\n",
    "data.sort_values(by='AGE', ascending=True).head(3)\n",
    "```\n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.6. Group data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html\n",
    "\n",
    "> By “group by” we are referring to a process involving one or more of the following steps:\n",
    "> * **Splitting** the data into groups based on some criteria.\n",
    "> * **Applying** a function to each group independently.\n",
    "> * **Combining** the results into a data structure.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Splitting**: Split data into groups based on criteria, e.g. the civil status."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.DataFrameGroupBy object at 0x7f23c3affba8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('PTMARRY')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Applying and combining**: Apply function to each group, e.g. get the number of entries in each group (function `size`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PTMARRY\n",
       "Divorced          1209\n",
       "Married          11116\n",
       "Never married      454\n",
       "Unknown             53\n",
       "White                3\n",
       "Widowed           1697\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('PTMARRY').size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is combined into a data structure, i.e. `Series`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data.groupby('PTMARRY').size())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Side note: Does anyone know what \"white\" in context of the civil status means? I do not. Maybe this is an artifact in the data?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also apply other functions to our grouped data. \n",
    "\n",
    "Return the first element in each group (`first`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RID</th>\n",
       "      <th>VISCODE</th>\n",
       "      <th>SITE</th>\n",
       "      <th>EXAMDATE</th>\n",
       "      <th>DX_bl</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PTGENDER</th>\n",
       "      <th>PTEDUCAT</th>\n",
       "      <th>WORK</th>\n",
       "      <th>PTETHCAT</th>\n",
       "      <th>PTRACCAT</th>\n",
       "      <th>APOE4</th>\n",
       "      <th>FDG</th>\n",
       "      <th>PIB</th>\n",
       "      <th>AV45</th>\n",
       "      <th>ABETA</th>\n",
       "      <th>TAU</th>\n",
       "      <th>PTAU</th>\n",
       "      <th>CDRSB</th>\n",
       "      <th>ADAS11</th>\n",
       "      <th>ADAS13</th>\n",
       "      <th>ADASQ4</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>RAVLT_immediate</th>\n",
       "      <th>RAVLT_learning</th>\n",
       "      <th>RAVLT_forgetting</th>\n",
       "      <th>RAVLT_perc_forgetting</th>\n",
       "      <th>LDELTOTAL</th>\n",
       "      <th>DIGITSCOR</th>\n",
       "      <th>TRABSCOR</th>\n",
       "      <th>FAQ</th>\n",
       "      <th>MOCA</th>\n",
       "      <th>EcogPtMem</th>\n",
       "      <th>EcogPtLang</th>\n",
       "      <th>EcogPtVisspat</th>\n",
       "      <th>EcogPtPlan</th>\n",
       "      <th>EcogPtOrgan</th>\n",
       "      <th>EcogPtDivatt</th>\n",
       "      <th>EcogPtTotal</th>\n",
       "      <th>EcogSPMem</th>\n",
       "      <th>EcogSPLang</th>\n",
       "      <th>EcogSPVisspat</th>\n",
       "      <th>EcogSPPlan</th>\n",
       "      <th>EcogSPOrgan</th>\n",
       "      <th>EcogSPDivatt</th>\n",
       "      <th>EcogSPTotal</th>\n",
       "      <th>FLDSTRENG</th>\n",
       "      <th>IMAGEUID</th>\n",
       "      <th>Ventricles</th>\n",
       "      <th>Hippocampus</th>\n",
       "      <th>WholeBrain</th>\n",
       "      <th>Entorhinal</th>\n",
       "      <th>Fusiform</th>\n",
       "      <th>MidTemp</th>\n",
       "      <th>ICV</th>\n",
       "      <th>DX</th>\n",
       "      <th>mPACCdigit</th>\n",
       "      <th>mPACCtrailsB</th>\n",
       "      <th>EXAMDATE_bl</th>\n",
       "      <th>CDRSB_bl</th>\n",
       "      <th>ADAS11_bl</th>\n",
       "      <th>ADAS13_bl</th>\n",
       "      <th>ADASQ4_bl</th>\n",
       "      <th>MMSE_bl</th>\n",
       "      <th>RAVLT_immediate_bl</th>\n",
       "      <th>RAVLT_learning_bl</th>\n",
       "      <th>RAVLT_forgetting_bl</th>\n",
       "      <th>RAVLT_perc_forgetting_bl</th>\n",
       "      <th>LDELTOTAL_BL</th>\n",
       "      <th>DIGITSCOR_bl</th>\n",
       "      <th>TRABSCOR_bl</th>\n",
       "      <th>FAQ_bl</th>\n",
       "      <th>mPACCdigit_bl</th>\n",
       "      <th>mPACCtrailsB_bl</th>\n",
       "      <th>FLDSTRENG_bl</th>\n",
       "      <th>Ventricles_bl</th>\n",
       "      <th>Hippocampus_bl</th>\n",
       "      <th>WholeBrain_bl</th>\n",
       "      <th>Entorhinal_bl</th>\n",
       "      <th>Fusiform_bl</th>\n",
       "      <th>MidTemp_bl</th>\n",
       "      <th>ICV_bl</th>\n",
       "      <th>MOCA_bl</th>\n",
       "      <th>EcogPtMem_bl</th>\n",
       "      <th>EcogPtLang_bl</th>\n",
       "      <th>EcogPtVisspat_bl</th>\n",
       "      <th>EcogPtPlan_bl</th>\n",
       "      <th>EcogPtOrgan_bl</th>\n",
       "      <th>EcogPtDivatt_bl</th>\n",
       "      <th>EcogPtTotal_bl</th>\n",
       "      <th>EcogSPMem_bl</th>\n",
       "      <th>EcogSPLang_bl</th>\n",
       "      <th>EcogSPVisspat_bl</th>\n",
       "      <th>EcogSPPlan_bl</th>\n",
       "      <th>EcogSPOrgan_bl</th>\n",
       "      <th>EcogSPDivatt_bl</th>\n",
       "      <th>EcogSPTotal_bl</th>\n",
       "      <th>ABETA_bl</th>\n",
       "      <th>TAU_bl</th>\n",
       "      <th>PTAU_bl</th>\n",
       "      <th>FDG_bl</th>\n",
       "      <th>PIB_bl</th>\n",
       "      <th>AV45_bl</th>\n",
       "      <th>Years_bl</th>\n",
       "      <th>Month_bl</th>\n",
       "      <th>Month</th>\n",
       "      <th>M</th>\n",
       "      <th>update_stamp</th>\n",
       "      <th>Unnamed: 109</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTMARRY</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Divorced</th>\n",
       "      <td>140</td>\n",
       "      <td>bl</td>\n",
       "      <td>175</td>\n",
       "      <td>2005-11-04</td>\n",
       "      <td>CN</td>\n",
       "      <td>78.3</td>\n",
       "      <td>Female</td>\n",
       "      <td>12</td>\n",
       "      <td>legal secretary</td>\n",
       "      <td>Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>1.25096</td>\n",
       "      <td>1.5800</td>\n",
       "      <td>1.354630</td>\n",
       "      <td>1582.0</td>\n",
       "      <td>203.6</td>\n",
       "      <td>16.68</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.33</td>\n",
       "      <td>8.33</td>\n",
       "      <td>4.00</td>\n",
       "      <td>29.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>36.36360</td>\n",
       "      <td>10.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.375</td>\n",
       "      <td>1.33333</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.16667</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.17949</td>\n",
       "      <td>1.12500</td>\n",
       "      <td>1.11111</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.05128</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59356.0</td>\n",
       "      <td>46281.0</td>\n",
       "      <td>6729.0</td>\n",
       "      <td>861745.0</td>\n",
       "      <td>3580.0</td>\n",
       "      <td>13781.0</td>\n",
       "      <td>17795.0</td>\n",
       "      <td>1269537.0</td>\n",
       "      <td>CN</td>\n",
       "      <td>-3.33317</td>\n",
       "      <td>-2.51998</td>\n",
       "      <td>2005-11-04</td>\n",
       "      <td>0</td>\n",
       "      <td>4.33</td>\n",
       "      <td>8.33</td>\n",
       "      <td>4.00</td>\n",
       "      <td>29</td>\n",
       "      <td>45.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>36.36360</td>\n",
       "      <td>10.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.33317</td>\n",
       "      <td>-2.51998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46281.0</td>\n",
       "      <td>6729.0</td>\n",
       "      <td>861745.0</td>\n",
       "      <td>3580.0</td>\n",
       "      <td>13781.0</td>\n",
       "      <td>17795.0</td>\n",
       "      <td>1269537.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.375</td>\n",
       "      <td>1.22222</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.20000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.17949</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1582</td>\n",
       "      <td>203.6</td>\n",
       "      <td>16.68</td>\n",
       "      <td>1.25096</td>\n",
       "      <td>1.67250</td>\n",
       "      <td>1.049550</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Married</th>\n",
       "      <td>128</td>\n",
       "      <td>bl</td>\n",
       "      <td>164</td>\n",
       "      <td>2005-09-08</td>\n",
       "      <td>CN</td>\n",
       "      <td>74.2</td>\n",
       "      <td>Male</td>\n",
       "      <td>16</td>\n",
       "      <td>technical writer and editor</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>1.36665</td>\n",
       "      <td>2.3575</td>\n",
       "      <td>1.326790</td>\n",
       "      <td>741.5</td>\n",
       "      <td>239.7</td>\n",
       "      <td>22.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.67</td>\n",
       "      <td>18.67</td>\n",
       "      <td>5.00</td>\n",
       "      <td>28.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>54.54550</td>\n",
       "      <td>10.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1.250</td>\n",
       "      <td>1.44444</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.16667</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.25641</td>\n",
       "      <td>1.62500</td>\n",
       "      <td>1.33333</td>\n",
       "      <td>1.14286</td>\n",
       "      <td>1.20000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.25641</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35479.0</td>\n",
       "      <td>118226.0</td>\n",
       "      <td>8340.0</td>\n",
       "      <td>1229736.0</td>\n",
       "      <td>4182.0</td>\n",
       "      <td>16562.0</td>\n",
       "      <td>27930.0</td>\n",
       "      <td>1984658.0</td>\n",
       "      <td>CN</td>\n",
       "      <td>-4.41005</td>\n",
       "      <td>-4.23545</td>\n",
       "      <td>2005-09-08</td>\n",
       "      <td>0</td>\n",
       "      <td>10.67</td>\n",
       "      <td>18.67</td>\n",
       "      <td>5.00</td>\n",
       "      <td>28</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>54.54550</td>\n",
       "      <td>10.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-4.41005</td>\n",
       "      <td>-4.23545</td>\n",
       "      <td>NaN</td>\n",
       "      <td>118226.0</td>\n",
       "      <td>8340.0</td>\n",
       "      <td>1229736.0</td>\n",
       "      <td>4182.0</td>\n",
       "      <td>16562.0</td>\n",
       "      <td>27930.0</td>\n",
       "      <td>1984658.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1.250</td>\n",
       "      <td>1.44444</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.16667</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.25641</td>\n",
       "      <td>1.62500</td>\n",
       "      <td>1.33333</td>\n",
       "      <td>1.14286</td>\n",
       "      <td>1.20000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.25641</td>\n",
       "      <td>741.5</td>\n",
       "      <td>239.7</td>\n",
       "      <td>22.83</td>\n",
       "      <td>1.36665</td>\n",
       "      <td>1.49000</td>\n",
       "      <td>1.326790</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Never married</th>\n",
       "      <td>5406</td>\n",
       "      <td>m24</td>\n",
       "      <td>253</td>\n",
       "      <td>2015-09-29</td>\n",
       "      <td>SMC</td>\n",
       "      <td>66.4</td>\n",
       "      <td>Male</td>\n",
       "      <td>16</td>\n",
       "      <td>Registered Nurse</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>Black</td>\n",
       "      <td>0</td>\n",
       "      <td>1.25096</td>\n",
       "      <td>1.2650</td>\n",
       "      <td>0.982566</td>\n",
       "      <td>731.8</td>\n",
       "      <td>101.2</td>\n",
       "      <td>10.08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>30.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>50.00000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1.625</td>\n",
       "      <td>1.11111</td>\n",
       "      <td>1.14286</td>\n",
       "      <td>1.20000</td>\n",
       "      <td>1.66667</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.35897</td>\n",
       "      <td>1.75000</td>\n",
       "      <td>1.11111</td>\n",
       "      <td>1.33333</td>\n",
       "      <td>1.20000</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.40000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>389179.0</td>\n",
       "      <td>33187.0</td>\n",
       "      <td>8297.0</td>\n",
       "      <td>1165500.0</td>\n",
       "      <td>4945.0</td>\n",
       "      <td>20148.0</td>\n",
       "      <td>21190.0</td>\n",
       "      <td>1656457.0</td>\n",
       "      <td>CN</td>\n",
       "      <td>1.86257</td>\n",
       "      <td>1.64240</td>\n",
       "      <td>2013-09-17</td>\n",
       "      <td>0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>30</td>\n",
       "      <td>42.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.00000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.05460</td>\n",
       "      <td>2.36566</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33187.0</td>\n",
       "      <td>8297.0</td>\n",
       "      <td>1165500.0</td>\n",
       "      <td>4945.0</td>\n",
       "      <td>20148.0</td>\n",
       "      <td>21190.0</td>\n",
       "      <td>1656457.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1.875</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.43590</td>\n",
       "      <td>1.62500</td>\n",
       "      <td>1.11111</td>\n",
       "      <td>1.28571</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.25000</td>\n",
       "      <td>731.8</td>\n",
       "      <td>101.2</td>\n",
       "      <td>10.08</td>\n",
       "      <td>1.25096</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.983143</td>\n",
       "      <td>2.03149</td>\n",
       "      <td>24.327900</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2019-12-04 04:20:18.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>1393</td>\n",
       "      <td>bl</td>\n",
       "      <td>247</td>\n",
       "      <td>2007-03-16</td>\n",
       "      <td>CN</td>\n",
       "      <td>73.4</td>\n",
       "      <td>Male</td>\n",
       "      <td>16</td>\n",
       "      <td>Educator</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>1.25804</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.004690</td>\n",
       "      <td>980.4</td>\n",
       "      <td>116.8</td>\n",
       "      <td>10.40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>30.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.66667</td>\n",
       "      <td>14.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2.250</td>\n",
       "      <td>2.11111</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.20000</td>\n",
       "      <td>1.20000</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.68421</td>\n",
       "      <td>2.75000</td>\n",
       "      <td>2.33333</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1.94444</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80695.0</td>\n",
       "      <td>19980.0</td>\n",
       "      <td>8657.0</td>\n",
       "      <td>1195613.0</td>\n",
       "      <td>4324.0</td>\n",
       "      <td>19230.0</td>\n",
       "      <td>21954.0</td>\n",
       "      <td>1731299.0</td>\n",
       "      <td>CN</td>\n",
       "      <td>4.27238</td>\n",
       "      <td>2.93081</td>\n",
       "      <td>2007-03-16</td>\n",
       "      <td>0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>30</td>\n",
       "      <td>56.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.66667</td>\n",
       "      <td>14.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.27238</td>\n",
       "      <td>2.93081</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19980.0</td>\n",
       "      <td>8657.0</td>\n",
       "      <td>1195613.0</td>\n",
       "      <td>4324.0</td>\n",
       "      <td>19230.0</td>\n",
       "      <td>21954.0</td>\n",
       "      <td>1731299.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2.250</td>\n",
       "      <td>2.11111</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.20000</td>\n",
       "      <td>1.20000</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.68421</td>\n",
       "      <td>2.75000</td>\n",
       "      <td>2.33333</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1.94444</td>\n",
       "      <td>980.4</td>\n",
       "      <td>116.8</td>\n",
       "      <td>10.40</td>\n",
       "      <td>1.25804</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.004690</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:20:06.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>982</td>\n",
       "      <td>m12</td>\n",
       "      <td>164</td>\n",
       "      <td>2007-09-25</td>\n",
       "      <td>LMCI</td>\n",
       "      <td>60.7</td>\n",
       "      <td>Male</td>\n",
       "      <td>13</td>\n",
       "      <td>Military</td>\n",
       "      <td>Air Force</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>Married</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.2512</td>\n",
       "      <td>1.435000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.50</td>\n",
       "      <td>17.33</td>\n",
       "      <td>29.33</td>\n",
       "      <td>10.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>26.000</td>\n",
       "      <td>3.14286</td>\n",
       "      <td>2.71429</td>\n",
       "      <td>1.16667</td>\n",
       "      <td>1.60000</td>\n",
       "      <td>2.50</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>2.48571</td>\n",
       "      <td>2.37500</td>\n",
       "      <td>1.55556</td>\n",
       "      <td>1.33333</td>\n",
       "      <td>1.20</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>1.76316</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78789.0</td>\n",
       "      <td>56830.0</td>\n",
       "      <td>6597.0</td>\n",
       "      <td>1141372.0</td>\n",
       "      <td>2780.0</td>\n",
       "      <td>16818.0</td>\n",
       "      <td>16880.0</td>\n",
       "      <td>1846260</td>\n",
       "      <td>Dementia</td>\n",
       "      <td>-11.59230</td>\n",
       "      <td>-11.7945</td>\n",
       "      <td>2006-09-29</td>\n",
       "      <td>1.00</td>\n",
       "      <td>17.33</td>\n",
       "      <td>28.33</td>\n",
       "      <td>9</td>\n",
       "      <td>27.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.00000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>-9.65127</td>\n",
       "      <td>-10.6964</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50745.0</td>\n",
       "      <td>6834.0</td>\n",
       "      <td>1163222.0</td>\n",
       "      <td>3440.0</td>\n",
       "      <td>17074.0</td>\n",
       "      <td>18077.0</td>\n",
       "      <td>1850330.0</td>\n",
       "      <td>26.000</td>\n",
       "      <td>3.14286</td>\n",
       "      <td>2.71429</td>\n",
       "      <td>1.16667</td>\n",
       "      <td>1.60000</td>\n",
       "      <td>2.50</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>2.48571</td>\n",
       "      <td>2.37500</td>\n",
       "      <td>1.55556</td>\n",
       "      <td>1.33333</td>\n",
       "      <td>1.20</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>1.76316</td>\n",
       "      <td>1308.0</td>\n",
       "      <td>213.70</td>\n",
       "      <td>18.62000</td>\n",
       "      <td>1.18734</td>\n",
       "      <td>1.567500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.988364</td>\n",
       "      <td>11.8361</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12</td>\n",
       "      <td>2019-12-04 04:20:03.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Widowed</th>\n",
       "      <td>149</td>\n",
       "      <td>bl</td>\n",
       "      <td>164</td>\n",
       "      <td>2005-11-08</td>\n",
       "      <td>CN</td>\n",
       "      <td>73.4</td>\n",
       "      <td>Male</td>\n",
       "      <td>14</td>\n",
       "      <td>Secretary</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>Black</td>\n",
       "      <td>0</td>\n",
       "      <td>1.36097</td>\n",
       "      <td>2.2700</td>\n",
       "      <td>0.964111</td>\n",
       "      <td>1647.0</td>\n",
       "      <td>181.1</td>\n",
       "      <td>16.74</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>26.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>63.63640</td>\n",
       "      <td>8.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.125</td>\n",
       "      <td>1.11111</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.05128</td>\n",
       "      <td>1.25000</td>\n",
       "      <td>1.11111</td>\n",
       "      <td>1.14286</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.10256</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32428.0</td>\n",
       "      <td>21895.0</td>\n",
       "      <td>8316.0</td>\n",
       "      <td>1040561.0</td>\n",
       "      <td>3622.0</td>\n",
       "      <td>18025.0</td>\n",
       "      <td>19499.0</td>\n",
       "      <td>1568537.0</td>\n",
       "      <td>CN</td>\n",
       "      <td>-5.37952</td>\n",
       "      <td>-6.12939</td>\n",
       "      <td>2005-11-08</td>\n",
       "      <td>0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>26</td>\n",
       "      <td>40.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>63.63640</td>\n",
       "      <td>8.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5.37952</td>\n",
       "      <td>-6.12939</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21895.0</td>\n",
       "      <td>8316.0</td>\n",
       "      <td>1040561.0</td>\n",
       "      <td>3622.0</td>\n",
       "      <td>18025.0</td>\n",
       "      <td>19499.0</td>\n",
       "      <td>1568537.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2.750</td>\n",
       "      <td>2.22222</td>\n",
       "      <td>1.14286</td>\n",
       "      <td>1.40000</td>\n",
       "      <td>1.40000</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.84211</td>\n",
       "      <td>1.87500</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.25641</td>\n",
       "      <td>1647</td>\n",
       "      <td>181.1</td>\n",
       "      <td>16.74</td>\n",
       "      <td>1.36097</td>\n",
       "      <td>1.90250</td>\n",
       "      <td>1.734130</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                RID VISCODE  SITE    EXAMDATE DX_bl   AGE PTGENDER  PTEDUCAT  \\\n",
       "PTMARRY                                                                        \n",
       "Divorced        140      bl   175  2005-11-04    CN  78.3   Female        12   \n",
       "Married         128      bl   164  2005-09-08    CN  74.2     Male        16   \n",
       "Never married  5406     m24   253  2015-09-29   SMC  66.4     Male        16   \n",
       "Unknown        1393      bl   247  2007-03-16    CN  73.4     Male        16   \n",
       "White           982     m12   164  2007-09-25  LMCI  60.7     Male        13   \n",
       "Widowed         149      bl   164  2005-11-08    CN  73.4     Male        14   \n",
       "\n",
       "                                      WORK         PTETHCAT         PTRACCAT  \\\n",
       "PTMARRY                                                                        \n",
       "Divorced                   legal secretary      Hisp/Latino            White   \n",
       "Married        technical writer and editor  Not Hisp/Latino            White   \n",
       "Never married             Registered Nurse  Not Hisp/Latino            Black   \n",
       "Unknown                           Educator  Not Hisp/Latino            White   \n",
       "White                             Military        Air Force  Not Hisp/Latino   \n",
       "Widowed                          Secretary  Not Hisp/Latino            Black   \n",
       "\n",
       "                 APOE4      FDG     PIB      AV45   ABETA    TAU   PTAU  \\\n",
       "PTMARRY                                                                   \n",
       "Divorced             0  1.25096  1.5800  1.354630  1582.0  203.6  16.68   \n",
       "Married              0  1.36665  2.3575  1.326790   741.5  239.7  22.83   \n",
       "Never married        0  1.25096  1.2650  0.982566   731.8  101.2  10.08   \n",
       "Unknown              0  1.25804     NaN  1.004690   980.4  116.8  10.40   \n",
       "White          Married  1.00000  1.2512  1.435000     NaN    NaN    NaN   \n",
       "Widowed              0  1.36097  2.2700  0.964111  1647.0  181.1  16.74   \n",
       "\n",
       "               CDRSB  ADAS11  ADAS13  ADASQ4  MMSE  RAVLT_immediate  \\\n",
       "PTMARRY                                                               \n",
       "Divorced         0.0    4.33    8.33    4.00  29.0             45.0   \n",
       "Married          0.0   10.67   18.67    5.00  28.0             44.0   \n",
       "Never married    0.0    2.00    3.00    1.00  30.0             38.0   \n",
       "Unknown          0.0    5.00    6.00    1.00  30.0             56.0   \n",
       "White            NaN    2.50   17.33   29.33  10.0             27.0   \n",
       "Widowed          0.0    4.00    8.00    4.00  26.0             40.0   \n",
       "\n",
       "               RAVLT_learning  RAVLT_forgetting  RAVLT_perc_forgetting  \\\n",
       "PTMARRY                                                                  \n",
       "Divorced                  6.0               4.0               36.36360   \n",
       "Married                   4.0               6.0               54.54550   \n",
       "Never married             6.0               5.0               50.00000   \n",
       "Unknown                  10.0               1.0                6.66667   \n",
       "White                    22.0               5.0                5.00000   \n",
       "Widowed                   7.0               7.0               63.63640   \n",
       "\n",
       "               LDELTOTAL  DIGITSCOR  TRABSCOR    FAQ  MOCA  EcogPtMem  \\\n",
       "PTMARRY                                                                 \n",
       "Divorced            10.0       30.0     101.0    0.0  21.0      1.375   \n",
       "Married             10.0       34.0     112.0    0.0  27.0      1.250   \n",
       "Never married       12.0       40.0      67.0    0.0  27.0      1.625   \n",
       "Unknown             14.0       69.0      50.0    0.0  25.0      2.250   \n",
       "White              100.0        0.0      29.0  161.0  14.0     26.000   \n",
       "Widowed              8.0       43.0     114.0    0.0  22.0      1.125   \n",
       "\n",
       "               EcogPtLang  EcogPtVisspat  EcogPtPlan  EcogPtOrgan  \\\n",
       "PTMARRY                                                             \n",
       "Divorced          1.33333        1.00000     1.00000      1.16667   \n",
       "Married           1.44444        1.00000     1.00000      1.16667   \n",
       "Never married     1.11111        1.14286     1.20000      1.66667   \n",
       "Unknown           2.11111        1.00000     1.20000      1.20000   \n",
       "White             3.14286        2.71429     1.16667      1.60000   \n",
       "Widowed           1.11111        1.00000     1.00000      1.00000   \n",
       "\n",
       "               EcogPtDivatt  EcogPtTotal  EcogSPMem  EcogSPLang  \\\n",
       "PTMARRY                                                           \n",
       "Divorced               1.00      1.17949    1.12500     1.11111   \n",
       "Married                1.75      1.25641    1.62500     1.33333   \n",
       "Never married          1.50      1.35897    1.75000     1.11111   \n",
       "Unknown                2.00      1.68421    2.75000     2.33333   \n",
       "White                  2.50      4.00000    2.48571     2.37500   \n",
       "Widowed                1.00      1.05128    1.25000     1.11111   \n",
       "\n",
       "               EcogSPVisspat  EcogSPPlan  EcogSPOrgan  EcogSPDivatt  \\\n",
       "PTMARRY                                                               \n",
       "Divorced             1.00000     1.00000         1.00          1.00   \n",
       "Married              1.14286     1.20000         1.00          1.00   \n",
       "Never married        1.33333     1.20000         1.25          1.25   \n",
       "Unknown              1.00000     1.00000         1.00          2.50   \n",
       "White                1.55556     1.33333         1.20          2.00   \n",
       "Widowed              1.14286     1.00000         1.00          1.00   \n",
       "\n",
       "               EcogSPTotal  FLDSTRENG  IMAGEUID  Ventricles  Hippocampus  \\\n",
       "PTMARRY                                                                    \n",
       "Divorced           1.05128        NaN   59356.0     46281.0       6729.0   \n",
       "Married            1.25641        NaN   35479.0    118226.0       8340.0   \n",
       "Never married      1.40000        NaN  389179.0     33187.0       8297.0   \n",
       "Unknown            1.94444        NaN   80695.0     19980.0       8657.0   \n",
       "White              2.00000    1.76316       NaN     78789.0      56830.0   \n",
       "Widowed            1.10256        NaN   32428.0     21895.0       8316.0   \n",
       "\n",
       "               WholeBrain  Entorhinal  Fusiform  MidTemp        ICV       DX  \\\n",
       "PTMARRY                                                                        \n",
       "Divorced         861745.0      3580.0   13781.0  17795.0  1269537.0       CN   \n",
       "Married         1229736.0      4182.0   16562.0  27930.0  1984658.0       CN   \n",
       "Never married   1165500.0      4945.0   20148.0  21190.0  1656457.0       CN   \n",
       "Unknown         1195613.0      4324.0   19230.0  21954.0  1731299.0       CN   \n",
       "White              6597.0   1141372.0    2780.0  16818.0    16880.0  1846260   \n",
       "Widowed         1040561.0      3622.0   18025.0  19499.0  1568537.0       CN   \n",
       "\n",
       "              mPACCdigit  mPACCtrailsB EXAMDATE_bl    CDRSB_bl  ADAS11_bl  \\\n",
       "PTMARRY                                                                     \n",
       "Divorced        -3.33317      -2.51998  2005-11-04           0       4.33   \n",
       "Married         -4.41005      -4.23545  2005-09-08           0      10.67   \n",
       "Never married    1.86257       1.64240  2013-09-17           0       3.00   \n",
       "Unknown          4.27238       2.93081  2007-03-16           0       5.00   \n",
       "White           Dementia     -11.59230    -11.7945  2006-09-29       1.00   \n",
       "Widowed         -5.37952      -6.12939  2005-11-08           0       4.00   \n",
       "\n",
       "               ADAS13_bl  ADASQ4_bl  MMSE_bl  RAVLT_immediate_bl  \\\n",
       "PTMARRY                                                            \n",
       "Divorced            8.33       4.00       29                45.0   \n",
       "Married            18.67       5.00       28                44.0   \n",
       "Never married       5.00       1.00       30                42.0   \n",
       "Unknown             6.00       1.00       30                56.0   \n",
       "White              17.33      28.33        9                27.0   \n",
       "Widowed             8.00       4.00       26                40.0   \n",
       "\n",
       "               RAVLT_learning_bl  RAVLT_forgetting_bl  \\\n",
       "PTMARRY                                                 \n",
       "Divorced                     6.0                  4.0   \n",
       "Married                      4.0                  6.0   \n",
       "Never married                7.0                  2.0   \n",
       "Unknown                     10.0                  1.0   \n",
       "White                       24.0                  4.0   \n",
       "Widowed                      7.0                  7.0   \n",
       "\n",
       "               RAVLT_perc_forgetting_bl  LDELTOTAL_BL  DIGITSCOR_bl  \\\n",
       "PTMARRY                                                               \n",
       "Divorced                       36.36360          10.0          30.0   \n",
       "Married                        54.54550          10.0          34.0   \n",
       "Never married                  20.00000          15.0          40.0   \n",
       "Unknown                         6.66667          14.0          69.0   \n",
       "White                           6.00000         100.0           2.0   \n",
       "Widowed                        63.63640           8.0          43.0   \n",
       "\n",
       "               TRABSCOR_bl  FAQ_bl  mPACCdigit_bl  mPACCtrailsB_bl  \\\n",
       "PTMARRY                                                              \n",
       "Divorced             101.0     0.0       -3.33317         -2.51998   \n",
       "Married              112.0     0.0       -4.41005         -4.23545   \n",
       "Never married         72.0     0.0        3.05460          2.36566   \n",
       "Unknown               50.0     0.0        4.27238          2.93081   \n",
       "White                 37.0   165.0        3.00000         -9.65127   \n",
       "Widowed              114.0     0.0       -5.37952         -6.12939   \n",
       "\n",
       "               FLDSTRENG_bl  Ventricles_bl  Hippocampus_bl  WholeBrain_bl  \\\n",
       "PTMARRY                                                                     \n",
       "Divorced                NaN        46281.0          6729.0       861745.0   \n",
       "Married                 NaN       118226.0          8340.0      1229736.0   \n",
       "Never married           NaN        33187.0          8297.0      1165500.0   \n",
       "Unknown                 NaN        19980.0          8657.0      1195613.0   \n",
       "White              -10.6964            NaN         50745.0         6834.0   \n",
       "Widowed                 NaN        21895.0          8316.0      1040561.0   \n",
       "\n",
       "               Entorhinal_bl  Fusiform_bl  MidTemp_bl     ICV_bl    MOCA_bl  \\\n",
