618_02_Pandas_Intro.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "# SI 618: Data Manipulation and Analysis\n",
    "## 02 - Introduction to pandas\n",
    "### Dr. Chris Teplovs, School of Information, University of Michigan\n",
    "<small><a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\"><img alt=\"Creative Commons License\" style=\"border-width:0\" src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png\" /></a>This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "id": "6xlxDwAkVZ6g"
   },
   "source": [
    "\n",
    "## Objectives:\n",
    "* Know how to manipulate Series and DataFrame\n",
    "* Draw a random sample of data\n",
    "* Select subset of data using boolean masking\n",
    "* Compute descriptive and summary statistics\n",
    "* Discretize continuous variables\n",
    "* Sort Series/DataFrame by index or column\n",
    "* Group data and calculate aggregate statistics\n",
    "* Use vectorized string methods\n",
    "* Make basic plots (scatter plot, histogram, bar chart, etc)\n",
    "\n",
    "## Submission Instructions:\n",
    "Please turn in this Jupyter notebook file (both .ipynb and .html formats) on Canvas before you leave the classroom. Try to get through as much as you can."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yzYmZ_ZRVZ6h"
   },
   "source": [
    "### IMPORTANT: Replace ```?``` in the following code with your uniqname."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NJ-VtIuoVZ6i"
   },
   "outputs": [],
   "source": [
    "MY_UNIQNAME = 'xinyexu'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-YzZjtKXVZ8D"
   },
   "source": [
    "## Preface\n",
    "This lab consists of Part 0, Part 1 and Part 2. Part 0 is an introduction to some Pandas basics. There are no points assocaited with Part 0 but we will be covering it in class. Part 1 is based on a survey dataset and Part 2 uses movies and cast data. We will guide you through Part 1, and let you work on Part 2 on your own. \n",
    "\n",
    "**Question 0 is worth 2 points.  Questions 1-12 are worth 1 point each.  Questions 13-23 are worth 2 points each.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 0 (2 points, 0.5 points each): \n",
    "What is the output of pandas.DataFrame.describe()? (Chapter 5)\n",
    "\n",
    "    put the basic summary of the dataframe: count, min, max, mean, std, some quantiles.\n",
    "\n",
    "What is the output of value_counts() work? (Chapter 5)\n",
    "\n",
    "    Return counts of each unique values. And it will be in descending order so that the first element is the most frequently-occurring element. Excludes NA values by default.\n",
    "\n",
    "What’s the difference between a series & a dataframe? (Chapter 5)\n",
    "\n",
    "    Dataframe is two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns).\n",
    "    Series is the datastructure for a single column of a DataFrame. It's one dimentional. \n",
    "\n",
    "Explain what foo_bar.groupby(’A’).sum() returns (Data Wrangling with Python and Pandas 1.9)\n",
    "\n",
    "    It calculates the sum of foo_bar values which is belonging to the group of 'A'. 'A' might be the another category in other columns. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4LttaTgGVZ8E"
   },
   "source": [
    "## Part 0: Background"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](assets/02.002.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](assets/02.003.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](assets/02.004.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tGLSanHxVZ8H"
   },
   "source": [
    "* less looping over elements\n",
    "* lots of built-in functionality\n",
    "* a \"paradigm shift\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](assets/02.005.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](assets/02.006.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](assets/02.007.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](assets/02.008.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](assets/02.009.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](assets/02.010.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](assets/02.011.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YjYixiP1VZ8H",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Data structures\n",
    "\n",
    "We're all familiar with lists:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xhegqV6SVZ8I"
   },
   "outputs": [],
   "source": [
    "names = [\"Charlotte\", \"Ingrid\", \"Ian\", \"Eric\"]\n",
    "scores = [80, 95, 85, 70]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zu3jCo_KVZ8N",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "Now let's say that we wanted to divide each of those scores by two and assign the results to another variable. Go ahead and write some code that does that... There are lots of ways to do this, so go ahead and write one way to do it (without importing any additional python packages) and assign the results to a \n",
    "variable called ```half```:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8mhhgNVAVZ8P"
   },
   "outputs": [],
   "source": [
    "half = [0.5 * score for score in scores]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QAnFgL-YVZ8Y"
   },
   "source": [
    "If you followed the above instructions, the following cell block should print\n",
    "a list of floats that looks like ```\n",
    "[40.0, 47.5, 42.5, 35.0]```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "niLrWWpCVZ8Z",
    "outputId": "a0bde7db-3edd-43ae-b5fe-05f4cd0367c4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[40.0, 47.5, 42.5, 35.0]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "half"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PmZ3WH5NVZ8e",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We can put data into an array structure that allows us to apply more powerful\n",
    "functions.  The data structure that we're interested in is called an ```ndarray``` and is from the ```numpy``` package:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Hp5a1NU_VZ8f",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "ascores = np.array(scores) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8uGqlkmZVZ8j",
    "outputId": "0a568c45-e6d4-4e49-947d-cf52e23e4762",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([80, 95, 85, 70])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ascores "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oMPWnT1RVZ8m"
   },
   "outputs": [],
   "source": [
    "ahalf = ascores / 2 # array: support universal functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dE9-NV3xVZ8q",
    "outputId": "cc580497-7fd1-475e-86b7-911cc9b9bbca"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([40, 47, 42, 35])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ahalf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CJqs59pPVZ8w",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Numpy arrays are powerful, but they have some limitations:  they can only \n",
    "consist of one type of data (e.g. int), etc.  pandas provides two additional\n",
    "data structures that are built on numpy ndarrays.\n",
    "\n",
    "The first are Series.  Let's create a simple pandas Series and examine it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zCY2O1qNVZ8x",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XxeUVoa4VZ83"
   },
   "outputs": [],
   "source": [
    "sscores = pd.Series(scores,name='the_scores') # the name would become the column name when get together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mLurEmmeVZ86",
    "outputId": "82a6e021-1b0a-44ae-992b-46062e40c66c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    80\n",
       "1    95\n",
       "2    85\n",
       "3    70\n",
       "Name: the_scores, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sscores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iH8UUuMEVZ89",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "So you see a couple of useful things: an index (0 to 3) and a data type (dtype), which in this case is an int64.\n",
    "\n",
    "**A Series is a one-dimensional ndarray with axis labels**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0eDglQ3VVZ8-",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Charlotte': 80, 'Eric': 70, 'Ian': 85, 'Ingrid': 95}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = dict(zip(names,scores))\n",
    "dict(zip(names,scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Charlotte', 80), ('Ingrid', 95), ('Ian', 85), ('Eric', 70)]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zip(names,scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Charlotte': 80, 'Eric': 70, 'Ian': 85, 'Ingrid': 95}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "w84Dm6g0VZ9B"
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xN7msMHZVZ9H",
    "outputId": "86fab839-41ef-40d2-88a3-417db686a732"
   },
   "outputs": [],
   "source": [
    "xdata = data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "THyCYrt9VZ9Q"
   },
   "outputs": [],
   "source": [
    "sData = pd.Series(data=xdata,name='score')  # put the mouse in the right side of (, then shift + tab => rules of function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yxxn9GEkVZ9W",
    "outputId": "d9c958a9-44eb-4c6c-f48a-b8bc4e95d7f0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Charlotte    80\n",
       "Eric         70\n",
       "Ian          85\n",
       "Ingrid       95\n",
       "Name: score, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sData"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iRq0P-5uVZ9b",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "So Series are a bit friendlier than numpy arrays, but they're still only one-dimensional.  Keep in mind that our basic data abstraction is a table, which can\n",
    "be thought of as a two-dimensional array.  Let's go ahead and create a simple DataFrame with just one column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rAyN0TCDVZ9f",
    "outputId": "af63b6af-d63a-454b-b3a4-044a5d1bcd6d"
   },
   "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>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Charlotte</th>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ingrid</th>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ian</th>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eric</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           score\n",
       "Charlotte     80\n",
       "Ingrid        95\n",
       "Ian           85\n",
       "Eric          70"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(scores,index=names,columns=['score'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zaNqugmcVZ9i",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's return to the code we ran last time and walk though it just to make sure we understand it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>name</th>\n",
       "      <th>gender</th>\n",
       "      <th>birth_count</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Simeon</td>\n",
       "      <td>M</td>\n",
       "      <td>23</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Raoul</td>\n",
       "      <td>M</td>\n",
       "      <td>7</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lou</td>\n",
       "      <td>M</td>\n",
       "      <td>14</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Myra</td>\n",
       "      <td>F</td>\n",
       "      <td>83</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alois</td>\n",
       "      <td>M</td>\n",
       "      <td>10</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     name gender  birth_count  year\n",
       "0  Simeon      M           23  1880\n",
       "1   Raoul      M            7  1880\n",
       "2     Lou      M           14  1880\n",
       "3    Myra      F           83  1880\n",
       "4   Alois      M           10  1880"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_names = pd.read_csv('data/names.csv')\n",
    "df_names.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Actually, that CSV file was created from a number of smaller CSV files.  Here's\n",
    "the code that will load the set of smaller CSV files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WyLINje5VZ9j"
   },
   "outputs": [],
   "source": [
    "years = range(1880, 2015)\n",
    "pieces = []\n",
    "for year in years:\n",
    "    path = 'data/names/yob%d.csv' % year\n",
    "    frame = pd.read_csv(path)\n",
    "    frame['year'] = year\n",
    "    pieces.append(frame) # why we need to append to pieces?\n",
    "df_names = pd.concat(pieces, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>gender</th>\n",
       "      <th>birth_count</th>\n",
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       "      <th>0</th>\n",
       "      <td>Simeon</td>\n",
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       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Raoul</td>\n",
       "      <td>M</td>\n",
       "      <td>7</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lou</td>\n",
