neural_net_test_notebook_in_a_conda_env.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# https://chrisalbon.com/\n",
    "Huge amount of notebooks with technical notes for python, data science, and deep learning "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We are importing and also printing out the versions of these main libraries:\n",
      "scikit-learn: 0.19.1\n",
      "numpy: 1.14.2\n",
      "pandas: 0.23.0\n",
      "download me matplotlib: matplotlib.__version__\n",
      "keras: 2.1.5\n",
      "tensorflow: 1.7.0\n"
     ]
    }
   ],
   "source": [
    "print('We are importing and also printing out the versions of these main libraries:')\n",
    "import sklearn\n",
    "print('scikit-learn: ' + sklearn.__version__)\n",
    "import numpy as np\n",
    "print('numpy: '+numpy.__version__)\n",
    "import pandas as pd\n",
    "print('pandas: ' + pandas.__version__)\n",
    "import matplotlib.pyplot as plt\n",
    "print('download me matplotlib: ' + 'matplotlib.__version__')\n",
    "import keras\n",
    "print('keras: ' + keras.__version__)\n",
    "import tensorflow\n",
    "print('tensorflow: ' + tensorflow.__version__) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot test just to make sure matplotlib works bc it was causing goofy errors before in the conda env"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1,2,3,4])\n",
    "plt.ylabel('some numbers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://chrisalbon.com/python/data_wrangling/pandas_dataframe_importing_csv/\n",
    "\n",
    "The following cells show how to use a little bit of pandas as a way to import and export data frames in various ways, fill in missing data, and remove commas from large numbers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a data frame that we will be importing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>age</th>\n",
       "      <th>preTestScore</th>\n",
       "      <th>postTestScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jason</td>\n",
       "      <td>Miller</td>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "      <td>25,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Molly</td>\n",
       "      <td>Jacobson</td>\n",
       "      <td>52</td>\n",
       "      <td>24</td>\n",
       "      <td>94,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tina</td>\n",
       "      <td>.</td>\n",
       "      <td>36</td>\n",
       "      <td>31</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Milner</td>\n",
       "      <td>24</td>\n",
       "      <td>.</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Amy</td>\n",
       "      <td>Cooze</td>\n",
       "      <td>73</td>\n",
       "      <td>.</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  first_name last_name  age preTestScore postTestScore\n",
       "0      Jason    Miller   42            4        25,000\n",
       "1      Molly  Jacobson   52           24        94,000\n",
       "2       Tina         .   36           31            57\n",
       "3       Jake    Milner   24            .            62\n",
       "4        Amy     Cooze   73            .            70"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = {'first_name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], \n",
    "        'last_name': ['Miller', 'Jacobson', \".\", 'Milner', 'Cooze'], \n",
    "        'age': [42, 52, 36, 24, 73], \n",
    "        'preTestScore': [4, 24, 31, \".\", \".\"],\n",
    "        'postTestScore': [\"25,000\", \"94,000\", 57, 62, 70]}\n",
    "df = pd.DataFrame(raw_data, columns = ['first_name', 'last_name', 'age', 'preTestScore', 'postTestScore'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save The data frame as a CSV in the working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "note that the backslashes create an absolute path, if you are using / than you are getting a variable path it also need the forwardslash in front of the first folder name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('\\pandas_dataframe_importing_csv\\example.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the data frame from the folder we are running the notebook in (currently a very messy virutual env within anaconda)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>age</th>\n",
       "      <th>preTestScore</th>\n",
       "      <th>postTestScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Jason</td>\n",
       "      <td>Miller</td>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "      <td>25,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Molly</td>\n",
       "      <td>Jacobson</td>\n",
       "      <td>52</td>\n",
       "      <td>24</td>\n",
       "      <td>94,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Tina</td>\n",
       "      <td>.</td>\n",
       "      <td>36</td>\n",
       "      <td>31</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Jake</td>\n",
       "      <td>Milner</td>\n",
       "      <td>24</td>\n",
       "      <td>.</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Amy</td>\n",
       "      <td>Cooze</td>\n",
       "      <td>73</td>\n",
       "      <td>.</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1 first_name last_name  age preTestScore  \\\n",
       "0           0             0      Jason    Miller   42            4   \n",
       "1           1             1      Molly  Jacobson   52           24   \n",
       "2           2             2       Tina         .   36           31   \n",
       "3           3             3       Jake    Milner   24            .   \n",
       "4           4             4        Amy     Cooze   73            .   \n",
       "\n",
       "  postTestScore  \n",
       "0        25,000  \n",
       "1        94,000  \n",
       "2            57  \n",
       "3            62  \n",
       "4            70  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('\\pandas_dataframe_importing_csv\\example.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load a CSV but with no headers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
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       "      <th>7</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>Unnamed: 0.1</td>\n",