       "PTMARRY                                                                       \n",
       "Divorced              3580.0      13781.0     17795.0  1269537.0       26.0   \n",
       "Married               4182.0      16562.0     27930.0  1984658.0       27.0   \n",
       "Never married         4945.0      20148.0     21190.0  1656457.0       28.0   \n",
       "Unknown               4324.0      19230.0     21954.0  1731299.0       25.0   \n",
       "White              1163222.0       3440.0     17074.0    18077.0  1850330.0   \n",
       "Widowed               3622.0      18025.0     19499.0  1568537.0       25.0   \n",
       "\n",
       "               EcogPtMem_bl  EcogPtLang_bl  EcogPtVisspat_bl  EcogPtPlan_bl  \\\n",
       "PTMARRY                                                                       \n",
       "Divorced              1.375        1.22222           1.00000        1.20000   \n",
       "Married               1.250        1.44444           1.00000        1.00000   \n",
       "Never married         1.875        1.00000           1.00000        1.00000   \n",
       "Unknown               2.250        2.11111           1.00000        1.20000   \n",
       "White                26.000        3.14286           2.71429        1.16667   \n",
       "Widowed               2.750        2.22222           1.14286        1.40000   \n",
       "\n",
       "               EcogPtOrgan_bl  EcogPtDivatt_bl  EcogPtTotal_bl  EcogSPMem_bl  \\\n",
       "PTMARRY                                                                        \n",
       "Divorced              1.00000             1.25         1.17949       1.00000   \n",
       "Married               1.16667             1.75         1.25641       1.62500   \n",
       "Never married         2.00000             2.00         1.43590       1.62500   \n",
       "Unknown               1.20000             2.00         1.68421       2.75000   \n",
       "White                 1.60000             2.50         4.00000       2.48571   \n",
       "Widowed               1.40000             1.50         1.84211       1.87500   \n",
       "\n",
       "               EcogSPLang_bl  EcogSPVisspat_bl  EcogSPPlan_bl  EcogSPOrgan_bl  \\\n",
       "PTMARRY                                                                         \n",
       "Divorced             1.00000           1.00000        1.00000            1.00   \n",
       "Married              1.33333           1.14286        1.20000            1.00   \n",
       "Never married        1.11111           1.28571        1.00000            1.25   \n",
       "Unknown              2.33333           1.00000        1.00000            1.00   \n",
       "White                2.37500           1.55556        1.33333            1.20   \n",
       "Widowed              1.00000           1.00000        1.00000            1.00   \n",
       "\n",
       "               EcogSPDivatt_bl  EcogSPTotal_bl ABETA_bl  TAU_bl  PTAU_bl  \\\n",
       "PTMARRY                                                                    \n",
       "Divorced                  1.00         1.00000     1582   203.6    16.68   \n",
       "Married                   1.00         1.25641    741.5   239.7    22.83   \n",
       "Never married             1.00         1.25000    731.8   101.2    10.08   \n",
       "Unknown                   2.50         1.94444    980.4   116.8    10.40   \n",
       "White                     2.00         2.00000  1.76316  1308.0   213.70   \n",
       "Widowed                   1.75         1.25641     1647   181.1    16.74   \n",
       "\n",
       "                 FDG_bl   PIB_bl   AV45_bl  Years_bl   Month_bl    Month  \\\n",
       "PTMARRY                                                                    \n",
       "Divorced        1.25096  1.67250  1.049550   0.00000   0.000000   0.0000   \n",
       "Married         1.36665  1.49000  1.326790   0.00000   0.000000   0.0000   \n",
       "Never married   1.25096      NaN  0.983143   2.03149  24.327900  24.0000   \n",
       "Unknown         1.25804      NaN  1.004690   0.00000   0.000000   0.0000   \n",
       "White          18.62000  1.18734  1.567500       NaN   0.988364  11.8361   \n",
       "Widowed         1.36097  1.90250  1.734130   0.00000   0.000000   0.0000   \n",
       "\n",
       "                  M           update_stamp           Unnamed: 109  \n",
       "PTMARRY                                                            \n",
       "Divorced        0.0  2019-12-04 04:19:56.0                    NaN  \n",
       "Married         0.0  2019-12-04 04:19:56.0                    NaN  \n",
       "Never married  24.0  2019-12-04 04:20:18.0                    NaN  \n",
       "Unknown         0.0  2019-12-04 04:20:06.0                    NaN  \n",
       "White          12.0                     12  2019-12-04 04:20:03.0  \n",
       "Widowed         0.0  2019-12-04 04:19:56.0                    NaN  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('PTMARRY').first()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Return the mean of each numerical column for each group (`mean`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RID</th>\n",
       "      <th>SITE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PTEDUCAT</th>\n",
       "      <th>FDG</th>\n",
       "      <th>PIB</th>\n",
       "      <th>AV45</th>\n",
       "      <th>CDRSB</th>\n",
       "      <th>ADAS11</th>\n",
       "      <th>ADAS13</th>\n",
       "      <th>ADASQ4</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>RAVLT_immediate</th>\n",
       "      <th>RAVLT_learning</th>\n",
       "      <th>RAVLT_forgetting</th>\n",
       "      <th>RAVLT_perc_forgetting</th>\n",
       "      <th>LDELTOTAL</th>\n",
       "      <th>DIGITSCOR</th>\n",
       "      <th>TRABSCOR</th>\n",
       "      <th>FAQ</th>\n",
       "      <th>MOCA</th>\n",
       "      <th>EcogPtMem</th>\n",
       "      <th>EcogPtLang</th>\n",
       "      <th>EcogPtVisspat</th>\n",
       "      <th>EcogPtPlan</th>\n",
       "      <th>EcogPtOrgan</th>\n",
       "      <th>EcogPtDivatt</th>\n",
       "      <th>EcogPtTotal</th>\n",
       "      <th>EcogSPMem</th>\n",
       "      <th>EcogSPLang</th>\n",
       "      <th>EcogSPVisspat</th>\n",
       "      <th>EcogSPPlan</th>\n",
       "      <th>EcogSPOrgan</th>\n",
       "      <th>EcogSPDivatt</th>\n",
       "      <th>EcogSPTotal</th>\n",
       "      <th>FLDSTRENG</th>\n",
       "      <th>IMAGEUID</th>\n",
       "      <th>Ventricles</th>\n",
       "      <th>Hippocampus</th>\n",
       "      <th>WholeBrain</th>\n",
       "      <th>Entorhinal</th>\n",
       "      <th>Fusiform</th>\n",
       "      <th>MidTemp</th>\n",
       "      <th>ICV</th>\n",
       "      <th>mPACCtrailsB</th>\n",
       "      <th>ADAS11_bl</th>\n",
       "      <th>ADAS13_bl</th>\n",
       "      <th>ADASQ4_bl</th>\n",
       "      <th>MMSE_bl</th>\n",
       "      <th>RAVLT_immediate_bl</th>\n",
       "      <th>RAVLT_learning_bl</th>\n",
       "      <th>RAVLT_forgetting_bl</th>\n",
       "      <th>RAVLT_perc_forgetting_bl</th>\n",
       "      <th>LDELTOTAL_BL</th>\n",
       "      <th>DIGITSCOR_bl</th>\n",
       "      <th>TRABSCOR_bl</th>\n",
       "      <th>FAQ_bl</th>\n",
       "      <th>mPACCdigit_bl</th>\n",
       "      <th>mPACCtrailsB_bl</th>\n",
       "      <th>FLDSTRENG_bl</th>\n",
       "      <th>Ventricles_bl</th>\n",
       "      <th>Hippocampus_bl</th>\n",
       "      <th>WholeBrain_bl</th>\n",
       "      <th>Entorhinal_bl</th>\n",
       "      <th>Fusiform_bl</th>\n",
       "      <th>MidTemp_bl</th>\n",
       "      <th>ICV_bl</th>\n",
       "      <th>MOCA_bl</th>\n",
       "      <th>EcogPtMem_bl</th>\n",
       "      <th>EcogPtLang_bl</th>\n",
       "      <th>EcogPtVisspat_bl</th>\n",
       "      <th>EcogPtPlan_bl</th>\n",
       "      <th>EcogPtOrgan_bl</th>\n",
       "      <th>EcogPtDivatt_bl</th>\n",
       "      <th>EcogPtTotal_bl</th>\n",
       "      <th>EcogSPMem_bl</th>\n",
       "      <th>EcogSPLang_bl</th>\n",
       "      <th>EcogSPVisspat_bl</th>\n",
       "      <th>EcogSPPlan_bl</th>\n",
       "      <th>EcogSPOrgan_bl</th>\n",
       "      <th>EcogSPDivatt_bl</th>\n",
       "      <th>EcogSPTotal_bl</th>\n",
       "      <th>FDG_bl</th>\n",
       "      <th>PIB_bl</th>\n",
       "      <th>AV45_bl</th>\n",
       "      <th>Years_bl</th>\n",
       "      <th>Month_bl</th>\n",
       "      <th>Month</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTMARRY</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Divorced</th>\n",
       "      <td>3291.654260</td>\n",
       "      <td>223.070306</td>\n",
       "      <td>70.829032</td>\n",
       "      <td>16.191067</td>\n",
       "      <td>1.244450</td>\n",
       "      <td>1.640000</td>\n",
       "      <td>1.160615</td>\n",
       "      <td>1.381640</td>\n",
       "      <td>9.692649</td>\n",
       "      <td>14.474584</td>\n",
       "      <td>4.128000</td>\n",
       "      <td>27.392449</td>\n",
       "      <td>39.161066</td>\n",
       "      <td>4.858633</td>\n",
       "      <td>3.844548</td>\n",
       "      <td>47.425462</td>\n",
       "      <td>10.141457</td>\n",
       "      <td>38.839286</td>\n",
       "      <td>102.003534</td>\n",
       "      <td>2.965157</td>\n",
       "      <td>24.570300</td>\n",
       "      <td>2.019086</td>\n",
       "      <td>1.657964</td>\n",
       "      <td>1.374131</td>\n",
       "      <td>1.348843</td>\n",
       "      <td>1.508141</td>\n",
       "      <td>1.850129</td>\n",
       "      <td>1.641122</td>\n",
       "      <td>1.798203</td>\n",
       "      <td>1.405836</td>\n",
       "      <td>1.361824</td>\n",
       "      <td>1.408663</td>\n",
       "      <td>1.488619</td>\n",
       "      <td>1.706600</td>\n",
       "      <td>1.529100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>270371.363914</td>\n",
       "      <td>31515.775610</td>\n",
       "      <td>6881.013722</td>\n",
       "      <td>1.006044e+06</td>\n",
       "      <td>3.516545e+03</td>\n",
       "      <td>17315.250000</td>\n",
       "      <td>18998.447917</td>\n",
       "      <td>1.463609e+06</td>\n",
       "      <td>-3.903863</td>\n",
       "      <td>8.332983</td>\n",
       "      <td>12.686830</td>\n",
       "      <td>3.826015</td>\n",
       "      <td>28.025641</td>\n",
       "      <td>40.929577</td>\n",
       "      <td>5.202983</td>\n",
       "      <td>4.025684</td>\n",
       "      <td>44.961698</td>\n",
       "      <td>9.201159</td>\n",
       "      <td>40.381201</td>\n",
       "      <td>94.118289</td>\n",
       "      <td>1.660550</td>\n",
       "      <td>-3.461543</td>\n",
       "      <td>-3.164937</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29943.786822</td>\n",
       "      <td>7114.693401</td>\n",
       "      <td>1.024601e+06</td>\n",
       "      <td>3.632987e+03</td>\n",
       "      <td>17789.262846</td>\n",
       "      <td>19573.578063</td>\n",
       "      <td>1.475378e+06</td>\n",
       "      <td>2.510037e+01</td>\n",
       "      <td>2.056265</td>\n",
       "      <td>1.673695</td>\n",
       "      <td>1.380301</td>\n",
       "      <td>1.388564</td>\n",
       "      <td>1.505799</td>\n",
       "      <td>1.813058</td>\n",
       "      <td>1.652824</td>\n",
       "      <td>1.692674</td>\n",
       "      <td>1.337108</td>\n",
       "      <td>1.242633</td>\n",
       "      <td>1.311714</td>\n",
       "      <td>1.343620</td>\n",
       "      <td>1.572745</td>\n",
       "      <td>1.420171</td>\n",
       "      <td>1.280540</td>\n",
       "      <td>1.742308</td>\n",
       "      <td>1.150095</td>\n",
       "      <td>2.171089</td>\n",
       "      <td>25.999675</td>\n",
       "      <td>25.957816</td>\n",
       "      <td>25.786600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Married</th>\n",
       "      <td>2641.822688</td>\n",
       "      <td>230.516823</td>\n",
       "      <td>73.121623</td>\n",
       "      <td>16.198813</td>\n",
       "      <td>1.196356</td>\n",
       "      <td>1.796808</td>\n",
       "      <td>1.190302</td>\n",
       "      <td>2.215350</td>\n",
       "      <td>11.861407</td>\n",
       "      <td>18.073441</td>\n",
       "      <td>5.255935</td>\n",
       "      <td>26.584115</td>\n",
       "      <td>34.616825</td>\n",
       "      <td>4.013591</td>\n",
       "      <td>4.213266</td>\n",
       "      <td>59.053171</td>\n",
       "      <td>8.361783</td>\n",
       "      <td>37.106649</td>\n",
       "      <td>118.497411</td>\n",
       "      <td>5.756061</td>\n",
       "      <td>23.131192</td>\n",
       "      <td>2.069483</td>\n",
       "      <td>1.728869</td>\n",
       "      <td>1.391380</td>\n",
       "      <td>1.423182</td>\n",
       "      <td>1.531277</td>\n",
       "      <td>1.835863</td>\n",
       "      <td>1.684390</td>\n",
       "      <td>2.165452</td>\n",
       "      <td>1.757264</td>\n",
       "      <td>1.611220</td>\n",
       "      <td>1.726930</td>\n",
       "      <td>1.805617</td>\n",
       "      <td>2.011747</td>\n",
       "      <td>1.851985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>235688.480256</td>\n",
       "      <td>43605.737458</td>\n",
       "      <td>6679.473350</td>\n",
       "      <td>1.021657e+06</td>\n",
       "      <td>3.453634e+03</td>\n",
       "      <td>17258.479726</td>\n",
       "      <td>19362.160379</td>\n",
       "      <td>1.553296e+06</td>\n",
       "      <td>-6.127014</td>\n",
       "      <td>10.037228</td>\n",
       "      <td>15.762419</td>\n",
       "      <td>4.976598</td>\n",
       "      <td>27.588881</td>\n",
       "      <td>36.525850</td>\n",
       "      <td>4.393215</td>\n",
       "      <td>4.288486</td>\n",
       "      <td>55.041650</td>\n",
       "      <td>7.723623</td>\n",
       "      <td>40.142704</td>\n",
       "      <td>110.331072</td>\n",
       "      <td>3.402205</td>\n",
       "      <td>-5.190128</td>\n",
       "      <td>-4.924596</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39932.131867</td>\n",
       "      <td>6901.944605</td>\n",
       "      <td>1.037495e+06</td>\n",
       "      <td>3.587217e+03</td>\n",
       "      <td>17625.947628</td>\n",
       "      <td>19919.337894</td>\n",
       "      <td>1.555202e+06</td>\n",
       "      <td>2.341527e+01</td>\n",
       "      <td>2.108906</td>\n",
       "      <td>1.722058</td>\n",
       "      <td>1.368785</td>\n",
       "      <td>1.399488</td>\n",
       "      <td>1.496558</td>\n",
       "      <td>1.809537</td>\n",
       "      <td>1.673526</td>\n",
       "      <td>2.050150</td>\n",
       "      <td>1.606915</td>\n",
       "      <td>1.419309</td>\n",
       "      <td>1.546458</td>\n",
       "      <td>1.640734</td>\n",
       "      <td>1.822297</td>\n",
       "      <td>1.687627</td>\n",
       "      <td>1.241390</td>\n",
       "      <td>1.543079</td>\n",
       "      <td>1.196235</td>\n",
       "      <td>2.443094</td>\n",
       "      <td>29.257051</td>\n",
       "      <td>29.174433</td>\n",
       "      <td>29.014575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Never married</th>\n",
       "      <td>3079.861233</td>\n",
       "      <td>247.359031</td>\n",
       "      <td>73.142952</td>\n",
       "      <td>16.288546</td>\n",
       "      <td>1.255222</td>\n",
       "      <td>1.574167</td>\n",
       "      <td>1.129356</td>\n",
       "      <td>1.050459</td>\n",
       "      <td>8.969072</td>\n",
       "      <td>13.779149</td>\n",
       "      <td>3.844311</td>\n",
       "      <td>27.997006</td>\n",
       "      <td>39.849850</td>\n",
       "      <td>4.732733</td>\n",
       "      <td>3.909910</td>\n",
       "      <td>48.169036</td>\n",
       "      <td>11.003636</td>\n",
       "      <td>39.308411</td>\n",
       "      <td>110.350769</td>\n",
       "      <td>2.705521</td>\n",
       "      <td>24.443439</td>\n",
       "      <td>1.810006</td>\n",
       "      <td>1.647400</td>\n",
       "      <td>1.351972</td>\n",
       "      <td>1.322646</td>\n",
       "      <td>1.500682</td>\n",
       "      <td>1.728979</td>\n",
       "      <td>1.578666</td>\n",
       "      <td>1.627326</td>\n",
       "      <td>1.392489</td>\n",
       "      <td>1.308530</td>\n",
       "      <td>1.360714</td>\n",
       "      <td>1.420957</td>\n",
       "      <td>1.619565</td>\n",
       "      <td>1.454961</td>\n",
       "      <td>NaN</td>\n",
       "      <td>255590.510288</td>\n",
       "      <td>40377.385650</td>\n",
       "      <td>6829.151111</td>\n",
       "      <td>9.890619e+05</td>\n",
       "      <td>3.517871e+03</td>\n",
       "      <td>16647.980861</td>\n",
       "      <td>19105.272727</td>\n",
       "      <td>1.482008e+06</td>\n",
       "      <td>-2.996565</td>\n",
       "      <td>7.628150</td>\n",
       "      <td>11.896872</td>\n",
       "      <td>3.572687</td>\n",
       "      <td>28.495595</td>\n",
       "      <td>39.973568</td>\n",
       "      <td>4.764317</td>\n",
       "      <td>3.995595</td>\n",
       "      <td>47.159533</td>\n",
       "      <td>10.074890</td>\n",
       "      <td>41.952128</td>\n",
       "      <td>109.788889</td>\n",
       "      <td>1.721854</td>\n",
       "      <td>-2.480422</td>\n",
       "      <td>-2.574614</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37799.040100</td>\n",
       "      <td>6977.434783</td>\n",
       "      <td>1.003797e+06</td>\n",
       "      <td>3.633925e+03</td>\n",
       "      <td>17030.840399</td>\n",
       "      <td>19769.970075</td>\n",
       "      <td>1.484635e+06</td>\n",
       "      <td>2.483142e+01</td>\n",
       "      <td>1.830189</td>\n",
       "      <td>1.652464</td>\n",
       "      <td>1.344593</td>\n",
       "      <td>1.314286</td>\n",
       "      <td>1.497782</td>\n",
       "      <td>1.799812</td>\n",
       "      <td>1.585091</td>\n",
       "      <td>1.584209</td>\n",
       "      <td>1.383333</td>\n",
       "      <td>1.270781</td>\n",
       "      <td>1.311280</td>\n",
       "      <td>1.379476</td>\n",
       "      <td>1.584651</td>\n",
       "      <td>1.416639</td>\n",
       "      <td>1.277663</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.136882</td>\n",
       "      <td>2.167781</td>\n",
       "      <td>25.960063</td>\n",
       "      <td>25.803965</td>\n",
       "      <td>25.638767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>2941.018868</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>68.700000</td>\n",
       "      <td>15.509434</td>\n",
       "      <td>1.306954</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.018476</td>\n",
       "      <td>0.959459</td>\n",
       "      <td>7.333250</td>\n",
       "      <td>11.316154</td>\n",
       "      <td>3.325000</td>\n",
       "      <td>28.650000</td>\n",
       "      <td>38.769231</td>\n",
       "      <td>6.025641</td>\n",
       "      <td>3.743590</td>\n",
       "      <td>42.535700</td>\n",
       "      <td>9.937500</td>\n",
       "      <td>62.200000</td>\n",
       "      <td>82.842105</td>\n",
       "      <td>1.631579</td>\n",
       "      <td>24.588235</td>\n",
       "      <td>2.419048</td>\n",
       "      <td>2.131745</td>\n",
       "      <td>1.386939</td>\n",
       "      <td>1.308571</td>\n",
       "      <td>1.552525</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>1.816112</td>\n",
       "      <td>2.033110</td>\n",
       "      <td>1.516617</td>\n",
       "      <td>1.189796</td>\n",
       "      <td>1.356250</td>\n",
       "      <td>1.331884</td>\n",
       "      <td>1.830357</td>\n",
       "      <td>1.557582</td>\n",
       "      <td>NaN</td>\n",
       "      <td>292398.205882</td>\n",
       "      <td>28855.878788</td>\n",
       "      <td>8057.655172</td>\n",
       "      <td>1.131052e+06</td>\n",
       "      <td>3.967667e+03</td>\n",
       "      <td>19804.500000</td>\n",
       "      <td>21685.500000</td>\n",
       "      <td>1.568945e+06</td>\n",
       "      <td>-1.784398</td>\n",
       "      <td>8.213774</td>\n",
       "      <td>12.270377</td>\n",
       "      <td>3.792453</td>\n",
       "      <td>29.622642</td>\n",
       "      <td>39.490566</td>\n",
       "      <td>5.716981</td>\n",
       "      <td>2.905660</td>\n",
       "      <td>33.165607</td>\n",
       "      <td>7.943396</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>85.521739</td>\n",
       "      <td>1.113208</td>\n",
       "      <td>-2.080783</td>\n",
       "      <td>-1.935192</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27664.211538</td>\n",
       "      <td>8053.133333</td>\n",
       "      <td>1.148639e+06</td>\n",
       "      <td>3.991452e+03</td>\n",
       "      <td>19908.238095</td>\n",
       "      <td>21757.952381</td>\n",
       "      <td>1.570393e+06</td>\n",
       "      <td>2.197826e+01</td>\n",
       "      <td>2.347826</td>\n",
       "      <td>2.134661</td>\n",
       "      <td>1.381988</td>\n",
       "      <td>1.304348</td>\n",
       "      <td>1.425362</td>\n",
       "      <td>1.489130</td>\n",
       "      <td>1.763424</td>\n",
       "      <td>2.090580</td>\n",
       "      <td>1.509962</td>\n",
       "      <td>1.329062</td>\n",
       "      <td>1.347826</td>\n",
       "      <td>1.499999</td>\n",
       "      <td>1.885870</td>\n",
       "      <td>1.618948</td>\n",
       "      <td>1.308734</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.020542</td>\n",
       "      <td>1.853266</td>\n",
       "      <td>22.193632</td>\n",
       "      <td>22.018868</td>\n",
       "      <td>21.396226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>3135.000000</td>\n",
       "      <td>208.666667</td>\n",
       "      <td>69.366667</td>\n",
       "      <td>16.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.251200</td>\n",
       "      <td>1.435000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>13.666667</td>\n",
       "      <td>20.666667</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>28.333333</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>5.333333</td>\n",
       "      <td>74.074100</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>99.333333</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>3.142860</td>\n",
       "      <td>2.714290</td>\n",
       "      <td>1.166670</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.485710</td>\n",
       "      <td>2.375000</td>\n",
       "      <td>1.555560</td>\n",
       "      <td>1.333330</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.76316</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100246.500000</td>\n",
       "      <td>40338.000000</td>\n",
       "      <td>6.771000e+03</td>\n",
       "      <td>1.093406e+06</td>\n",
       "      <td>2743.500000</td>\n",
       "      <td>16969.500000</td>\n",
       "      <td>1.921900e+04</td>\n",
       "      <td>-6.449037</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>11.220000</td>\n",
       "      <td>17.220000</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>31.666667</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>5.333333</td>\n",
       "      <td>75.661400</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>38.500000</td>\n",
       "      <td>128.666667</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-5.468220</td>\n",
       "      <td>-5.80532</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37793.000000</td>\n",
       "      <td>6.813000e+03</td>\n",
       "      <td>1.104006e+06</td>\n",
       "      <td>3357.000000</td>\n",
       "      <td>16902.500000</td>\n",
       "      <td>1.964700e+04</td>\n",
       "      <td>1.725966e+06</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>3.142860</td>\n",
       "      <td>2.714290</td>\n",
       "      <td>1.166670</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.485710</td>\n",
       "      <td>2.375000</td>\n",
       "      <td>1.555560</td>\n",
       "      <td>1.333330</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>18.620000</td>\n",
       "      <td>1.187340</td>\n",
       "      <td>1.567500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.667121</td>\n",
       "      <td>7.989067</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Widowed</th>\n",
       "      <td>2458.219210</td>\n",
       "      <td>226.511491</td>\n",
       "      <td>78.326458</td>\n",
       "      <td>14.945197</td>\n",
       "      <td>1.223788</td>\n",
       "      <td>1.774485</td>\n",
       "      <td>1.235103</td>\n",
       "      <td>2.063839</td>\n",
       "      <td>11.259461</td>\n",
       "      <td>17.450841</td>\n",
       "      <td>5.179916</td>\n",
       "      <td>26.769231</td>\n",
       "      <td>35.263783</td>\n",
       "      <td>4.273113</td>\n",
       "      <td>3.424061</td>\n",
       "      <td>51.019401</td>\n",
       "      <td>8.210856</td>\n",
       "      <td>37.775551</td>\n",
       "      <td>128.854167</td>\n",
       "      <td>5.114050</td>\n",
       "      <td>22.719585</td>\n",
       "      <td>2.039231</td>\n",
       "      <td>1.701440</td>\n",
       "      <td>1.394168</td>\n",
       "      <td>1.393874</td>\n",
       "      <td>1.523945</td>\n",
       "      <td>1.744265</td>\n",
       "      <td>1.658422</td>\n",
       "      <td>2.141702</td>\n",
       "      <td>1.639487</td>\n",
       "      <td>1.669640</td>\n",
       "      <td>1.709104</td>\n",
       "      <td>1.797419</td>\n",
       "      <td>2.003234</td>\n",
       "      <td>1.817711</td>\n",
       "      <td>NaN</td>\n",
       "      <td>226681.464052</td>\n",
       "      <td>40968.026995</td>\n",
       "      <td>6443.295958</td>\n",
       "      <td>9.512258e+05</td>\n",
       "      <td>3.271105e+03</td>\n",
       "      <td>16236.510057</td>\n",
       "      <td>18166.413793</td>\n",
       "      <td>1.469116e+06</td>\n",
       "      <td>-6.039665</td>\n",
       "      <td>9.122096</td>\n",
       "      <td>14.746559</td>\n",
       "      <td>4.758986</td>\n",
       "      <td>27.692398</td>\n",
       "      <td>37.732386</td>\n",
       "      <td>4.957963</td>\n",
       "      <td>4.343991</td>\n",
       "      <td>53.368305</td>\n",
       "      <td>8.086034</td>\n",
       "      <td>40.006390</td>\n",
       "      <td>118.372176</td>\n",
       "      <td>2.898405</td>\n",
       "      <td>-4.882019</td>\n",
       "      <td>-4.823704</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36126.831365</td>\n",
       "      <td>6706.048816</td>\n",
       "      <td>9.642041e+05</td>\n",
       "      <td>3.371200e+03</td>\n",
       "      <td>16519.855951</td>\n",
       "      <td>18690.333586</td>\n",
       "      <td>1.461396e+06</td>\n",
       "      <td>2.364103e+01</td>\n",
       "      <td>2.033463</td>\n",
       "      <td>1.724068</td>\n",
       "      <td>1.325367</td>\n",
       "      <td>1.343875</td>\n",
       "      <td>1.474285</td>\n",
       "      <td>1.660643</td>\n",
       "      <td>1.624628</td>\n",
       "      <td>1.972430</td>\n",
       "      <td>1.451835</td>\n",
       "      <td>1.418806</td>\n",
       "      <td>1.499909</td>\n",
       "      <td>1.604590</td>\n",
       "      <td>1.753841</td>\n",
       "      <td>1.610115</td>\n",
       "      <td>1.260279</td>\n",
       "      <td>1.664583</td>\n",
       "      <td>1.246703</td>\n",
       "      <td>2.551963</td>\n",
       "      <td>30.560797</td>\n",
       "      <td>30.496759</td>\n",
       "      <td>30.194461</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       RID        SITE        AGE   PTEDUCAT       FDG  \\\n",
       "PTMARRY                                                                  \n",
       "Divorced       3291.654260  223.070306  70.829032  16.191067  1.244450   \n",
       "Married        2641.822688  230.516823  73.121623  16.198813  1.196356   \n",
       "Never married  3079.861233  247.359031  73.142952  16.288546  1.255222   \n",
       "Unknown        2941.018868  250.000000  68.700000  15.509434  1.306954   \n",
       "White          3135.000000  208.666667  69.366667  16.333333  1.000000   \n",
       "Widowed        2458.219210  226.511491  78.326458  14.945197  1.223788   \n",
       "\n",
       "                    PIB      AV45     CDRSB     ADAS11     ADAS13     ADASQ4  \\\n",
       "PTMARRY                                                                        \n",
       "Divorced       1.640000  1.160615  1.381640   9.692649  14.474584   4.128000   \n",
       "Married        1.796808  1.190302  2.215350  11.861407  18.073441   5.255935   \n",
       "Never married  1.574167  1.129356  1.050459   8.969072  13.779149   3.844311   \n",
       "Unknown             NaN  1.018476  0.959459   7.333250  11.316154   3.325000   \n",
       "White          1.251200  1.435000       NaN   1.666667  13.666667  20.666667   \n",
       "Widowed        1.774485  1.235103  2.063839  11.259461  17.450841   5.179916   \n",
       "\n",
       "                    MMSE  RAVLT_immediate  RAVLT_learning  RAVLT_forgetting  \\\n",
       "PTMARRY                                                                       \n",
       "Divorced       27.392449        39.161066        4.858633          3.844548   \n",
       "Married        26.584115        34.616825        4.013591          4.213266   \n",
       "Never married  27.997006        39.849850        4.732733          3.909910   \n",
       "Unknown        28.650000        38.769231        6.025641          3.743590   \n",
       "White           5.666667        28.333333       35.000000          3.333333   \n",
       "Widowed        26.769231        35.263783        4.273113          3.424061   \n",
       "\n",
       "               RAVLT_perc_forgetting  LDELTOTAL  DIGITSCOR    TRABSCOR  \\\n",
       "PTMARRY                                                                  \n",
       "Divorced                   47.425462  10.141457  38.839286  102.003534   \n",
       "Married                    59.053171   8.361783  37.106649  118.497411   \n",
       "Never married              48.169036  11.003636  39.308411  110.350769   \n",
       "Unknown                    42.535700   9.937500  62.200000   82.842105   \n",
       "White                       5.333333  74.074100   3.333333   32.000000   \n",
       "Widowed                    51.019401   8.210856  37.775551  128.854167   \n",
       "\n",
       "                     FAQ       MOCA  EcogPtMem  EcogPtLang  EcogPtVisspat  \\\n",
       "PTMARRY                                                                     \n",
       "Divorced        2.965157  24.570300   2.019086    1.657964       1.374131   \n",
       "Married         5.756061  23.131192   2.069483    1.728869       1.391380   \n",
       "Never married   2.705521  24.443439   1.810006    1.647400       1.351972   \n",
       "Unknown         1.631579  24.588235   2.419048    2.131745       1.386939   \n",
       "White          99.333333   5.000000  26.000000    3.142860       2.714290   \n",
       "Widowed         5.114050  22.719585   2.039231    1.701440       1.394168   \n",
       "\n",
       "               EcogPtPlan  EcogPtOrgan  EcogPtDivatt  EcogPtTotal  EcogSPMem  \\\n",
       "PTMARRY                                                                        \n",
       "Divorced         1.348843     1.508141      1.850129     1.641122   1.798203   \n",
       "Married          1.423182     1.531277      1.835863     1.684390   2.165452   \n",
       "Never married    1.322646     1.500682      1.728979     1.578666   1.627326   \n",
       "Unknown          1.308571     1.552525      1.700000     1.816112   2.033110   \n",
       "White            1.166670     1.600000      2.500000     4.000000   2.485710   \n",
       "Widowed          1.393874     1.523945      1.744265     1.658422   2.141702   \n",
       "\n",
       "               EcogSPLang  EcogSPVisspat  EcogSPPlan  EcogSPOrgan  \\\n",
       "PTMARRY                                                             \n",
       "Divorced         1.405836       1.361824    1.408663     1.488619   \n",
       "Married          1.757264       1.611220    1.726930     1.805617   \n",
       "Never married    1.392489       1.308530    1.360714     1.420957   \n",
       "Unknown          1.516617       1.189796    1.356250     1.331884   \n",
       "White            2.375000       1.555560    1.333330     1.200000   \n",
       "Widowed          1.639487       1.669640    1.709104     1.797419   \n",
       "\n",
       "               EcogSPDivatt  EcogSPTotal  FLDSTRENG       IMAGEUID  \\\n",
       "PTMARRY                                                              \n",
       "Divorced           1.706600     1.529100        NaN  270371.363914   \n",
       "Married            2.011747     1.851985        NaN  235688.480256   \n",
       "Never married      1.619565     1.454961        NaN  255590.510288   \n",
       "Unknown            1.830357     1.557582        NaN  292398.205882   \n",
       "White              2.000000     2.000000    1.76316            NaN   \n",
       "Widowed            2.003234     1.817711        NaN  226681.464052   \n",
       "\n",
       "                  Ventricles   Hippocampus    WholeBrain    Entorhinal  \\\n",
       "PTMARRY                                                                  \n",
       "Divorced        31515.775610   6881.013722  1.006044e+06  3.516545e+03   \n",
       "Married         43605.737458   6679.473350  1.021657e+06  3.453634e+03   \n",
       "Never married   40377.385650   6829.151111  9.890619e+05  3.517871e+03   \n",
       "Unknown         28855.878788   8057.655172  1.131052e+06  3.967667e+03   \n",
       "White          100246.500000  40338.000000  6.771000e+03  1.093406e+06   \n",
       "Widowed         40968.026995   6443.295958  9.512258e+05  3.271105e+03   \n",
       "\n",
       "                   Fusiform       MidTemp           ICV  mPACCtrailsB  \\\n",
       "PTMARRY                                                                 \n",
       "Divorced       17315.250000  18998.447917  1.463609e+06     -3.903863   \n",
       "Married        17258.479726  19362.160379  1.553296e+06     -6.127014   \n",
       "Never married  16647.980861  19105.272727  1.482008e+06     -2.996565   \n",
       "Unknown        19804.500000  21685.500000  1.568945e+06     -1.784398   \n",
       "White           2743.500000  16969.500000  1.921900e+04     -6.449037   \n",
       "Widowed        16236.510057  18166.413793  1.469116e+06     -6.039665   \n",