       "      <td>M</td>\n",
       "      <td>14</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Myra</td>\n",
       "      <td>F</td>\n",
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       "      <td>1880</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alois</td>\n",
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       "      <td>1880</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     name gender  birth_count  year\n",
       "0  Simeon      M           23  1880\n",
       "1   Raoul      M            7  1880\n",
       "2     Lou      M           14  1880\n",
       "3    Myra      F           83  1880\n",
       "4   Alois      M           10  1880"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_names.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's discuss what just happened there."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rE4uriL0VZ9l"
   },
   "source": [
    "# Part 1 (as a group): Mental Health Disorders In the Tech Workplace\n",
    "From https://www.kaggle.com/osmi/mental-health-in-tech-survey"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "N1djnH6yVZ9m"
   },
   "source": [
    "## Data Description\n",
    "\n",
    "This dataset is from a 2014 survey that measures attitudes towards mental health and frequency of mental health disorders in the tech workplace."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PhOC6r2AVZ9n"
   },
   "source": [
    "## Metadata\n",
    "\n",
    "**Timestamp**\n",
    "\n",
    "**Age**\n",
    "\n",
    "**Gender**\n",
    "\n",
    "**Country**\n",
    "\n",
    "**state**: If you live in the United States, which state or territory do you live in?\n",
    "\n",
    "**self_employed**: Are you self-employed?\n",
    "\n",
    "**family_history**: Do you have a family history of mental illness?\n",
    "\n",
    "**treatment**: Have you sought treatment for a mental health condition?\n",
    "\n",
    "**work_interfere**: If you have a mental health condition, do you feel that it interferes with your work?\n",
    "\n",
    "**no_employees**: How many employees does your company or organization have?\n",
    "\n",
    "**remote_work**: Do you work remotely (outside of an office) at least 50% of the time?\n",
    "\n",
    "**tech_company**: Is your employer primarily a tech company/organization?\n",
    "\n",
    "**benefits**: Does your employer provide mental health benefits?\n",
    "\n",
    "**care_options**: Do you know the options for mental health care your employer provides?\n",
    "\n",
    "**wellness_program**: Has your employer ever discussed mental health as part of an employee wellness program?\n",
    "\n",
    "**seek_help**: Does your employer provide resources to learn more about mental health issues and how to seek help?\n",
    "\n",
    "**anonymity**: Is your anonymity protected if you choose to take advantage of mental health or substance abuse treatment resources?\n",
    "\n",
    "**leave**: How easy is it for you to take medical leave for a mental health condition?\n",
    "\n",
    "**mental_health_consequence**: Do you think that discussing a mental health issue with your employer would have negative consequences?\n",
    "\n",
    "**phys_health_consequence**: Do you think that discussing a physical health issue with your employer would have negative consequences?\n",
    "\n",
    "**coworkers**: Would you be willing to discuss a mental health issue with your coworkers?\n",
    "\n",
    "**supervisor**: Would you be willing to discuss a mental health issue with your direct supervisor(s)?\n",
    "\n",
    "**mental_health_interview**: Would you bring up a mental health issue with a potential employer in an interview?\n",
    "\n",
    "**phys_health_interview**: Would you bring up a physical health issue with a potential employer in an interview?\n",
    "\n",
    "**mental_vs_physical**: Do you feel that your employer takes mental health as seriously as physical health?\n",
    "\n",
    "**obs_consequence**: Have you heard of or observed negative consequences for coworkers with mental health conditions in your workplace?\n",
    "\n",
    "**comments**: Any additional notes or comments\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's load the usual libraries and also ask for plots to be rendered inside the notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7WzcvvbBVZ9p"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    " # inline: % magic comment, tells the jupyter display right in the notebook.\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JSRo9nBsVZ9p"
   },
   "source": [
    "Then read the CSV file into a DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7WzcvvbBVZ9p"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/survey.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Sviw9MgTVZ9s"
   },
   "source": [
    "It's common to look at the resulting DataFrame using .head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vfZrtu7xVZ9t",
    "outputId": "9981f8bc-789d-4243-c701-4f518e781373"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Timestamp</th>\n",
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       "      <th>0</th>\n",
       "      <td>2014-08-27 11:29:31</td>\n",
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       "      <td>United States</td>\n",
       "      <td>IL</td>\n",
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       "      <td>Often</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-08-27 11:29:37</td>\n",
       "      <td>44</td>\n",
       "      <td>M</td>\n",
       "      <td>United States</td>\n",
       "      <td>IN</td>\n",
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       "      <td>No</td>\n",
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       "      <td>No</td>\n",
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       "      <td>No</td>\n",
       "      <td>No</td>\n",
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       "      <th>2</th>\n",
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       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-08-27 11:29:46</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>26-100</td>\n",
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       "      <td>Maybe</td>\n",
       "      <td>Maybe</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-08-27 11:30:22</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>United States</td>\n",
       "      <td>TX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Never</td>\n",
       "      <td>100-500</td>\n",
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       "      <td>Some of them</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Timestamp  Age  Gender         Country state self_employed  \\\n",
       "0  2014-08-27 11:29:31   37  Female   United States    IL           NaN   \n",
       "1  2014-08-27 11:29:37   44       M   United States    IN           NaN   \n",
       "2  2014-08-27 11:29:44   32    Male          Canada   NaN           NaN   \n",
       "3  2014-08-27 11:29:46   31    Male  United Kingdom   NaN           NaN   \n",
       "4  2014-08-27 11:30:22   31    Male   United States    TX           NaN   \n",
       "\n",
       "  family_history treatment work_interfere    no_employees   ...     \\\n",
       "0             No       Yes          Often            6-25   ...      \n",
       "1             No        No         Rarely  More than 1000   ...      \n",
       "2             No        No         Rarely            6-25   ...      \n",
       "3            Yes       Yes          Often          26-100   ...      \n",
       "4             No        No          Never         100-500   ...      \n",
       "\n",
       "                leave mental_health_consequence phys_health_consequence  \\\n",
       "0       Somewhat easy                        No                      No   \n",
       "1          Don't know                     Maybe                      No   \n",
       "2  Somewhat difficult                        No                      No   \n",
       "3  Somewhat difficult                       Yes                     Yes   \n",
       "4          Don't know                        No                      No   \n",
       "\n",
       "      coworkers supervisor mental_health_interview phys_health_interview  \\\n",
       "0  Some of them        Yes                      No                 Maybe   \n",
       "1            No         No                      No                    No   \n",
       "2           Yes        Yes                     Yes                   Yes   \n",
       "3  Some of them         No                   Maybe                 Maybe   \n",
       "4  Some of them        Yes                     Yes                   Yes   \n",
       "\n",
       "  mental_vs_physical obs_consequence comments  \n",
       "0                Yes              No      NaN  \n",
       "1         Don't know              No      NaN  \n",
       "2                 No              No      NaN  \n",
       "3                 No             Yes      NaN  \n",
       "4         Don't know              No      NaN  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head() # df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PZLAWq-AVZ9y"
   },
   "source": [
    "If you want to look at a random sample, you can use .sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZTZNPV6vVZ9z",
    "outputId": "0c41c5a3-117a-4194-bdfa-2ac568d4a071"
   },
   "outputs": [
    {
     "data": {
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       "      <th>121</th>\n",
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       "      <td>Some of them</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>2014-08-27 12:52:08</td>\n",
       "      <td>43</td>\n",
       "      <td>Male</td>\n",
       "      <td>United States</td>\n",
       "      <td>WA</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Often</td>\n",
       "      <td>More than 1000</td>\n",
       "      <td>...</td>\n",
       "      <td>Very difficult</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Timestamp  Age  Gender        Country state self_employed  \\\n",
       "954   2014-08-29 02:10:57   38    Male  United States    TN            No   \n",
       "1050  2014-08-29 11:32:44   26  female  United States    WA            No   \n",
       "820   2014-08-28 14:52:46   40  Female  United States    WA            No   \n",
       "121   2014-08-27 12:33:00   25    Male  United States    DC            No   \n",
       "184   2014-08-27 12:52:08   43    Male  United States    WA            No   \n",
       "\n",
       "     family_history treatment work_interfere    no_employees  \\\n",
       "954              No        No            NaN            6-25   \n",
       "1050            Yes       Yes      Sometimes  More than 1000   \n",
       "820             Yes       Yes      Sometimes         100-500   \n",
       "121              No        No            NaN          26-100   \n",
       "184             Yes       Yes          Often  More than 1000   \n",
       "\n",
       "                            ...                                   leave  \\\n",
       "954                         ...                              Don't know   \n",
       "1050                        ...                              Don't know   \n",
       "820                         ...                              Don't know   \n",
       "121                         ...                               Very easy   \n",
       "184                         ...                          Very difficult   \n",
       "\n",
       "     mental_health_consequence phys_health_consequence     coworkers  \\\n",
       "954                      Maybe                      No  Some of them   \n",
       "1050                        No                      No  Some of them   \n",
       "820                        Yes                   Maybe  Some of them   \n",
       "121                         No                      No  Some of them   \n",
       "184                        Yes                   Maybe            No   \n",
       "\n",
       "     supervisor mental_health_interview phys_health_interview  \\\n",
       "954         Yes                   Maybe                 Maybe   \n",
       "1050        Yes                      No                 Maybe   \n",
       "820          No                      No                    No   \n",
       "121         Yes                      No                    No   \n",
       "184          No                      No                    No   \n",
       "\n",
       "     mental_vs_physical obs_consequence  \\\n",
       "954          Don't know              No   \n",
       "1050                 No             Yes   \n",
       "820          Don't know              No   \n",
       "121          Don't know              No   \n",
       "184                  No              No   \n",
       "\n",
       "                                               comments  \n",