       "      <td>first_name</td>\n",
       "      <td>last_name</td>\n",
       "      <td>age</td>\n",
       "      <td>preTestScore</td>\n",
       "      <td>postTestScore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Jason</td>\n",
       "      <td>Miller</td>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "      <td>25,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Molly</td>\n",
       "      <td>Jacobson</td>\n",
       "      <td>52</td>\n",
       "      <td>24</td>\n",
       "      <td>94,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Tina</td>\n",
       "      <td>.</td>\n",
       "      <td>36</td>\n",
       "      <td>31</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Jake</td>\n",
       "      <td>Milner</td>\n",
       "      <td>24</td>\n",
       "      <td>.</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Amy</td>\n",
       "      <td>Cooze</td>\n",
       "      <td>73</td>\n",
       "      <td>.</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0           1             2           3          4    5             6  \\\n",
       "0  NaN  Unnamed: 0  Unnamed: 0.1  first_name  last_name  age  preTestScore   \n",
       "1  0.0           0             0       Jason     Miller   42             4   \n",
       "2  1.0           1             1       Molly   Jacobson   52            24   \n",
       "3  2.0           2             2        Tina          .   36            31   \n",
       "4  3.0           3             3        Jake     Milner   24             .   \n",
       "5  4.0           4             4         Amy      Cooze   73             .   \n",
       "\n",
       "               7  \n",
       "0  postTestScore  \n",
       "1         25,000  \n",
       "2         94,000  \n",
       "3             57  \n",
       "4             62  \n",
       "5             70  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('\\pandas_dataframe_importing_csv\\example.csv', header=None)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load a CSV while specifying column names "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>UID</th>\n",
       "      <th>First Name</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Pre-Test Score</th>\n",
       "      <th>Post-Test Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <td>Unnamed: 0.1</td>\n",
       "      <td>first_name</td>\n",
       "      <td>last_name</td>\n",
       "      <td>age</td>\n",
       "      <td>preTestScore</td>\n",
       "      <td>postTestScore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Jason</td>\n",
       "      <td>Miller</td>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "      <td>25,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Molly</td>\n",
       "      <td>Jacobson</td>\n",
       "      <td>52</td>\n",
       "      <td>24</td>\n",
       "      <td>94,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Tina</td>\n",
       "      <td>.</td>\n",
       "      <td>36</td>\n",
       "      <td>31</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Jake</td>\n",
       "      <td>Milner</td>\n",
       "      <td>24</td>\n",
       "      <td>.</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Amy</td>\n",
       "      <td>Cooze</td>\n",
       "      <td>73</td>\n",
       "      <td>.</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         UID  First Name  Last Name  Age Pre-Test Score  \\\n",
       "NaN Unnamed: 0  Unnamed: 0.1  first_name  last_name  age   preTestScore   \n",
       "0.0 0                      0       Jason     Miller   42              4   \n",
       "1.0 1                      1       Molly   Jacobson   52             24   \n",
       "2.0 2                      2        Tina          .   36             31   \n",
       "3.0 3                      3        Jake     Milner   24              .   \n",
       "4.0 4                      4         Amy      Cooze   73              .   \n",
       "\n",
       "               Post-Test Score  \n",
       "NaN Unnamed: 0   postTestScore  \n",
       "0.0 0                   25,000  \n",
       "1.0 1                   94,000  \n",
       "2.0 2                       57  \n",
       "3.0 3                       62  \n",
       "4.0 4                       70  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('\\pandas_dataframe_importing_csv\\example.csv', names=['UID', 'First Name', 'Last Name', 'Age', 'Pre-Test Score', 'Post-Test Score'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "load a csv while setting the column names to UID\n",
    "Should be this:\n",
    "\tFirst Name\tLast Name\tAge\tPre-Test Score\tPost-Test Score\n",
    "UID\t\t\t\t\t\n",
    "NaN\tfirst_name\tlast_name\tage\tpreTestScore\tpostTestScore\n",
    "0.0\tJason\tMiller\t42\t4\t25,000\n",
    "1.0\tMolly\tJacobson\t52\t24\t94,000\n",
    "2.0\tTina\t.\t36\t31\t57\n",
    "3.0\tJake\tMilner\t24\t.\t62\n",
    "4.0\tAmy\tCooze\t73\t.\t70\n",
    "\n",
    "Error for KeyError :'UID'\n",
    "https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html\n",
    "index_col : int or sequence or False, default None\n",
    "\n",
    "Column to use as the row labels of the DataFrame. If a sequence is given, a MultiIndex is used. If you have a malformed file with delimiters at the end of each line, you might consider index_col=False to force pandas to _not_ use the first column as the index (row names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'False'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-78-df26469b5ce3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\pandas_dataframe_importing_csv\\example.csv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex_col\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'False'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'UID'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'First Name'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Last Name'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Age'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Pre-Test Score'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Post-Test