       "\n",
       "               ADAS11_bl  ADAS13_bl  ADASQ4_bl    MMSE_bl  RAVLT_immediate_bl  \\\n",
       "PTMARRY                                                                         \n",
       "Divorced        8.332983  12.686830   3.826015  28.025641           40.929577   \n",
       "Married        10.037228  15.762419   4.976598  27.588881           36.525850   \n",
       "Never married   7.628150  11.896872   3.572687  28.495595           39.973568   \n",
       "Unknown         8.213774  12.270377   3.792453  29.622642           39.490566   \n",
       "White           1.166667  11.220000  17.220000   4.666667           28.000000   \n",
       "Widowed         9.122096  14.746559   4.758986  27.692398           37.732386   \n",
       "\n",
       "               RAVLT_learning_bl  RAVLT_forgetting_bl  \\\n",
       "PTMARRY                                                 \n",
       "Divorced                5.202983             4.025684   \n",
       "Married                 4.393215             4.288486   \n",
       "Never married           4.764317             3.995595   \n",
       "Unknown                 5.716981             2.905660   \n",
       "White                  31.666667             2.666667   \n",
       "Widowed                 4.957963             4.343991   \n",
       "\n",
       "               RAVLT_perc_forgetting_bl  LDELTOTAL_BL  DIGITSCOR_bl  \\\n",
       "PTMARRY                                                               \n",
       "Divorced                      44.961698      9.201159     40.381201   \n",
       "Married                       55.041650      7.723623     40.142704   \n",
       "Never married                 47.159533     10.074890     41.952128   \n",
       "Unknown                       33.165607      7.943396     69.000000   \n",
       "White                          5.333333     75.661400      4.333333   \n",
       "Widowed                       53.368305      8.086034     40.006390   \n",
       "\n",
       "               TRABSCOR_bl      FAQ_bl  mPACCdigit_bl  mPACCtrailsB_bl  \\\n",
       "PTMARRY                                                                  \n",
       "Divorced         94.118289    1.660550      -3.461543        -3.164937   \n",
       "Married         110.331072    3.402205      -5.190128        -4.924596   \n",
       "Never married   109.788889    1.721854      -2.480422        -2.574614   \n",
       "Unknown          85.521739    1.113208      -2.080783        -1.935192   \n",
       "White            38.500000  128.666667       2.000000        -5.468220   \n",
       "Widowed         118.372176    2.898405      -4.882019        -4.823704   \n",
       "\n",
       "               FLDSTRENG_bl  Ventricles_bl  Hippocampus_bl  WholeBrain_bl  \\\n",
       "PTMARRY                                                                     \n",
       "Divorced                NaN   29943.786822     7114.693401   1.024601e+06   \n",
       "Married                 NaN   39932.131867     6901.944605   1.037495e+06   \n",
       "Never married           NaN   37799.040100     6977.434783   1.003797e+06   \n",
       "Unknown                 NaN   27664.211538     8053.133333   1.148639e+06   \n",
       "White              -5.80532            NaN    37793.000000   6.813000e+03   \n",
       "Widowed                 NaN   36126.831365     6706.048816   9.642041e+05   \n",
       "\n",
       "               Entorhinal_bl   Fusiform_bl    MidTemp_bl        ICV_bl  \\\n",
       "PTMARRY                                                                  \n",
       "Divorced        3.632987e+03  17789.262846  19573.578063  1.475378e+06   \n",
       "Married         3.587217e+03  17625.947628  19919.337894  1.555202e+06   \n",
       "Never married   3.633925e+03  17030.840399  19769.970075  1.484635e+06   \n",
       "Unknown         3.991452e+03  19908.238095  21757.952381  1.570393e+06   \n",
       "White           1.104006e+06   3357.000000  16902.500000  1.964700e+04   \n",
       "Widowed         3.371200e+03  16519.855951  18690.333586  1.461396e+06   \n",
       "\n",
       "                    MOCA_bl  EcogPtMem_bl  EcogPtLang_bl  EcogPtVisspat_bl  \\\n",
       "PTMARRY                                                                      \n",
       "Divorced       2.510037e+01      2.056265       1.673695          1.380301   \n",
       "Married        2.341527e+01      2.108906       1.722058          1.368785   \n",
       "Never married  2.483142e+01      1.830189       1.652464          1.344593   \n",
       "Unknown        2.197826e+01      2.347826       2.134661          1.381988   \n",
       "White          1.725966e+06     26.000000       3.142860          2.714290   \n",
       "Widowed        2.364103e+01      2.033463       1.724068          1.325367   \n",
       "\n",
       "               EcogPtPlan_bl  EcogPtOrgan_bl  EcogPtDivatt_bl  EcogPtTotal_bl  \\\n",
       "PTMARRY                                                                         \n",
       "Divorced            1.388564        1.505799         1.813058        1.652824   \n",
       "Married             1.399488        1.496558         1.809537        1.673526   \n",
       "Never married       1.314286        1.497782         1.799812        1.585091   \n",
       "Unknown             1.304348        1.425362         1.489130        1.763424   \n",
       "White               1.166670        1.600000         2.500000        4.000000   \n",
       "Widowed             1.343875        1.474285         1.660643        1.624628   \n",
       "\n",
       "               EcogSPMem_bl  EcogSPLang_bl  EcogSPVisspat_bl  EcogSPPlan_bl  \\\n",
       "PTMARRY                                                                       \n",
       "Divorced           1.692674       1.337108          1.242633       1.311714   \n",
       "Married            2.050150       1.606915          1.419309       1.546458   \n",
       "Never married      1.584209       1.383333          1.270781       1.311280   \n",
       "Unknown            2.090580       1.509962          1.329062       1.347826   \n",
       "White              2.485710       2.375000          1.555560       1.333330   \n",
       "Widowed            1.972430       1.451835          1.418806       1.499909   \n",
       "\n",
       "               EcogSPOrgan_bl  EcogSPDivatt_bl  EcogSPTotal_bl     FDG_bl  \\\n",
       "PTMARRY                                                                     \n",
       "Divorced             1.343620         1.572745        1.420171   1.280540   \n",
       "Married              1.640734         1.822297        1.687627   1.241390   \n",
       "Never married        1.379476         1.584651        1.416639   1.277663   \n",
       "Unknown              1.499999         1.885870        1.618948   1.308734   \n",
       "White                1.200000         2.000000        2.000000  18.620000   \n",
       "Widowed              1.604590         1.753841        1.610115   1.260279   \n",
       "\n",
       "                 PIB_bl   AV45_bl  Years_bl   Month_bl      Month          M  \n",
       "PTMARRY                                                                       \n",
       "Divorced       1.742308  1.150095  2.171089  25.999675  25.957816  25.786600  \n",
       "Married        1.543079  1.196235  2.443094  29.257051  29.174433  29.014575  \n",
       "Never married       NaN  1.136882  2.167781  25.960063  25.803965  25.638767  \n",
       "Unknown             NaN  1.020542  1.853266  22.193632  22.018868  21.396226  \n",
       "White          1.187340  1.567500       NaN   0.667121   7.989067   8.000000  \n",
       "Widowed        1.664583  1.246703  2.551963  30.560797  30.496759  30.194461  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('PTMARRY').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise\n",
    "\n",
    "Group data by the patients' occupation. If you wanted to statistically analyse the patient's occupation, which problems in the data would you need to solve first?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary> > Solution</summary>\n",
    "    \n",
    "```python\n",
    "data.groupby('WORK').size()\n",
    "```\n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.7. Subset data\n",
    "\n",
    "We can select data by column label (e.g. column name):\n",
    "\n",
    "- A single label (as *string*), e.g. `'a'`\n",
    "- A *list* or array of labels `['a', 'b', 'c']`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select data by a single label (column name) in the form of a string or list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    74.2\n",
       "1    82.4\n",
       "2    81.4\n",
       "3    81.3\n",
       "4    80.5\n",
       "Name: AGE, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['AGE'].head()  # Returns Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AGE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>74.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>82.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>81.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>81.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>80.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    AGE\n",
       "0  74.2\n",
       "1  82.4\n",
       "2  81.4\n",
       "3  81.3\n",
       "4  80.5"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['AGE']].head()  # Returns DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select data by a list of labels (column names):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PTGENDER</th>\n",
       "      <th>AGE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>74.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>82.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>81.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>81.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>80.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PTGENDER   AGE\n",
       "0     Male  74.2\n",
       "1     Male  82.4\n",
       "2     Male  81.4\n",
       "3     Male  81.3\n",
       "4     Male  80.5"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['PTGENDER', 'AGE']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is possible to check for the elements of a column whether they fulfill a certain condition (`True`) or not (`False`), returning a `Series` of boolean values (`True`/`False`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        False\n",
       "1        False\n",
       "2        False\n",
       "3        False\n",
       "4        False\n",
       "5        False\n",
       "6        False\n",
       "7        False\n",
       "8        False\n",
       "9        False\n",
       "         ...  \n",
       "14522    False\n",
       "14523    False\n",
       "14524     True\n",
       "14525     True\n",
       "14526    False\n",
       "14527    False\n",
       "14528    False\n",
       "14529    False\n",
       "14530    False\n",
       "14531     True\n",
       "Name: PTGENDER, dtype: bool"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['PTGENDER']  == 'Female'  # Per row: Is the patient female?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This list of boolean values can be used to select only those rows of the DataFrame that fullfill a condition. \n",
    "\n",
    "**Important**: The number of boolean values must be equal to the number of DataFrame rows. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RID</th>\n",
       "      <th>VISCODE</th>\n",
       "      <th>SITE</th>\n",
       "      <th>EXAMDATE</th>\n",
       "      <th>DX_bl</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PTGENDER</th>\n",
       "      <th>PTEDUCAT</th>\n",
       "      <th>WORK</th>\n",
       "      <th>PTETHCAT</th>\n",
       "      <th>PTRACCAT</th>\n",
       "      <th>PTMARRY</th>\n",
       "      <th>APOE4</th>\n",
       "      <th>FDG</th>\n",
       "      <th>PIB</th>\n",
       "      <th>AV45</th>\n",
       "      <th>ABETA</th>\n",
       "      <th>TAU</th>\n",
       "      <th>PTAU</th>\n",
       "      <th>CDRSB</th>\n",
       "      <th>ADAS11</th>\n",
       "      <th>ADAS13</th>\n",
       "      <th>ADASQ4</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>RAVLT_immediate</th>\n",
       "      <th>RAVLT_learning</th>\n",
       "      <th>RAVLT_forgetting</th>\n",
       "      <th>RAVLT_perc_forgetting</th>\n",
       "      <th>LDELTOTAL</th>\n",
       "      <th>DIGITSCOR</th>\n",
       "      <th>TRABSCOR</th>\n",
       "      <th>FAQ</th>\n",
       "      <th>MOCA</th>\n",
       "      <th>EcogPtMem</th>\n",
       "      <th>EcogPtLang</th>\n",
       "      <th>EcogPtVisspat</th>\n",
       "      <th>EcogPtPlan</th>\n",
       "      <th>EcogPtOrgan</th>\n",
       "      <th>EcogPtDivatt</th>\n",
       "      <th>EcogPtTotal</th>\n",
       "      <th>EcogSPMem</th>\n",
       "      <th>EcogSPLang</th>\n",
       "      <th>EcogSPVisspat</th>\n",
       "      <th>EcogSPPlan</th>\n",
       "      <th>EcogSPOrgan</th>\n",
       "      <th>EcogSPDivatt</th>\n",
       "      <th>EcogSPTotal</th>\n",
       "      <th>FLDSTRENG</th>\n",
       "      <th>IMAGEUID</th>\n",
       "      <th>Ventricles</th>\n",
       "      <th>Hippocampus</th>\n",
       "      <th>WholeBrain</th>\n",
       "      <th>Entorhinal</th>\n",
       "      <th>Fusiform</th>\n",
       "      <th>MidTemp</th>\n",
       "      <th>ICV</th>\n",
       "      <th>DX</th>\n",
       "      <th>mPACCdigit</th>\n",
       "      <th>mPACCtrailsB</th>\n",
       "      <th>EXAMDATE_bl</th>\n",
       "      <th>CDRSB_bl</th>\n",
       "      <th>ADAS11_bl</th>\n",
       "      <th>ADAS13_bl</th>\n",
       "      <th>ADASQ4_bl</th>\n",
       "      <th>MMSE_bl</th>\n",
       "      <th>RAVLT_immediate_bl</th>\n",
       "      <th>RAVLT_learning_bl</th>\n",
       "      <th>RAVLT_forgetting_bl</th>\n",
       "      <th>RAVLT_perc_forgetting_bl</th>\n",
       "      <th>LDELTOTAL_BL</th>\n",
       "      <th>DIGITSCOR_bl</th>\n",
       "      <th>TRABSCOR_bl</th>\n",
       "      <th>FAQ_bl</th>\n",
       "      <th>mPACCdigit_bl</th>\n",
       "      <th>mPACCtrailsB_bl</th>\n",
       "      <th>FLDSTRENG_bl</th>\n",
       "      <th>Ventricles_bl</th>\n",
       "      <th>Hippocampus_bl</th>\n",
       "      <th>WholeBrain_bl</th>\n",
       "      <th>Entorhinal_bl</th>\n",
       "      <th>Fusiform_bl</th>\n",
       "      <th>MidTemp_bl</th>\n",
       "      <th>ICV_bl</th>\n",
       "      <th>MOCA_bl</th>\n",
       "      <th>EcogPtMem_bl</th>\n",
       "      <th>EcogPtLang_bl</th>\n",
       "      <th>EcogPtVisspat_bl</th>\n",
       "      <th>EcogPtPlan_bl</th>\n",
       "      <th>EcogPtOrgan_bl</th>\n",
       "      <th>EcogPtDivatt_bl</th>\n",
       "      <th>EcogPtTotal_bl</th>\n",
       "      <th>EcogSPMem_bl</th>\n",
       "      <th>EcogSPLang_bl</th>\n",
       "      <th>EcogSPVisspat_bl</th>\n",
       "      <th>EcogSPPlan_bl</th>\n",
       "      <th>EcogSPOrgan_bl</th>\n",
       "      <th>EcogSPDivatt_bl</th>\n",
       "      <th>EcogSPTotal_bl</th>\n",
       "      <th>ABETA_bl</th>\n",
       "      <th>TAU_bl</th>\n",
       "      <th>PTAU_bl</th>\n",
       "      <th>FDG_bl</th>\n",
       "      <th>PIB_bl</th>\n",
       "      <th>AV45_bl</th>\n",
       "      <th>Years_bl</th>\n",
       "      <th>Month_bl</th>\n",
       "      <th>Month</th>\n",
       "      <th>M</th>\n",
       "      <th>update_stamp</th>\n",
       "      <th>Unnamed: 109</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>132</td>\n",
       "      <td>bl</td>\n",
       "      <td>253</td>\n",
       "      <td>2005-11-29</td>\n",
       "      <td>LMCI</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>13</td>\n",
       "      <td>Transportation Operation Sales</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5</td>\n",
       "      <td>18.67</td>\n",
       "      <td>25.67</td>\n",
       "      <td>7.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>83.3333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33012.0</td>\n",
       "      <td>39822.0</td>\n",
       "      <td>5342.0</td>\n",
       "      <td>927504.0</td>\n",
       "      <td>2276.0</td>\n",
       "      <td>17966.0</td>\n",
       "      <td>17802.0</td>\n",
       "      <td>1485836.0</td>\n",
       "      <td>MCI</td>\n",
       "      <td>-10.3423</td>\n",
       "      <td>-11.136500</td>\n",
       "      <td>2005-11-29</td>\n",
       "      <td>0.5</td>\n",
       "      <td>18.67</td>\n",
       "      <td>25.67</td>\n",
       "      <td>7.0</td>\n",
       "      <td>25</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>83.33330</td>\n",
       "      <td>3.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-10.342300</td>\n",
       "      <td>-11.13650</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39822.0</td>\n",
       "      <td>5342.0</td>\n",
       "      <td>927504.0</td>\n",
       "      <td>2276.0</td>\n",
       "      <td>17966.0</td>\n",
       "      <td>17802.0</td>\n",
       "      <td>1485836.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>132</td>\n",
       "      <td>m06</td>\n",
       "      <td>253</td>\n",
       "      <td>2006-06-01</td>\n",
       "      <td>LMCI</td>\n",
       "      <td>79.4</td>\n",
       "      <td>Female</td>\n",
       "      <td>13</td>\n",
       "      <td>clerical worker</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.00</td>\n",
       "      <td>26.00</td>\n",
       "      <td>7.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>63.6364</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33031.0</td>\n",
       "      <td>40161.0</td>\n",
       "      <td>5536.0</td>\n",
       "      <td>957215.0</td>\n",
       "      <td>2479.0</td>\n",
       "      <td>18461.0</td>\n",
       "      <td>18689.0</td>\n",
       "      <td>1514266.0</td>\n",
       "      <td>MCI</td>\n",
       "      <td>-14.7828</td>\n",
       "      <td>-15.110800</td>\n",
       "      <td>2005-11-29</td>\n",
       "      <td>0.5</td>\n",
       "      <td>18.67</td>\n",
       "      <td>25.67</td>\n",
       "      <td>7.0</td>\n",
       "      <td>25</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>83.33330</td>\n",
       "      <td>3.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-10.342300</td>\n",
       "      <td>-11.13650</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39822.0</td>\n",
       "      <td>5342.0</td>\n",
       "      <td>927504.0</td>\n",
       "      <td>2276.0</td>\n",
       "      <td>17966.0</td>\n",
       "      <td>17802.0</td>\n",
       "      <td>1485836.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.503765</td>\n",
       "      <td>6.03279</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>132</td>\n",
       "      <td>m12</td>\n",
       "      <td>253</td>\n",
       "      <td>2006-11-20</td>\n",
       "      <td>LMCI</td>\n",
       "      <td>80.3</td>\n",
       "      <td>Female</td>\n",
       "      <td>13</td>\n",
       "      <td>Special Education Teacher</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.5</td>\n",
       "      <td>18.67</td>\n",
       "      <td>25.67</td>\n",
       "      <td>7.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62329.0</td>\n",
       "      <td>40672.0</td>\n",
       "      <td>5528.0</td>\n",
       "      <td>954562.0</td>\n",
       "      <td>2419.0</td>\n",
       "      <td>17851.0</td>\n",
       "      <td>18289.0</td>\n",
       "      <td>1512746.0</td>\n",
       "      <td>MCI</td>\n",
       "      <td>-10.4261</td>\n",
       "      <td>-10.898500</td>\n",
       "      <td>2005-11-29</td>\n",
       "      <td>0.5</td>\n",
       "      <td>18.67</td>\n",
       "      <td>25.67</td>\n",
       "      <td>7.0</td>\n",
       "      <td>25</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>83.33330</td>\n",
       "      <td>3.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-10.342300</td>\n",
       "      <td>-11.13650</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39822.0</td>\n",
       "      <td>5342.0</td>\n",
       "      <td>927504.0</td>\n",
       "      <td>2276.0</td>\n",
       "      <td>17966.0</td>\n",
       "      <td>17802.0</td>\n",
       "      <td>1485836.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.974675</td>\n",
       "      <td>11.67210</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>132</td>\n",
       "      <td>m18</td>\n",
       "      <td>253</td>\n",
       "      <td>2007-05-15</td>\n",
       "      <td>LMCI</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>13</td>\n",
       "      <td>RN</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.33</td>\n",
       "      <td>21.33</td>\n",
       "      <td>9.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>85.7143</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72772.0</td>\n",
       "      <td>41166.0</td>\n",
       "      <td>5202.0</td>\n",
       "      <td>921976.0</td>\n",
       "      <td>2278.0</td>\n",
       "      <td>16852.0</td>\n",
       "      <td>17430.0</td>\n",
       "      <td>1482666.0</td>\n",
       "      <td>MCI</td>\n",
       "      <td>-12.5145</td>\n",
       "      <td>-11.594200</td>\n",
       "      <td>2005-11-29</td>\n",
       "      <td>0.5</td>\n",
       "      <td>18.67</td>\n",
       "      <td>25.67</td>\n",
       "      <td>7.0</td>\n",
       "      <td>25</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>83.33330</td>\n",
       "      <td>3.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-10.342300</td>\n",
       "      <td>-11.13650</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39822.0</td>\n",
       "      <td>5342.0</td>\n",
       "      <td>927504.0</td>\n",
       "      <td>2276.0</td>\n",
       "      <td>17966.0</td>\n",
       "      <td>17802.0</td>\n",
       "      <td>1485836.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.456540</td>\n",
       "      <td>17.44260</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>132</td>\n",
       "      <td>m36</td>\n",
       "      <td>253</td>\n",
       "      <td>2008-12-08</td>\n",
       "      <td>LMCI</td>\n",
       "      <td>81.2</td>\n",
       "      <td>Female</td>\n",
       "      <td>13</td>\n",
       "      <td>Book keeper</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17.67</td>\n",
       "      <td>26.67</td>\n",
       "      <td>9.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>85.7143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>160845.0</td>\n",
       "      <td>46545.0</td>\n",
       "      <td>4903.0</td>\n",
       "      <td>924397.0</td>\n",
       "      <td>2308.0</td>\n",
       "      <td>17675.0</td>\n",
       "      <td>17356.0</td>\n",
       "      <td>1492366.0</td>\n",
       "      <td>MCI</td>\n",
       "      <td>-15.6707</td>\n",
       "      <td>-16.399400</td>\n",
       "      <td>2005-11-29</td>\n",
       "      <td>0.5</td>\n",
       "      <td>18.67</td>\n",
       "      <td>25.67</td>\n",
       "      <td>7.0</td>\n",
       "      <td>25</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>83.33330</td>\n",
       "      <td>3.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-10.342300</td>\n",
       "      <td>-11.13650</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39822.0</td>\n",
       "      <td>5342.0</td>\n",
       "      <td>927504.0</td>\n",
       "      <td>2276.0</td>\n",
       "      <td>17966.0</td>\n",
       "      <td>17802.0</td>\n",
       "      <td>1485836.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.025330</td>\n",
       "      <td>36.22950</td>\n",
       "      <td>36.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>136</td>\n",
       "      <td>bl</td>\n",
       "      <td>164</td>\n",
       "      <td>2005-11-10</td>\n",
       "      <td>AD</td>\n",
       "      <td>73.9</td>\n",
       "      <td>Female</td>\n",
       "      <td>12</td>\n",
       "      <td>Professor of Neurophysiology</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>1.115320</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>357.4</td>\n",
       "      <td>329.9</td>\n",
       "      <td>31.26</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.33</td>\n",
       "      <td>24.33</td>\n",
       "      <td>10.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32253.0</td>\n",
       "      <td>26819.0</td>\n",
       "      <td>5479.0</td>\n",
       "      <td>1033537.0</td>\n",
       "      <td>2674.0</td>\n",
       "      <td>16758.0</td>\n",
       "      <td>19736.0</td>\n",
       "      <td>1471182.0</td>\n",
       "      <td>Dementia</td>\n",
       "      <td>-13.917</td>\n",
       "      <td>-12.786000</td>\n",
       "      <td>2005-11-10</td>\n",
       "      <td>5</td>\n",
       "      <td>12.33</td>\n",
       "      <td>24.33</td>\n",
       "      <td>10.0</td>\n",
       "      <td>24</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>100.00000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>-13.917000</td>\n",
       "      <td>-12.78600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26819.0</td>\n",
       "      <td>5479.0</td>\n",
       "      <td>1033537.0</td>\n",
       "      <td>2674.0</td>\n",
       "      <td>16758.0</td>\n",
       "      <td>19736.0</td>\n",
       "      <td>1471182.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>357.4</td>\n",
       "      <td>329.9</td>\n",
       "      <td>31.26</td>\n",
       "      <td>1.11532</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>136</td>\n",
       "      <td>m06</td>\n",
       "      <td>164</td>\n",
       "      <td>2006-05-09</td>\n",
       "      <td>AD</td>\n",
       "      <td>74.4</td>\n",
       "      <td>Female</td>\n",
       "      <td>12</td>\n",
       "      <td>Registered Nurse</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>1.075580</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.5</td>\n",
       "      <td>14.33</td>\n",
       "      <td>24.33</td>\n",
       "      <td>9.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>75.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31901.0</td>\n",
       "      <td>29615.0</td>\n",
       "      <td>5313.0</td>\n",
       "      <td>989042.0</td>\n",
       "      <td>2417.0</td>\n",
       "      <td>15889.0</td>\n",
       "      <td>18590.0</td>\n",
       "      <td>1459662.0</td>\n",
       "      <td>Dementia</td>\n",
       "      <td>-11.1382</td>\n",
       "      <td>-11.852500</td>\n",
       "      <td>2005-11-10</td>\n",
       "      <td>5</td>\n",
       "      <td>12.33</td>\n",
       "      <td>24.33</td>\n",
       "      <td>10.0</td>\n",
       "      <td>24</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>100.00000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>-13.917000</td>\n",
       "      <td>-12.78600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26819.0</td>\n",
       "      <td>5479.0</td>\n",
       "      <td>1033537.0</td>\n",
       "      <td>2674.0</td>\n",
       "      <td>16758.0</td>\n",
       "      <td>19736.0</td>\n",
       "      <td>1471182.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>357.4</td>\n",
       "      <td>329.9</td>\n",
       "      <td>31.26</td>\n",
       "      <td>1.11532</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.492813</td>\n",
       "      <td>5.90164</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>136</td>\n",
       "      <td>m12</td>\n",
       "      <td>164</td>\n",
       "      <td>2006-11-09</td>\n",
       "      <td>AD</td>\n",
       "      <td>74.9</td>\n",
       "      <td>Female</td>\n",
       "      <td>12</td>\n",
       "      <td>Retail Store Owner/paint</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>1.007210</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>340</td>\n",
       "      <td>287.7</td>\n",
       "      <td>27.37</td>\n",
       "      <td>4.0</td>\n",
       "      <td>23.33</td>\n",
       "      <td>35.33</td>\n",
       "      <td>9.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94390.0</td>\n",
       "      <td>31565.0</td>\n",
       "      <td>5260.0</td>\n",
       "      <td>1010657.0</td>\n",
       "      <td>2182.0</td>\n",
       "      <td>15756.0</td>\n",
       "      <td>18013.0</td>\n",
       "      <td>1467272.0</td>\n",
       "      <td>Dementia</td>\n",
       "      <td>-13.0401</td>\n",
       "      <td>-12.485300</td>\n",
       "      <td>2005-11-10</td>\n",
       "      <td>5</td>\n",
       "      <td>12.33</td>\n",
       "      <td>24.33</td>\n",
       "      <td>10.0</td>\n",
       "      <td>24</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>100.00000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>-13.917000</td>\n",
       "      <td>-12.78600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26819.0</td>\n",
       "      <td>5479.0</td>\n",
       "      <td>1033537.0</td>\n",
       "      <td>2674.0</td>\n",
       "      <td>16758.0</td>\n",
       "      <td>19736.0</td>\n",
       "      <td>1471182.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>357.4</td>\n",
       "      <td>329.9</td>\n",
       "      <td>31.26</td>\n",
       "      <td>1.11532</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.996578</td>\n",
       "      <td>11.93440</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>136</td>\n",
       "      <td>m24</td>\n",
       "      <td>164</td>\n",
       "      <td>2007-11-07</td>\n",
       "      <td>AD</td>\n",
       "      <td>73.4</td>\n",
       "      <td>Female</td>\n",
       "      <td>12</td>\n",
       "      <td>Director of Purchasing</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>0.959766</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19.33</td>\n",
       "      <td>30.33</td>\n",
       "      <td>10.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94371.0</td>\n",
       "      <td>35428.0</td>\n",
       "      <td>4862.0</td>\n",
       "      <td>989150.0</td>\n",
       "      <td>2253.0</td>\n",
       "      <td>15649.0</td>\n",
       "      <td>18014.0</td>\n",
       "      <td>1469532.0</td>\n",
       "      <td>Dementia</td>\n",
       "      <td>-11.9584</td>\n",
       "      <td>-12.211800</td>\n",
       "      <td>2005-11-10</td>\n",
       "      <td>5</td>\n",
       "      <td>12.33</td>\n",
       "      <td>24.33</td>\n",
       "      <td>10.0</td>\n",
       "      <td>24</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>100.00000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>-13.917000</td>\n",
       "      <td>-12.78600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26819.0</td>\n",
       "      <td>5479.0</td>\n",
       "      <td>1033537.0</td>\n",
       "      <td>2674.0</td>\n",
       "      <td>16758.0</td>\n",
       "      <td>19736.0</td>\n",
       "      <td>1471182.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>357.4</td>\n",
       "      <td>329.9</td>\n",
       "      <td>31.26</td>\n",
       "      <td>1.11532</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.990420</td>\n",
       "      <td>23.83610</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>140</td>\n",
       "      <td>bl</td>\n",
       "      <td>175</td>\n",
       "      <td>2005-11-04</td>\n",
       "      <td>CN</td>\n",
       "      <td>78.3</td>\n",
       "      <td>Female</td>\n",
       "      <td>12</td>\n",
       "      <td>legal secretary</td>\n",
       "      <td>Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>0</td>\n",
       "      <td>1.250960</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1582</td>\n",
       "      <td>203.6</td>\n",
       "      <td>16.68</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.33</td>\n",
       "      <td>8.33</td>\n",
       "      <td>4.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>36.3636</td>\n",
       "      <td>10.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59356.0</td>\n",
       "      <td>46281.0</td>\n",
       "      <td>6729.0</td>\n",
       "      <td>861745.0</td>\n",
       "      <td>3580.0</td>\n",
       "      <td>13781.0</td>\n",
       "      <td>17795.0</td>\n",
       "      <td>1269537.0</td>\n",
       "      <td>CN</td>\n",
       "      <td>-3.33317</td>\n",
       "      <td>-2.519980</td>\n",
       "      <td>2005-11-04</td>\n",
       "      <td>0</td>\n",
       "      <td>4.33</td>\n",
       "      <td>8.33</td>\n",
       "      <td>4.0</td>\n",
       "      <td>29</td>\n",
       "      <td>45.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>36.36360</td>\n",
       "      <td>10.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.333170</td>\n",
       "      <td>-2.51998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46281.0</td>\n",
       "      <td>6729.0</td>\n",
       "      <td>861745.0</td>\n",
       "      <td>3580.0</td>\n",
       "      <td>13781.0</td>\n",
       "      <td>17795.0</td>\n",
       "      <td>1269537.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1582</td>\n",
       "      <td>203.6</td>\n",
       "      <td>16.68</td>\n",
       "      <td>1.25096</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14511</th>\n",
       "      <td>5398</td>\n",
       "      <td>m72</td>\n",
       "      <td>206</td>\n",
       "      <td>2019-11-25</td>\n",
       "      <td>SMC</td>\n",
       "      <td>69.4</td>\n",
       "      <td>Female</td>\n",
       "      <td>8</td>\n",
       "      <td>banker's engineer</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-09-04</td>\n",
       "      <td>0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>28</td>\n",
       "      <td>42.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>63.63640</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.152710</td>\n",
       "      <td>-1.60840</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24203.0</td>\n",
       "      <td>6041.0</td>\n",
       "      <td>957894.0</td>\n",
       "      <td>3380.0</td>\n",
       "      <td>17666.0</td>\n",
       "      <td>17810.0</td>\n",
       "      <td>1379247.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1.625</td>\n",
       "      <td>1.22222</td>\n",
       "      <td>1.14286</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.23077</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>&gt;1700</td>\n",
       "      <td>305</td>\n",
       "      <td>25.05</td>\n",
       "      <td>1.38561</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.234360</td>\n",