       "954               Some of these should not be required.  \n",
       "1050  I should note one of the places my employer fa...  \n",
       "820                                                 NaN  \n",
       "121                                                 NaN  \n",
       "184                                                 NaN  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lVXzJKBwVZ93"
   },
   "source": [
    "Finally, you can get some basic information about the size and shape of the DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "35wmK1eeVZ94",
    "outputId": "6d530143-54a5-49ef-bd13-74fbb6cbb257"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('The number of rows of the dataset is: ', 1259)\n",
      "('The number of columns of the dataset is: ', 27)\n",
      "('The shape of the dataset is: ', (1259, 27))\n"
     ]
    }
   ],
   "source": [
    "print(\"The number of rows of the dataset is: \", len(df))\n",
    "print(\"The number of columns of the dataset is: \", len(df.columns))\n",
    "print(\"The shape of the dataset is: \", df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WM6QabC9VZ98"
   },
   "source": [
    "You can list the columns:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Oz5UXsevVZ98",
    "outputId": "753c7815-7f15-47c5-887b-f2df74868924"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'Timestamp', u'Age', u'Gender', u'Country', u'state', u'self_employed',\n",
       "       u'family_history', u'treatment', u'work_interfere', u'no_employees',\n",
       "       u'remote_work', u'tech_company', u'benefits', u'care_options',\n",
       "       u'wellness_program', u'seek_help', u'anonymity', u'leave',\n",
       "       u'mental_health_consequence', u'phys_health_consequence', u'coworkers',\n",
       "       u'supervisor', u'mental_health_interview', u'phys_health_interview',\n",
       "       u'mental_vs_physical', u'obs_consequence', u'comments'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns # get columns name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MLVrG97_VZ-B"
   },
   "source": [
    "And you can extract one or more columns.  The following pair of \n",
    "commands do exactly the same thing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2HuKMyd3VZ-B",
    "outputId": "7c05d6db-55e9-4b0d-aea5-fe6282365fda"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0        United States\n",
      "1        United States\n",
      "2               Canada\n",
      "3       United Kingdom\n",
      "4        United States\n",
      "5        United States\n",
      "6        United States\n",
      "7               Canada\n",
      "8        United States\n",
      "9               Canada\n",
      "10       United States\n",
      "11            Bulgaria\n",
      "12       United States\n",
      "13       United States\n",
      "14              Canada\n",
      "15       United States\n",
      "16      United Kingdom\n",
      "17       United States\n",
      "18       United States\n",
      "19              France\n",
      "20       United States\n",
      "21       United States\n",
      "22       United States\n",
      "23       United States\n",
      "24       United States\n",
      "25       United States\n",
      "26       United States\n",
      "27       United States\n",
      "28       United States\n",
      "29      United Kingdom\n",
      "             ...      \n",
      "1229            Greece\n",
      "1230    United Kingdom\n",
      "1231    United Kingdom\n",
      "1232    United Kingdom\n",
      "1233    United Kingdom\n",
      "1234    United Kingdom\n",
      "1235            Canada\n",
      "1236     United States\n",
      "1237           Ireland\n",
      "1238     United States\n",
      "1239       Netherlands\n",
      "1240           Germany\n",
      "1241            Poland\n",
      "1242           Ireland\n",
      "1243     United States\n",
      "1244    United Kingdom\n",
      "1245         Australia\n",
      "1246     United States\n",
      "1247           Finland\n",
      "1248     United States\n",
      "1249     United States\n",
      "1250     United States\n",
      "1251      South Africa\n",
      "1252     United States\n",
      "1253     United States\n",
      "1254    United Kingdom\n",
      "1255     United States\n",
      "1256     United States\n",
      "1257     United States\n",
      "1258     United States\n",
      "Name: Country, Length: 1259, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df['Country']) # that's a series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sc5m2sFKVZ-H",
    "outputId": "fe857af6-702d-47f3-a0d3-e408bb9ac0dc"
   },
   "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>Country</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United States</td>\n",
       "      <td>IL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>United States</td>\n",
       "      <td>IN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Canada</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United States</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country state\n",
       "0   United States    IL\n",
       "1   United States    IN\n",
       "2          Canada   NaN\n",
       "3  United Kingdom   NaN\n",
       "4   United States    TX"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_state = df[['Country','state']]\n",
    "country_state.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "sCdBNks5VZ-K"
   },
   "source": [
    "## Extracting rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yvJlk3mRVZ-K",
    "outputId": "15aedfc7-b423-4694-a4f7-f631479ba7cf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp                    2014-08-27 11:29:31\n",
       "Age                                           37\n",
       "Gender                                    Female\n",
       "Country                            United States\n",
       "state                                         IL\n",
       "self_employed                                NaN\n",
       "family_history                                No\n",
       "treatment                                    Yes\n",
       "work_interfere                             Often\n",
       "no_employees                                6-25\n",
       "remote_work                                   No\n",
       "tech_company                                 Yes\n",
       "benefits                                     Yes\n",
       "care_options                            Not sure\n",
       "wellness_program                              No\n",
       "seek_help                                    Yes\n",
       "anonymity                                    Yes\n",
       "leave                              Somewhat easy\n",
       "mental_health_consequence                     No\n",
       "phys_health_consequence                       No\n",
       "coworkers                           Some of them\n",
       "supervisor                                   Yes\n",
       "mental_health_interview                       No\n",
       "phys_health_interview                      Maybe\n",
       "mental_vs_physical                           Yes\n",
       "obs_consequence                               No\n",
       "comments                                     NaN\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0] # iloc should use integer: give the zero row of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "t2ixMmwDVZ-P",
    "outputId": "b2faeb7e-e480-42d0-d38c-f5dc3b72cb37"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp                    2014-08-27 11:29:31\n",
       "Age                                           37\n",
       "Gender                                    Female\n",
       "Country                            United States\n",
       "state                                         IL\n",
       "self_employed                                NaN\n",
       "family_history                                No\n",
       "treatment                                    Yes\n",
       "work_interfere                             Often\n",
       "no_employees                                6-25\n",
       "remote_work                                   No\n",
       "tech_company                                 Yes\n",
       "benefits                                     Yes\n",
       "care_options                            Not sure\n",
       "wellness_program                              No\n",
       "seek_help                                    Yes\n",
       "anonymity                                    Yes\n",
       "leave                              Somewhat easy\n",
       "mental_health_consequence                     No\n",
       "phys_health_consequence                       No\n",
       "coworkers                           Some of them\n",
       "supervisor                                   Yes\n",
       "mental_health_interview                       No\n",
       "phys_health_interview                      Maybe\n",
       "mental_vs_physical                           Yes\n",
       "obs_consequence                               No\n",
       "comments                                     NaN\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[0] # give the row with the index value is zero!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DjG0tSoNVZ-S"
   },
   "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>Timestamp</th>\n",
       "      <th>Age</th>\n",
       "      <th>Country</th>\n",
       "      <th>state</th>\n",
       "      <th>self_employed</th>\n",
       "      <th>family_history</th>\n",
       "      <th>treatment</th>\n",
       "      <th>work_interfere</th>\n",
       "      <th>no_employees</th>\n",
       "      <th>remote_work</th>\n",
       "      <th>...</th>\n",
       "      <th>leave</th>\n",
       "      <th>mental_health_consequence</th>\n",
       "      <th>phys_health_consequence</th>\n",
       "      <th>coworkers</th>\n",
       "      <th>supervisor</th>\n",
       "      <th>mental_health_interview</th>\n",
       "      <th>phys_health_interview</th>\n",
       "      <th>mental_vs_physical</th>\n",
       "      <th>obs_consequence</th>\n",
       "      <th>comments</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</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>Female</th>\n",
       "      <td>2014-08-27 11:29:31</td>\n",
       "      <td>37</td>\n",
       "      <td>United States</td>\n",
       "      <td>IL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Often</td>\n",
       "      <td>6-25</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat easy</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>2014-08-27 11:29:37</td>\n",
       "      <td>44</td>\n",
       "      <td>United States</td>\n",
       "      <td>IN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Rarely</td>\n",
       "      <td>More than 1000</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2014-08-27 11:29:44</td>\n",
       "      <td>32</td>\n",
       "      <td>Canada</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Rarely</td>\n",
       "      <td>6-25</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat difficult</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2014-08-27 11:29:46</td>\n",
       "      <td>31</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Often</td>\n",
       "      <td>26-100</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat difficult</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2014-08-27 11:30:22</td>\n",
       "      <td>31</td>\n",
       "      <td>United States</td>\n",
       "      <td>TX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Never</td>\n",
       "      <td>100-500</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Timestamp  Age         Country state self_employed  \\\n",
       "Gender                                                                 \n",
       "Female  2014-08-27 11:29:31   37   United States    IL           NaN   \n",
       "M       2014-08-27 11:29:37   44   United States    IN           NaN   \n",
       "Male    2014-08-27 11:29:44   32          Canada   NaN           NaN   \n",
       "Male    2014-08-27 11:29:46   31  United Kingdom   NaN           NaN   \n",
       "Male    2014-08-27 11:30:22   31   United States    TX           NaN   \n",
       "\n",
       "       family_history treatment work_interfere    no_employees remote_work  \\\n",
       "Gender                                                                       \n",
       "Female             No       Yes          Often            6-25          No   \n",
       "M                  No        No         Rarely  More than 1000          No   \n",
       "Male               No        No         Rarely            6-25          No   \n",
       "Male              Yes       Yes          Often          26-100          No   \n",
       "Male               No        No          Never         100-500         Yes   \n",
       "\n",