Score'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/my_env/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, doublequote, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    676\u001b[0m                     skip_blank_lines=skip_blank_lines)\n\u001b[1;32m    677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 678\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    679\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    680\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/my_env/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    444\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    445\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 446\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    447\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    448\u001b[0m         \u001b[0mparser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/my_env/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m   1034\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'skipfooter not supported for iteration'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1035\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1036\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1037\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1038\u001b[0m         \u001b[0;31m# May alter columns / col_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/my_env/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m   1884\u001b[0m                     \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1885\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1886\u001b[0;31m                     \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex_col\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1887\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1888\u001b[0m                 values = self._maybe_parse_dates(values, i,\n",
      "\u001b[0;31mKeyError\u001b[0m: 'False'"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('\\pandas_dataframe_importing_csv\\example.csv', index_col='UID', names=['UID', 'First Name', 'Last Name', 'Age', 'Pre-Test Score', 'Post-Test Score'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading an index column while setting the first and last names\n",
    "should look like this:\n",
    "\t\tUID\tAge\tPre-Test Score\tPost-Test Score\n",
    "First Name\tLast Name\t\t\t\t\n",
    "first_name\tlast_name\tNaN\tage\tpreTestScore\tpostTestScore\n",
    "Jason\tMiller\t0.0\t42\t4\t25,000\n",
    "Molly\tJacobson\t1.0\t52\t24\t94,000\n",
    "Tina\t.\t2.0\t36\t31\t57\n",
    "Jake\tMilner\t3.0\t24\t.\t62\n",
    "Amy\tCooze\t4.0\t73\t.\t70"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'First Name'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-57-594a42649f35>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\pandas_dataframe_importing_csv\\example.csv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex_col\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'First Name'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Last Name'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'UID'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'First Name'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Last Name'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Age'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Pre-Test Score'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Post-Test Score'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/my_env/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, doublequote, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    676\u001b[0m                     skip_blank_lines=skip_blank_lines)\n\u001b[1;32m    677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 678\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    679\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    680\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/my_env/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    444\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    445\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 446\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    447\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    448\u001b[0m         \u001b[0mparser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/my_env/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m   1034\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'skipfooter not supported for iteration'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1035\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1036\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1037\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1038\u001b[0m         \u001b[0;31m# May alter columns / col_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/my_env/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m   1884\u001b[0m                     \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1885\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1886\u001b[0;31m                     \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex_col\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1887\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1888\u001b[0m                 values = self._maybe_parse_dates(values, i,\n",
      "\u001b[0;31mKeyError\u001b[0m: 'First Name'"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('\\pandas_dataframe_importing_csv\\example.csv', index_col=['First Name', 'Last Name'], names=['UID', 'First Name', 'Last Name', 'Age', 'Pre-Test Score', 'Post-Test Score'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load a CSV while specifying \".\" as a missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>Unnamed: 0.1.1</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>age</th>\n",
       "      <th>preTestScore</th>\n",
       "      <th>postTestScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1  Unnamed: 0.1.1  first_name  last_name    age  \\\n",
       "0       False         False           False       False      False  False   \n",
       "1       False         False           False       False      False  False   \n",