       "      <td>6.223130</td>\n",
       "      <td>74.52460</td>\n",
       "      <td>72.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2019-12-11 04:20:38.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14513</th>\n",
       "      <td>6195</td>\n",
       "      <td>m24</td>\n",
       "      <td>175</td>\n",
       "      <td>2019-12-10</td>\n",
       "      <td>SMC</td>\n",
       "      <td>73.1</td>\n",
       "      <td>Female</td>\n",
       "      <td>18</td>\n",
       "      <td>VP Supermarket</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.33</td>\n",
       "      <td>16.33</td>\n",
       "      <td>3.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.3333</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.25000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.05714</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.54031</td>\n",
       "      <td>0.780165</td>\n",
       "      <td>2017-09-06</td>\n",
       "      <td>0</td>\n",
       "      <td>7.33</td>\n",
       "      <td>9.33</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29</td>\n",
       "      <td>59.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>26.66670</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.385990</td>\n",
       "      <td>2.85682</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.750</td>\n",
       "      <td>1.77778</td>\n",
       "      <td>1.16667</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.16667</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.50000</td>\n",
       "      <td>1.125</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.02632</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.258730</td>\n",
       "      <td>27.04920</td>\n",
       "      <td>30.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2019-12-12 04:20:11.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14514</th>\n",
       "      <td>6741</td>\n",
       "      <td>m12</td>\n",
       "      <td>454</td>\n",
       "      <td>2019-12-05</td>\n",
       "      <td>LMCI</td>\n",
       "      <td>75.6</td>\n",
       "      <td>Female</td>\n",
       "      <td>15</td>\n",
       "      <td>stock broker</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31.00</td>\n",
       "      <td>41.00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>191.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.125</td>\n",
       "      <td>3.55556</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.16667</td>\n",
       "      <td>1.25000</td>\n",
       "      <td>1.92105</td>\n",
       "      <td>1.62500</td>\n",
       "      <td>2.55556</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.58333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MCI</td>\n",
       "      <td>-17.9589</td>\n",
       "      <td>-15.721900</td>\n",
       "      <td>2018-12-03</td>\n",
       "      <td>2</td>\n",
       "      <td>16.33</td>\n",
       "      <td>27.33</td>\n",
       "      <td>8.0</td>\n",
       "      <td>24</td>\n",
       "      <td>26.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>125.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-15.007700</td>\n",
       "      <td>-12.49480</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.250</td>\n",
       "      <td>2.66667</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.66667</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>3.375</td>\n",
       "      <td>3.44444</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.66667</td>\n",
       "      <td>2.61765</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.09878</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.004790</td>\n",
       "      <td>12.03280</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2019-12-12 04:20:12.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14515</th>\n",
       "      <td>4220</td>\n",
       "      <td>m96</td>\n",
       "      <td>165</td>\n",
       "      <td>2019-12-10</td>\n",
       "      <td>LMCI</td>\n",
       "      <td>58.5</td>\n",
       "      <td>Female</td>\n",
       "      <td>12</td>\n",
       "      <td>Judge</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>26.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>44.4444</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.500</td>\n",
       "      <td>3.22222</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.66667</td>\n",
       "      <td>3.50000</td>\n",
       "      <td>3.13158</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-7.00404</td>\n",
       "      <td>-5.862460</td>\n",
       "      <td>2011-07-25</td>\n",
       "      <td>3.5</td>\n",
       "      <td>16.00</td>\n",
       "      <td>22.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>26</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>42.85710</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>130.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>-9.548080</td>\n",
       "      <td>-8.49370</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14643.0</td>\n",
       "      <td>6934.0</td>\n",
       "      <td>844742.0</td>\n",
       "      <td>2862.0</td>\n",
       "      <td>14659.0</td>\n",
       "      <td>19362.0</td>\n",
       "      <td>1174864.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3.500</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>2.85714</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.50000</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.25641</td>\n",
       "      <td>3.000</td>\n",
       "      <td>2.22222</td>\n",
       "      <td>1.71429</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>2.20513</td>\n",
       "      <td>&gt;1700</td>\n",
       "      <td>159.4</td>\n",
       "      <td>12.99</td>\n",
       "      <td>1.31685</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.060480</td>\n",
       "      <td>8.377820</td>\n",
       "      <td>100.32800</td>\n",
       "      <td>102.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2019-12-12 04:20:06.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14517</th>\n",
       "      <td>6717</td>\n",
       "      <td>m12</td>\n",
       "      <td>321</td>\n",
       "      <td>2019-12-11</td>\n",
       "      <td>EMCI</td>\n",
       "      <td>75.6</td>\n",
       "      <td>Female</td>\n",
       "      <td>18</td>\n",
       "      <td>Vice President of Publishing Company</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>33.3333</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.125</td>\n",
       "      <td>2.88889</td>\n",
       "      <td>1.83333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.80000</td>\n",
       "      <td>3.66667</td>\n",
       "      <td>2.97222</td>\n",
       "      <td>1.37500</td>\n",
       "      <td>1.22222</td>\n",
       "      <td>1.33333</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.27027</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.16191</td>\n",
       "      <td>0.389376</td>\n",
       "      <td>2018-11-07</td>\n",
       "      <td>1</td>\n",
       "      <td>8.67</td>\n",
       "      <td>12.67</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30</td>\n",
       "      <td>46.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.09091</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.824804</td>\n",
       "      <td>-1.04776</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.125</td>\n",
       "      <td>2.44444</td>\n",
       "      <td>1.33333</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.66667</td>\n",
       "      <td>3.75</td>\n",
       "      <td>2.73684</td>\n",
       "      <td>1.500</td>\n",
       "      <td>1.11111</td>\n",
       "      <td>1.33333</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.21622</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.25368</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.987947</td>\n",
       "      <td>1.092400</td>\n",
       "      <td>13.08200</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2019-12-12 04:20:12.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14518</th>\n",
       "      <td>6937</td>\n",
       "      <td>bl</td>\n",
       "      <td>454</td>\n",
       "      <td>2019-12-03</td>\n",
       "      <td>LMCI</td>\n",
       "      <td>75.2</td>\n",
       "      <td>Female</td>\n",
       "      <td>18</td>\n",
       "      <td>medical doctor</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.67</td>\n",
       "      <td>13.67</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.125</td>\n",
       "      <td>3.44444</td>\n",
       "      <td>2.33333</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.16667</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>3.18421</td>\n",
       "      <td>2.25000</td>\n",
       "      <td>1.33333</td>\n",
       "      <td>1.60000</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.60000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MCI</td>\n",
       "      <td>-2.1298</td>\n",
       "      <td>-1.010180</td>\n",
       "      <td>2019-12-03</td>\n",
       "      <td>1</td>\n",
       "      <td>9.67</td>\n",
       "      <td>13.67</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29</td>\n",
       "      <td>48.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>100.00000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.129800</td>\n",
       "      <td>-1.01018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.125</td>\n",
       "      <td>3.44444</td>\n",
       "      <td>2.33333</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.16667</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.18421</td>\n",
       "      <td>2.250</td>\n",
       "      <td>1.33333</td>\n",
       "      <td>1.60000</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.50000</td>\n",
       "      <td>1.60000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-13 04:20:29.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14519</th>\n",
       "      <td>6959</td>\n",
       "      <td>bl</td>\n",
       "      <td>156</td>\n",
       "      <td>2019-12-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87.7</td>\n",
       "      <td>Female</td>\n",
       "      <td>16</td>\n",
       "      <td>registered Nurse</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>Asian</td>\n",
       "      <td>Married</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.5</td>\n",
       "      <td>27.67</td>\n",
       "      <td>38.67</td>\n",
       "      <td>9.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>156.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2.125</td>\n",
       "      <td>1.77778</td>\n",
       "      <td>2.50000</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.83333</td>\n",
       "      <td>1.75000</td>\n",
       "      <td>1.89474</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>1.66667</td>\n",
       "      <td>1.66667</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.84211</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-17.3637</td>\n",
       "      <td>-14.791200</td>\n",
       "      <td>2019-12-11</td>\n",
       "      <td>4.5</td>\n",
       "      <td>27.67</td>\n",
       "      <td>38.67</td>\n",
       "      <td>9.0</td>\n",
       "      <td>23</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>100.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>156.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>-17.363700</td>\n",
       "      <td>-14.79120</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2.125</td>\n",
       "      <td>1.77778</td>\n",
       "      <td>2.50000</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.83333</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.89474</td>\n",
       "      <td>2.000</td>\n",
       "      <td>1.66667</td>\n",
       "      <td>1.66667</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.75000</td>\n",
       "      <td>1.84211</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-13 04:20:30.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14524</th>\n",
       "      <td>4326</td>\n",
       "      <td>m96</td>\n",
       "      <td>194</td>\n",
       "      <td>2019-12-10</td>\n",
       "      <td>CN</td>\n",
       "      <td>69.3</td>\n",
       "      <td>Female</td>\n",
       "      <td>18</td>\n",
       "      <td>US ARMY</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011-10-17</td>\n",
       "      <td>0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30</td>\n",
       "      <td>60.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.66667</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.657260</td>\n",
       "      <td>2.97847</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18864.0</td>\n",
       "      <td>8227.0</td>\n",
       "      <td>1059274.0</td>\n",
       "      <td>4056.0</td>\n",
       "      <td>16272.0</td>\n",
       "      <td>22422.0</td>\n",
       "      <td>1502419.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1.500</td>\n",
       "      <td>1.44444</td>\n",
       "      <td>1.57143</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.50000</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.48718</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>1.15385</td>\n",
       "      <td>1544</td>\n",
       "      <td>146.5</td>\n",
       "      <td>13.05</td>\n",
       "      <td>1.45546</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.061610</td>\n",
       "      <td>8.147840</td>\n",
       "      <td>97.57380</td>\n",
       "      <td>96.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2019-12-14 04:20:23.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14525</th>\n",
       "      <td>4477</td>\n",
       "      <td>m96</td>\n",
       "      <td>290</td>\n",
       "      <td>2019-11-13</td>\n",
       "      <td>EMCI</td>\n",
       "      <td>67.2</td>\n",
       "      <td>Female</td>\n",
       "      <td>16</td>\n",
       "      <td>Housewife</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5</td>\n",
       "      <td>13.00</td>\n",
       "      <td>23.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>50.0000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1.750</td>\n",
       "      <td>1.55556</td>\n",
       "      <td>1.33333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.34211</td>\n",
       "      <td>1.71429</td>\n",
       "      <td>1.88889</td>\n",
       "      <td>1.66667</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.57143</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-5.37679</td>\n",
       "      <td>-3.445430</td>\n",
       "      <td>2011-12-13</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10.00</td>\n",
       "      <td>17.00</td>\n",
       "      <td>7.0</td>\n",
       "      <td>29</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25.00000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5.026460</td>\n",
       "      <td>-4.42840</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26384.0</td>\n",
       "      <td>6804.0</td>\n",
       "      <td>1035941.0</td>\n",
       "      <td>3917.0</td>\n",
       "      <td>17523.0</td>\n",
       "      <td>22537.0</td>\n",
       "      <td>1466235.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.500</td>\n",
       "      <td>1.11111</td>\n",
       "      <td>1.28571</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.20513</td>\n",
       "      <td>1.125</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.02564</td>\n",
       "      <td>703.7</td>\n",
       "      <td>233.7</td>\n",
       "      <td>24.86</td>\n",
       "      <td>1.43161</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.359710</td>\n",
       "      <td>7.917860</td>\n",
       "      <td>94.81970</td>\n",
       "      <td>96.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2019-12-14 04:20:25.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14531</th>\n",
       "      <td>6960</td>\n",
       "      <td>bl</td>\n",
       "      <td>169</td>\n",
       "      <td>2019-12-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81.3</td>\n",
       "      <td>Female</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>Black</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>69.2308</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2.250</td>\n",
       "      <td>1.55556</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.33333</td>\n",
       "      <td>2.25000</td>\n",
       "      <td>2.05263</td>\n",
       "      <td>1.50000</td>\n",
       "      <td>1.22222</td>\n",
       "      <td>1.50000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.26316</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.92281</td>\n",
       "      <td>-1.789670</td>\n",
       "      <td>2019-12-11</td>\n",
       "      <td>1</td>\n",
       "      <td>10.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>30</td>\n",
       "      <td>46.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>69.23080</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.922810</td>\n",
       "      <td>-1.78967</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2.250</td>\n",
       "      <td>1.55556</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.33333</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2.05263</td>\n",
       "      <td>1.500</td>\n",
       "      <td>1.22222</td>\n",
       "      <td>1.50000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.25000</td>\n",
       "      <td>1.26316</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-17 04:20:26.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6453 rows × 110 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        RID VISCODE  SITE    EXAMDATE DX_bl   AGE PTGENDER  PTEDUCAT  \\\n",
       "15      132      bl   253  2005-11-29  LMCI  80.0   Female        13   \n",
       "16      132     m06   253  2006-06-01  LMCI  79.4   Female        13   \n",
       "17      132     m12   253  2006-11-20  LMCI  80.3   Female        13   \n",
       "18      132     m18   253  2007-05-15  LMCI  80.0   Female        13   \n",
       "19      132     m36   253  2008-12-08  LMCI  81.2   Female        13   \n",
       "22      136      bl   164  2005-11-10    AD  73.9   Female        12   \n",
       "23      136     m06   164  2006-05-09    AD  74.4   Female        12   \n",
       "24      136     m12   164  2006-11-09    AD  74.9   Female        12   \n",
       "25      136     m24   164  2007-11-07    AD  73.4   Female        12   \n",
       "26      140      bl   175  2005-11-04    CN  78.3   Female        12   \n",
       "...     ...     ...   ...         ...   ...   ...      ...       ...   \n",
       "14511  5398     m72   206  2019-11-25   SMC  69.4   Female         8   \n",
       "14513  6195     m24   175  2019-12-10   SMC  73.1   Female        18   \n",
       "14514  6741     m12   454  2019-12-05  LMCI  75.6   Female        15   \n",
       "14515  4220     m96   165  2019-12-10  LMCI  58.5   Female        12   \n",
       "14517  6717     m12   321  2019-12-11  EMCI  75.6   Female        18   \n",
       "14518  6937      bl   454  2019-12-03  LMCI  75.2   Female        18   \n",
       "14519  6959      bl   156  2019-12-11   NaN  87.7   Female        16   \n",
       "14524  4326     m96   194  2019-12-10    CN  69.3   Female        18   \n",
       "14525  4477     m96   290  2019-11-13  EMCI  67.2   Female        16   \n",
       "14531  6960      bl   169  2019-12-11   NaN  81.3   Female        16   \n",
       "\n",
       "                                       WORK         PTETHCAT PTRACCAT  \\\n",
       "15           Transportation Operation Sales  Not Hisp/Latino    White   \n",
       "16                          clerical worker  Not Hisp/Latino    White   \n",
       "17                Special Education Teacher  Not Hisp/Latino    White   \n",
       "18                                       RN  Not Hisp/Latino    White   \n",
       "19                              Book keeper  Not Hisp/Latino    White   \n",
       "22             Professor of Neurophysiology  Not Hisp/Latino    White   \n",
       "23                         Registered Nurse  Not Hisp/Latino    White   \n",
       "24                 Retail Store Owner/paint  Not Hisp/Latino    White   \n",
       "25                   Director of Purchasing  Not Hisp/Latino    White   \n",
       "26                          legal secretary      Hisp/Latino    White   \n",
       "...                                     ...              ...      ...   \n",
       "14511                     banker's engineer  Not Hisp/Latino    White   \n",
       "14513                        VP Supermarket  Not Hisp/Latino    White   \n",
       "14514                          stock broker  Not Hisp/Latino    White   \n",
       "14515                                 Judge  Not Hisp/Latino    White   \n",
       "14517  Vice President of Publishing Company  Not Hisp/Latino    White   \n",
       "14518                        medical doctor  Not Hisp/Latino    White   \n",
       "14519                      registered Nurse  Not Hisp/Latino    Asian   \n",
       "14524                               US ARMY  Not Hisp/Latino    White   \n",
       "14525                             Housewife  Not Hisp/Latino    White   \n",
       "14531                                   NaN  Not Hisp/Latino    Black   \n",
       "\n",
       "        PTMARRY APOE4       FDG  PIB  AV45  ABETA    TAU   PTAU  CDRSB  \\\n",
       "15      Married     0       NaN  NaN   NaN    NaN    NaN    NaN    0.5   \n",
       "16      Married     0       NaN  NaN   NaN    NaN    NaN    NaN    1.0   \n",
       "17      Married     0       NaN  NaN   NaN    NaN    NaN    NaN    1.5   \n",
       "18      Married     0       NaN  NaN   NaN    NaN    NaN    NaN    2.0   \n",
       "19      Married     0       NaN  NaN   NaN    NaN    NaN    NaN    2.0   \n",
       "22      Married     1  1.115320  NaN   NaN  357.4  329.9  31.26    5.0   \n",
       "23      Married     1  1.075580  NaN   NaN    NaN    NaN    NaN    2.5   \n",
       "24      Married     1  1.007210  NaN   NaN    340  287.7  27.37    4.0   \n",
       "25      Married     1  0.959766  NaN   NaN    NaN    NaN    NaN    5.0   \n",
       "26     Divorced     0  1.250960  NaN   NaN   1582  203.6  16.68    0.0   \n",
       "...         ...   ...       ...  ...   ...    ...    ...    ...    ...   \n",
       "14511   Married     0       NaN  NaN   NaN    NaN    NaN    NaN    NaN   \n",
       "14513   Married     1       NaN  NaN   NaN    NaN    NaN    NaN    0.0   \n",
       "14514   Married   NaN       NaN  NaN   NaN    NaN    NaN    NaN    1.0   \n",
       "14515   Married     0       NaN  NaN   NaN    NaN    NaN    NaN    0.0   \n",
       "14517   Married   NaN       NaN  NaN   NaN    NaN    NaN    NaN    1.0   \n",
       "14518  Divorced   NaN       NaN  NaN   NaN    NaN    NaN    NaN    1.0   \n",
       "14519   Married   NaN       NaN  NaN   NaN    NaN    NaN    NaN    4.5   \n",
       "14524  Divorced     0       NaN  NaN   NaN    NaN    NaN    NaN    NaN   \n",
       "14525   Married     1       NaN  NaN   NaN    NaN    NaN    NaN    0.5   \n",
       "14531  Divorced   NaN       NaN  NaN   NaN    NaN    NaN    NaN    1.0   \n",
       "\n",
       "       ADAS11  ADAS13  ADASQ4  MMSE  RAVLT_immediate  RAVLT_learning  \\\n",
       "15      18.67   25.67     7.0  25.0             30.0             1.0   \n",
       "16      19.00   26.00     7.0  21.0             41.0             6.0   \n",
       "17      18.67   25.67     7.0  26.0             31.0             4.0   \n",
       "18      11.33   21.33     9.0  24.0             31.0             3.0   \n",
       "19      17.67   26.67     9.0  22.0             26.0             3.0   \n",
       "22      12.33   24.33    10.0  24.0             20.0             2.0   \n",
       "23      14.33   24.33     9.0  26.0             20.0             1.0   \n",
       "24      23.33   35.33     9.0  25.0             21.0             2.0   \n",
       "25      19.33   30.33    10.0  28.0             15.0             0.0   \n",
       "26       4.33    8.33     4.0  29.0             45.0             6.0   \n",
       "...       ...     ...     ...   ...              ...             ...   \n",
       "14511     NaN     NaN     NaN   NaN              NaN             NaN   \n",
       "14513   10.33   16.33     3.0  29.0             60.0             6.0   \n",
       "14514   31.00   41.00     6.0  20.0              8.0             2.0   \n",
       "14515   15.00   26.00     8.0  27.0             36.0             4.0   \n",
       "14517    8.00   12.00     3.0  30.0             48.0             7.0   \n",
       "14518    9.67   13.67     1.0  29.0             48.0             7.0   \n",
       "14519   27.67   38.67     9.0  23.0             26.0             0.0   \n",
       "14524     NaN     NaN     NaN   NaN              NaN             NaN   \n",
       "14525   13.00   23.00     8.0  29.0             33.0             6.0   \n",
       "14531   10.00   16.00     5.0  30.0             46.0             9.0   \n",
       "\n",
       "       RAVLT_forgetting  RAVLT_perc_forgetting  LDELTOTAL  DIGITSCOR  \\\n",
       "15                  5.0                83.3333        3.0       34.0   \n",
       "16                  7.0                63.6364        NaN       32.0   \n",
       "17                  8.0               100.0000        0.0       33.0   \n",
       "18                  6.0                85.7143        NaN       33.0   \n",
       "19                  6.0                85.7143        0.0       28.0   \n",
       "22                  5.0               100.0000        2.0       27.0   \n",
       "23                  3.0                75.0000        NaN       25.0   \n",
       "24                  5.0               100.0000        1.0       24.0   \n",
       "25                  3.0               100.0000        0.0       16.0   \n",
       "26                  4.0                36.3636       10.0       30.0   \n",
       "...                 ...                    ...        ...        ...   \n",
       "14511               NaN                    NaN        NaN        NaN   \n",
       "14513               2.0                13.3333       18.0        NaN   \n",
       "14514               3.0               100.0000        2.0        NaN   \n",
       "14515               4.0                44.4444       12.0        NaN   \n",
       "14517               4.0                33.3333       14.0        NaN   \n",
       "14518              12.0               100.0000        5.0        NaN   \n",
       "14519               5.0               100.0000        0.0        NaN   \n",
       "14524               NaN                    NaN        NaN        NaN   \n",
       "14525               5.0                50.0000       10.0        NaN   \n",
       "14531               9.0                69.2308       10.0        NaN   \n",
       "\n",
       "       TRABSCOR   FAQ  MOCA  EcogPtMem  EcogPtLang  EcogPtVisspat  EcogPtPlan  \\\n",
       "15        168.0   0.0   NaN        NaN         NaN            NaN         NaN   \n",
       "16        145.0   0.0   NaN        NaN         NaN            NaN         NaN   \n",
       "17        153.0   0.0   NaN        NaN         NaN            NaN         NaN   \n",
       "18         94.0   2.0   NaN        NaN         NaN            NaN         NaN   \n",
       "19        209.0   6.0   NaN        NaN         NaN            NaN         NaN   \n",
       "22        100.0  11.0   NaN        NaN         NaN            NaN         NaN   \n",
       "23        218.0  12.0   NaN        NaN         NaN            NaN         NaN   \n",
       "24        144.0  15.0   NaN        NaN         NaN            NaN         NaN   \n",
       "25        280.0  20.0   NaN        NaN         NaN            NaN         NaN   \n",
       "26        101.0   0.0   NaN        NaN         NaN            NaN         NaN   \n",
       "...         ...   ...   ...        ...         ...            ...         ...   \n",
       "14511       NaN   NaN   NaN        NaN         NaN            NaN         NaN   \n",
       "14513      87.0   0.0  29.0        NaN         NaN            NaN         NaN   \n",
       "14514     191.0   0.0   9.0      1.125     3.55556        2.00000         1.6   \n",
       "14515      96.0   0.0   NaN      2.500     3.22222        3.00000         3.2   \n",
       "14517      91.0   0.0  25.0      3.125     2.88889        1.83333         3.0   \n",
       "14518      58.0   0.0  24.0      3.125     3.44444        2.33333         3.2   \n",
       "14519     156.0  15.0  21.0      2.125     1.77778        2.50000         1.2   \n",
       "14524       NaN   NaN   NaN        NaN         NaN            NaN         NaN   \n",
       "14525      58.0   0.0  27.0      1.750     1.55556        1.33333         1.0   \n",
       "14531      86.0   0.0  24.0      2.250     1.55556        2.00000         2.2   \n",
       "\n",
       "       EcogPtOrgan  EcogPtDivatt  EcogPtTotal  EcogSPMem  EcogSPLang  \\\n",
       "15             NaN           NaN          NaN        NaN         NaN   \n",
       "16             NaN           NaN          NaN        NaN         NaN   \n",
       "17             NaN           NaN          NaN        NaN         NaN   \n",
       "18             NaN           NaN          NaN        NaN         NaN   \n",
       "19             NaN           NaN          NaN        NaN         NaN   \n",
       "22             NaN           NaN          NaN        NaN         NaN   \n",
       "23             NaN           NaN          NaN        NaN         NaN   \n",
       "24             NaN           NaN          NaN        NaN         NaN   \n",
       "25             NaN           NaN          NaN        NaN         NaN   \n",
       "26             NaN           NaN          NaN        NaN         NaN   \n",
       "...            ...           ...          ...        ...         ...   \n",
       "14511          NaN           NaN          NaN        NaN         NaN   \n",
       "14513          NaN           NaN          NaN    1.25000     1.00000   \n",
       "14514      1.16667       1.25000      1.92105    1.62500     2.55556   \n",
       "14515      3.66667       3.50000      3.13158    1.00000     1.00000   \n",
       "14517      3.80000       3.66667      2.97222    1.37500     1.22222   \n",
       "14518      3.16667       4.00000      3.18421    2.25000     1.33333   \n",
       "14519      1.83333       1.75000      1.89474    2.00000     1.66667   \n",
       "14524          NaN           NaN          NaN        NaN         NaN   \n",
       "14525      1.00000       1.00000      1.34211    1.71429     1.88889   \n",
       "14531      2.33333       2.25000      2.05263    1.50000     1.22222   \n",
       "\n",
       "       EcogSPVisspat  EcogSPPlan  EcogSPOrgan  EcogSPDivatt  EcogSPTotal  \\\n",
       "15               NaN         NaN          NaN           NaN          NaN   \n",
       "16               NaN         NaN          NaN           NaN          NaN   \n",
       "17               NaN         NaN          NaN           NaN          NaN   \n",
       "18               NaN         NaN          NaN           NaN          NaN   \n",
       "19               NaN         NaN          NaN           NaN          NaN   \n",
       "22               NaN         NaN          NaN           NaN          NaN   \n",
       "23               NaN         NaN          NaN           NaN          NaN   \n",
       "24               NaN         NaN          NaN           NaN          NaN   \n",
       "25               NaN         NaN          NaN           NaN          NaN   \n",
       "26               NaN         NaN          NaN           NaN          NaN   \n",
       "...              ...         ...          ...           ...          ...   \n",
       "14511            NaN         NaN          NaN           NaN          NaN   \n",
       "14513            NaN        1.00          1.0          1.00      1.05714   \n",
       "14514        1.00000        1.00          1.0          1.50      1.58333   \n",
       "14515        1.00000        1.00          1.0          1.00      1.00000   \n",
       "14517        1.33333        1.00          1.0          1.75      1.27027   \n",
       "14518        1.60000        1.25          1.4          1.50      1.60000   \n",
       "14519        1.66667        2.00          2.0          1.75      1.84211   \n",
       "14524            NaN         NaN          NaN           NaN          NaN   \n",
       "14525        1.66667        1.20          1.5          1.00      1.57143   \n",
       "14531        1.50000        1.00          1.0          1.25      1.26316   \n",
       "\n",
       "       FLDSTRENG  IMAGEUID  Ventricles  Hippocampus  WholeBrain  Entorhinal  \\\n",
       "15           NaN   33012.0     39822.0       5342.0    927504.0      2276.0   \n",
       "16           NaN   33031.0     40161.0       5536.0    957215.0      2479.0   \n",
       "17           NaN   62329.0     40672.0       5528.0    954562.0      2419.0   \n",
       "18           NaN   72772.0     41166.0       5202.0    921976.0      2278.0   \n",
       "19           NaN  160845.0     46545.0       4903.0    924397.0      2308.0   \n",
       "22           NaN   32253.0     26819.0       5479.0   1033537.0      2674.0   \n",
       "23           NaN   31901.0     29615.0       5313.0    989042.0      2417.0   \n",
       "24           NaN   94390.0     31565.0       5260.0   1010657.0      2182.0   \n",
       "25           NaN   94371.0     35428.0       4862.0    989150.0      2253.0   \n",
       "26           NaN   59356.0     46281.0       6729.0    861745.0      3580.0   \n",
       "...          ...       ...         ...          ...         ...         ...   \n",
       "14511        NaN       NaN         NaN          NaN         NaN         NaN   \n",
       "14513        NaN       NaN         NaN          NaN         NaN         NaN   \n",
       "14514        NaN       NaN         NaN          NaN         NaN         NaN   \n",
       "14515        NaN       NaN         NaN          NaN         NaN         NaN   \n",
       "14517        NaN       NaN         NaN          NaN         NaN         NaN   \n",
       "14518        NaN       NaN         NaN          NaN         NaN         NaN   \n",
       "14519        NaN       NaN         NaN          NaN         NaN         NaN   \n",
       "14524        NaN       NaN         NaN          NaN         NaN         NaN   \n",