       "         ...                  leave mental_health_consequence  \\\n",
       "Gender   ...                                                    \n",
       "Female   ...          Somewhat easy                        No   \n",
       "M        ...             Don't know                     Maybe   \n",
       "Male     ...     Somewhat difficult                        No   \n",
       "Male     ...     Somewhat difficult                       Yes   \n",
       "Male     ...             Don't know                        No   \n",
       "\n",
       "       phys_health_consequence     coworkers supervisor  \\\n",
       "Gender                                                    \n",
       "Female                      No  Some of them        Yes   \n",
       "M                           No            No         No   \n",
       "Male                        No           Yes        Yes   \n",
       "Male                       Yes  Some of them         No   \n",
       "Male                        No  Some of them        Yes   \n",
       "\n",
       "       mental_health_interview phys_health_interview mental_vs_physical  \\\n",
       "Gender                                                                    \n",
       "Female                      No                 Maybe                Yes   \n",
       "M                           No                    No         Don't know   \n",
       "Male                       Yes                   Yes                 No   \n",
       "Male                     Maybe                 Maybe                 No   \n",
       "Male                       Yes                   Yes         Don't know   \n",
       "\n",
       "       obs_consequence comments  \n",
       "Gender                           \n",
       "Female              No      NaN  \n",
       "M                   No      NaN  \n",
       "Male                No      NaN  \n",
       "Male               Yes      NaN  \n",
       "Male                No      NaN  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gender = df.set_index('Gender')\n",
    "df_gender.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UpkWJyHGVZ-U",
    "outputId": "f17d6766-c2fe-46d0-c746-e4c657792cf3"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Age</th>\n",
       "      <th>Country</th>\n",
       "      <th>state</th>\n",
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       "      <th>phys_health_interview</th>\n",
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       "    <tr>\n",
       "      <th>Gender</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2014-08-27 11:29:44</td>\n",
       "      <td>32</td>\n",
       "      <td>Canada</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>Male</th>\n",
       "      <td>2014-08-27 11:29:46</td>\n",
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       "      <td>Maybe</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2014-08-27 11:30:22</td>\n",
       "      <td>31</td>\n",
       "      <td>United States</td>\n",
       "      <td>TX</td>\n",
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       "      <td>Yes</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2014-08-27 11:31:22</td>\n",
       "      <td>33</td>\n",
       "      <td>United States</td>\n",
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       "      <td>No</td>\n",
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       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2014-08-27 11:32:43</td>\n",
       "      <td>23</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2014-08-27 11:32:44</td>\n",
       "      <td>31</td>\n",
       "      <td>United States</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2014-08-27 11:33:26</td>\n",
       "      <td>36</td>\n",
       "      <td>United States</td>\n",
       "      <td>CT</td>\n",
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       "      <td>500-1000</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
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       "      <td>No</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>I'm not on my company's health insurance which...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2014-08-27 11:33:57</td>\n",
       "      <td>27</td>\n",
       "      <td>Canada</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "      <td>2014-08-27 11:34:20</td>\n",
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       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>My company does provide healthcare but not to ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2014-08-27 11:34:37</td>\n",
       "      <td>32</td>\n",
       "      <td>United States</td>\n",
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       "      <td>No</td>\n",
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       "      <td>Don't know</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2014-08-27 11:35:08</td>\n",
       "      <td>36</td>\n",
       "      <td>France</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>Yes</td>\n",
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       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2014-08-27 11:35:12</td>\n",
       "      <td>29</td>\n",
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       "      <td>Yes</td>\n",
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       "      <td>Sometimes</td>\n",
       "      <td>100-500</td>\n",
       "      <td>No</td>\n",
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       "      <td>Somewhat difficult</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2014-08-27 11:35:48</td>\n",
       "      <td>46</td>\n",
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       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2014-08-27 11:36:24</td>\n",
       "      <td>41</td>\n",
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       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Don't know</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2014-08-27 11:38:18</td>\n",
       "      <td>37</td>\n",
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       "      <th>Male</th>\n",
       "      <td>2014-08-27 11:39:03</td>\n",
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       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2014-08-27 11:38:55</td>\n",
       "      <td>31</td>\n",
       "      <td>United States</td>\n",
       "      <td>PA</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Rarely</td>\n",
       "      <td>1-5</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat difficult</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2014-08-27 11:39:36</td>\n",
       "      <td>42</td>\n",
       "      <td>United States</td>\n",
       "      <td>WA</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>26-100</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
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       "      <td>Maybe</td>\n",
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       "      <td>Some of them</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>I selected my current employer based on its po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2014-08-27 11:41:17</td>\n",
       "      <td>27</td>\n",
       "      <td>United States</td>\n",
       "      <td>NY</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Rarely</td>\n",
       "      <td>6-25</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Very easy</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2014-08-27 11:41:37</td>\n",
       "      <td>29</td>\n",
       "      <td>Canada</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Rarely</td>\n",
       "      <td>1-5</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Very easy</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2014-08-27 11:41:50</td>\n",
       "      <td>38</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100-500</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat easy</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2014-08-27 11:42:25</td>\n",
       "      <td>24</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>6-25</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2014-08-27 11:42:31</td>\n",
       "      <td>35</td>\n",
       "      <td>United States</td>\n",
       "      <td>MI</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>More than 1000</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat difficult</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2014-08-27 11:43:07</td>\n",
       "      <td>27</td>\n",
       "      <td>Canada</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>1-5</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Very difficult</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2014-08-27 11:43:10</td>\n",
       "      <td>18</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Often</td>\n",
       "      <td>6-25</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat difficult</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2014-08-27 11:43:22</td>\n",
       "      <td>30</td>\n",
       "      <td>United States</td>\n",
       "      <td>IN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>26-100</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2014-08-27 11:43:45</td>\n",
       "      <td>28</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26-100</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2014-08-27 11:43:48</td>\n",
       "      <td>34</td>\n",
       "      <td>United States</td>\n",
       "      <td>TN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6-25</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2014-08-27 11:45:32</td>\n",
       "      <td>33</td>\n",
       "      <td>United States</td>\n",
       "      <td>UT</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100-500</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2014-08-27 11:45:51</td>\n",
       "      <td>32</td>\n",
       "      <td>United States</td>\n",
       "      <td>TN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Never</td>\n",
       "      <td>1-5</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2015-02-21 04:41:28</td>\n",
       "      <td>32</td>\n",
       "      <td>United States</td>\n",
       "      <td>TX</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Often</td>\n",
       "      <td>26-100</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Very difficult</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2015-02-21 04:55:11</td>\n",
       "      <td>28</td>\n",
       "      <td>Australia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Often</td>\n",
       "      <td>100-500</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2015-02-21 05:34:27</td>\n",
       "      <td>31</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1-5</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2015-02-21 05:55:13</td>\n",
       "      <td>24</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100-500</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat easy</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2015-02-21 05:55:17</td>\n",
       "      <td>34</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Never</td>\n",
       "      <td>1-5</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2015-02-21 06:19:41</td>\n",
       "      <td>34</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Often</td>\n",
       "      <td>26-100</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat easy</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2015-02-21 08:21:36</td>\n",
       "      <td>23</td>\n",
       "      <td>United States</td>\n",
       "      <td>TX</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>1-5</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Very easy</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2015-02-21 08:54:40</td>\n",
       "      <td>33</td>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Often</td>\n",
       "      <td>100-500</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat easy</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2015-02-21 09:19:12</td>\n",
       "      <td>31</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>More than 1000</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Very difficult</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2015-02-21 09:22:23</td>\n",
       "      <td>33</td>\n",
       "      <td>United States</td>\n",
       "      <td>FL</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Often</td>\n",
       "      <td>26-100</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2015-02-21 09:30:14</td>\n",
       "      <td>27</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Rarely</td>\n",
       "      <td>More than 1000</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2015-02-21 09:48:13</td>\n",
       "      <td>38</td>\n",
       "      <td>United States</td>\n",