       "2       False         False           False       False       True  False   \n",
       "3       False         False           False       False      False  False   \n",
       "4       False         False           False       False      False  False   \n",
       "\n",
       "   preTestScore  postTestScore  \n",
       "0         False          False  \n",
       "1         False          False  \n",
       "2         False          False  \n",
       "3          True          False  \n",
       "4          True          False  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('\\pandas_dataframe_importing_csv\\example.csv', na_values=['.'])\n",
    "pd.isnull(df) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load a csv while specifying “.” and “NA” as missing values in the Last Name column and “.” as missing values in Pre-Test Score column note the dictionary keys and the lists of lists withing it all set to a variable and then assigned to sentinels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentinels = {'Last Name': ['.', 'NA'], 'Pre-Test Score': ['.']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>Unnamed: 0.1.1</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>age</th>\n",
       "      <th>preTestScore</th>\n",
       "      <th>postTestScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Jason</td>\n",
       "      <td>Miller</td>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "      <td>25,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Molly</td>\n",
       "      <td>Jacobson</td>\n",
       "      <td>52</td>\n",
       "      <td>24</td>\n",
       "      <td>94,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Tina</td>\n",
       "      <td>.</td>\n",
       "      <td>36</td>\n",
       "      <td>31</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Jake</td>\n",
       "      <td>Milner</td>\n",
       "      <td>24</td>\n",
       "      <td>.</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Amy</td>\n",
       "      <td>Cooze</td>\n",
       "      <td>73</td>\n",
       "      <td>.</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1  Unnamed: 0.1.1 first_name last_name  age  \\\n",
       "0           0             0               0      Jason    Miller   42   \n",
       "1           1             1               1      Molly  Jacobson   52   \n",
       "2           2             2               2       Tina         .   36   \n",
       "3           3             3               3       Jake    Milner   24   \n",
       "4           4             4               4        Amy     Cooze   73   \n",
       "\n",
       "  preTestScore postTestScore  \n",
       "0            4        25,000  \n",
       "1           24        94,000  \n",
       "2           31            57  \n",
       "3            .            62  \n",
       "4            .            70  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('\\pandas_dataframe_importing_csv\\example.csv', na_values=sentinels)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load a CSV while skipping the top 3 rows (aka 0,1,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2</th>\n",
       "      <th>2.1</th>\n",
       "      <th>2.2</th>\n",
       "      <th>Tina</th>\n",
       "      <th>.</th>\n",
       "      <th>36</th>\n",
       "      <th>31</th>\n",
       "      <th>57</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Jake</td>\n",
       "      <td>Milner</td>\n",
       "      <td>24</td>\n",
       "      <td>.</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Amy</td>\n",
       "      <td>Cooze</td>\n",
       "      <td>73</td>\n",
       "      <td>.</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   2  2.1  2.2  Tina       .  36 31  57\n",
       "0  3    3    3  Jake  Milner  24  .  62\n",
       "1  4    4    4   Amy   Cooze  73  .  70"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('\\pandas_dataframe_importing_csv\\example.csv', na_values=sentinels, skiprows=3)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load a csv while interpreting “,” in strings around numbers as thousands seperators aka removing the comma in large numbers to that they don't mess up the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>Unnamed: 0.1.1</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>age</th>\n",
       "      <th>preTestScore</th>\n",
       "      <th>postTestScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Jason</td>\n",
       "      <td>Miller</td>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "      <td>25000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Molly</td>\n",
       "      <td>Jacobson</td>\n",
       "      <td>52</td>\n",
       "      <td>24</td>\n",
       "      <td>94000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Tina</td>\n",
       "      <td>.</td>\n",
       "      <td>36</td>\n",
       "      <td>31</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Jake</td>\n",
       "      <td>Milner</td>\n",
       "      <td>24</td>\n",
       "      <td>.</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Amy</td>\n",
       "      <td>Cooze</td>\n",
       "      <td>73</td>\n",
       "      <td>.</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1  Unnamed: 0.1.1 first_name last_name  age  \\\n",
       "0           0             0               0      Jason    Miller   42   \n",
       "1           1             1               1      Molly  Jacobson   52   \n",
       "2           2             2               2       Tina         .   36   \n",
       "3           3             3               3       Jake    Milner   24   \n",
       "4           4             4               4        Amy     Cooze   73   \n",
       "\n",
       "  preTestScore  postTestScore  \n",
       "0            4          25000  \n",
       "1           24          94000  \n",
       "2           31             57  \n",
       "3            .             62  \n",
       "4            .             70  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('\\pandas_dataframe_importing_csv\\example.csv', thousands=',')\n",
    "df"
   ]
  }
 ],
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