       "14525        NaN       NaN         NaN          NaN         NaN         NaN   \n",
       "14531        NaN       NaN         NaN          NaN         NaN         NaN   \n",
       "\n",
       "       Fusiform  MidTemp        ICV        DX mPACCdigit  mPACCtrailsB  \\\n",
       "15      17966.0  17802.0  1485836.0       MCI   -10.3423    -11.136500   \n",
       "16      18461.0  18689.0  1514266.0       MCI   -14.7828    -15.110800   \n",
       "17      17851.0  18289.0  1512746.0       MCI   -10.4261    -10.898500   \n",
       "18      16852.0  17430.0  1482666.0       MCI   -12.5145    -11.594200   \n",
       "19      17675.0  17356.0  1492366.0       MCI   -15.6707    -16.399400   \n",
       "22      16758.0  19736.0  1471182.0  Dementia    -13.917    -12.786000   \n",
       "23      15889.0  18590.0  1459662.0  Dementia   -11.1382    -11.852500   \n",
       "24      15756.0  18013.0  1467272.0  Dementia   -13.0401    -12.485300   \n",
       "25      15649.0  18014.0  1469532.0  Dementia   -11.9584    -12.211800   \n",
       "26      13781.0  17795.0  1269537.0        CN   -3.33317     -2.519980   \n",
       "...         ...      ...        ...       ...        ...           ...   \n",
       "14511       NaN      NaN        NaN       NaN        NaN           NaN   \n",
       "14513       NaN      NaN        NaN       NaN    1.54031      0.780165   \n",
       "14514       NaN      NaN        NaN       MCI   -17.9589    -15.721900   \n",
       "14515       NaN      NaN        NaN       NaN   -7.00404     -5.862460   \n",
       "14517       NaN      NaN        NaN       NaN    1.16191      0.389376   \n",
       "14518       NaN      NaN        NaN       MCI    -2.1298     -1.010180   \n",
       "14519       NaN      NaN        NaN       NaN   -17.3637    -14.791200   \n",
       "14524       NaN      NaN        NaN       NaN        NaN           NaN   \n",
       "14525       NaN      NaN        NaN       NaN   -5.37679     -3.445430   \n",
       "14531       NaN      NaN        NaN       NaN   -1.92281     -1.789670   \n",
       "\n",
       "      EXAMDATE_bl CDRSB_bl  ADAS11_bl  ADAS13_bl  ADASQ4_bl  MMSE_bl  \\\n",
       "15     2005-11-29      0.5      18.67      25.67        7.0       25   \n",
       "16     2005-11-29      0.5      18.67      25.67        7.0       25   \n",
       "17     2005-11-29      0.5      18.67      25.67        7.0       25   \n",
       "18     2005-11-29      0.5      18.67      25.67        7.0       25   \n",
       "19     2005-11-29      0.5      18.67      25.67        7.0       25   \n",
       "22     2005-11-10        5      12.33      24.33       10.0       24   \n",
       "23     2005-11-10        5      12.33      24.33       10.0       24   \n",
       "24     2005-11-10        5      12.33      24.33       10.0       24   \n",
       "25     2005-11-10        5      12.33      24.33       10.0       24   \n",
       "26     2005-11-04        0       4.33       8.33        4.0       29   \n",
       "...           ...      ...        ...        ...        ...      ...   \n",
       "14511  2013-09-04        0       5.00      10.00        5.0       28   \n",
       "14513  2017-09-06        0       7.33       9.33        0.0       29   \n",
       "14514  2018-12-03        2      16.33      27.33        8.0       24   \n",
       "14515  2011-07-25      3.5      16.00      22.00        5.0       26   \n",
       "14517  2018-11-07        1       8.67      12.67        3.0       30   \n",
       "14518  2019-12-03        1       9.67      13.67        1.0       29   \n",
       "14519  2019-12-11      4.5      27.67      38.67        9.0       23   \n",
       "14524  2011-10-17        0       4.00       5.00        1.0       30   \n",
       "14525  2011-12-13      0.5      10.00      17.00        7.0       29   \n",
       "14531  2019-12-11        1      10.00      16.00        5.0       30   \n",
       "\n",
       "       RAVLT_immediate_bl  RAVLT_learning_bl  RAVLT_forgetting_bl  \\\n",
       "15                   30.0                1.0                  5.0   \n",
       "16                   30.0                1.0                  5.0   \n",
       "17                   30.0                1.0                  5.0   \n",
       "18                   30.0                1.0                  5.0   \n",
       "19                   30.0                1.0                  5.0   \n",
       "22                   20.0                2.0                  5.0   \n",
       "23                   20.0                2.0                  5.0   \n",
       "24                   20.0                2.0                  5.0   \n",
       "25                   20.0                2.0                  5.0   \n",
       "26                   45.0                6.0                  4.0   \n",
       "...                   ...                ...                  ...   \n",
       "14511                42.0                5.0                  7.0   \n",
       "14513                59.0               10.0                  4.0   \n",
       "14514                26.0                9.0                  2.0   \n",
       "14515                25.0                2.0                  3.0   \n",
       "14517                46.0                4.0                  1.0   \n",
       "14518                48.0                7.0                 12.0   \n",
       "14519                26.0                0.0                  5.0   \n",
       "14524                60.0                7.0                  1.0   \n",
       "14525                33.0                3.0                  2.0   \n",
       "14531                46.0                9.0                  9.0   \n",
       "\n",
       "       RAVLT_perc_forgetting_bl  LDELTOTAL_BL  DIGITSCOR_bl  TRABSCOR_bl  \\\n",
       "15                     83.33330           3.0          34.0        168.0   \n",
       "16                     83.33330           3.0          34.0        168.0   \n",
       "17                     83.33330           3.0          34.0        168.0   \n",
       "18                     83.33330           3.0          34.0        168.0   \n",
       "19                     83.33330           3.0          34.0        168.0   \n",
       "22                    100.00000           2.0          27.0        100.0   \n",
       "23                    100.00000           2.0          27.0        100.0   \n",
       "24                    100.00000           2.0          27.0        100.0   \n",
       "25                    100.00000           2.0          27.0        100.0   \n",
       "26                     36.36360          10.0          30.0        101.0   \n",
       "...                         ...           ...           ...          ...   \n",
       "14511                  63.63640          13.0           NaN         54.0   \n",
       "14513                  26.66670          17.0           NaN         65.0   \n",
       "14514                  20.00000           1.0           NaN        125.0   \n",
       "14515                  42.85710           3.0           NaN        130.0   \n",
       "14517                   9.09091           9.0           NaN         89.0   \n",
       "14518                 100.00000           5.0           NaN         58.0   \n",
       "14519                 100.00000           0.0           NaN        156.0   \n",
       "14524                   6.66667          14.0           NaN         49.0   \n",
       "14525                  25.00000           9.0           NaN         98.0   \n",
       "14531                  69.23080          10.0           NaN         86.0   \n",
       "\n",
       "       FAQ_bl  mPACCdigit_bl  mPACCtrailsB_bl  FLDSTRENG_bl  Ventricles_bl  \\\n",
       "15        0.0     -10.342300        -11.13650           NaN        39822.0   \n",
       "16        0.0     -10.342300        -11.13650           NaN        39822.0   \n",
       "17        0.0     -10.342300        -11.13650           NaN        39822.0   \n",
       "18        0.0     -10.342300        -11.13650           NaN        39822.0   \n",
       "19        0.0     -10.342300        -11.13650           NaN        39822.0   \n",
       "22       11.0     -13.917000        -12.78600           NaN        26819.0   \n",
       "23       11.0     -13.917000        -12.78600           NaN        26819.0   \n",
       "24       11.0     -13.917000        -12.78600           NaN        26819.0   \n",
       "25       11.0     -13.917000        -12.78600           NaN        26819.0   \n",
       "26        0.0      -3.333170         -2.51998           NaN        46281.0   \n",
       "...       ...            ...              ...           ...            ...   \n",
       "14511     0.0      -3.152710         -1.60840           NaN        24203.0   \n",
       "14513     0.0       3.385990          2.85682           NaN            NaN   \n",
       "14514     2.0     -15.007700        -12.49480           NaN            NaN   \n",
       "14515     7.0      -9.548080         -8.49370           NaN        14643.0   \n",
       "14517     1.0      -0.824804         -1.04776           NaN            NaN   \n",
       "14518     0.0      -2.129800         -1.01018           NaN            NaN   \n",
       "14519    15.0     -17.363700        -14.79120           NaN            NaN   \n",
       "14524     0.0       2.657260          2.97847           NaN        18864.0   \n",
       "14525     0.0      -5.026460         -4.42840           NaN        26384.0   \n",
       "14531     0.0      -1.922810         -1.78967           NaN            NaN   \n",
       "\n",
       "       Hippocampus_bl  WholeBrain_bl  Entorhinal_bl  Fusiform_bl  MidTemp_bl  \\\n",
       "15             5342.0       927504.0         2276.0      17966.0     17802.0   \n",
       "16             5342.0       927504.0         2276.0      17966.0     17802.0   \n",
       "17             5342.0       927504.0         2276.0      17966.0     17802.0   \n",
       "18             5342.0       927504.0         2276.0      17966.0     17802.0   \n",
       "19             5342.0       927504.0         2276.0      17966.0     17802.0   \n",
       "22             5479.0      1033537.0         2674.0      16758.0     19736.0   \n",
       "23             5479.0      1033537.0         2674.0      16758.0     19736.0   \n",
       "24             5479.0      1033537.0         2674.0      16758.0     19736.0   \n",
       "25             5479.0      1033537.0         2674.0      16758.0     19736.0   \n",
       "26             6729.0       861745.0         3580.0      13781.0     17795.0   \n",
       "...               ...            ...            ...          ...         ...   \n",
       "14511          6041.0       957894.0         3380.0      17666.0     17810.0   \n",
       "14513             NaN            NaN            NaN          NaN         NaN   \n",
       "14514             NaN            NaN            NaN          NaN         NaN   \n",
       "14515          6934.0       844742.0         2862.0      14659.0     19362.0   \n",
       "14517             NaN            NaN            NaN          NaN         NaN   \n",
       "14518             NaN            NaN            NaN          NaN         NaN   \n",
       "14519             NaN            NaN            NaN          NaN         NaN   \n",
       "14524          8227.0      1059274.0         4056.0      16272.0     22422.0   \n",
       "14525          6804.0      1035941.0         3917.0      17523.0     22537.0   \n",
       "14531             NaN            NaN            NaN          NaN         NaN   \n",
       "\n",
       "          ICV_bl  MOCA_bl  EcogPtMem_bl  EcogPtLang_bl  EcogPtVisspat_bl  \\\n",
       "15     1485836.0      NaN           NaN            NaN               NaN   \n",
       "16     1485836.0      NaN           NaN            NaN               NaN   \n",
       "17     1485836.0      NaN           NaN            NaN               NaN   \n",
       "18     1485836.0      NaN           NaN            NaN               NaN   \n",
       "19     1485836.0      NaN           NaN            NaN               NaN   \n",
       "22     1471182.0      NaN           NaN            NaN               NaN   \n",
       "23     1471182.0      NaN           NaN            NaN               NaN   \n",
       "24     1471182.0      NaN           NaN            NaN               NaN   \n",
       "25     1471182.0      NaN           NaN            NaN               NaN   \n",
       "26     1269537.0      NaN           NaN            NaN               NaN   \n",
       "...          ...      ...           ...            ...               ...   \n",
       "14511  1379247.0     27.0         1.625        1.22222           1.14286   \n",
       "14513        NaN     26.0         1.750        1.77778           1.16667   \n",
       "14514        NaN     16.0         2.250        2.66667           2.00000   \n",
       "14515  1174864.0     15.0         3.500        3.00000           2.85714   \n",
       "14517        NaN     24.0         3.125        2.44444           1.33333   \n",
       "14518        NaN     24.0         3.125        3.44444           2.33333   \n",
       "14519        NaN     21.0         2.125        1.77778           2.50000   \n",
       "14524  1502419.0     29.0         1.500        1.44444           1.57143   \n",
       "14525  1466235.0     24.0         1.500        1.11111           1.28571   \n",
       "14531        NaN     24.0         2.250        1.55556           2.00000   \n",
       "\n",
       "       EcogPtPlan_bl  EcogPtOrgan_bl  EcogPtDivatt_bl  EcogPtTotal_bl  \\\n",
       "15               NaN             NaN              NaN             NaN   \n",
       "16               NaN             NaN              NaN             NaN   \n",
       "17               NaN             NaN              NaN             NaN   \n",
       "18               NaN             NaN              NaN             NaN   \n",
       "19               NaN             NaN              NaN             NaN   \n",
       "22               NaN             NaN              NaN             NaN   \n",
       "23               NaN             NaN              NaN             NaN   \n",
       "24               NaN             NaN              NaN             NaN   \n",
       "25               NaN             NaN              NaN             NaN   \n",
       "26               NaN             NaN              NaN             NaN   \n",
       "...              ...             ...              ...             ...   \n",
       "14511            1.0         1.00000             1.25         1.23077   \n",
       "14513            1.2         1.16667             1.75         1.50000   \n",
       "14514            1.6         1.66667             1.00         2.00000   \n",
       "14515            3.0         3.50000             4.00         3.25641   \n",
       "14517            2.4         3.66667             3.75         2.73684   \n",
       "14518            3.2         3.16667             4.00         3.18421   \n",
       "14519            1.2         1.83333             1.75         1.89474   \n",
       "14524            1.4         1.50000             1.50         1.48718   \n",
       "14525            1.0         1.00000             1.25         1.20513   \n",
       "14531            2.2         2.33333             2.25         2.05263   \n",
       "\n",
       "       EcogSPMem_bl  EcogSPLang_bl  EcogSPVisspat_bl  EcogSPPlan_bl  \\\n",
       "15              NaN            NaN               NaN            NaN   \n",
       "16              NaN            NaN               NaN            NaN   \n",
       "17              NaN            NaN               NaN            NaN   \n",
       "18              NaN            NaN               NaN            NaN   \n",
       "19              NaN            NaN               NaN            NaN   \n",
       "22              NaN            NaN               NaN            NaN   \n",
       "23              NaN            NaN               NaN            NaN   \n",
       "24              NaN            NaN               NaN            NaN   \n",
       "25              NaN            NaN               NaN            NaN   \n",
       "26              NaN            NaN               NaN            NaN   \n",
       "...             ...            ...               ...            ...   \n",
       "14511         1.000        1.00000           1.00000           1.00   \n",
       "14513         1.125        1.00000           1.00000           1.00   \n",
       "14514         3.375        3.44444           2.00000           2.00   \n",
       "14515         3.000        2.22222           1.71429           2.00   \n",
       "14517         1.500        1.11111           1.33333           1.00   \n",
       "14518         2.250        1.33333           1.60000           1.25   \n",
       "14519         2.000        1.66667           1.66667           2.00   \n",
       "14524         1.000        1.00000           1.00000           1.40   \n",
       "14525         1.125        1.00000           1.00000           1.00   \n",
       "14531         1.500        1.22222           1.50000           1.00   \n",
       "\n",
       "       EcogSPOrgan_bl  EcogSPDivatt_bl  EcogSPTotal_bl ABETA_bl TAU_bl  \\\n",
       "15                NaN              NaN             NaN      NaN    NaN   \n",
       "16                NaN              NaN             NaN      NaN    NaN   \n",
       "17                NaN              NaN             NaN      NaN    NaN   \n",
       "18                NaN              NaN             NaN      NaN    NaN   \n",
       "19                NaN              NaN             NaN      NaN    NaN   \n",
       "22                NaN              NaN             NaN    357.4  329.9   \n",
       "23                NaN              NaN             NaN    357.4  329.9   \n",
       "24                NaN              NaN             NaN    357.4  329.9   \n",
       "25                NaN              NaN             NaN    357.4  329.9   \n",
       "26                NaN              NaN             NaN     1582  203.6   \n",
       "...               ...              ...             ...      ...    ...   \n",
       "14511             1.0          1.00000         1.00000    >1700    305   \n",
       "14513             1.0          1.00000         1.02632      NaN    NaN   \n",
       "14514             1.5          1.66667         2.61765      NaN    NaN   \n",
       "14515             2.0          2.00000         2.20513    >1700  159.4   \n",
       "14517             1.2          1.00000         1.21622      NaN    NaN   \n",
       "14518             1.4          1.50000         1.60000      NaN    NaN   \n",
       "14519             2.0          1.75000         1.84211      NaN    NaN   \n",
       "14524             1.0          2.00000         1.15385     1544  146.5   \n",
       "14525             1.0          1.00000         1.02564    703.7  233.7   \n",
       "14531             1.0          1.25000         1.26316      NaN    NaN   \n",
       "\n",
       "      PTAU_bl   FDG_bl  PIB_bl   AV45_bl  Years_bl   Month_bl  Month     M  \\\n",
       "15        NaN      NaN     NaN       NaN  0.000000    0.00000    0.0   0.0   \n",
       "16        NaN      NaN     NaN       NaN  0.503765    6.03279    6.0   6.0   \n",
       "17        NaN      NaN     NaN       NaN  0.974675   11.67210   12.0  12.0   \n",
       "18        NaN      NaN     NaN       NaN  1.456540   17.44260   18.0  18.0   \n",
       "19        NaN      NaN     NaN       NaN  3.025330   36.22950   36.0  36.0   \n",
       "22      31.26  1.11532     NaN       NaN  0.000000    0.00000    0.0   0.0   \n",
       "23      31.26  1.11532     NaN       NaN  0.492813    5.90164    6.0   6.0   \n",
       "24      31.26  1.11532     NaN       NaN  0.996578   11.93440   12.0  12.0   \n",
       "25      31.26  1.11532     NaN       NaN  1.990420   23.83610   24.0  24.0   \n",
       "26      16.68  1.25096     NaN       NaN  0.000000    0.00000    0.0   0.0   \n",
       "...       ...      ...     ...       ...       ...        ...    ...   ...   \n",
       "14511   25.05  1.38561     NaN  1.234360  6.223130   74.52460   72.0  72.0   \n",
       "14513     NaN      NaN     NaN       NaN  2.258730   27.04920   30.0  24.0   \n",
       "14514     NaN  1.09878     NaN       NaN  1.004790   12.03280   12.0  12.0   \n",
       "14515   12.99  1.31685     NaN  1.060480  8.377820  100.32800  102.0  96.0   \n",
       "14517     NaN  1.25368     NaN  0.987947  1.092400   13.08200   12.0  12.0   \n",
       "14518     NaN      NaN     NaN       NaN  0.000000    0.00000    0.0   0.0   \n",
       "14519     NaN      NaN     NaN       NaN  0.000000    0.00000    0.0   0.0   \n",
       "14524   13.05  1.45546     NaN  1.061610  8.147840   97.57380   96.0  96.0   \n",
       "14525   24.86  1.43161     NaN  1.359710  7.917860   94.81970   96.0  96.0   \n",
       "14531     NaN      NaN     NaN       NaN  0.000000    0.00000    0.0   0.0   \n",
       "\n",
       "                update_stamp Unnamed: 109  \n",
       "15     2019-12-04 04:19:56.0          NaN  \n",
       "16     2019-12-04 04:19:56.0          NaN  \n",
       "17     2019-12-04 04:19:56.0          NaN  \n",
       "18     2019-12-04 04:19:56.0          NaN  \n",
       "19     2019-12-04 04:19:56.0          NaN  \n",
       "22     2019-12-04 04:19:56.0          NaN  \n",
       "23     2019-12-04 04:19:56.0          NaN  \n",
       "24     2019-12-04 04:19:56.0          NaN  \n",
       "25     2019-12-04 04:19:56.0          NaN  \n",
       "26     2019-12-04 04:19:56.0          NaN  \n",
       "...                      ...          ...  \n",
       "14511  2019-12-11 04:20:38.0          NaN  \n",
       "14513  2019-12-12 04:20:11.0          NaN  \n",
       "14514  2019-12-12 04:20:12.0          NaN  \n",
       "14515  2019-12-12 04:20:06.0          NaN  \n",
       "14517  2019-12-12 04:20:12.0          NaN  \n",
       "14518  2019-12-13 04:20:29.0          NaN  \n",
       "14519  2019-12-13 04:20:30.0          NaN  \n",
       "14524  2019-12-14 04:20:23.0          NaN  \n",
       "14525  2019-12-14 04:20:25.0          NaN  \n",
       "14531  2019-12-17 04:20:26.0          NaN  \n",
       "\n",
       "[6453 rows x 110 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['PTGENDER']  == 'Female']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get all patients' civil/marital status (column name *PTMARRY*). Return the selection as (a) Series and (b) DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary> > Solution</summary>\n",
    "    \n",
    "```python\n",
    "data['PTMARRY']\n",
    "data[['PTMARRY']]\n",
    "```\n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get all patients' age and gender."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary> > Solution</summary>\n",
    "    \n",
    "```python\n",
    "data[['AGE', 'PTGENDER']]\n",
    "```\n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get all patients' age and gender but show only the last 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary> > Solution</summary>\n",
    "    \n",
    "```python\n",
    "data[['AGE', 'PTGENDER']].tail(5)\n",
    "```\n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select only divorced patients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary> > Solution</summary>\n",
    "    \n",
    "```python\n",
    "\n",
    "data[data['PTMARRY'] == 'Divorced']\n",
    "```\n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many patients are divorced?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary> > Solution</summary>\n",
    "    \n",
    "```python\n",
    "\n",
    "data[data['PTMARRY'] == 'Divorced'].shape[0]\n",
    "```\n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select cross-sectional baseline data, i.e. where column with name `VISCODE` (visit code) is `bl` (baseline)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary> > Solution</summary>\n",
    "    \n",
    "```python\n",
    "\n",
    "data[data['VISCODE'] == 'bl']\n",
    "```\n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many patients are dropped by this action?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary> > Solution</summary>\n",
    "    \n",
    "```python\n",
    "\n",
    "data.shape[0] - data[data['VISCODE'] == 'bl'].shape[0]\n",
    "```\n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save this selection to the new variable `data_reduced` (use `copy()` to make a copy of this object’s indices and data). Use this variable from now on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_reduced = data[data['VISCODE'] == 'bl'].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.8. Drop columns and rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Drop columns by label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the `drop` function to remove rows (`axis=0`, default) or columns (`axis=1`) by their labels. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RID</th>\n",
       "      <th>VISCODE</th>\n",
       "      <th>SITE</th>\n",
       "      <th>EXAMDATE</th>\n",
       "      <th>DX_bl</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PTGENDER</th>\n",
       "      <th>PTEDUCAT</th>\n",
       "      <th>WORK</th>\n",
       "      <th>PTETHCAT</th>\n",
       "      <th>PTRACCAT</th>\n",
       "      <th>PTMARRY</th>\n",
       "      <th>APOE4</th>\n",
       "      <th>FDG</th>\n",
       "      <th>PIB</th>\n",
       "      <th>AV45</th>\n",
       "      <th>ABETA</th>\n",
       "      <th>TAU</th>\n",
       "      <th>PTAU</th>\n",
       "      <th>CDRSB</th>\n",
       "      <th>ADAS11</th>\n",
       "      <th>ADAS13</th>\n",
       "      <th>ADASQ4</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>RAVLT_immediate</th>\n",
       "      <th>RAVLT_learning</th>\n",
       "      <th>RAVLT_forgetting</th>\n",
       "      <th>RAVLT_perc_forgetting</th>\n",
       "      <th>LDELTOTAL</th>\n",
       "      <th>DIGITSCOR</th>\n",
       "      <th>TRABSCOR</th>\n",
       "      <th>FAQ</th>\n",
       "      <th>MOCA</th>\n",
       "      <th>EcogPtMem</th>\n",
       "      <th>EcogPtLang</th>\n",
       "      <th>EcogPtVisspat</th>\n",
       "      <th>EcogPtPlan</th>\n",
       "      <th>EcogPtOrgan</th>\n",
       "      <th>EcogPtDivatt</th>\n",
       "      <th>EcogPtTotal</th>\n",
       "      <th>EcogSPMem</th>\n",
       "      <th>EcogSPLang</th>\n",
       "      <th>EcogSPVisspat</th>\n",
       "      <th>EcogSPPlan</th>\n",
       "      <th>EcogSPOrgan</th>\n",
       "      <th>EcogSPDivatt</th>\n",
       "      <th>EcogSPTotal</th>\n",
       "      <th>FLDSTRENG</th>\n",
       "      <th>IMAGEUID</th>\n",
       "      <th>Ventricles</th>\n",
       "      <th>Hippocampus</th>\n",
       "      <th>WholeBrain</th>\n",
       "      <th>Entorhinal</th>\n",
       "      <th>Fusiform</th>\n",
       "      <th>MidTemp</th>\n",
       "      <th>ICV</th>\n",
       "      <th>DX</th>\n",
       "      <th>mPACCdigit</th>\n",
       "      <th>mPACCtrailsB</th>\n",
       "      <th>EXAMDATE_bl</th>\n",
       "      <th>CDRSB_bl</th>\n",
       "      <th>ADAS11_bl</th>\n",
       "      <th>ADAS13_bl</th>\n",
       "      <th>ADASQ4_bl</th>\n",
       "      <th>MMSE_bl</th>\n",
       "      <th>RAVLT_immediate_bl</th>\n",
       "      <th>RAVLT_learning_bl</th>\n",
       "      <th>RAVLT_forgetting_bl</th>\n",
       "      <th>RAVLT_perc_forgetting_bl</th>\n",
       "      <th>LDELTOTAL_BL</th>\n",
       "      <th>DIGITSCOR_bl</th>\n",
       "      <th>TRABSCOR_bl</th>\n",
       "      <th>FAQ_bl</th>\n",
       "      <th>mPACCdigit_bl</th>\n",
       "      <th>mPACCtrailsB_bl</th>\n",
       "      <th>FLDSTRENG_bl</th>\n",
       "      <th>Ventricles_bl</th>\n",
       "      <th>Hippocampus_bl</th>\n",
       "      <th>WholeBrain_bl</th>\n",
       "      <th>Entorhinal_bl</th>\n",
       "      <th>Fusiform_bl</th>\n",
       "      <th>MidTemp_bl</th>\n",
       "      <th>ICV_bl</th>\n",
       "      <th>MOCA_bl</th>\n",
       "      <th>EcogPtMem_bl</th>\n",
       "      <th>EcogPtLang_bl</th>\n",
       "      <th>EcogPtVisspat_bl</th>\n",
       "      <th>EcogPtPlan_bl</th>\n",
       "      <th>EcogPtOrgan_bl</th>\n",
       "      <th>EcogPtDivatt_bl</th>\n",
       "      <th>EcogPtTotal_bl</th>\n",
       "      <th>EcogSPMem_bl</th>\n",
       "      <th>EcogSPLang_bl</th>\n",
       "      <th>EcogSPVisspat_bl</th>\n",
       "      <th>EcogSPPlan_bl</th>\n",
       "      <th>EcogSPOrgan_bl</th>\n",
       "      <th>EcogSPDivatt_bl</th>\n",
       "      <th>EcogSPTotal_bl</th>\n",
       "      <th>ABETA_bl</th>\n",
       "      <th>TAU_bl</th>\n",
       "      <th>PTAU_bl</th>\n",
       "      <th>FDG_bl</th>\n",
       "      <th>PIB_bl</th>\n",
       "      <th>AV45_bl</th>\n",
       "      <th>Years_bl</th>\n",
       "      <th>Month_bl</th>\n",
       "      <th>Month</th>\n",
       "      <th>M</th>\n",
       "      <th>update_stamp</th>\n",
       "      <th>Unnamed: 109</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>128</td>\n",
       "      <td>bl</td>\n",
       "      <td>164</td>\n",
       "      <td>2005-09-08</td>\n",
       "      <td>CN</td>\n",
       "      <td>74.2</td>\n",
       "      <td>Male</td>\n",
       "      <td>16</td>\n",
       "      <td>technical writer and editor</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>1.36665</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.67</td>\n",
       "      <td>18.67</td>\n",
       "      <td>5.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>54.5455</td>\n",
       "      <td>10.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35479.0</td>\n",
       "      <td>118226.0</td>\n",
       "      <td>8340.0</td>\n",
       "      <td>1229736.0</td>\n",
       "      <td>4182.0</td>\n",
       "      <td>16562.0</td>\n",
       "      <td>27930.0</td>\n",
       "      <td>1984658.0</td>\n",
       "      <td>CN</td>\n",
       "      <td>-4.41005</td>\n",
       "      <td>-4.23545</td>\n",
       "      <td>2005-09-08</td>\n",
       "      <td>0</td>\n",
       "      <td>10.67</td>\n",
       "      <td>18.67</td>\n",
       "      <td>5.0</td>\n",
       "      <td>28</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>54.5455</td>\n",
       "      <td>10.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-4.41005</td>\n",
       "      <td>-4.23545</td>\n",
       "      <td>NaN</td>\n",
       "      <td>118226.0</td>\n",
       "      <td>8340.0</td>\n",
       "      <td>1229736.0</td>\n",
       "      <td>4182.0</td>\n",
       "      <td>16562.0</td>\n",
       "      <td>27930.0</td>\n",
       "      <td>1984658.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.36665</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>129</td>\n",
       "      <td>bl</td>\n",
       "      <td>164</td>\n",
       "      <td>2005-09-12</td>\n",
       "      <td>AD</td>\n",
       "      <td>82.4</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>Secretary</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>1.08355</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>741.5</td>\n",
       "      <td>239.7</td>\n",
       "      <td>22.83</td>\n",
       "      <td>4.5</td>\n",
       "      <td>22.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32241.0</td>\n",
       "      <td>84599.0</td>\n",
       "      <td>5318.0</td>\n",
       "      <td>1129825.0</td>\n",
       "      <td>1790.0</td>\n",
       "      <td>15507.0</td>\n",
       "      <td>18425.0</td>\n",
       "      <td>1920689.0</td>\n",
       "      <td>Dementia</td>\n",
       "      <td>-16.6244</td>\n",
       "      <td>-16.23320</td>\n",
       "      <td>2005-09-12</td>\n",
       "      <td>4.5</td>\n",
       "      <td>22.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-16.62440</td>\n",
       "      <td>-16.23320</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84599.0</td>\n",
       "      <td>5318.0</td>\n",
       "      <td>1129825.0</td>\n",
       "      <td>1790.0</td>\n",
       "      <td>15507.0</td>\n",
       "      <td>18425.0</td>\n",
       "      <td>1920689.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>741.5</td>\n",
       "      <td>239.7</td>\n",
       "      <td>22.83</td>\n",
       "      <td>1.08355</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>130</td>\n",
       "      <td>bl</td>\n",
       "      <td>175</td>\n",
       "      <td>2005-11-08</td>\n",
       "      <td>LMCI</td>\n",
       "      <td>67.1</td>\n",
       "      <td>Male</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1501</td>\n",
       "      <td>153.1</td>\n",
       "      <td>13.29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.33</td>\n",
       "      <td>21.33</td>\n",
       "      <td>6.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>36.3636</td>\n",
       "      <td>4.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>271.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64621.0</td>\n",
       "      <td>39604.0</td>\n",
       "      <td>6870.0</td>\n",
       "      <td>1154979.0</td>\n",
       "      <td>3981.0</td>\n",
       "      <td>19035.0</td>\n",
       "      <td>19614.0</td>\n",
       "      <td>1679439.0</td>\n",
       "      <td>MCI</td>\n",
       "      <td>-8.54931</td>\n",
       "      <td>-9.60664</td>\n",
       "      <td>2005-11-08</td>\n",
       "      <td>1</td>\n",
       "      <td>14.33</td>\n",
       "      <td>21.33</td>\n",
       "      <td>6.0</td>\n",
       "      <td>27</td>\n",
       "      <td>37.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>36.3636</td>\n",
       "      <td>4.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>271.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.54931</td>\n",
       "      <td>-9.60664</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39604.0</td>\n",
       "      <td>6870.0</td>\n",
       "      <td>1154979.0</td>\n",
       "      <td>3981.0</td>\n",
       "      <td>19035.0</td>\n",
       "      <td>19614.0</td>\n",
       "      <td>1679439.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1501</td>\n",
       "      <td>153.1</td>\n",
       "      <td>13.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RID VISCODE  SITE    EXAMDATE DX_bl   AGE PTGENDER  PTEDUCAT  \\\n",
       "0  128      bl   164  2005-09-08    CN  74.2     Male        16   \n",
       "1  129      bl   164  2005-09-12    AD  82.4     Male        18   \n",