       "      <td>TX</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>More than 1000</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Very easy</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>I openly discuss my mental health struggles. I...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2015-02-21 10:00:50</td>\n",
       "      <td>46</td>\n",
       "      <td>United States</td>\n",
       "      <td>CA</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Often</td>\n",
       "      <td>26-100</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2015-02-21 17:19:18</td>\n",
       "      <td>39</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>More than 1000</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat easy</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2015-02-21 18:59:05</td>\n",
       "      <td>25</td>\n",
       "      <td>United States</td>\n",
       "      <td>MN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>26-100</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat easy</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2015-02-22 02:40:32</td>\n",
       "      <td>39</td>\n",
       "      <td>Greece</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6-25</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2015-02-24 09:00:56</td>\n",
       "      <td>25</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>More than 1000</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Very easy</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>I work at a large university with a track reco...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2015-02-24 09:13:49</td>\n",
       "      <td>23</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Rarely</td>\n",
       "      <td>6-25</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2015-02-24 09:18:25</td>\n",
       "      <td>23</td>\n",
       "      <td>Canada</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Often</td>\n",
       "      <td>26-100</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2015-02-24 10:32:32</td>\n",
       "      <td>60</td>\n",
       "      <td>United States</td>\n",
       "      <td>CA</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Often</td>\n",
       "      <td>More than 1000</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat easy</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2015-04-02 15:47:43</td>\n",
       "      <td>28</td>\n",
       "      <td>United States</td>\n",
       "      <td>TN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Often</td>\n",
       "      <td>More than 1000</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat easy</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2015-04-11 14:35:22</td>\n",
       "      <td>31</td>\n",
       "      <td>Poland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Often</td>\n",
       "      <td>6-25</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat easy</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2015-04-23 14:03:51</td>\n",
       "      <td>28</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Rarely</td>\n",
       "      <td>26-100</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2015-05-06 10:14:50</td>\n",
       "      <td>22</td>\n",
       "      <td>Australia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Often</td>\n",
       "      <td>100-500</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Yes</td>\n",
       "      <td>In australia all organisations of a certain si...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2015-05-06 16:55:58</td>\n",
       "      <td>32</td>\n",
       "      <td>United States</td>\n",
       "      <td>OR</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Never</td>\n",
       "      <td>100-500</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat easy</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2015-07-27 23:25:34</td>\n",
       "      <td>30</td>\n",
       "      <td>United States</td>\n",
       "      <td>CA</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Often</td>\n",
       "      <td>26-100</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2015-08-17 09:38:35</td>\n",
       "      <td>36</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Often</td>\n",
       "      <td>100-500</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat easy</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2015-08-25 19:59:38</td>\n",
       "      <td>36</td>\n",
       "      <td>United States</td>\n",
       "      <td>UT</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Rarely</td>\n",
       "      <td>More than 1000</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat easy</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2015-09-26 01:07:35</td>\n",
       "      <td>32</td>\n",
       "      <td>United States</td>\n",
       "      <td>IL</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Often</td>\n",
       "      <td>26-100</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat difficult</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2016-02-01 23:04:31</td>\n",
       "      <td>25</td>\n",
       "      <td>United States</td>\n",
       "      <td>IL</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>26-100</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>615 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Timestamp  Age         Country state self_employed  \\\n",
       "Gender                                                                 \n",
       "Male    2014-08-27 11:29:44   32          Canada   NaN           NaN   \n",
       "Male    2014-08-27 11:29:46   31  United Kingdom   NaN           NaN   \n",
       "Male    2014-08-27 11:30:22   31   United States    TX           NaN   \n",
       "Male    2014-08-27 11:31:22   33   United States    TN           NaN   \n",
       "Male    2014-08-27 11:32:43   23          Canada   NaN           NaN   \n",
       "Male    2014-08-27 11:32:44   31   United States    OH           NaN   \n",
       "Male    2014-08-27 11:33:26   36   United States    CT           NaN   \n",
       "Male    2014-08-27 11:33:57   27          Canada   NaN           NaN   \n",
       "Male    2014-08-27 11:34:20   23  United Kingdom   NaN           NaN   \n",
       "Male    2014-08-27 11:34:37   32   United States    TN           NaN   \n",
       "Male    2014-08-27 11:35:08   36          France   NaN           Yes   \n",
       "Male    2014-08-27 11:35:12   29   United States    NY            No   \n",
       "Male    2014-08-27 11:35:48   46   United States    MA            No   \n",
       "Male    2014-08-27 11:36:24   41   United States    IA            No   \n",
       "Male    2014-08-27 11:38:18   37  United Kingdom   NaN            No   \n",
       "Male    2014-08-27 11:39:03   32  United Kingdom   NaN            No   \n",
       "Male    2014-08-27 11:38:55   31   United States    PA           Yes   \n",
       "Male    2014-08-27 11:39:36   42   United States    WA            No   \n",
       "Male    2014-08-27 11:41:17   27   United States    NY            No   \n",
       "Male    2014-08-27 11:41:37   29          Canada   NaN            No   \n",
       "Male    2014-08-27 11:41:50   38        Portugal   NaN            No   \n",
       "Male    2014-08-27 11:42:25   24  United Kingdom   NaN            No   \n",
       "Male    2014-08-27 11:42:31   35   United States    MI            No   \n",
       "Male    2014-08-27 11:43:07   27          Canada   NaN            No   \n",
       "Male    2014-08-27 11:43:10   18     Netherlands   NaN            No   \n",
       "Male    2014-08-27 11:43:22   30   United States    IN            No   \n",
       "Male    2014-08-27 11:43:45   28  United Kingdom   NaN            No   \n",
       "Male    2014-08-27 11:43:48   34   United States    TN            No   \n",
       "Male    2014-08-27 11:45:32   33   United States    UT            No   \n",
       "Male    2014-08-27 11:45:51   32   United States    TN            No   \n",
       "...                     ...  ...             ...   ...           ...   \n",
       "Male    2015-02-21 04:41:28   32   United States    TX            No   \n",
       "Male    2015-02-21 04:55:11   28       Australia   NaN            No   \n",
       "Male    2015-02-21 05:34:27   31          Brazil   NaN           Yes   \n",
       "Male    2015-02-21 05:55:13   24  United Kingdom   NaN            No   \n",
       "Male    2015-02-21 05:55:17   34         Ireland   NaN            No   \n",
       "Male    2015-02-21 06:19:41   34  United Kingdom   NaN            No   \n",
       "Male    2015-02-21 08:21:36   23   United States    TX            No   \n",
       "Male    2015-02-21 08:54:40   33  Czech Republic   NaN            No   \n",
       "Male    2015-02-21 09:19:12   31     Philippines   NaN            No   \n",
       "Male    2015-02-21 09:22:23   33   United States    FL            No   \n",
       "Male    2015-02-21 09:30:14   27  United Kingdom   NaN            No   \n",
       "Male    2015-02-21 09:48:13   38   United States    TX            No   \n",
       "Male    2015-02-21 10:00:50   46   United States    CA            No   \n",
       "Male    2015-02-21 17:19:18   39     Netherlands   NaN            No   \n",
       "Male    2015-02-21 18:59:05   25   United States    MN            No   \n",
       "Male    2015-02-22 02:40:32   39          Greece   NaN            No   \n",
       "Male    2015-02-24 09:00:56   25  United Kingdom   NaN            No   \n",
       "Male    2015-02-24 09:13:49   23  United Kingdom   NaN            No   \n",
       "Male    2015-02-24 09:18:25   23          Canada   NaN            No   \n",
       "Male    2015-02-24 10:32:32   60   United States    CA            No   \n",
       "Male    2015-04-02 15:47:43   28   United States    TN            No   \n",
       "Male    2015-04-11 14:35:22   31          Poland   NaN           Yes   \n",
       "Male    2015-04-23 14:03:51   28         Ireland   NaN            No   \n",
       "Male    2015-05-06 10:14:50   22       Australia   NaN            No   \n",
       "Male    2015-05-06 16:55:58   32   United States    OR            No   \n",
       "Male    2015-07-27 23:25:34   30   United States    CA           Yes   \n",
       "Male    2015-08-17 09:38:35   36    South Africa   NaN            No   \n",
       "Male    2015-08-25 19:59:38   36   United States    UT            No   \n",
       "Male    2015-09-26 01:07:35   32   United States    IL            No   \n",
       "Male    2016-02-01 23:04:31   25   United States    IL            No   \n",
       "\n",
       "       family_history treatment work_interfere    no_employees remote_work  \\\n",
       "Gender                                                                       \n",
       "Male               No        No         Rarely            6-25          No   \n",
       "Male              Yes       Yes          Often          26-100          No   \n",
       "Male               No        No          Never         100-500         Yes   \n",
       "Male              Yes        No      Sometimes            6-25          No   \n",
       "Male               No        No          Never          26-100          No   \n",
       "Male               No       Yes      Sometimes            6-25         Yes   \n",
       "Male              Yes        No          Never        500-1000          No   \n",
       "Male               No        No          Never            6-25          No   \n",
       "Male               No       Yes      Sometimes          26-100         Yes   \n",
       "Male               No       Yes      Sometimes            6-25          No   \n",
       "Male              Yes        No            NaN            6-25         Yes   \n",
       "Male              Yes       Yes      Sometimes         100-500          No   \n",
       "Male               No       Yes          Often          26-100         Yes   \n",
       "Male               No       Yes          Never  More than 1000          No   \n",
       "Male               No        No      Sometimes            6-25          No   \n",
       "Male               No        No          Never            6-25         Yes   \n",
       "Male              Yes        No         Rarely             1-5         Yes   \n",
       "Male              Yes       Yes      Sometimes          26-100         Yes   \n",
       "Male               No       Yes         Rarely            6-25          No   \n",
       "Male               No        No         Rarely             1-5          No   \n",
       "Male               No        No            NaN         100-500          No   \n",
       "Male               No       Yes      Sometimes            6-25          No   \n",
       "Male               No        No            NaN  More than 1000         Yes   \n",
       "Male              Yes       Yes      Sometimes             1-5          No   \n",
       "Male               No        No          Often            6-25          No   \n",
       "Male               No       Yes      Sometimes          26-100          No   \n",
       "Male               No        No            NaN          26-100          No   \n",
       "Male               No        No            NaN            6-25          No   \n",
       "Male               No        No            NaN         100-500         Yes   \n",
       "Male               No        No          Never             1-5         Yes   \n",
       "...               ...       ...            ...             ...         ...   \n",
       "Male               No       Yes          Often          26-100         Yes   \n",
       "Male               No       Yes          Often         100-500         Yes   \n",
       "Male               No       Yes            NaN             1-5          No   \n",
       "Male               No        No            NaN         100-500          No   \n",
       "Male              Yes       Yes          Never             1-5         Yes   \n",
       "Male              Yes        No          Often          26-100         Yes   \n",
       "Male               No       Yes      Sometimes             1-5          No   \n",
       "Male              Yes        No          Often         100-500          No   \n",
       "Male               No        No      Sometimes  More than 1000          No   \n",
       "Male               No       Yes          Often          26-100         Yes   \n",
       "Male               No        No         Rarely  More than 1000         Yes   \n",
       "Male               No       Yes      Sometimes  More than 1000         Yes   \n",
       "Male              Yes       Yes          Often          26-100          No   \n",
       "Male               No        No            NaN  More than 1000          No   \n",
       "Male              Yes       Yes      Sometimes          26-100          No   \n",
       "Male               No        No            NaN            6-25         Yes   \n",
       "Male              Yes       Yes      Sometimes  More than 1000          No   \n",
       "Male               No       Yes         Rarely            6-25          No   \n",
       "Male               No       Yes          Often          26-100          No   \n",
       "Male               No       Yes          Often  More than 1000         Yes   \n",
       "Male              Yes       Yes          Often  More than 1000          No   \n",
       "Male               No       Yes          Often            6-25         Yes   \n",
       "Male               No       Yes         Rarely          26-100          No   \n",
       "Male              Yes       Yes          Often         100-500          No   \n",
       "Male               No        No          Never         100-500          No   \n",
       "Male              Yes       Yes          Often          26-100          No   \n",
       "Male              Yes       Yes          Often         100-500          No   \n",
       "Male              Yes        No         Rarely  More than 1000          No   \n",
       "Male              Yes       Yes          Often          26-100         Yes   \n",
       "Male              Yes       Yes      Sometimes          26-100          No   \n",
       "\n",
       "                              ...                                       leave  \\\n",
       "Gender                        ...                                               \n",
       "Male                          ...                          Somewhat difficult   \n",
       "Male                          ...                          Somewhat difficult   \n",
       "Male                          ...                                  Don't know   \n",
       "Male                          ...                                  Don't know   \n",
       "Male                          ...                                  Don't know   \n",
       "Male                          ...                                  Don't know   \n",
       "Male                          ...                                  Don't know   \n",
       "Male                          ...                               Somewhat easy   \n",
       "Male                          ...                                   Very easy   \n",
       "Male                          ...                                  Don't know   \n",
       "Male                          ...                               Somewhat easy   \n",
       "Male                          ...                          Somewhat difficult   \n",
       "Male                          ...                                  Don't know   \n",
       "Male                          ...                                  Don't know   \n",
       "Male                          ...                              Very difficult   \n",
       "Male                          ...                                  Don't know   \n",
       "Male                          ...                          Somewhat difficult   \n",
       "Male                          ...                                   Very easy   \n",
       "Male                          ...                                   Very easy   \n",
       "Male                          ...                                   Very easy   \n",
       "Male                          ...                               Somewhat easy   \n",
       "Male                          ...                                  Don't know   \n",
       "Male                          ...                          Somewhat difficult   \n",
       "Male                          ...                              Very difficult   \n",
       "Male                          ...                          Somewhat difficult   \n",
       "Male                          ...                                  Don't know   \n",
       "Male                          ...                                  Don't know   \n",
       "Male                          ...                                  Don't know   \n",
       "Male                          ...                                  Don't know   \n",
       "Male                          ...                                  Don't know   \n",
       "...                           ...                                         ...   \n",
       "Male                          ...                              Very difficult   \n",
       "Male                          ...                                  Don't know   \n",
       "Male                          ...                                  Don't know   \n",
       "Male                          ...                               Somewhat easy   \n",
       "Male                          ...                                  Don't know   \n",
       "Male                          ...                               Somewhat easy   \n",
       "Male                          ...                                   Very easy   \n",
       "Male                          ...                               Somewhat easy   \n",
       "Male                          ...                              Very difficult   \n",
       "Male                          ...                                  Don't know   \n",
       "Male                          ...                                  Don't know   \n",
       "Male                          ...                                   Very easy   \n",
       "Male                          ...                                  Don't know   \n",
       "Male                          ...                               Somewhat easy   \n",
       "Male                          ...                               Somewhat easy   \n",
       "Male                          ...                                  Don't know   \n",
       "Male                          ...                                   Very easy   \n",
       "Male                          ...                                  Don't know   \n",
       "Male                          ...                                  Don't know   \n",
       "Male                          ...                               Somewhat easy   \n",
       "Male                          ...                               Somewhat easy   \n",
       "Male                          ...                               Somewhat easy   \n",
       "Male                          ...                                  Don't know   \n",
       "Male                          ...                                  Don't know   \n",
       "Male                          ...                               Somewhat easy   \n",
       "Male                          ...                                  Don't know   \n",
       "Male                          ...                               Somewhat easy   \n",
       "Male                          ...                               Somewhat easy   \n",
       "Male                          ...                          Somewhat difficult   \n",
       "Male                          ...                                  Don't know   \n",
       "\n",
       "       mental_health_consequence phys_health_consequence     coworkers  \\\n",
       "Gender                                                                   \n",
       "Male                          No                      No           Yes   \n",
       "Male                         Yes                     Yes  Some of them   \n",
       "Male                          No                      No  Some of them   \n",
       "Male                          No                      No           Yes   \n",
       "Male                          No                      No           Yes   \n",
       "Male                          No                      No  Some of them   \n",
       "Male                          No                      No           Yes   \n",
       "Male                          No                      No  Some of them   \n",
       "Male                       Maybe                      No  Some of them   \n",
       "Male                       Maybe                      No  Some of them   \n",
       "Male                          No                      No  Some of them   \n",
       "Male                       Maybe                      No  Some of them   \n",
       "Male                       Maybe                      No  Some of them   \n",
       "Male                       Maybe                      No            No   \n",
       "Male                         Yes                   Maybe  Some of them   \n",
       "Male                         Yes                     Yes  Some of them   \n",
       "Male                         Yes                      No            No   \n",
       "Male                       Maybe                      No  Some of them   \n",
       "Male                          No                      No           Yes   \n",
       "Male                         Yes                   Maybe  Some of them   \n",
       "Male                       Maybe                      No  Some of them   \n",
       "Male                       Maybe                   Maybe  Some of them   \n",
       "Male                         Yes                     Yes  Some of them   \n",
       "Male                       Maybe                      No  Some of them   \n",
       "Male                         Yes                   Maybe            No   \n",
       "Male                          No                      No            No   \n",
       "Male                          No                   Maybe  Some of them   \n",
       "Male                          No                      No           Yes   \n",
       "Male                          No                      No  Some of them   \n",
       "Male                       Maybe                      No  Some of them   \n",
       "...                          ...                     ...           ...   \n",
       "Male                         Yes                   Maybe  Some of them   \n",
       "Male                         Yes                      No            No   \n",
       "Male                          No                      No           Yes   \n",
       "Male                       Maybe                   Maybe  Some of them   \n",
       "Male                       Maybe                      No  Some of them   \n",
       "Male                       Maybe                      No  Some of them   \n",
       "Male                       Maybe                   Maybe  Some of them   \n",
       "Male                          No                      No  Some of them   \n",
       "Male                         Yes                     Yes  Some of them   \n",
       "Male                       Maybe                      No  Some of them   \n",
       "Male                          No                      No  Some of them   \n",
       "Male                          No                      No           Yes   \n",
       "Male                       Maybe                      No  Some of them   \n",
       "Male                       Maybe                   Maybe            No   \n",
       "Male                         Yes                      No  Some of them   \n",