       "5  130      bl   175  2005-11-08  LMCI  67.1     Male        10   \n",
       "\n",
       "                          WORK         PTETHCAT PTRACCAT  PTMARRY APOE4  \\\n",
       "0  technical writer and editor  Not Hisp/Latino    White  Married     0   \n",
       "1                    Secretary  Not Hisp/Latino    White  Married     1   \n",
       "5                          NaN      Hisp/Latino    White  Married     0   \n",
       "\n",
       "       FDG  PIB  AV45  ABETA    TAU   PTAU  CDRSB  ADAS11  ADAS13  ADASQ4  \\\n",
       "0  1.36665  NaN   NaN    NaN    NaN    NaN    0.0   10.67   18.67     5.0   \n",
       "1  1.08355  NaN   NaN  741.5  239.7  22.83    4.5   22.00   31.00     8.0   \n",
       "5      NaN  NaN   NaN   1501  153.1  13.29    1.0   14.33   21.33     6.0   \n",
       "\n",
       "   MMSE  RAVLT_immediate  RAVLT_learning  RAVLT_forgetting  \\\n",
       "0  28.0             44.0             4.0               6.0   \n",
       "1  20.0             22.0             1.0               4.0   \n",
       "5  27.0             37.0             7.0               4.0   \n",
       "\n",
       "   RAVLT_perc_forgetting  LDELTOTAL  DIGITSCOR  TRABSCOR   FAQ  MOCA  \\\n",
       "0                54.5455       10.0       34.0     112.0   0.0   NaN   \n",
       "1               100.0000        2.0       25.0     148.0  10.0   NaN   \n",
       "5                36.3636        4.0       25.0     271.0   0.0   NaN   \n",
       "\n",
       "   EcogPtMem  EcogPtLang  EcogPtVisspat  EcogPtPlan  EcogPtOrgan  \\\n",
       "0        NaN         NaN            NaN         NaN          NaN   \n",
       "1        NaN         NaN            NaN         NaN          NaN   \n",
       "5        NaN         NaN            NaN         NaN          NaN   \n",
       "\n",
       "   EcogPtDivatt  EcogPtTotal  EcogSPMem  EcogSPLang  EcogSPVisspat  \\\n",
       "0           NaN          NaN        NaN         NaN            NaN   \n",
       "1           NaN          NaN        NaN         NaN            NaN   \n",
       "5           NaN          NaN        NaN         NaN            NaN   \n",
       "\n",
       "   EcogSPPlan  EcogSPOrgan  EcogSPDivatt  EcogSPTotal  FLDSTRENG  IMAGEUID  \\\n",
       "0         NaN          NaN           NaN          NaN        NaN   35479.0   \n",
       "1         NaN          NaN           NaN          NaN        NaN   32241.0   \n",
       "5         NaN          NaN           NaN          NaN        NaN   64621.0   \n",
       "\n",
       "   Ventricles  Hippocampus  WholeBrain  Entorhinal  Fusiform  MidTemp  \\\n",
       "0    118226.0       8340.0   1229736.0      4182.0   16562.0  27930.0   \n",
       "1     84599.0       5318.0   1129825.0      1790.0   15507.0  18425.0   \n",
       "5     39604.0       6870.0   1154979.0      3981.0   19035.0  19614.0   \n",
       "\n",
       "         ICV        DX mPACCdigit  mPACCtrailsB EXAMDATE_bl CDRSB_bl  \\\n",
       "0  1984658.0        CN   -4.41005      -4.23545  2005-09-08        0   \n",
       "1  1920689.0  Dementia   -16.6244     -16.23320  2005-09-12      4.5   \n",
       "5  1679439.0       MCI   -8.54931      -9.60664  2005-11-08        1   \n",
       "\n",
       "   ADAS11_bl  ADAS13_bl  ADASQ4_bl  MMSE_bl  RAVLT_immediate_bl  \\\n",
       "0      10.67      18.67        5.0       28                44.0   \n",
       "1      22.00      31.00        8.0       20                22.0   \n",
       "5      14.33      21.33        6.0       27                37.0   \n",
       "\n",
       "   RAVLT_learning_bl  RAVLT_forgetting_bl  RAVLT_perc_forgetting_bl  \\\n",
       "0                4.0                  6.0                   54.5455   \n",
       "1                1.0                  4.0                  100.0000   \n",
       "5                7.0                  4.0                   36.3636   \n",
       "\n",
       "   LDELTOTAL_BL  DIGITSCOR_bl  TRABSCOR_bl  FAQ_bl  mPACCdigit_bl  \\\n",
       "0          10.0          34.0        112.0     0.0       -4.41005   \n",
       "1           2.0          25.0        148.0    10.0      -16.62440   \n",
       "5           4.0          25.0        271.0     0.0       -8.54931   \n",
       "\n",
       "   mPACCtrailsB_bl  FLDSTRENG_bl  Ventricles_bl  Hippocampus_bl  \\\n",
       "0         -4.23545           NaN       118226.0          8340.0   \n",
       "1        -16.23320           NaN        84599.0          5318.0   \n",
       "5         -9.60664           NaN        39604.0          6870.0   \n",
       "\n",
       "   WholeBrain_bl  Entorhinal_bl  Fusiform_bl  MidTemp_bl     ICV_bl  MOCA_bl  \\\n",
       "0      1229736.0         4182.0      16562.0     27930.0  1984658.0      NaN   \n",
       "1      1129825.0         1790.0      15507.0     18425.0  1920689.0      NaN   \n",
       "5      1154979.0         3981.0      19035.0     19614.0  1679439.0      NaN   \n",
       "\n",
       "   EcogPtMem_bl  EcogPtLang_bl  EcogPtVisspat_bl  EcogPtPlan_bl  \\\n",
       "0           NaN            NaN               NaN            NaN   \n",
       "1           NaN            NaN               NaN            NaN   \n",
       "5           NaN            NaN               NaN            NaN   \n",
       "\n",
       "   EcogPtOrgan_bl  EcogPtDivatt_bl  EcogPtTotal_bl  EcogSPMem_bl  \\\n",
       "0             NaN              NaN             NaN           NaN   \n",
       "1             NaN              NaN             NaN           NaN   \n",
       "5             NaN              NaN             NaN           NaN   \n",
       "\n",
       "   EcogSPLang_bl  EcogSPVisspat_bl  EcogSPPlan_bl  EcogSPOrgan_bl  \\\n",
       "0            NaN               NaN            NaN             NaN   \n",
       "1            NaN               NaN            NaN             NaN   \n",
       "5            NaN               NaN            NaN             NaN   \n",
       "\n",
       "   EcogSPDivatt_bl  EcogSPTotal_bl ABETA_bl TAU_bl PTAU_bl   FDG_bl  PIB_bl  \\\n",
       "0              NaN             NaN      NaN    NaN     NaN  1.36665     NaN   \n",
       "1              NaN             NaN    741.5  239.7   22.83  1.08355     NaN   \n",
       "5              NaN             NaN     1501  153.1   13.29      NaN     NaN   \n",
       "\n",
       "   AV45_bl  Years_bl  Month_bl  Month    M           update_stamp Unnamed: 109  \n",
       "0      NaN       0.0       0.0    0.0  0.0  2019-12-04 04:19:56.0          NaN  \n",
       "1      NaN       0.0       0.0    0.0  0.0  2019-12-04 04:19:56.0          NaN  \n",
       "5      NaN       0.0       0.0    0.0  0.0  2019-12-04 04:19:56.0          NaN  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_reduced.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RID</th>\n",
       "      <th>VISCODE</th>\n",
       "      <th>SITE</th>\n",
       "      <th>EXAMDATE</th>\n",
       "      <th>DX_bl</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PTGENDER</th>\n",
       "      <th>PTEDUCAT</th>\n",
       "      <th>WORK</th>\n",
       "      <th>PTETHCAT</th>\n",
       "      <th>PTRACCAT</th>\n",
       "      <th>PTMARRY</th>\n",
       "      <th>APOE4</th>\n",
       "      <th>FDG</th>\n",
       "      <th>PIB</th>\n",
       "      <th>AV45</th>\n",
       "      <th>ABETA</th>\n",
       "      <th>TAU</th>\n",
       "      <th>PTAU</th>\n",
       "      <th>CDRSB</th>\n",
       "      <th>ADAS11</th>\n",
       "      <th>ADAS13</th>\n",
       "      <th>ADASQ4</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>RAVLT_immediate</th>\n",
       "      <th>RAVLT_learning</th>\n",
       "      <th>RAVLT_forgetting</th>\n",
       "      <th>RAVLT_perc_forgetting</th>\n",
       "      <th>LDELTOTAL</th>\n",
       "      <th>DIGITSCOR</th>\n",
       "      <th>TRABSCOR</th>\n",
       "      <th>FAQ</th>\n",
       "      <th>MOCA</th>\n",
       "      <th>EcogPtMem</th>\n",
       "      <th>EcogPtLang</th>\n",
       "      <th>EcogPtVisspat</th>\n",
       "      <th>EcogPtPlan</th>\n",
       "      <th>EcogPtOrgan</th>\n",
       "      <th>EcogPtDivatt</th>\n",
       "      <th>EcogPtTotal</th>\n",
       "      <th>EcogSPMem</th>\n",
       "      <th>EcogSPLang</th>\n",
       "      <th>EcogSPVisspat</th>\n",
       "      <th>EcogSPPlan</th>\n",
       "      <th>EcogSPOrgan</th>\n",
       "      <th>EcogSPDivatt</th>\n",
       "      <th>EcogSPTotal</th>\n",
       "      <th>FLDSTRENG</th>\n",
       "      <th>IMAGEUID</th>\n",
       "      <th>Ventricles</th>\n",
       "      <th>Hippocampus</th>\n",
       "      <th>WholeBrain</th>\n",
       "      <th>Entorhinal</th>\n",
       "      <th>Fusiform</th>\n",
       "      <th>MidTemp</th>\n",
       "      <th>ICV</th>\n",
       "      <th>DX</th>\n",
       "      <th>mPACCdigit</th>\n",
       "      <th>mPACCtrailsB</th>\n",
       "      <th>EXAMDATE_bl</th>\n",
       "      <th>CDRSB_bl</th>\n",
       "      <th>ADAS11_bl</th>\n",
       "      <th>ADAS13_bl</th>\n",
       "      <th>ADASQ4_bl</th>\n",
       "      <th>MMSE_bl</th>\n",
       "      <th>RAVLT_immediate_bl</th>\n",
       "      <th>RAVLT_learning_bl</th>\n",
       "      <th>RAVLT_forgetting_bl</th>\n",
       "      <th>RAVLT_perc_forgetting_bl</th>\n",
       "      <th>LDELTOTAL_BL</th>\n",
       "      <th>DIGITSCOR_bl</th>\n",
       "      <th>TRABSCOR_bl</th>\n",
       "      <th>FAQ_bl</th>\n",
       "      <th>mPACCdigit_bl</th>\n",
       "      <th>mPACCtrailsB_bl</th>\n",
       "      <th>FLDSTRENG_bl</th>\n",
       "      <th>Ventricles_bl</th>\n",
       "      <th>Hippocampus_bl</th>\n",
       "      <th>WholeBrain_bl</th>\n",
       "      <th>Entorhinal_bl</th>\n",
       "      <th>Fusiform_bl</th>\n",
       "      <th>MidTemp_bl</th>\n",
       "      <th>ICV_bl</th>\n",
       "      <th>MOCA_bl</th>\n",
       "      <th>EcogPtMem_bl</th>\n",
       "      <th>EcogPtLang_bl</th>\n",
       "      <th>EcogPtVisspat_bl</th>\n",
       "      <th>EcogPtPlan_bl</th>\n",
       "      <th>EcogPtOrgan_bl</th>\n",
       "      <th>EcogPtDivatt_bl</th>\n",
       "      <th>EcogPtTotal_bl</th>\n",
       "      <th>EcogSPMem_bl</th>\n",
       "      <th>EcogSPLang_bl</th>\n",
       "      <th>EcogSPVisspat_bl</th>\n",
       "      <th>EcogSPPlan_bl</th>\n",
       "      <th>EcogSPOrgan_bl</th>\n",
       "      <th>EcogSPDivatt_bl</th>\n",
       "      <th>EcogSPTotal_bl</th>\n",
       "      <th>ABETA_bl</th>\n",
       "      <th>TAU_bl</th>\n",
       "      <th>PTAU_bl</th>\n",
       "      <th>FDG_bl</th>\n",
       "      <th>PIB_bl</th>\n",
       "      <th>AV45_bl</th>\n",
       "      <th>Years_bl</th>\n",
       "      <th>Month_bl</th>\n",
       "      <th>Month</th>\n",
       "      <th>M</th>\n",
       "      <th>update_stamp</th>\n",
       "      <th>Unnamed: 109</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>129</td>\n",
       "      <td>bl</td>\n",
       "      <td>164</td>\n",
       "      <td>2005-09-12</td>\n",
       "      <td>AD</td>\n",
       "      <td>82.4</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>Secretary</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>1.08355</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>741.5</td>\n",
       "      <td>239.7</td>\n",
       "      <td>22.83</td>\n",
       "      <td>4.5</td>\n",
       "      <td>22.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32241.0</td>\n",
       "      <td>84599.0</td>\n",
       "      <td>5318.0</td>\n",
       "      <td>1129825.0</td>\n",
       "      <td>1790.0</td>\n",
       "      <td>15507.0</td>\n",
       "      <td>18425.0</td>\n",
       "      <td>1920689.0</td>\n",
       "      <td>Dementia</td>\n",
       "      <td>-16.6244</td>\n",
       "      <td>-16.23320</td>\n",
       "      <td>2005-09-12</td>\n",
       "      <td>4.5</td>\n",
       "      <td>22.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-16.62440</td>\n",
       "      <td>-16.23320</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84599.0</td>\n",
       "      <td>5318.0</td>\n",
       "      <td>1129825.0</td>\n",
       "      <td>1790.0</td>\n",
       "      <td>15507.0</td>\n",
       "      <td>18425.0</td>\n",
       "      <td>1920689.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>741.5</td>\n",
       "      <td>239.7</td>\n",
       "      <td>22.83</td>\n",
       "      <td>1.08355</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>130</td>\n",
       "      <td>bl</td>\n",
       "      <td>175</td>\n",
       "      <td>2005-11-08</td>\n",
       "      <td>LMCI</td>\n",
       "      <td>67.1</td>\n",
       "      <td>Male</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1501</td>\n",
       "      <td>153.1</td>\n",
       "      <td>13.29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.33</td>\n",
       "      <td>21.33</td>\n",
       "      <td>6.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>36.3636</td>\n",
       "      <td>4.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>271.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64621.0</td>\n",
       "      <td>39604.0</td>\n",
       "      <td>6870.0</td>\n",
       "      <td>1154979.0</td>\n",
       "      <td>3981.0</td>\n",
       "      <td>19035.0</td>\n",
       "      <td>19614.0</td>\n",
       "      <td>1679439.0</td>\n",
       "      <td>MCI</td>\n",
       "      <td>-8.54931</td>\n",
       "      <td>-9.60664</td>\n",
       "      <td>2005-11-08</td>\n",
       "      <td>1</td>\n",
       "      <td>14.33</td>\n",
       "      <td>21.33</td>\n",
       "      <td>6.0</td>\n",
       "      <td>27</td>\n",
       "      <td>37.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>36.3636</td>\n",
       "      <td>4.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>271.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.54931</td>\n",
       "      <td>-9.60664</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39604.0</td>\n",
       "      <td>6870.0</td>\n",
       "      <td>1154979.0</td>\n",
       "      <td>3981.0</td>\n",
       "      <td>19035.0</td>\n",
       "      <td>19614.0</td>\n",
       "      <td>1679439.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1501</td>\n",
       "      <td>153.1</td>\n",
       "      <td>13.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>131</td>\n",
       "      <td>bl</td>\n",
       "      <td>164</td>\n",
       "      <td>2005-09-07</td>\n",
       "      <td>CN</td>\n",
       "      <td>75.7</td>\n",
       "      <td>Male</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>1.29343</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>547.3</td>\n",
       "      <td>337</td>\n",
       "      <td>33.43</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.67</td>\n",
       "      <td>14.67</td>\n",
       "      <td>4.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>44.4444</td>\n",
       "      <td>12.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32253.0</td>\n",
       "      <td>34061.0</td>\n",
       "      <td>7068.0</td>\n",
       "      <td>1116634.0</td>\n",
       "      <td>4428.0</td>\n",
       "      <td>24789.0</td>\n",
       "      <td>21616.0</td>\n",
       "      <td>1640772.0</td>\n",
       "      <td>CN</td>\n",
       "      <td>-1.95295</td>\n",
       "      <td>-1.64932</td>\n",
       "      <td>2005-09-07</td>\n",
       "      <td>0</td>\n",
       "      <td>8.67</td>\n",
       "      <td>14.67</td>\n",
       "      <td>4.0</td>\n",
       "      <td>29</td>\n",
       "      <td>37.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>44.4444</td>\n",
       "      <td>12.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.95295</td>\n",
       "      <td>-1.64932</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34061.0</td>\n",
       "      <td>7068.0</td>\n",
       "      <td>1116634.0</td>\n",
       "      <td>4428.0</td>\n",
       "      <td>24789.0</td>\n",
       "      <td>21616.0</td>\n",
       "      <td>1640772.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>547.3</td>\n",
       "      <td>337</td>\n",
       "      <td>33.43</td>\n",
       "      <td>1.29343</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    RID VISCODE  SITE    EXAMDATE DX_bl   AGE PTGENDER  PTEDUCAT       WORK  \\\n",
       "1   129      bl   164  2005-09-12    AD  82.4     Male        18  Secretary   \n",
       "5   130      bl   175  2005-11-08  LMCI  67.1     Male        10        NaN   \n",
       "10  131      bl   164  2005-09-07    CN  75.7     Male        16        NaN   \n",
       "\n",
       "           PTETHCAT PTRACCAT  PTMARRY APOE4      FDG  PIB  AV45  ABETA    TAU  \\\n",
       "1   Not Hisp/Latino    White  Married     1  1.08355  NaN   NaN  741.5  239.7   \n",
       "5       Hisp/Latino    White  Married     0      NaN  NaN   NaN   1501  153.1   \n",
       "10  Not Hisp/Latino    White  Married     0  1.29343  NaN   NaN  547.3    337   \n",
       "\n",
       "     PTAU  CDRSB  ADAS11  ADAS13  ADASQ4  MMSE  RAVLT_immediate  \\\n",
       "1   22.83    4.5   22.00   31.00     8.0  20.0             22.0   \n",
       "5   13.29    1.0   14.33   21.33     6.0  27.0             37.0   \n",
       "10  33.43    0.0    8.67   14.67     4.0  29.0             37.0   \n",
       "\n",
       "    RAVLT_learning  RAVLT_forgetting  RAVLT_perc_forgetting  LDELTOTAL  \\\n",
       "1              1.0               4.0               100.0000        2.0   \n",
       "5              7.0               4.0                36.3636        4.0   \n",
       "10             4.0               4.0                44.4444       12.0   \n",
       "\n",
       "    DIGITSCOR  TRABSCOR   FAQ  MOCA  EcogPtMem  EcogPtLang  EcogPtVisspat  \\\n",
       "1        25.0     148.0  10.0   NaN        NaN         NaN            NaN   \n",
       "5        25.0     271.0   0.0   NaN        NaN         NaN            NaN   \n",
       "10       38.0      90.0   0.0   NaN        NaN         NaN            NaN   \n",
       "\n",
       "    EcogPtPlan  EcogPtOrgan  EcogPtDivatt  EcogPtTotal  EcogSPMem  EcogSPLang  \\\n",
       "1          NaN          NaN           NaN          NaN        NaN         NaN   \n",
       "5          NaN          NaN           NaN          NaN        NaN         NaN   \n",
       "10         NaN          NaN           NaN          NaN        NaN         NaN   \n",
       "\n",
       "    EcogSPVisspat  EcogSPPlan  EcogSPOrgan  EcogSPDivatt  EcogSPTotal  \\\n",
       "1             NaN         NaN          NaN           NaN          NaN   \n",
       "5             NaN         NaN          NaN           NaN          NaN   \n",
       "10            NaN         NaN          NaN           NaN          NaN   \n",
       "\n",
       "    FLDSTRENG  IMAGEUID  Ventricles  Hippocampus  WholeBrain  Entorhinal  \\\n",
       "1         NaN   32241.0     84599.0       5318.0   1129825.0      1790.0   \n",
       "5         NaN   64621.0     39604.0       6870.0   1154979.0      3981.0   \n",
       "10        NaN   32253.0     34061.0       7068.0   1116634.0      4428.0   \n",
       "\n",
       "    Fusiform  MidTemp        ICV        DX mPACCdigit  mPACCtrailsB  \\\n",
       "1    15507.0  18425.0  1920689.0  Dementia   -16.6244     -16.23320   \n",
       "5    19035.0  19614.0  1679439.0       MCI   -8.54931      -9.60664   \n",
       "10   24789.0  21616.0  1640772.0        CN   -1.95295      -1.64932   \n",
       "\n",
       "   EXAMDATE_bl CDRSB_bl  ADAS11_bl  ADAS13_bl  ADASQ4_bl  MMSE_bl  \\\n",
       "1   2005-09-12      4.5      22.00      31.00        8.0       20   \n",
       "5   2005-11-08        1      14.33      21.33        6.0       27   \n",
       "10  2005-09-07        0       8.67      14.67        4.0       29   \n",
       "\n",
       "    RAVLT_immediate_bl  RAVLT_learning_bl  RAVLT_forgetting_bl  \\\n",
       "1                 22.0                1.0                  4.0   \n",
       "5                 37.0                7.0                  4.0   \n",
       "10                37.0                4.0                  4.0   \n",
       "\n",
       "    RAVLT_perc_forgetting_bl  LDELTOTAL_BL  DIGITSCOR_bl  TRABSCOR_bl  FAQ_bl  \\\n",
       "1                   100.0000           2.0          25.0        148.0    10.0   \n",
       "5                    36.3636           4.0          25.0        271.0     0.0   \n",
       "10                   44.4444          12.0          38.0         90.0     0.0   \n",
       "\n",
       "    mPACCdigit_bl  mPACCtrailsB_bl  FLDSTRENG_bl  Ventricles_bl  \\\n",
       "1       -16.62440        -16.23320           NaN        84599.0   \n",
       "5        -8.54931         -9.60664           NaN        39604.0   \n",
       "10       -1.95295         -1.64932           NaN        34061.0   \n",
       "\n",
       "    Hippocampus_bl  WholeBrain_bl  Entorhinal_bl  Fusiform_bl  MidTemp_bl  \\\n",
       "1           5318.0      1129825.0         1790.0      15507.0     18425.0   \n",
       "5           6870.0      1154979.0         3981.0      19035.0     19614.0   \n",
       "10          7068.0      1116634.0         4428.0      24789.0     21616.0   \n",
       "\n",
       "       ICV_bl  MOCA_bl  EcogPtMem_bl  EcogPtLang_bl  EcogPtVisspat_bl  \\\n",
       "1   1920689.0      NaN           NaN            NaN               NaN   \n",
       "5   1679439.0      NaN           NaN            NaN               NaN   \n",
       "10  1640772.0      NaN           NaN            NaN               NaN   \n",
       "\n",
       "    EcogPtPlan_bl  EcogPtOrgan_bl  EcogPtDivatt_bl  EcogPtTotal_bl  \\\n",
       "1             NaN             NaN              NaN             NaN   \n",
       "5             NaN             NaN              NaN             NaN   \n",
       "10            NaN             NaN              NaN             NaN   \n",
       "\n",
       "    EcogSPMem_bl  EcogSPLang_bl  EcogSPVisspat_bl  EcogSPPlan_bl  \\\n",
       "1            NaN            NaN               NaN            NaN   \n",
       "5            NaN            NaN               NaN            NaN   \n",
       "10           NaN            NaN               NaN            NaN   \n",
       "\n",
       "    EcogSPOrgan_bl  EcogSPDivatt_bl  EcogSPTotal_bl ABETA_bl TAU_bl PTAU_bl  \\\n",
       "1              NaN              NaN             NaN    741.5  239.7   22.83   \n",
       "5              NaN              NaN             NaN     1501  153.1   13.29   \n",
       "10             NaN              NaN             NaN    547.3    337   33.43   \n",
       "\n",
       "     FDG_bl  PIB_bl  AV45_bl  Years_bl  Month_bl  Month    M  \\\n",
       "1   1.08355     NaN      NaN       0.0       0.0    0.0  0.0   \n",
       "5       NaN     NaN      NaN       0.0       0.0    0.0  0.0   \n",
       "10  1.29343     NaN      NaN       0.0       0.0    0.0  0.0   \n",
       "\n",
       "             update_stamp Unnamed: 109  \n",
       "1   2019-12-04 04:19:56.0          NaN  \n",
       "5   2019-12-04 04:19:56.0          NaN  \n",
       "10  2019-12-04 04:19:56.0          NaN  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_reduced.drop(0, axis=0).head(3)  # Remove first row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VISCODE</th>\n",
       "      <th>SITE</th>\n",
       "      <th>EXAMDATE</th>\n",
       "      <th>DX_bl</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PTGENDER</th>\n",
       "      <th>PTEDUCAT</th>\n",
       "      <th>WORK</th>\n",
       "      <th>PTETHCAT</th>\n",
       "      <th>PTRACCAT</th>\n",
       "      <th>PTMARRY</th>\n",
       "      <th>APOE4</th>\n",
       "      <th>FDG</th>\n",
       "      <th>PIB</th>\n",
       "      <th>AV45</th>\n",
       "      <th>ABETA</th>\n",
       "      <th>TAU</th>\n",
       "      <th>PTAU</th>\n",
       "      <th>CDRSB</th>\n",
       "      <th>ADAS11</th>\n",
       "      <th>ADAS13</th>\n",
       "      <th>ADASQ4</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>RAVLT_immediate</th>\n",
       "      <th>RAVLT_learning</th>\n",
       "      <th>RAVLT_forgetting</th>\n",
       "      <th>RAVLT_perc_forgetting</th>\n",
       "      <th>LDELTOTAL</th>\n",
       "      <th>DIGITSCOR</th>\n",
       "      <th>TRABSCOR</th>\n",
       "      <th>FAQ</th>\n",
       "      <th>MOCA</th>\n",
       "      <th>EcogPtMem</th>\n",
       "      <th>EcogPtLang</th>\n",
       "      <th>EcogPtVisspat</th>\n",
       "      <th>EcogPtPlan</th>\n",
       "      <th>EcogPtOrgan</th>\n",
       "      <th>EcogPtDivatt</th>\n",
       "      <th>EcogPtTotal</th>\n",
       "      <th>EcogSPMem</th>\n",
       "      <th>EcogSPLang</th>\n",
       "      <th>EcogSPVisspat</th>\n",
       "      <th>EcogSPPlan</th>\n",
       "      <th>EcogSPOrgan</th>\n",
       "      <th>EcogSPDivatt</th>\n",
       "      <th>EcogSPTotal</th>\n",
       "      <th>FLDSTRENG</th>\n",
       "      <th>IMAGEUID</th>\n",
       "      <th>Ventricles</th>\n",
       "      <th>Hippocampus</th>\n",
       "      <th>WholeBrain</th>\n",
       "      <th>Entorhinal</th>\n",
       "      <th>Fusiform</th>\n",
       "      <th>MidTemp</th>\n",
       "      <th>ICV</th>\n",
       "      <th>DX</th>\n",
       "      <th>mPACCdigit</th>\n",
       "      <th>mPACCtrailsB</th>\n",
       "      <th>EXAMDATE_bl</th>\n",
       "      <th>CDRSB_bl</th>\n",
       "      <th>ADAS11_bl</th>\n",
       "      <th>ADAS13_bl</th>\n",
       "      <th>ADASQ4_bl</th>\n",
       "      <th>MMSE_bl</th>\n",
       "      <th>RAVLT_immediate_bl</th>\n",
       "      <th>RAVLT_learning_bl</th>\n",
       "      <th>RAVLT_forgetting_bl</th>\n",
       "      <th>RAVLT_perc_forgetting_bl</th>\n",
       "      <th>LDELTOTAL_BL</th>\n",
       "      <th>DIGITSCOR_bl</th>\n",
       "      <th>TRABSCOR_bl</th>\n",
       "      <th>FAQ_bl</th>\n",
       "      <th>mPACCdigit_bl</th>\n",
       "      <th>mPACCtrailsB_bl</th>\n",
       "      <th>FLDSTRENG_bl</th>\n",
       "      <th>Ventricles_bl</th>\n",
       "      <th>Hippocampus_bl</th>\n",
       "      <th>WholeBrain_bl</th>\n",
       "      <th>Entorhinal_bl</th>\n",
       "      <th>Fusiform_bl</th>\n",
       "      <th>MidTemp_bl</th>\n",
       "      <th>ICV_bl</th>\n",
       "      <th>MOCA_bl</th>\n",
       "      <th>EcogPtMem_bl</th>\n",
       "      <th>EcogPtLang_bl</th>\n",
       "      <th>EcogPtVisspat_bl</th>\n",
       "      <th>EcogPtPlan_bl</th>\n",
       "      <th>EcogPtOrgan_bl</th>\n",
       "      <th>EcogPtDivatt_bl</th>\n",
       "      <th>EcogPtTotal_bl</th>\n",
       "      <th>EcogSPMem_bl</th>\n",
       "      <th>EcogSPLang_bl</th>\n",
       "      <th>EcogSPVisspat_bl</th>\n",
       "      <th>EcogSPPlan_bl</th>\n",
       "      <th>EcogSPOrgan_bl</th>\n",
       "      <th>EcogSPDivatt_bl</th>\n",
       "      <th>EcogSPTotal_bl</th>\n",
       "      <th>ABETA_bl</th>\n",
       "      <th>TAU_bl</th>\n",
       "      <th>PTAU_bl</th>\n",
       "      <th>FDG_bl</th>\n",
       "      <th>PIB_bl</th>\n",
       "      <th>AV45_bl</th>\n",
       "      <th>Years_bl</th>\n",
       "      <th>Month_bl</th>\n",
       "      <th>Month</th>\n",
       "      <th>M</th>\n",
       "      <th>update_stamp</th>\n",
       "      <th>Unnamed: 109</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bl</td>\n",
       "      <td>164</td>\n",
       "      <td>2005-09-08</td>\n",
       "      <td>CN</td>\n",
       "      <td>74.2</td>\n",
       "      <td>Male</td>\n",
       "      <td>16</td>\n",
       "      <td>technical writer and editor</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>1.36665</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.67</td>\n",
       "      <td>18.67</td>\n",
       "      <td>5.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>54.5455</td>\n",
       "      <td>10.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35479.0</td>\n",
       "      <td>118226.0</td>\n",
       "      <td>8340.0</td>\n",
       "      <td>1229736.0</td>\n",
       "      <td>4182.0</td>\n",
       "      <td>16562.0</td>\n",
       "      <td>27930.0</td>\n",
       "      <td>1984658.0</td>\n",
       "      <td>CN</td>\n",
       "      <td>-4.41005</td>\n",
       "      <td>-4.23545</td>\n",
       "      <td>2005-09-08</td>\n",
       "      <td>0</td>\n",
       "      <td>10.67</td>\n",
       "      <td>18.67</td>\n",
       "      <td>5.0</td>\n",
       "      <td>28</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>54.5455</td>\n",
       "      <td>10.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-4.41005</td>\n",
       "      <td>-4.23545</td>\n",
       "      <td>NaN</td>\n",
       "      <td>118226.0</td>\n",
       "      <td>8340.0</td>\n",
       "      <td>1229736.0</td>\n",
       "      <td>4182.0</td>\n",
       "      <td>16562.0</td>\n",
       "      <td>27930.0</td>\n",
       "      <td>1984658.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.36665</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bl</td>\n",
       "      <td>164</td>\n",
       "      <td>2005-09-12</td>\n",
       "      <td>AD</td>\n",
       "      <td>82.4</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>Secretary</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>1.08355</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>741.5</td>\n",
       "      <td>239.7</td>\n",
       "      <td>22.83</td>\n",
       "      <td>4.5</td>\n",
       "      <td>22.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32241.0</td>\n",
       "      <td>84599.0</td>\n",
       "      <td>5318.0</td>\n",
       "      <td>1129825.0</td>\n",
       "      <td>1790.0</td>\n",
       "      <td>15507.0</td>\n",
       "      <td>18425.0</td>\n",
       "      <td>1920689.0</td>\n",
       "      <td>Dementia</td>\n",
       "      <td>-16.6244</td>\n",
       "      <td>-16.23320</td>\n",
       "      <td>2005-09-12</td>\n",
       "      <td>4.5</td>\n",
       "      <td>22.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-16.62440</td>\n",
       "      <td>-16.23320</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84599.0</td>\n",
       "      <td>5318.0</td>\n",
       "      <td>1129825.0</td>\n",
       "      <td>1790.0</td>\n",
       "      <td>15507.0</td>\n",
       "      <td>18425.0</td>\n",
       "      <td>1920689.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>741.5</td>\n",
       "      <td>239.7</td>\n",
       "      <td>22.83</td>\n",
       "      <td>1.08355</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bl</td>\n",
       "      <td>175</td>\n",
       "      <td>2005-11-08</td>\n",
       "      <td>LMCI</td>\n",
       "      <td>67.1</td>\n",
       "      <td>Male</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1501</td>\n",
       "      <td>153.1</td>\n",
       "      <td>13.29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.33</td>\n",
       "      <td>21.33</td>\n",
       "      <td>6.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>36.3636</td>\n",
       "      <td>4.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>271.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64621.0</td>\n",
       "      <td>39604.0</td>\n",
       "      <td>6870.0</td>\n",
       "      <td>1154979.0</td>\n",
       "      <td>3981.0</td>\n",
       "      <td>19035.0</td>\n",
       "      <td>19614.0</td>\n",
       "      <td>1679439.0</td>\n",
       "      <td>MCI</td>\n",
       "      <td>-8.54931</td>\n",
       "      <td>-9.60664</td>\n",
       "      <td>2005-11-08</td>\n",
       "      <td>1</td>\n",
       "      <td>14.33</td>\n",
       "      <td>21.33</td>\n",
       "      <td>6.0</td>\n",
       "      <td>27</td>\n",
       "      <td>37.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>36.3636</td>\n",
       "      <td>4.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>271.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.54931</td>\n",
       "      <td>-9.60664</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39604.0</td>\n",
       "      <td>6870.0</td>\n",
       "      <td>1154979.0</td>\n",
       "      <td>3981.0</td>\n",
       "      <td>19035.0</td>\n",
       "      <td>19614.0</td>\n",
       "      <td>1679439.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1501</td>\n",
       "      <td>153.1</td>\n",
       "      <td>13.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  VISCODE  SITE    EXAMDATE DX_bl   AGE PTGENDER  PTEDUCAT  \\\n",
       "0      bl   164  2005-09-08    CN  74.2     Male        16   \n",
       "1      bl   164  2005-09-12    AD  82.4     Male        18   \n",
       "5      bl   175  2005-11-08  LMCI  67.1     Male        10   \n",
       "\n",
       "                          WORK         PTETHCAT PTRACCAT  PTMARRY APOE4  \\\n",
       "0  technical writer and editor  Not Hisp/Latino    White  Married     0   \n",
       "1                    Secretary  Not Hisp/Latino    White  Married     1   \n",
       "5                          NaN      Hisp/Latino    White  Married     0   \n",
       "\n",
       "       FDG  PIB  AV45  ABETA    TAU   PTAU  CDRSB  ADAS11  ADAS13  ADASQ4  \\\n",
       "0  1.36665  NaN   NaN    NaN    NaN    NaN    0.0   10.67   18.67     5.0   \n",
       "1  1.08355  NaN   NaN  741.5  239.7  22.83    4.5   22.00   31.00     8.0   \n",
       "5      NaN  NaN   NaN   1501  153.1  13.29    1.0   14.33   21.33     6.0   \n",
       "\n",
       "   MMSE  RAVLT_immediate  RAVLT_learning  RAVLT_forgetting  \\\n",
       "0  28.0             44.0             4.0               6.0   \n",
       "1  20.0             22.0             1.0               4.0   \n",
       "5  27.0             37.0             7.0               4.0   \n",
       "\n",
       "   RAVLT_perc_forgetting  LDELTOTAL  DIGITSCOR  TRABSCOR   FAQ  MOCA  \\\n",
       "0                54.5455       10.0       34.0     112.0   0.0   NaN   \n",
       "1               100.0000        2.0       25.0     148.0  10.0   NaN   \n",
       "5                36.3636        4.0       25.0     271.0   0.0   NaN   \n",
       "\n",
       "   EcogPtMem  EcogPtLang  EcogPtVisspat  EcogPtPlan  EcogPtOrgan  \\\n",
       "0        NaN         NaN            NaN         NaN          NaN   \n",
       "1        NaN         NaN            NaN         NaN          NaN   \n",
       "5        NaN         NaN            NaN         NaN          NaN   \n",
       "\n",
       "   EcogPtDivatt  EcogPtTotal  EcogSPMem  EcogSPLang  EcogSPVisspat  \\\n",
       "0           NaN          NaN        NaN         NaN            NaN   \n",