       "Male                         Yes                      No            No   \n",
       "Male                          No                      No           Yes   \n",
       "Male                          No                      No           Yes   \n",
       "Male                       Maybe                      No           Yes   \n",
       "Male                       Maybe                   Maybe  Some of them   \n",
       "Male                         Yes                   Maybe  Some of them   \n",
       "Male                       Maybe                      No  Some of them   \n",
       "Male                       Maybe                      No            No   \n",
       "Male                       Maybe                   Maybe            No   \n",
       "Male                          No                      No            No   \n",
       "Male                          No                      No  Some of them   \n",
       "Male                          No                      No  Some of them   \n",
       "Male                       Maybe                   Maybe  Some of them   \n",
       "Male                          No                      No  Some of them   \n",
       "Male                       Maybe                      No  Some of them   \n",
       "\n",
       "          supervisor mental_health_interview phys_health_interview  \\\n",
       "Gender                                                               \n",
       "Male             Yes                     Yes                   Yes   \n",
       "Male              No                   Maybe                 Maybe   \n",
       "Male             Yes                     Yes                   Yes   \n",
       "Male             Yes                      No                 Maybe   \n",
       "Male             Yes                   Maybe                 Maybe   \n",
       "Male             Yes                      No                    No   \n",
       "Male             Yes                      No                    No   \n",
       "Male    Some of them                   Maybe                   Yes   \n",
       "Male              No                   Maybe                 Maybe   \n",
       "Male             Yes                      No                    No   \n",
       "Male    Some of them                   Maybe                 Maybe   \n",
       "Male    Some of them                      No                    No   \n",
       "Male             Yes                      No                 Maybe   \n",
       "Male              No                      No                   Yes   \n",
       "Male              No                      No                 Maybe   \n",
       "Male    Some of them                      No                 Maybe   \n",
       "Male              No                      No                    No   \n",
       "Male    Some of them                   Maybe                   Yes   \n",
       "Male             Yes                   Maybe                 Maybe   \n",
       "Male              No                      No                    No   \n",
       "Male    Some of them                      No                 Maybe   \n",
       "Male              No                      No                   Yes   \n",
       "Male              No                      No                 Maybe   \n",
       "Male              No                      No                    No   \n",
       "Male    Some of them                      No                    No   \n",
       "Male              No                      No                 Maybe   \n",
       "Male             Yes                   Maybe                   Yes   \n",
       "Male             Yes                   Maybe                   Yes   \n",
       "Male             Yes                   Maybe                 Maybe   \n",
       "Male             Yes                      No                    No   \n",
       "...              ...                     ...                   ...   \n",
       "Male              No                      No                    No   \n",
       "Male              No                      No                 Maybe   \n",
       "Male             Yes                      No                 Maybe   \n",
       "Male             Yes                      No                 Maybe   \n",
       "Male             Yes                      No                    No   \n",
       "Male             Yes                   Maybe                   Yes   \n",
       "Male             Yes                      No                    No   \n",
       "Male             Yes                   Maybe                 Maybe   \n",
       "Male              No                      No                 Maybe   \n",
       "Male              No                      No                 Maybe   \n",
       "Male             Yes                      No                    No   \n",
       "Male             Yes                      No                    No   \n",
       "Male              No                      No                    No   \n",
       "Male    Some of them                      No                    No   \n",
       "Male    Some of them                      No                 Maybe   \n",
       "Male              No                      No                    No   \n",
       "Male             Yes                     Yes                   Yes   \n",
       "Male             Yes                      No                 Maybe   \n",
       "Male    Some of them                      No                    No   \n",
       "Male              No                      No                 Maybe   \n",
       "Male             Yes                      No                    No   \n",
       "Male              No                      No                    No   \n",
       "Male              No                      No                 Maybe   \n",
       "Male             Yes                      No                 Maybe   \n",
       "Male    Some of them                   Maybe                   Yes   \n",
       "Male             Yes                   Maybe                 Maybe   \n",
       "Male             Yes                      No                   Yes   \n",
       "Male    Some of them                      No                    No   \n",
       "Male             Yes                      No                    No   \n",
       "Male              No                      No                    No   \n",
       "\n",
       "       mental_vs_physical obs_consequence  \\\n",
       "Gender                                      \n",
       "Male                   No              No   \n",
       "Male                   No             Yes   \n",
       "Male           Don't know              No   \n",
       "Male           Don't know              No   \n",
       "Male                  Yes              No   \n",
       "Male           Don't know              No   \n",
       "Male           Don't know              No   \n",
       "Male                  Yes              No   \n",
       "Male                   No              No   \n",
       "Male                   No              No   \n",
       "Male           Don't know              No   \n",
       "Male                   No              No   \n",
       "Male                   No              No   \n",
       "Male           Don't know              No   \n",
       "Male                   No              No   \n",
       "Male                   No              No   \n",
       "Male                   No             Yes   \n",
       "Male           Don't know              No   \n",
       "Male           Don't know              No   \n",
       "Male           Don't know              No   \n",
       "Male                   No              No   \n",
       "Male                   No             Yes   \n",
       "Male           Don't know              No   \n",
       "Male                  Yes              No   \n",
       "Male                   No              No   \n",
       "Male           Don't know              No   \n",
       "Male           Don't know              No   \n",
       "Male           Don't know              No   \n",
       "Male           Don't know              No   \n",
       "Male           Don't know              No   \n",
       "...                   ...             ...   \n",
       "Male           Don't know              No   \n",
       "Male                   No              No   \n",
       "Male           Don't know              No   \n",
       "Male           Don't know              No   \n",
       "Male           Don't know              No   \n",
       "Male                  Yes              No   \n",
       "Male                   No              No   \n",
       "Male           Don't know             Yes   \n",
       "Male                   No             Yes   \n",
       "Male           Don't know              No   \n",
       "Male           Don't know              No   \n",
       "Male           Don't know              No   \n",
       "Male                   No              No   \n",
       "Male                  Yes              No   \n",
       "Male           Don't know              No   \n",
       "Male           Don't know              No   \n",
       "Male                  Yes              No   \n",
       "Male           Don't know              No   \n",
       "Male           Don't know              No   \n",
       "Male           Don't know              No   \n",
       "Male                   No             Yes   \n",
       "Male           Don't know              No   \n",
       "Male           Don't know              No   \n",
       "Male           Don't know             Yes   \n",
       "Male           Don't know              No   \n",
       "Male                  Yes              No   \n",
       "Male                  Yes              No   \n",
       "Male           Don't know              No   \n",
       "Male                  Yes              No   \n",
       "Male           Don't know              No   \n",
       "\n",
       "                                                 comments  \n",
       "Gender                                                     \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male    I'm not on my company's health insurance which...  \n",
       "Male                                                  NaN  \n",
       "Male    My company does provide healthcare but not to ...  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male    I selected my current employer based on its po...  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "...                                                   ...  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male    I openly discuss my mental health struggles. I...  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male    I work at a large university with a track reco...  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male    In australia all organisations of a certain si...  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "Male                                                  NaN  \n",
       "\n",
       "[615 rows x 26 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gender.loc['Male'] # index can be different in python\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vQx4rzahVZ-a",
    "outputId": "95d2d95a-2888-4284-93f6-dd1673f1b429"