       "1           NaN          NaN        NaN         NaN            NaN   \n",
       "5           NaN          NaN        NaN         NaN            NaN   \n",
       "\n",
       "   EcogSPPlan  EcogSPOrgan  EcogSPDivatt  EcogSPTotal  FLDSTRENG  IMAGEUID  \\\n",
       "0         NaN          NaN           NaN          NaN        NaN   35479.0   \n",
       "1         NaN          NaN           NaN          NaN        NaN   32241.0   \n",
       "5         NaN          NaN           NaN          NaN        NaN   64621.0   \n",
       "\n",
       "   Ventricles  Hippocampus  WholeBrain  Entorhinal  Fusiform  MidTemp  \\\n",
       "0    118226.0       8340.0   1229736.0      4182.0   16562.0  27930.0   \n",
       "1     84599.0       5318.0   1129825.0      1790.0   15507.0  18425.0   \n",
       "5     39604.0       6870.0   1154979.0      3981.0   19035.0  19614.0   \n",
       "\n",
       "         ICV        DX mPACCdigit  mPACCtrailsB EXAMDATE_bl CDRSB_bl  \\\n",
       "0  1984658.0        CN   -4.41005      -4.23545  2005-09-08        0   \n",
       "1  1920689.0  Dementia   -16.6244     -16.23320  2005-09-12      4.5   \n",
       "5  1679439.0       MCI   -8.54931      -9.60664  2005-11-08        1   \n",
       "\n",
       "   ADAS11_bl  ADAS13_bl  ADASQ4_bl  MMSE_bl  RAVLT_immediate_bl  \\\n",
       "0      10.67      18.67        5.0       28                44.0   \n",
       "1      22.00      31.00        8.0       20                22.0   \n",
       "5      14.33      21.33        6.0       27                37.0   \n",
       "\n",
       "   RAVLT_learning_bl  RAVLT_forgetting_bl  RAVLT_perc_forgetting_bl  \\\n",
       "0                4.0                  6.0                   54.5455   \n",
       "1                1.0                  4.0                  100.0000   \n",
       "5                7.0                  4.0                   36.3636   \n",
       "\n",
       "   LDELTOTAL_BL  DIGITSCOR_bl  TRABSCOR_bl  FAQ_bl  mPACCdigit_bl  \\\n",
       "0          10.0          34.0        112.0     0.0       -4.41005   \n",
       "1           2.0          25.0        148.0    10.0      -16.62440   \n",
       "5           4.0          25.0        271.0     0.0       -8.54931   \n",
       "\n",
       "   mPACCtrailsB_bl  FLDSTRENG_bl  Ventricles_bl  Hippocampus_bl  \\\n",
       "0         -4.23545           NaN       118226.0          8340.0   \n",
       "1        -16.23320           NaN        84599.0          5318.0   \n",
       "5         -9.60664           NaN        39604.0          6870.0   \n",
       "\n",
       "   WholeBrain_bl  Entorhinal_bl  Fusiform_bl  MidTemp_bl     ICV_bl  MOCA_bl  \\\n",
       "0      1229736.0         4182.0      16562.0     27930.0  1984658.0      NaN   \n",
       "1      1129825.0         1790.0      15507.0     18425.0  1920689.0      NaN   \n",
       "5      1154979.0         3981.0      19035.0     19614.0  1679439.0      NaN   \n",
       "\n",
       "   EcogPtMem_bl  EcogPtLang_bl  EcogPtVisspat_bl  EcogPtPlan_bl  \\\n",
       "0           NaN            NaN               NaN            NaN   \n",
       "1           NaN            NaN               NaN            NaN   \n",
       "5           NaN            NaN               NaN            NaN   \n",
       "\n",
       "   EcogPtOrgan_bl  EcogPtDivatt_bl  EcogPtTotal_bl  EcogSPMem_bl  \\\n",
       "0             NaN              NaN             NaN           NaN   \n",
       "1             NaN              NaN             NaN           NaN   \n",
       "5             NaN              NaN             NaN           NaN   \n",
       "\n",
       "   EcogSPLang_bl  EcogSPVisspat_bl  EcogSPPlan_bl  EcogSPOrgan_bl  \\\n",
       "0            NaN               NaN            NaN             NaN   \n",
       "1            NaN               NaN            NaN             NaN   \n",
       "5            NaN               NaN            NaN             NaN   \n",
       "\n",
       "   EcogSPDivatt_bl  EcogSPTotal_bl ABETA_bl TAU_bl PTAU_bl   FDG_bl  PIB_bl  \\\n",
       "0              NaN             NaN      NaN    NaN     NaN  1.36665     NaN   \n",
       "1              NaN             NaN    741.5  239.7   22.83  1.08355     NaN   \n",
       "5              NaN             NaN     1501  153.1   13.29      NaN     NaN   \n",
       "\n",
       "   AV45_bl  Years_bl  Month_bl  Month    M           update_stamp Unnamed: 109  \n",
       "0      NaN       0.0       0.0    0.0  0.0  2019-12-04 04:19:56.0          NaN  \n",
       "1      NaN       0.0       0.0    0.0  0.0  2019-12-04 04:19:56.0          NaN  \n",
       "5      NaN       0.0       0.0    0.0  0.0  2019-12-04 04:19:56.0          NaN  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_reduced.drop('RID', axis=1).head(3)  # Remove RID column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SITE</th>\n",
       "      <th>EXAMDATE</th>\n",
       "      <th>DX_bl</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PTGENDER</th>\n",
       "      <th>PTEDUCAT</th>\n",
       "      <th>WORK</th>\n",
       "      <th>PTETHCAT</th>\n",
       "      <th>PTRACCAT</th>\n",
       "      <th>PTMARRY</th>\n",
       "      <th>APOE4</th>\n",
       "      <th>FDG</th>\n",
       "      <th>PIB</th>\n",
       "      <th>AV45</th>\n",
       "      <th>ABETA</th>\n",
       "      <th>TAU</th>\n",
       "      <th>PTAU</th>\n",
       "      <th>CDRSB</th>\n",
       "      <th>ADAS11</th>\n",
       "      <th>ADAS13</th>\n",
       "      <th>ADASQ4</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>RAVLT_immediate</th>\n",
       "      <th>RAVLT_learning</th>\n",
       "      <th>RAVLT_forgetting</th>\n",
       "      <th>RAVLT_perc_forgetting</th>\n",
       "      <th>LDELTOTAL</th>\n",
       "      <th>DIGITSCOR</th>\n",
       "      <th>TRABSCOR</th>\n",
       "      <th>FAQ</th>\n",
       "      <th>MOCA</th>\n",
       "      <th>EcogPtMem</th>\n",
       "      <th>EcogPtLang</th>\n",
       "      <th>EcogPtVisspat</th>\n",
       "      <th>EcogPtPlan</th>\n",
       "      <th>EcogPtOrgan</th>\n",
       "      <th>EcogPtDivatt</th>\n",
       "      <th>EcogPtTotal</th>\n",
       "      <th>EcogSPMem</th>\n",
       "      <th>EcogSPLang</th>\n",
       "      <th>EcogSPVisspat</th>\n",
       "      <th>EcogSPPlan</th>\n",
       "      <th>EcogSPOrgan</th>\n",
       "      <th>EcogSPDivatt</th>\n",
       "      <th>EcogSPTotal</th>\n",
       "      <th>FLDSTRENG</th>\n",
       "      <th>IMAGEUID</th>\n",
       "      <th>Ventricles</th>\n",
       "      <th>Hippocampus</th>\n",
       "      <th>WholeBrain</th>\n",
       "      <th>Entorhinal</th>\n",
       "      <th>Fusiform</th>\n",
       "      <th>MidTemp</th>\n",
       "      <th>ICV</th>\n",
       "      <th>DX</th>\n",
       "      <th>mPACCdigit</th>\n",
       "      <th>mPACCtrailsB</th>\n",
       "      <th>EXAMDATE_bl</th>\n",
       "      <th>CDRSB_bl</th>\n",
       "      <th>ADAS11_bl</th>\n",
       "      <th>ADAS13_bl</th>\n",
       "      <th>ADASQ4_bl</th>\n",
       "      <th>MMSE_bl</th>\n",
       "      <th>RAVLT_immediate_bl</th>\n",
       "      <th>RAVLT_learning_bl</th>\n",
       "      <th>RAVLT_forgetting_bl</th>\n",
       "      <th>RAVLT_perc_forgetting_bl</th>\n",
       "      <th>LDELTOTAL_BL</th>\n",
       "      <th>DIGITSCOR_bl</th>\n",
       "      <th>TRABSCOR_bl</th>\n",
       "      <th>FAQ_bl</th>\n",
       "      <th>mPACCdigit_bl</th>\n",
       "      <th>mPACCtrailsB_bl</th>\n",
       "      <th>FLDSTRENG_bl</th>\n",
       "      <th>Ventricles_bl</th>\n",
       "      <th>Hippocampus_bl</th>\n",
       "      <th>WholeBrain_bl</th>\n",
       "      <th>Entorhinal_bl</th>\n",
       "      <th>Fusiform_bl</th>\n",
       "      <th>MidTemp_bl</th>\n",
       "      <th>ICV_bl</th>\n",
       "      <th>MOCA_bl</th>\n",
       "      <th>EcogPtMem_bl</th>\n",
       "      <th>EcogPtLang_bl</th>\n",
       "      <th>EcogPtVisspat_bl</th>\n",
       "      <th>EcogPtPlan_bl</th>\n",
       "      <th>EcogPtOrgan_bl</th>\n",
       "      <th>EcogPtDivatt_bl</th>\n",
       "      <th>EcogPtTotal_bl</th>\n",
       "      <th>EcogSPMem_bl</th>\n",
       "      <th>EcogSPLang_bl</th>\n",
       "      <th>EcogSPVisspat_bl</th>\n",
       "      <th>EcogSPPlan_bl</th>\n",
       "      <th>EcogSPOrgan_bl</th>\n",
       "      <th>EcogSPDivatt_bl</th>\n",
       "      <th>EcogSPTotal_bl</th>\n",
       "      <th>ABETA_bl</th>\n",
       "      <th>TAU_bl</th>\n",
       "      <th>PTAU_bl</th>\n",
       "      <th>FDG_bl</th>\n",
       "      <th>PIB_bl</th>\n",
       "      <th>AV45_bl</th>\n",
       "      <th>Years_bl</th>\n",
       "      <th>Month_bl</th>\n",
       "      <th>Month</th>\n",
       "      <th>M</th>\n",
       "      <th>update_stamp</th>\n",
       "      <th>Unnamed: 109</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>164</td>\n",
       "      <td>2005-09-08</td>\n",
       "      <td>CN</td>\n",
       "      <td>74.2</td>\n",
       "      <td>Male</td>\n",
       "      <td>16</td>\n",
       "      <td>technical writer and editor</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>1.36665</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.67</td>\n",
       "      <td>18.67</td>\n",
       "      <td>5.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>54.5455</td>\n",
       "      <td>10.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35479.0</td>\n",
       "      <td>118226.0</td>\n",
       "      <td>8340.0</td>\n",
       "      <td>1229736.0</td>\n",
       "      <td>4182.0</td>\n",
       "      <td>16562.0</td>\n",
       "      <td>27930.0</td>\n",
       "      <td>1984658.0</td>\n",
       "      <td>CN</td>\n",
       "      <td>-4.41005</td>\n",
       "      <td>-4.23545</td>\n",
       "      <td>2005-09-08</td>\n",
       "      <td>0</td>\n",
       "      <td>10.67</td>\n",
       "      <td>18.67</td>\n",
       "      <td>5.0</td>\n",
       "      <td>28</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>54.5455</td>\n",
       "      <td>10.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-4.41005</td>\n",
       "      <td>-4.23545</td>\n",
       "      <td>NaN</td>\n",
       "      <td>118226.0</td>\n",
       "      <td>8340.0</td>\n",
       "      <td>1229736.0</td>\n",
       "      <td>4182.0</td>\n",
       "      <td>16562.0</td>\n",
       "      <td>27930.0</td>\n",
       "      <td>1984658.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.36665</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>164</td>\n",
       "      <td>2005-09-12</td>\n",
       "      <td>AD</td>\n",
       "      <td>82.4</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>Secretary</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>1.08355</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>741.5</td>\n",
       "      <td>239.7</td>\n",
       "      <td>22.83</td>\n",
       "      <td>4.5</td>\n",
       "      <td>22.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32241.0</td>\n",
       "      <td>84599.0</td>\n",
       "      <td>5318.0</td>\n",
       "      <td>1129825.0</td>\n",
       "      <td>1790.0</td>\n",
       "      <td>15507.0</td>\n",
       "      <td>18425.0</td>\n",
       "      <td>1920689.0</td>\n",
       "      <td>Dementia</td>\n",
       "      <td>-16.6244</td>\n",
       "      <td>-16.23320</td>\n",
       "      <td>2005-09-12</td>\n",
       "      <td>4.5</td>\n",
       "      <td>22.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-16.62440</td>\n",
       "      <td>-16.23320</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84599.0</td>\n",
       "      <td>5318.0</td>\n",
       "      <td>1129825.0</td>\n",
       "      <td>1790.0</td>\n",
       "      <td>15507.0</td>\n",
       "      <td>18425.0</td>\n",
       "      <td>1920689.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>741.5</td>\n",
       "      <td>239.7</td>\n",
       "      <td>22.83</td>\n",
       "      <td>1.08355</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>175</td>\n",
       "      <td>2005-11-08</td>\n",
       "      <td>LMCI</td>\n",
       "      <td>67.1</td>\n",
       "      <td>Male</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1501</td>\n",
       "      <td>153.1</td>\n",
       "      <td>13.29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.33</td>\n",
       "      <td>21.33</td>\n",
       "      <td>6.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>36.3636</td>\n",
       "      <td>4.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>271.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64621.0</td>\n",
       "      <td>39604.0</td>\n",
       "      <td>6870.0</td>\n",
       "      <td>1154979.0</td>\n",
       "      <td>3981.0</td>\n",
       "      <td>19035.0</td>\n",
       "      <td>19614.0</td>\n",
       "      <td>1679439.0</td>\n",
       "      <td>MCI</td>\n",
       "      <td>-8.54931</td>\n",
       "      <td>-9.60664</td>\n",
       "      <td>2005-11-08</td>\n",
       "      <td>1</td>\n",
       "      <td>14.33</td>\n",
       "      <td>21.33</td>\n",
       "      <td>6.0</td>\n",
       "      <td>27</td>\n",
       "      <td>37.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>36.3636</td>\n",
       "      <td>4.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>271.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.54931</td>\n",
       "      <td>-9.60664</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39604.0</td>\n",
       "      <td>6870.0</td>\n",
       "      <td>1154979.0</td>\n",
       "      <td>3981.0</td>\n",
       "      <td>19035.0</td>\n",
       "      <td>19614.0</td>\n",
       "      <td>1679439.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1501</td>\n",
       "      <td>153.1</td>\n",
       "      <td>13.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SITE    EXAMDATE DX_bl   AGE PTGENDER  PTEDUCAT  \\\n",
       "0   164  2005-09-08    CN  74.2     Male        16   \n",
       "1   164  2005-09-12    AD  82.4     Male        18   \n",
       "5   175  2005-11-08  LMCI  67.1     Male        10   \n",
       "\n",
       "                          WORK         PTETHCAT PTRACCAT  PTMARRY APOE4  \\\n",
       "0  technical writer and editor  Not Hisp/Latino    White  Married     0   \n",
       "1                    Secretary  Not Hisp/Latino    White  Married     1   \n",
       "5                          NaN      Hisp/Latino    White  Married     0   \n",
       "\n",
       "       FDG  PIB  AV45  ABETA    TAU   PTAU  CDRSB  ADAS11  ADAS13  ADASQ4  \\\n",
       "0  1.36665  NaN   NaN    NaN    NaN    NaN    0.0   10.67   18.67     5.0   \n",
       "1  1.08355  NaN   NaN  741.5  239.7  22.83    4.5   22.00   31.00     8.0   \n",
       "5      NaN  NaN   NaN   1501  153.1  13.29    1.0   14.33   21.33     6.0   \n",
       "\n",
       "   MMSE  RAVLT_immediate  RAVLT_learning  RAVLT_forgetting  \\\n",
       "0  28.0             44.0             4.0               6.0   \n",
       "1  20.0             22.0             1.0               4.0   \n",
       "5  27.0             37.0             7.0               4.0   \n",
       "\n",
       "   RAVLT_perc_forgetting  LDELTOTAL  DIGITSCOR  TRABSCOR   FAQ  MOCA  \\\n",
       "0                54.5455       10.0       34.0     112.0   0.0   NaN   \n",
       "1               100.0000        2.0       25.0     148.0  10.0   NaN   \n",
       "5                36.3636        4.0       25.0     271.0   0.0   NaN   \n",
       "\n",
       "   EcogPtMem  EcogPtLang  EcogPtVisspat  EcogPtPlan  EcogPtOrgan  \\\n",
       "0        NaN         NaN            NaN         NaN          NaN   \n",
       "1        NaN         NaN            NaN         NaN          NaN   \n",
       "5        NaN         NaN            NaN         NaN          NaN   \n",
       "\n",
       "   EcogPtDivatt  EcogPtTotal  EcogSPMem  EcogSPLang  EcogSPVisspat  \\\n",
       "0           NaN          NaN        NaN         NaN            NaN   \n",
       "1           NaN          NaN        NaN         NaN            NaN   \n",
       "5           NaN          NaN        NaN         NaN            NaN   \n",
       "\n",
       "   EcogSPPlan  EcogSPOrgan  EcogSPDivatt  EcogSPTotal  FLDSTRENG  IMAGEUID  \\\n",
       "0         NaN          NaN           NaN          NaN        NaN   35479.0   \n",
       "1         NaN          NaN           NaN          NaN        NaN   32241.0   \n",
       "5         NaN          NaN           NaN          NaN        NaN   64621.0   \n",
       "\n",
       "   Ventricles  Hippocampus  WholeBrain  Entorhinal  Fusiform  MidTemp  \\\n",
       "0    118226.0       8340.0   1229736.0      4182.0   16562.0  27930.0   \n",
       "1     84599.0       5318.0   1129825.0      1790.0   15507.0  18425.0   \n",
       "5     39604.0       6870.0   1154979.0      3981.0   19035.0  19614.0   \n",
       "\n",
       "         ICV        DX mPACCdigit  mPACCtrailsB EXAMDATE_bl CDRSB_bl  \\\n",
       "0  1984658.0        CN   -4.41005      -4.23545  2005-09-08        0   \n",
       "1  1920689.0  Dementia   -16.6244     -16.23320  2005-09-12      4.5   \n",
       "5  1679439.0       MCI   -8.54931      -9.60664  2005-11-08        1   \n",
       "\n",
       "   ADAS11_bl  ADAS13_bl  ADASQ4_bl  MMSE_bl  RAVLT_immediate_bl  \\\n",
       "0      10.67      18.67        5.0       28                44.0   \n",
       "1      22.00      31.00        8.0       20                22.0   \n",
       "5      14.33      21.33        6.0       27                37.0   \n",
       "\n",
       "   RAVLT_learning_bl  RAVLT_forgetting_bl  RAVLT_perc_forgetting_bl  \\\n",
       "0                4.0                  6.0                   54.5455   \n",
       "1                1.0                  4.0                  100.0000   \n",
       "5                7.0                  4.0                   36.3636   \n",
       "\n",
       "   LDELTOTAL_BL  DIGITSCOR_bl  TRABSCOR_bl  FAQ_bl  mPACCdigit_bl  \\\n",
       "0          10.0          34.0        112.0     0.0       -4.41005   \n",
       "1           2.0          25.0        148.0    10.0      -16.62440   \n",
       "5           4.0          25.0        271.0     0.0       -8.54931   \n",
       "\n",
       "   mPACCtrailsB_bl  FLDSTRENG_bl  Ventricles_bl  Hippocampus_bl  \\\n",
       "0         -4.23545           NaN       118226.0          8340.0   \n",
       "1        -16.23320           NaN        84599.0          5318.0   \n",
       "5         -9.60664           NaN        39604.0          6870.0   \n",
       "\n",
       "   WholeBrain_bl  Entorhinal_bl  Fusiform_bl  MidTemp_bl     ICV_bl  MOCA_bl  \\\n",
       "0      1229736.0         4182.0      16562.0     27930.0  1984658.0      NaN   \n",
       "1      1129825.0         1790.0      15507.0     18425.0  1920689.0      NaN   \n",
       "5      1154979.0         3981.0      19035.0     19614.0  1679439.0      NaN   \n",
       "\n",
       "   EcogPtMem_bl  EcogPtLang_bl  EcogPtVisspat_bl  EcogPtPlan_bl  \\\n",
       "0           NaN            NaN               NaN            NaN   \n",
       "1           NaN            NaN               NaN            NaN   \n",
       "5           NaN            NaN               NaN            NaN   \n",
       "\n",
       "   EcogPtOrgan_bl  EcogPtDivatt_bl  EcogPtTotal_bl  EcogSPMem_bl  \\\n",
       "0             NaN              NaN             NaN           NaN   \n",
       "1             NaN              NaN             NaN           NaN   \n",
       "5             NaN              NaN             NaN           NaN   \n",
       "\n",
       "   EcogSPLang_bl  EcogSPVisspat_bl  EcogSPPlan_bl  EcogSPOrgan_bl  \\\n",
       "0            NaN               NaN            NaN             NaN   \n",
       "1            NaN               NaN            NaN             NaN   \n",
       "5            NaN               NaN            NaN             NaN   \n",
       "\n",
       "   EcogSPDivatt_bl  EcogSPTotal_bl ABETA_bl TAU_bl PTAU_bl   FDG_bl  PIB_bl  \\\n",
       "0              NaN             NaN      NaN    NaN     NaN  1.36665     NaN   \n",
       "1              NaN             NaN    741.5  239.7   22.83  1.08355     NaN   \n",
       "5              NaN             NaN     1501  153.1   13.29      NaN     NaN   \n",
       "\n",
       "   AV45_bl  Years_bl  Month_bl  Month    M           update_stamp Unnamed: 109  \n",
       "0      NaN       0.0       0.0    0.0  0.0  2019-12-04 04:19:56.0          NaN  \n",
       "1      NaN       0.0       0.0    0.0  0.0  2019-12-04 04:19:56.0          NaN  \n",
       "5      NaN       0.0       0.0    0.0  0.0  2019-12-04 04:19:56.0          NaN  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_reduced.drop(['RID', 'VISCODE'], axis=1).head(3)  # Remove RID and VISCODE columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise\n",
    "\n",
    "We want to remove all column with column names containing the patterns **\\*_bl** and **Ecog\\***."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Look again at column names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['RID',\n",
       " 'VISCODE',\n",
       " 'SITE',\n",
       " 'EXAMDATE',\n",
       " 'DX_bl',\n",
       " 'AGE',\n",
       " 'PTGENDER',\n",
       " 'PTEDUCAT',\n",
       " 'WORK',\n",
       " 'PTETHCAT',\n",
       " 'PTRACCAT',\n",
       " 'PTMARRY',\n",
       " 'APOE4',\n",
       " 'FDG',\n",
       " 'PIB',\n",
       " 'AV45',\n",
       " 'ABETA',\n",
       " 'TAU',\n",
       " 'PTAU',\n",
       " 'CDRSB',\n",
       " 'ADAS11',\n",
       " 'ADAS13',\n",
       " 'ADASQ4',\n",
       " 'MMSE',\n",
       " 'RAVLT_immediate',\n",
       " 'RAVLT_learning',\n",
       " 'RAVLT_forgetting',\n",
       " 'RAVLT_perc_forgetting',\n",
       " 'LDELTOTAL',\n",
       " 'DIGITSCOR',\n",
       " 'TRABSCOR',\n",
       " 'FAQ',\n",
       " 'MOCA',\n",
       " 'EcogPtMem',\n",
       " 'EcogPtLang',\n",
       " 'EcogPtVisspat',\n",
       " 'EcogPtPlan',\n",
       " 'EcogPtOrgan',\n",
       " 'EcogPtDivatt',\n",
       " 'EcogPtTotal',\n",
       " 'EcogSPMem',\n",
       " 'EcogSPLang',\n",
       " 'EcogSPVisspat',\n",
       " 'EcogSPPlan',\n",
       " 'EcogSPOrgan',\n",
       " 'EcogSPDivatt',\n",
       " 'EcogSPTotal',\n",
       " 'FLDSTRENG',\n",
       " 'IMAGEUID',\n",
       " 'Ventricles',\n",
       " 'Hippocampus',\n",
       " 'WholeBrain',\n",
       " 'Entorhinal',\n",
       " 'Fusiform',\n",
       " 'MidTemp',\n",
       " 'ICV',\n",
       " 'DX',\n",
       " 'mPACCdigit',\n",
       " 'mPACCtrailsB',\n",
       " 'EXAMDATE_bl',\n",
       " 'CDRSB_bl',\n",
       " 'ADAS11_bl',\n",
       " 'ADAS13_bl',\n",
       " 'ADASQ4_bl',\n",
       " 'MMSE_bl',\n",
       " 'RAVLT_immediate_bl',\n",
       " 'RAVLT_learning_bl',\n",
       " 'RAVLT_forgetting_bl',\n",
       " 'RAVLT_perc_forgetting_bl',\n",
       " 'LDELTOTAL_BL',\n",
       " 'DIGITSCOR_bl',\n",
       " 'TRABSCOR_bl',\n",
       " 'FAQ_bl',\n",
       " 'mPACCdigit_bl',\n",
       " 'mPACCtrailsB_bl',\n",
       " 'FLDSTRENG_bl',\n",
       " 'Ventricles_bl',\n",
       " 'Hippocampus_bl',\n",
       " 'WholeBrain_bl',\n",
       " 'Entorhinal_bl',\n",
       " 'Fusiform_bl',\n",
       " 'MidTemp_bl',\n",
       " 'ICV_bl',\n",
       " 'MOCA_bl',\n",
       " 'EcogPtMem_bl',\n",
       " 'EcogPtLang_bl',\n",
       " 'EcogPtVisspat_bl',\n",
       " 'EcogPtPlan_bl',\n",
       " 'EcogPtOrgan_bl',\n",
       " 'EcogPtDivatt_bl',\n",
       " 'EcogPtTotal_bl',\n",
       " 'EcogSPMem_bl',\n",
       " 'EcogSPLang_bl',\n",
       " 'EcogSPVisspat_bl',\n",
       " 'EcogSPPlan_bl',\n",
       " 'EcogSPOrgan_bl',\n",
       " 'EcogSPDivatt_bl',\n",
       " 'EcogSPTotal_bl',\n",
       " 'ABETA_bl',\n",
       " 'TAU_bl',\n",
       " 'PTAU_bl',\n",
       " 'FDG_bl',\n",
       " 'PIB_bl',\n",
       " 'AV45_bl',\n",
       " 'Years_bl',\n",
       " 'Month_bl',\n",
       " 'Month',\n",
       " 'M',\n",
       " 'update_stamp',\n",
       " 'Unnamed: 109']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(data_reduced.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Get all column names with the patterns **\\*_bl** or **Ecog\\*** and save them in a list `column_names_unwanted`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's walk through the following `for` loop with a condition (`if-else` statement):\n",
    "\n",
    "- We initialize an empty list `column_names_unwanted` which will store all column names for all columns that we want to drop from our DataFrame.\n",
    "- We iterate over all column names (`data_reduced.columns`) using the `for` loop.\n",
    "- For each column name (`column_name`) we test a condition:\n",
    "  - If the column name contains the patterns **\\*_bl** or **Ecog\\***, append this column name to our `column_names_unwanted` list.\n",
    "  - Else do nothing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DX_bl',\n",
       " 'EcogPtMem',\n",
       " 'EcogPtLang',\n",
       " 'EcogPtVisspat',\n",
       " 'EcogPtPlan',\n",
       " 'EcogPtOrgan',\n",
       " 'EcogPtDivatt',\n",
       " 'EcogPtTotal',\n",
       " 'EcogSPMem',\n",
       " 'EcogSPLang',\n",
       " 'EcogSPVisspat',\n",
       " 'EcogSPPlan',\n",
       " 'EcogSPOrgan',\n",
       " 'EcogSPDivatt',\n",
       " 'EcogSPTotal',\n",
       " 'EXAMDATE_bl',\n",
       " 'CDRSB_bl',\n",
       " 'ADAS11_bl',\n",
       " 'ADAS13_bl',\n",
       " 'ADASQ4_bl',\n",
       " 'MMSE_bl',\n",
       " 'RAVLT_immediate_bl',\n",
       " 'RAVLT_learning_bl',\n",
       " 'RAVLT_forgetting_bl',\n",
       " 'RAVLT_perc_forgetting_bl',\n",
       " 'DIGITSCOR_bl',\n",
       " 'TRABSCOR_bl',\n",
       " 'FAQ_bl',\n",
       " 'mPACCdigit_bl',\n",
       " 'mPACCtrailsB_bl',\n",
       " 'FLDSTRENG_bl',\n",
       " 'Ventricles_bl',\n",
       " 'Hippocampus_bl',\n",
       " 'WholeBrain_bl',\n",
       " 'Entorhinal_bl',\n",
       " 'Fusiform_bl',\n",
       " 'MidTemp_bl',\n",
       " 'ICV_bl',\n",
       " 'MOCA_bl',\n",
       " 'EcogPtMem_bl',\n",
       " 'EcogPtLang_bl',\n",
       " 'EcogPtVisspat_bl',\n",
       " 'EcogPtPlan_bl',\n",
       " 'EcogPtOrgan_bl',\n",
       " 'EcogPtDivatt_bl',\n",
       " 'EcogPtTotal_bl',\n",
       " 'EcogSPMem_bl',\n",
       " 'EcogSPLang_bl',\n",
       " 'EcogSPVisspat_bl',\n",
       " 'EcogSPPlan_bl',\n",
       " 'EcogSPOrgan_bl',\n",
       " 'EcogSPDivatt_bl',\n",
       " 'EcogSPTotal_bl',\n",
       " 'ABETA_bl',\n",
       " 'TAU_bl',\n",
       " 'PTAU_bl',\n",
       " 'FDG_bl',\n",
       " 'PIB_bl',\n",
       " 'AV45_bl',\n",
       " 'Years_bl',\n",
       " 'Month_bl']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_names_unwanted = []\n",
    "\n",
    "for column_name in data_reduced.columns:\n",
    "    if ('_bl' in column_name) or ('Ecog' in column_name):\n",
    "        column_names_unwanted.append(column_name)\n",
    "    else:\n",
    "        pass\n",
    "\n",
    "column_names_unwanted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Drop all unwanted column names listed in `column_names_unwanted`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary> > Solution</summary>\n",
    "    \n",
    "```python\n",
    "data_reduced.drop(column_names_unwanted, axis=1)\n",
    "```\n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Save this selection by overwriting the variable `data_reduced`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_reduced = data_reduced.drop(column_names_unwanted, axis=1).copy()  # Note .copy()!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many columns are left in our dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary> > Solution</summary>\n",
    "    \n",
    "```python\n",
    "data_reduced.shape\n",
    "```\n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. Drop also the last column that contains only NaN values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_reduced = data_reduced.drop('Unnamed: 109', axis=1).copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Drop rows that contain NaN values in certain columns\n",
    "\n",
    "We want to drop rows that contain NaN values in important columns such as the \n",
    "- diagnosis (`'DX'`) \n",
    "- Mini Mental Status Examination (`'MMSE'`) and\n",
    "- Hippocampus volume (`'Hippocampus'`)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Check if these columns contain NaN values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The diagnosis is a categorial parameter and can be checked for NaN values using the `unique()` function, which returns a list of unique values contained in selected column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CN', 'Dementia', 'MCI', nan], dtype=object)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_reduced['DX'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The MMSE and hippocampus volume parameters are numerical values. Here, we can check if any (`any()`) NaN value (`isnull()`) exists in the selected column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_reduced['MMSE'].isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_reduced['Hippocampus'].isnull().values.any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Now drop the rows using the function `dropna()` with the parameters `axis=0` (for index) and `subset` to specify which columns are to be checked for NaN values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RID</th>\n",
       "      <th>VISCODE</th>\n",
       "      <th>SITE</th>\n",
       "      <th>EXAMDATE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PTGENDER</th>\n",
       "      <th>PTEDUCAT</th>\n",
       "      <th>WORK</th>\n",
       "      <th>PTETHCAT</th>\n",
       "      <th>PTRACCAT</th>\n",
       "      <th>PTMARRY</th>\n",
       "      <th>APOE4</th>\n",
       "      <th>FDG</th>\n",
       "      <th>PIB</th>\n",
       "      <th>AV45</th>\n",
       "      <th>ABETA</th>\n",
       "      <th>TAU</th>\n",
       "      <th>PTAU</th>\n",
       "      <th>CDRSB</th>\n",
       "      <th>ADAS11</th>\n",
       "      <th>ADAS13</th>\n",
       "      <th>ADASQ4</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>RAVLT_immediate</th>\n",
       "      <th>RAVLT_learning</th>\n",
       "      <th>RAVLT_forgetting</th>\n",
       "      <th>RAVLT_perc_forgetting</th>\n",
       "      <th>LDELTOTAL</th>\n",
       "      <th>DIGITSCOR</th>\n",
       "      <th>TRABSCOR</th>\n",
       "      <th>FAQ</th>\n",
       "      <th>MOCA</th>\n",
       "      <th>FLDSTRENG</th>\n",
       "      <th>IMAGEUID</th>\n",
       "      <th>Ventricles</th>\n",
       "      <th>Hippocampus</th>\n",
       "      <th>WholeBrain</th>\n",
       "      <th>Entorhinal</th>\n",
       "      <th>Fusiform</th>\n",
       "      <th>MidTemp</th>\n",
       "      <th>ICV</th>\n",
       "      <th>DX</th>\n",
       "      <th>mPACCdigit</th>\n",
       "      <th>mPACCtrailsB</th>\n",
       "      <th>LDELTOTAL_BL</th>\n",
       "      <th>Month</th>\n",
       "      <th>M</th>\n",
       "      <th>update_stamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>128</td>\n",
       "      <td>bl</td>\n",
       "      <td>164</td>\n",
       "      <td>2005-09-08</td>\n",
       "      <td>74.2</td>\n",
       "      <td>Male</td>\n",
       "      <td>16</td>\n",
       "      <td>technical writer and editor</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>1.36665</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.67</td>\n",
       "      <td>18.67</td>\n",
       "      <td>5.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>54.5455</td>\n",
       "      <td>10.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35479.0</td>\n",
       "      <td>118226.0</td>\n",
       "      <td>8340.0</td>\n",
       "      <td>1229736.0</td>\n",
       "      <td>4182.0</td>\n",
       "      <td>16562.0</td>\n",
       "      <td>27930.0</td>\n",
       "      <td>1984658.0</td>\n",
       "      <td>CN</td>\n",
       "      <td>-4.41005</td>\n",
       "      <td>-4.235450</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>129</td>\n",
       "      <td>bl</td>\n",
       "      <td>164</td>\n",
       "      <td>2005-09-12</td>\n",
       "      <td>82.4</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>Secretary</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>1.08355</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>741.5</td>\n",
       "      <td>239.7</td>\n",
       "      <td>22.83</td>\n",
       "      <td>4.5</td>\n",
       "      <td>22.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32241.0</td>\n",
       "      <td>84599.0</td>\n",
       "      <td>5318.0</td>\n",
       "      <td>1129825.0</td>\n",
       "      <td>1790.0</td>\n",
       "      <td>15507.0</td>\n",
       "      <td>18425.0</td>\n",
       "      <td>1920689.0</td>\n",
       "      <td>Dementia</td>\n",
       "      <td>-16.6244</td>\n",
       "      <td>-16.233200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>130</td>\n",
       "      <td>bl</td>\n",
       "      <td>175</td>\n",
       "      <td>2005-11-08</td>\n",
       "      <td>67.1</td>\n",
       "      <td>Male</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1501</td>\n",
       "      <td>153.1</td>\n",
       "      <td>13.29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.33</td>\n",
       "      <td>21.33</td>\n",
       "      <td>6.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>36.3636</td>\n",
       "      <td>4.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>271.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64621.0</td>\n",
       "      <td>39604.0</td>\n",
       "      <td>6870.0</td>\n",
       "      <td>1154979.0</td>\n",
       "      <td>3981.0</td>\n",
       "      <td>19035.0</td>\n",
       "      <td>19614.0</td>\n",
       "      <td>1679439.0</td>\n",
       "      <td>MCI</td>\n",
       "      <td>-8.54931</td>\n",
       "      <td>-9.606640</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>131</td>\n",
       "      <td>bl</td>\n",
       "      <td>164</td>\n",
       "      <td>2005-09-07</td>\n",
       "      <td>75.7</td>\n",
       "      <td>Male</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>1.29343</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>547.3</td>\n",
       "      <td>337</td>\n",
       "      <td>33.43</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.67</td>\n",
       "      <td>14.67</td>\n",
       "      <td>4.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>44.4444</td>\n",
       "      <td>12.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32253.0</td>\n",
       "      <td>34061.0</td>\n",
       "      <td>7068.0</td>\n",
       "      <td>1116634.0</td>\n",
       "      <td>4428.0</td>\n",
       "      <td>24789.0</td>\n",
       "      <td>21616.0</td>\n",
       "      <td>1640772.0</td>\n",
       "      <td>CN</td>\n",
       "      <td>-1.95295</td>\n",
       "      <td>-1.649320</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>132</td>\n",
       "      <td>bl</td>\n",
       "      <td>253</td>\n",
       "      <td>2005-11-29</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>13</td>\n",
       "      <td>Transportation Operation Sales</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5</td>\n",
       "      <td>18.67</td>\n",
       "      <td>25.67</td>\n",
       "      <td>7.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>83.3333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33012.0</td>\n",
       "      <td>39822.0</td>\n",
       "      <td>5342.0</td>\n",
       "      <td>927504.0</td>\n",
       "      <td>2276.0</td>\n",
       "      <td>17966.0</td>\n",
       "      <td>17802.0</td>\n",
       "      <td>1485836.0</td>\n",
       "      <td>MCI</td>\n",
       "      <td>-10.3423</td>\n",
       "      <td>-11.136500</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>133</td>\n",
       "      <td>bl</td>\n",
       "      <td>175</td>\n",
       "      <td>2005-10-06</td>\n",
       "      <td>75.3</td>\n",
       "      <td>Male</td>\n",
       "      <td>10</td>\n",
       "      <td>chartered accountant</td>\n",
       "      <td>Hisp/Latino</td>\n",
       "      <td>More than one</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>27.33</td>\n",
       "      <td>40.33</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>75.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59346.0</td>\n",
       "      <td>25699.0</td>\n",
       "      <td>6725.0</td>\n",
       "      <td>875793.0</td>\n",
       "      <td>2046.0</td>\n",
       "      <td>12067.0</td>\n",
       "      <td>15379.0</td>\n",
       "      <td>1353521.0</td>\n",
       "      <td>Dementia</td>\n",
       "      <td>-19.9104</td>\n",
       "      <td>-19.643100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>136</td>\n",
       "      <td>bl</td>\n",
       "      <td>164</td>\n",
       "      <td>2005-11-10</td>\n",
       "      <td>73.9</td>\n",
       "      <td>Female</td>\n",
       "      <td>12</td>\n",
       "      <td>Professor of Neurophysiology</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>1.11532</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>357.4</td>\n",
       "      <td>329.9</td>\n",
       "      <td>31.26</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.33</td>\n",
       "      <td>24.33</td>\n",
       "      <td>10.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32253.0</td>\n",
       "      <td>26819.0</td>\n",
       "      <td>5479.0</td>\n",
       "      <td>1033537.0</td>\n",
       "      <td>2674.0</td>\n",
       "      <td>16758.0</td>\n",
       "      <td>19736.0</td>\n",
       "      <td>1471182.0</td>\n",
       "      <td>Dementia</td>\n",
       "      <td>-13.917</td>\n",
       "      <td>-12.786000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>140</td>\n",
       "      <td>bl</td>\n",
       "      <td>175</td>\n",
       "      <td>2005-11-04</td>\n",
       "      <td>78.3</td>\n",
       "      <td>Female</td>\n",
       "      <td>12</td>\n",
       "      <td>legal secretary</td>\n",
       "      <td>Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>0</td>\n",
       "      <td>1.25096</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1582</td>\n",
       "      <td>203.6</td>\n",
       "      <td>16.68</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.33</td>\n",
       "      <td>8.33</td>\n",
       "      <td>4.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>36.3636</td>\n",
       "      <td>10.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59356.0</td>\n",
       "      <td>46281.0</td>\n",
       "      <td>6729.0</td>\n",
       "      <td>861745.0</td>\n",
       "      <td>3580.0</td>\n",
       "      <td>13781.0</td>\n",
       "      <td>17795.0</td>\n",
       "      <td>1269537.0</td>\n",
       "      <td>CN</td>\n",
       "      <td>-3.33317</td>\n",
       "      <td>-2.519980</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>141</td>\n",
       "      <td>bl</td>\n",
       "      <td>253</td>\n",
       "      <td>2005-10-18</td>\n",
       "      <td>80.5</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>Homemaker</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>19.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33053.0</td>\n",
       "      <td>33418.0</td>\n",
       "      <td>6731.0</td>\n",
       "      <td>942729.0</td>\n",
       "      <td>4307.0</td>\n",
       "      <td>14949.0</td>\n",
       "      <td>17272.0</td>\n",
       "      <td>1500997.0</td>\n",
       "      <td>CN</td>\n",
       "      <td>3.70528</td>\n",
       "      <td>2.999520</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>142</td>\n",
       "      <td>bl</td>\n",
       "      <td>164</td>\n",
       "      <td>2005-10-13</td>\n",
       "      <td>64.6</td>\n",
       "      <td>Male</td>\n",
       "      <td>9</td>\n",
       "      <td>Ombudsman</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>Black</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>1.38702</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>550.6</td>\n",
       "      <td>170.5</td>\n",
       "      <td>15.88</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.33</td>\n",
       "      <td>14.33</td>\n",
       "      <td>4.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>50.0000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32291.0</td>\n",
       "      <td>17310.0</td>\n",
       "      <td>7306.0</td>\n",
       "      <td>936538.0</td>\n",
       "      <td>3463.0</td>\n",
       "      <td>15931.0</td>\n",
       "      <td>17596.0</td>\n",
       "      <td>1351994.0</td>\n",
       "      <td>CN</td>\n",
       "      <td>-2.97098</td>\n",
       "      <td>-3.141490</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:19:56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13086</th>\n",
       "      <td>5392</td>\n",
       "      <td>bl</td>\n",
       "      <td>280</td>\n",
       "      <td>2013-08-15</td>\n",
       "      <td>67.3</td>\n",
       "      <td>Male</td>\n",
       "      <td>16</td>\n",
       "      <td>Physicist/Professor</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>1.31821</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.999911</td>\n",
       "      <td>&gt;1700</td>\n",
       "      <td>279.9</td>\n",
       "      <td>24.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.3333</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>414594.0</td>\n",
       "      <td>33689.0</td>\n",
       "      <td>9191.0</td>\n",
       "      <td>1114536.0</td>\n",
       "      <td>4761.0</td>\n",
       "      <td>19816.0</td>\n",
       "      <td>22405.0</td>\n",
       "      <td>1582954.0</td>\n",
       "      <td>CN</td>\n",
       "      <td>-1.30704</td>\n",
       "      <td>-0.588895</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:20:18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13090</th>\n",
       "      <td>5397</td>\n",
       "      <td>bl</td>\n",
       "      <td>189</td>\n",
       "      <td>2013-08-06</td>\n",
       "      <td>80.5</td>\n",
       "      <td>Male</td>\n",
       "      <td>20</td>\n",
       "      <td>special education instructional assistant</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>1.38876</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.975491</td>\n",
       "      <td>&gt;1700</td>\n",
       "      <td>314.6</td>\n",
       "      <td>28.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>38.4615</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>388440.0</td>\n",
       "      <td>54311.0</td>\n",
       "      <td>7895.0</td>\n",
       "      <td>1239304.0</td>\n",
       "      <td>3768.0</td>\n",
       "      <td>23273.0</td>\n",
       "      <td>23541.0</td>\n",
       "      <td>1775377.0</td>\n",
       "      <td>CN</td>\n",
       "      <td>-0.777792</td>\n",
       "      <td>-0.036629</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:20:18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13093</th>\n",
       "      <td>5398</td>\n",
       "      <td>bl</td>\n",
       "      <td>206</td>\n",
       "      <td>2013-09-04</td>\n",
       "      <td>69.6</td>\n",
       "      <td>Female</td>\n",
       "      <td>8</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>1.38561</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.234360</td>\n",
       "      <td>&gt;1700</td>\n",
       "      <td>305</td>\n",
       "      <td>25.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>63.6364</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>388011.0</td>\n",
       "      <td>24203.0</td>\n",
       "      <td>6041.0</td>\n",
       "      <td>957894.0</td>\n",
       "      <td>3380.0</td>\n",
       "      <td>17666.0</td>\n",
       "      <td>17810.0</td>\n",
       "      <td>1379247.0</td>\n",
       "      <td>CN</td>\n",
       "      <td>-3.15271</td>\n",
       "      <td>-1.608400</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:20:18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13096</th>\n",
       "      <td>5413</td>\n",
       "      <td>bl</td>\n",
       "      <td>206</td>\n",
       "      <td>2013-09-18</td>\n",
       "      <td>79.1</td>\n",
       "      <td>Female</td>\n",
       "      <td>16</td>\n",
       "      <td>homemaker</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>1.57952</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.234870</td>\n",
       "      <td>&gt;1700</td>\n",
       "      <td>268.4</td>\n",
       "      <td>26.11</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>26.6667</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>391045.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5643.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1476497.0</td>\n",
       "      <td>CN</td>\n",
       "      <td>1.04895</td>\n",
       "      <td>0.152593</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:20:18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13098</th>\n",
       "      <td>5414</td>\n",
       "      <td>bl</td>\n",
       "      <td>180</td>\n",
       "      <td>2013-09-25</td>\n",
       "      <td>80.5</td>\n",
       "      <td>Female</td>\n",
       "      <td>18</td>\n",
       "      <td>Accountant</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Never married</td>\n",
       "      <td>0</td>\n",
       "      <td>1.47256</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.041580</td>\n",
       "      <td>&gt;1700</td>\n",
       "      <td>300.1</td>\n",
       "      <td>26.59</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>23.0769</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>392141.0</td>\n",
       "      <td>47734.0</td>\n",
       "      <td>6990.0</td>\n",
       "      <td>994711.0</td>\n",
       "      <td>3436.0</td>\n",
       "      <td>19222.0</td>\n",
       "      <td>20816.0</td>\n",
       "      <td>1415018.0</td>\n",
       "      <td>CN</td>\n",
       "      <td>1.51224</td>\n",
       "      <td>0.814441</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:20:18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13123</th>\n",
       "      <td>716</td>\n",
       "      <td>bl</td>\n",
       "      <td>169</td>\n",
       "      <td>2006-07-18</td>\n",
       "      <td>78.4</td>\n",
       "      <td>Female</td>\n",
       "      <td>18</td>\n",
       "      <td>Dentist</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>1.05766</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>609.9</td>\n",
       "      <td>&gt;1300</td>\n",
       "      <td>&gt;120</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>21.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51496.0</td>\n",
       "      <td>19450.0</td>\n",
       "      <td>5482.0</td>\n",
       "      <td>900860.0</td>\n",
       "      <td>2488.0</td>\n",
       "      <td>18090.0</td>\n",
       "      <td>14702.0</td>\n",
       "      <td>1303081.0</td>\n",
       "      <td>MCI</td>\n",
       "      <td>-5.25167</td>\n",
       "      <td>-7.527730</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:20:01.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13127</th>\n",
       "      <td>818</td>\n",
       "      <td>bl</td>\n",
       "      <td>247</td>\n",
       "      <td>2006-08-11</td>\n",
       "      <td>76.4</td>\n",
       "      <td>Female</td>\n",
       "      <td>19</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>390.9</td>\n",
       "      <td>&lt;80</td>\n",
       "      <td>&lt;8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.33</td>\n",
       "      <td>11.33</td>\n",
       "      <td>4.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>75.0000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47289.0</td>\n",
       "      <td>41873.0</td>\n",
       "      <td>6983.0</td>\n",
       "      <td>900322.0</td>\n",
       "      <td>4492.0</td>\n",
       "      <td>17146.0</td>\n",
       "      <td>18454.0</td>\n",
       "      <td>1501798.0</td>\n",
       "      <td>CN</td>\n",
       "      <td>-1.47067</td>\n",
       "      <td>-2.423690</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:20:01.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13133</th>\n",
       "      <td>1002</td>\n",
       "      <td>bl</td>\n",
       "      <td>262</td>\n",
       "      <td>2006-10-05</td>\n",
       "      <td>75.6</td>\n",
       "      <td>Female</td>\n",
       "      <td>16</td>\n",
       "      <td>Teacher</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>Black</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>529.3</td>\n",
       "      <td>&lt;80</td>\n",
       "      <td>&lt;8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>30.7692</td>\n",
       "      <td>11.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>119249.0</td>\n",
       "      <td>32593.0</td>\n",
       "      <td>5264.0</td>\n",
       "      <td>746254.0</td>\n",
       "      <td>3298.0</td>\n",
       "      <td>12490.0</td>\n",
       "      <td>12819.0</td>\n",
       "      <td>1189742.0</td>\n",
       "      <td>CN</td>\n",
       "      <td>0.562994</td>\n",
       "      <td>-0.501791</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:20:03.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13134</th>\n",
       "      <td>1402</td>\n",
       "      <td>bl</td>\n",
       "      <td>166</td>\n",
       "      <td>2007-03-23</td>\n",
       "      <td>71.7</td>\n",
       "      <td>Female</td>\n",
       "      <td>14</td>\n",
       "      <td>unk</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>Never married</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>428.8</td>\n",
       "      <td>&lt;80</td>\n",
       "      <td>&lt;8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>50.0000</td>\n",
       "      <td>16.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62693.0</td>\n",
       "      <td>71358.0</td>\n",
       "      <td>6177.0</td>\n",
       "      <td>871254.0</td>\n",
       "      <td>4164.0</td>\n",
       "      <td>16819.0</td>\n",
       "      <td>15610.0</td>\n",
       "      <td>1417134.0</td>\n",
       "      <td>CN</td>\n",
       "      <td>1.75895</td>\n",
       "      <td>1.520130</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:20:06.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13141</th>\n",
       "      <td>4147</td>\n",
       "      <td>bl</td>\n",
       "      <td>184</td>\n",
       "      <td>2011-05-05</td>\n",
       "      <td>66.4</td>\n",
       "      <td>Male</td>\n",
       "      <td>20</td>\n",
       "      <td>Engineer</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>Black</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>1.32109</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.957524</td>\n",
       "      <td>690.2</td>\n",
       "      <td>81.54</td>\n",
       "      <td>&lt;8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>92.3077</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>232859.0</td>\n",
       "      <td>50944.0</td>\n",
       "      <td>7837.0</td>\n",
       "      <td>1144969.0</td>\n",
       "      <td>3690.0</td>\n",
       "      <td>20020.0</td>\n",
       "      <td>20722.0</td>\n",
       "      <td>1630489.0</td>\n",
       "      <td>CN</td>\n",
       "      <td>0.301273</td>\n",
       "      <td>1.071240</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-12-04 04:20:09.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1485 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        RID VISCODE  SITE    EXAMDATE   AGE PTGENDER  PTEDUCAT  \\\n",
       "0       128      bl   164  2005-09-08  74.2     Male        16   \n",
       "1       129      bl   164  2005-09-12  82.4     Male        18   \n",
       "5       130      bl   175  2005-11-08  67.1     Male        10   \n",
       "10      131      bl   164  2005-09-07  75.7     Male        16   \n",
       "15      132      bl   253  2005-11-29  80.0   Female        13   \n",
       "20      133      bl   175  2005-10-06  75.3     Male        10   \n",
       "22      136      bl   164  2005-11-10  73.9   Female        12   \n",
       "26      140      bl   175  2005-11-04  78.3   Female        12   \n",
       "31      141      bl   253  2005-10-18  80.5     Male        18   \n",
       "34      142      bl   164  2005-10-13  64.6     Male         9   \n",
       "...     ...     ...   ...         ...   ...      ...       ...   \n",
       "13086  5392      bl   280  2013-08-15  67.3     Male        16   \n",
       "13090  5397      bl   189  2013-08-06  80.5     Male        20   \n",
       "13093  5398      bl   206  2013-09-04  69.6   Female         8   \n",
       "13096  5413      bl   206  2013-09-18  79.1   Female        16   \n",
       "13098  5414      bl   180  2013-09-25  80.5   Female        18   \n",
       "13123   716      bl   169  2006-07-18  78.4   Female        18   \n",
       "13127   818      bl   247  2006-08-11  76.4   Female        19   \n",
       "13133  1002      bl   262  2006-10-05  75.6   Female        16   \n",
       "13134  1402      bl   166  2007-03-23  71.7   Female        14   \n",
       "13141  4147      bl   184  2011-05-05  66.4     Male        20   \n",
       "\n",
       "                                            WORK         PTETHCAT  \\\n",
       "0                    technical writer and editor  Not Hisp/Latino   \n",
       "1                                      Secretary  Not Hisp/Latino   \n",
       "5                                            NaN      Hisp/Latino   \n",
       "10                                           NaN  Not Hisp/Latino   \n",
       "15                Transportation Operation Sales  Not Hisp/Latino   \n",
       "20                          chartered accountant      Hisp/Latino   \n",
       "22                  Professor of Neurophysiology  Not Hisp/Latino   \n",
       "26                               legal secretary      Hisp/Latino   \n",
       "31                                     Homemaker  Not Hisp/Latino   \n",
       "34                                     Ombudsman  Not Hisp/Latino   \n",
       "...                                          ...              ...   \n",
       "13086                        Physicist/Professor  Not Hisp/Latino   \n",
       "13090  special education instructional assistant  Not Hisp/Latino   \n",
       "13093                                      Sales  Not Hisp/Latino   \n",
       "13096                                  homemaker  Not Hisp/Latino   \n",
       "13098                                 Accountant  Not Hisp/Latino   \n",
       "13123                                    Dentist  Not Hisp/Latino   \n",
       "13127                                   Clerical  Not Hisp/Latino   \n",
       "13133                                    Teacher  Not Hisp/Latino   \n",
       "13134                                        unk  Not Hisp/Latino   \n",
       "13141                                   Engineer  Not Hisp/Latino   \n",
       "\n",
       "            PTRACCAT        PTMARRY APOE4      FDG  PIB      AV45  ABETA  \\\n",
       "0              White        Married     0  1.36665  NaN       NaN    NaN   \n",
       "1              White        Married     1  1.08355  NaN       NaN  741.5   \n",
       "5              White        Married     0      NaN  NaN       NaN   1501   \n",
       "10             White        Married     0  1.29343  NaN       NaN  547.3   \n",
       "15             White        Married     0      NaN  NaN       NaN    NaN   \n",
       "20     More than one        Married     1      NaN  NaN       NaN    NaN   \n",
       "22             White        Married     1  1.11532  NaN       NaN  357.4   \n",
       "26             White       Divorced     0  1.25096  NaN       NaN   1582   \n",
       "31             White        Married     1      NaN  NaN       NaN    NaN   \n",
       "34             Black        Married     1  1.38702  NaN       NaN  550.6   \n",
       "...              ...            ...   ...      ...  ...       ...    ...   \n",
       "13086          White        Married     0  1.31821  NaN  0.999911  >1700   \n",
       "13090          White        Married     0  1.38876  NaN  0.975491  >1700   \n",
       "13093          White        Married     0  1.38561  NaN  1.234360  >1700   \n",
       "13096          White        Married     0  1.57952  NaN  1.234870  >1700   \n",
       "13098          White  Never married     0  1.47256  NaN  1.041580  >1700   \n",
       "13123          White        Married     1  1.05766  NaN       NaN  609.9   \n",
       "13127          White        Widowed     1      NaN  NaN       NaN  390.9   \n",
       "13133          Black        Married     0      NaN  NaN       NaN  529.3   \n",
       "13134          White  Never married     0      NaN  NaN       NaN  428.8   \n",
       "13141          Black        Married     0  1.32109  NaN  0.957524  690.2   \n",
       "\n",
       "         TAU   PTAU  CDRSB  ADAS11  ADAS13  ADASQ4  MMSE  RAVLT_immediate  \\\n",
       "0        NaN    NaN    0.0   10.67   18.67     5.0  28.0             44.0   \n",
       "1      239.7  22.83    4.5   22.00   31.00     8.0  20.0             22.0   \n",
       "5      153.1  13.29    1.0   14.33   21.33     6.0  27.0             37.0   \n",
       "10       337  33.43    0.0    8.67   14.67     4.0  29.0             37.0   \n",
       "15       NaN    NaN    0.5   18.67   25.67     7.0  25.0             30.0   \n",
       "20       NaN    NaN    6.0   27.33   40.33    10.0  20.0             17.0   \n",
       "22     329.9  31.26    5.0   12.33   24.33    10.0  24.0             20.0   \n",
       "26     203.6  16.68    0.0    4.33    8.33     4.0  29.0             45.0   \n",
       "31       NaN    NaN    0.0    7.00    9.00     2.0  29.0             50.0   \n",
       "34     170.5  15.88    0.0   10.33   14.33     4.0  28.0             40.0   \n",
       "...      ...    ...    ...     ...     ...     ...   ...              ...   \n",
       "13086  279.9  24.28    0.0    3.00    5.00     2.0  28.0             51.0   \n",
       "13090  314.6  28.56    0.0    4.00    8.00     4.0  30.0             45.0   \n",
       "13093    305  25.05    0.0    5.00   10.00     5.0  28.0             42.0   \n",
       "13096  268.4  26.11    0.5    3.00    4.00     1.0  29.0             50.0   \n",
       "13098  300.1  26.59    0.0    6.00    8.00     2.0  30.0             53.0   \n",
       "13123  >1300   >120    3.0   15.00   21.00     5.0  27.0             23.0   \n",
       "13127    <80     <8    0.0    7.33   11.33     4.0  29.0             42.0   \n",
       "13133    <80     <8    0.5    5.00    8.00     2.0  30.0             50.0   \n",
       "13134    <80     <8    0.0    8.00   10.00     2.0  30.0             29.0   \n",
       "13141  81.54     <8    0.0    2.00    4.00     2.0  29.0             58.0   \n",
       "\n",
       "       RAVLT_learning  RAVLT_forgetting  RAVLT_perc_forgetting  LDELTOTAL  \\\n",
       "0                 4.0               6.0                54.5455       10.0   \n",
       "1                 1.0               4.0               100.0000        2.0   \n",
       "5                 7.0               4.0                36.3636        4.0   \n",
       "10                4.0               4.0                44.4444       12.0   \n",
       "15                1.0               5.0                83.3333        3.0   \n",
       "20                2.0               3.0                75.0000        0.0   \n",
       "22                2.0               5.0               100.0000        2.0   \n",
       "26                6.0               4.0                36.3636       10.0   \n",
       "31                5.0               3.0                25.0000       19.0   \n",
       "34                8.0               6.0                50.0000        9.0   \n",
       "...               ...               ...                    ...        ...   \n",
       "13086             9.0               2.0                13.3333       12.0   \n",
       "13090             7.0               5.0                38.4615       11.0   \n",
       "13093             5.0               7.0                63.6364       13.0   \n",
       "13096            10.0               4.0                26.6667       13.0   \n",
       "13098             6.0               3.0                23.0769       13.0   \n",
       "13123             2.0               6.0               100.0000        2.0   \n",
       "13127             2.0               6.0                75.0000       10.0   \n",
       "13133             4.0               4.0                30.7692       11.0   \n",
       "13134             4.0               4.0                50.0000       16.0   \n",
       "13141             8.0              12.0                92.3077       13.0   \n",
       "\n",
       "       DIGITSCOR  TRABSCOR   FAQ  MOCA  FLDSTRENG  IMAGEUID  Ventricles  \\\n",
       "0           34.0     112.0   0.0   NaN        NaN   35479.0    118226.0   \n",
       "1           25.0     148.0  10.0   NaN        NaN   32241.0     84599.0   \n",
       "5           25.0     271.0   0.0   NaN        NaN   64621.0     39604.0   \n",
       "10          38.0      90.0   0.0   NaN        NaN   32253.0     34061.0   \n",
       "15          34.0     168.0   0.0   NaN        NaN   33012.0     39822.0   \n",
       "20           9.0     300.0  17.0   NaN        NaN   59346.0     25699.0   \n",
       "22          27.0     100.0  11.0   NaN        NaN   32253.0     26819.0   \n",
       "26          30.0     101.0   0.0   NaN        NaN   59356.0     46281.0   \n",
       "31          63.0      49.0   0.0   NaN        NaN   33053.0     33418.0   \n",
       "34          46.0      79.0   0.0   NaN        NaN   32291.0     17310.0   \n",
       "...          ...       ...   ...   ...        ...       ...         ...   \n",
       "13086        NaN      63.0   0.0  26.0        NaN  414594.0     33689.0   \n",
       "13090        NaN      59.0   0.0  28.0        NaN  388440.0     54311.0   \n",
       "13093        NaN      54.0   0.0  27.0        NaN  388011.0     24203.0   \n",
       "13096        NaN      97.0   0.0  28.0        NaN  391045.0         NaN   \n",
       "13098        NaN      85.0   0.0  29.0        NaN  392141.0     47734.0   \n",
       "13123       59.0     112.0   8.0   NaN        NaN   51496.0     19450.0   \n",
       "13127       49.0      97.0   0.0   NaN        NaN   47289.0     41873.0   \n",
       "13133       46.0     115.0   0.0   NaN        NaN  119249.0     32593.0   \n",
       "13134       43.0      92.0   0.0   NaN        NaN   62693.0     71358.0   \n",
       "13141        NaN      52.0   0.0  26.0        NaN  232859.0     50944.0   \n",
       "\n",
       "       Hippocampus  WholeBrain  Entorhinal  Fusiform  MidTemp        ICV  \\\n",
       "0           8340.0   1229736.0      4182.0   16562.0  27930.0  1984658.0   \n",
       "1           5318.0   1129825.0      1790.0   15507.0  18425.0  1920689.0   \n",
       "5           6870.0   1154979.0      3981.0   19035.0  19614.0  1679439.0   \n",
       "10          7068.0   1116634.0      4428.0   24789.0  21616.0  1640772.0   \n",
       "15          5342.0    927504.0      2276.0   17966.0  17802.0  1485836.0   \n",
       "20          6725.0    875793.0      2046.0   12067.0  15379.0  1353521.0   \n",
       "22          5479.0   1033537.0      2674.0   16758.0  19736.0  1471182.0   \n",
       "26          6729.0    861745.0      3580.0   13781.0  17795.0  1269537.0   \n",
       "31          6731.0    942729.0      4307.0   14949.0  17272.0  1500997.0   \n",
       "34          7306.0    936538.0      3463.0   15931.0  17596.0  1351994.0   \n",
       "...            ...         ...         ...       ...      ...        ...   \n",
       "13086       9191.0   1114536.0      4761.0   19816.0  22405.0  1582954.0   \n",
       "13090       7895.0   1239304.0      3768.0   23273.0  23541.0  1775377.0   \n",
       "13093       6041.0    957894.0      3380.0   17666.0  17810.0  1379247.0   \n",
       "13096       5643.0         NaN         NaN       NaN      NaN  1476497.0   \n",
       "13098       6990.0    994711.0      3436.0   19222.0  20816.0  1415018.0   \n",
       "13123       5482.0    900860.0      2488.0   18090.0  14702.0  1303081.0   \n",
       "13127       6983.0    900322.0      4492.0   17146.0  18454.0  1501798.0   \n",
       "13133       5264.0    746254.0      3298.0   12490.0  12819.0  1189742.0   \n",
       "13134       6177.0    871254.0      4164.0   16819.0  15610.0  1417134.0   \n",
       "13141       7837.0   1144969.0      3690.0   20020.0  20722.0  1630489.0   \n",
       "\n",
       "             DX mPACCdigit  mPACCtrailsB  LDELTOTAL_BL  Month    M  \\\n",
       "0            CN   -4.41005     -4.235450          10.0    0.0  0.0   \n",
       "1      Dementia   -16.6244    -16.233200           2.0    0.0  0.0   \n",
       "5           MCI   -8.54931     -9.606640           4.0    0.0  0.0   \n",
       "10           CN   -1.95295     -1.649320          12.0    0.0  0.0   \n",
       "15          MCI   -10.3423    -11.136500           3.0    0.0  0.0   \n",
       "20     Dementia   -19.9104    -19.643100           0.0    0.0  0.0   \n",
       "22     Dementia    -13.917    -12.786000           2.0    0.0  0.0   \n",
       "26           CN   -3.33317     -2.519980          10.0    0.0  0.0   \n",
       "31           CN    3.70528      2.999520          19.0    0.0  0.0   \n",
       "34           CN   -2.97098     -3.141490           9.0    0.0  0.0   \n",
       "...         ...        ...           ...           ...    ...  ...   \n",
       "13086        CN   -1.30704     -0.588895          12.0    0.0  0.0   \n",
       "13090        CN  -0.777792     -0.036629          11.0    0.0  0.0   \n",
       "13093        CN   -3.15271     -1.608400          13.0    0.0  0.0   \n",
       "13096        CN    1.04895      0.152593          13.0    0.0  0.0   \n",
       "13098        CN    1.51224      0.814441          13.0    0.0  0.0   \n",
       "13123       MCI   -5.25167     -7.527730           2.0    0.0  0.0   \n",
       "13127        CN   -1.47067     -2.423690          10.0    0.0  0.0   \n",
       "13133        CN   0.562994     -0.501791          11.0    0.0  0.0   \n",
       "13134        CN    1.75895      1.520130          16.0    0.0  0.0   \n",
       "13141        CN   0.301273      1.071240          13.0    0.0  0.0   \n",
       "\n",
       "                update_stamp  \n",
       "0      2019-12-04 04:19:56.0  \n",
       "1      2019-12-04 04:19:56.0  \n",
       "5      2019-12-04 04:19:56.0  \n",
       "10     2019-12-04 04:19:56.0  \n",
       "15     2019-12-04 04:19:56.0  \n",
       "20     2019-12-04 04:19:56.0  \n",
       "22     2019-12-04 04:19:56.0  \n",
       "26     2019-12-04 04:19:56.0  \n",
       "31     2019-12-04 04:19:56.0  \n",
       "34     2019-12-04 04:19:56.0  \n",
       "...                      ...  \n",
       "13086  2019-12-04 04:20:18.0  \n",
       "13090  2019-12-04 04:20:18.0  \n",
       "13093  2019-12-04 04:20:18.0  \n",
       "13096  2019-12-04 04:20:18.0  \n",
       "13098  2019-12-04 04:20:18.0  \n",
       "13123  2019-12-04 04:20:01.0  \n",
       "13127  2019-12-04 04:20:01.0  \n",
       "13133  2019-12-04 04:20:03.0  \n",
       "13134  2019-12-04 04:20:06.0  \n",
       "13141  2019-12-04 04:20:09.0  \n",
       "\n",
       "[1485 rows x 48 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_reduced.dropna(axis=0, subset=['DX', 'MMSE', 'Hippocampus'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise**\n",
    "\n",
    "How many patients contain values in the diagnosis, MMSE, and Hippocampus volume column?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary> > Solution</summary>\n",
    "    \n",
    "```python\n",
    "data_reduced.shape\n",
    "data_reduced.dropna(axis=0, subset=['DX', 'MMSE', 'Hippocampus']).shape\n",
    "```\n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.9 Write data\n",
    "\n",
    "Save our reduced DataFrame `data_reduced` to a csv file next to the original csv file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_reduced.to_csv('data/alzheimers_disease_reduced.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Discussion\n",
    "\n",
    "With the help of **Python's *numpy* and *pandas* packages**, we performed basic **data science** tasks on data for the early detection and tracking of **Alzheimer's disease**: \n",
    "\n",
    "- Read in a csv file\n",
    "- Look a basic descriptive statistics of the dataset\n",
    "- Selected columns and rows of importance\n",
    "- Saved the reduced dataset for later sessions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Quiz\n",
    "\n",
    "- Name typical steps during data cleaning/preparation.\n",
    "- Name challenges/artifacts you can face when working with a dataset.\n",
    "- Name important measures in descriptive statistics."
   ]
  }
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