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "cannot do positional indexing on <class 'pandas.core.indexes.range.RangeIndex'> with these indexers [Gender] of <type 'str'>",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-43-80e648bff291>\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[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Gender'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m# generates error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/anaconda2/lib/python2.7/site-packages/pandas/core/indexing.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1371\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1372\u001b[0m             \u001b[0mmaybe_callable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1373\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1374\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1375\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda2/lib/python2.7/site-packages/pandas/core/indexing.pyc\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1821\u001b[0m         \u001b[0;31m# a single integer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1822\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1823\u001b[0;31m             \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_scalar_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1824\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1825\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda2/lib/python2.7/site-packages/pandas/core/indexing.pyc\u001b[0m in \u001b[0;36m_convert_scalar_indexer\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m    254\u001b[0m         \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m         \u001b[0;31m# a scalar\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 256\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_scalar_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    257\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    258\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_convert_slice_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda2/lib/python2.7/site-packages/pandas/core/indexes/numeric.pyc\u001b[0m in \u001b[0;36m_convert_scalar_indexer\u001b[0;34m(self, key, kind)\u001b[0m\n\u001b[1;32m    166\u001b[0m             \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    167\u001b[0m         return (super(Int64Index, self)\n\u001b[0;32m--> 168\u001b[0;31m                 ._convert_scalar_indexer(key, kind=kind))\n\u001b[0m\u001b[1;32m    169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    170\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_wrap_joined_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjoined\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda2/lib/python2.7/site-packages/pandas/core/indexes/base.pyc\u001b[0m in \u001b[0;36m_convert_scalar_indexer\u001b[0;34m(self, key, kind)\u001b[0m\n\u001b[1;32m   1363\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1364\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mkind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'iloc'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1365\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'positional'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1367\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mABCMultiIndex\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda2/lib/python2.7/site-packages/pandas/core/indexes/base.pyc\u001b[0m in \u001b[0;36m_validate_indexer\u001b[0;34m(self, form, key, kind)\u001b[0m\n\u001b[1;32m   3493\u001b[0m             \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3494\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mkind\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'iloc'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'getitem'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3495\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_invalid_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mform\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3496\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3497\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda2/lib/python2.7/site-packages/pandas/core/indexes/base.pyc\u001b[0m in \u001b[0;36m_invalid_indexer\u001b[0;34m(self, form, key)\u001b[0m\n\u001b[1;32m   1574\u001b[0m                         \"indexers [{key}] of {kind}\".format(\n\u001b[1;32m   1575\u001b[0m                             \u001b[0mform\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mform\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1576\u001b[0;31m                             kind=type(key)))\n\u001b[0m\u001b[1;32m   1577\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1578\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_duplicates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: cannot do positional indexing on <class 'pandas.core.indexes.range.RangeIndex'> with these indexers [Gender] of <type 'str'>"
     ]
    }
   ],
   "source": [
    "df.iloc['Gender'] # generates error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Y5gxfBo5VZ-c",
    "outputId": "fe806a10-ebfc-49a8-eb38-3b17ed407ca2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp                    2014-08-27 11:29:31\n",
       "Age                                           37\n",
       "Gender                                    Female\n",
       "Country                            United States\n",
       "state                                         IL\n",
       "self_employed                                NaN\n",
       "family_history                                No\n",
       "treatment                                    Yes\n",
       "work_interfere                             Often\n",
       "no_employees                                6-25\n",
       "remote_work                                   No\n",
       "tech_company                                 Yes\n",
       "benefits                                     Yes\n",
       "care_options                            Not sure\n",
       "wellness_program                              No\n",
       "seek_help                                    Yes\n",
       "anonymity                                    Yes\n",
       "leave                              Somewhat easy\n",
       "mental_health_consequence                     No\n",
       "phys_health_consequence                       No\n",
       "coworkers                           Some of them\n",
       "supervisor                                   Yes\n",
       "mental_health_interview                       No\n",
       "phys_health_interview                      Maybe\n",
       "mental_vs_physical                           Yes\n",
       "obs_consequence                               No\n",
       "comments                                     NaN\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zBjjcPHZVZ-i"
   },
   "source": [
    "## Sorting\n",
    "You can use either sort_values() or sort_index():\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2TQQb4fkVZ-i",
    "outputId": "d52d4824-f7f5-41ea-ff1e-2f460b2d1277"
   },
   "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>Timestamp</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Country</th>\n",
       "      <th>state</th>\n",
       "      <th>self_employed</th>\n",
       "      <th>family_history</th>\n",
       "      <th>treatment</th>\n",
       "      <th>work_interfere</th>\n",
       "      <th>no_employees</th>\n",
       "      <th>...</th>\n",
       "      <th>leave</th>\n",
       "      <th>mental_health_consequence</th>\n",
       "      <th>phys_health_consequence</th>\n",
       "      <th>coworkers</th>\n",
       "      <th>supervisor</th>\n",
       "      <th>mental_health_interview</th>\n",
       "      <th>phys_health_interview</th>\n",
       "      <th>mental_vs_physical</th>\n",
       "      <th>obs_consequence</th>\n",
       "      <th>comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>715</th>\n",
       "      <td>2014-08-28 10:07:53</td>\n",
       "      <td>-1726</td>\n",
       "      <td>male</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>26-100</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat difficult</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>2014-08-27 12:39:14</td>\n",
       "      <td>-29</td>\n",
       "      <td>Male</td>\n",
       "      <td>United States</td>\n",
       "      <td>MN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>More than 1000</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1127</th>\n",
       "      <td>2014-08-30 20:55:11</td>\n",
       "      <td>-1</td>\n",
       "      <td>p</td>\n",
       "      <td>United States</td>\n",
       "      <td>AL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Often</td>\n",
       "      <td>1-5</td>\n",
       "      <td>...</td>\n",
       "      <td>Very easy</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>password: testered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>734</th>\n",
       "      <td>2014-08-28 10:35:55</td>\n",
       "      <td>5</td>\n",
       "      <td>Male</td>\n",
       "      <td>United States</td>\n",
       "      <td>OH</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100-500</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat easy</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>We had a developer suffer from depression and ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989</th>\n",
       "      <td>2014-08-29 09:10:58</td>\n",
       "      <td>8</td>\n",
       "      <td>A little about you</td>\n",
       "      <td>Bahamas, The</td>\n",
       "      <td>IL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Often</td>\n",
       "      <td>1-5</td>\n",
       "      <td>...</td>\n",
       "      <td>Very easy</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1090</th>\n",
       "      <td>2014-08-29 17:26:15</td>\n",
       "      <td>11</td>\n",
       "      <td>male</td>\n",
       "      <td>United States</td>\n",
       "      <td>OH</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Never</td>\n",
       "      <td>1-5</td>\n",
       "      <td>...</td>\n",
       "      <td>Very easy</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>2014-08-27 14:10:15</td>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>United States</td>\n",
       "      <td>TX</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>6-25</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>2014-08-27 14:11:55</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>United States</td>\n",
       "      <td>WA</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Never</td>\n",
       "      <td>26-100</td>\n",
       "      <td>...</td>\n",
       "      <td>Very easy</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2014-08-27 11:43:10</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Often</td>\n",
       "      <td>6-25</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat difficult</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>2014-08-27 16:19:05</td>\n",
       "      <td>18</td>\n",
       "      <td>Female</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>1-5</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>I don't have a job :D</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Timestamp   Age              Gender         Country state  \\\n",
       "715   2014-08-28 10:07:53 -1726                male  United Kingdom   NaN   \n",
       "143   2014-08-27 12:39:14   -29                Male   United States    MN   \n",
       "1127  2014-08-30 20:55:11    -1                   p   United States    AL   \n",
       "734   2014-08-28 10:35:55     5                Male   United States    OH   \n",
       "989   2014-08-29 09:10:58     8  A little about you    Bahamas, The    IL   \n",
       "1090  2014-08-29 17:26:15    11                male   United States    OH   \n",
       "282   2014-08-27 14:10:15    18                male   United States    TX   \n",
       "287   2014-08-27 14:11:55    18                Male   United States    WA   \n",
       "43    2014-08-27 11:43:10    18                Male     Netherlands   NaN   \n",
       "482   2014-08-27 16:19:05    18              Female  United Kingdom   NaN   \n",
       "\n",
       "     self_employed family_history treatment work_interfere    no_employees  \\\n",
       "715             No             No       Yes      Sometimes          26-100   \n",
       "143             No             No        No            NaN  More than 1000   \n",
       "1127           Yes            Yes       Yes          Often             1-5   \n",
       "734             No             No        No            NaN         100-500   \n",
       "989            Yes            Yes       Yes          Often             1-5   \n",
       "1090           Yes             No        No          Never             1-5   \n",
       "282             No             No       Yes      Sometimes            6-25   \n",
       "287             No             No        No          Never          26-100   \n",
       "43              No             No        No          Often            6-25   \n",
       "482             No            Yes       Yes      Sometimes             1-5   \n",
       "\n",
       "                            ...                                       leave  \\\n",
       "715                         ...                          Somewhat difficult   \n",
       "143                         ...                                  Don't know   \n",
       "1127                        ...                                   Very easy   \n",
       "734                         ...                               Somewhat easy   \n",
       "989                         ...                                   Very easy   \n",
       "1090                        ...                                   Very easy   \n",
       "282                         ...                                  Don't know   \n",
       "287                         ...                                   Very easy   \n",
       "43                          ...                          Somewhat difficult   \n",
       "482                         ...                                  Don't know   \n",
       "\n",
       "     mental_health_consequence phys_health_consequence     coworkers  \\\n",
       "715                        Yes                      No            No   \n",
       "143                         No                      No  Some of them   \n",
       "1127                       Yes                     Yes           Yes   \n",
       "734                         No                      No           Yes   \n",
       "989                        Yes                     Yes           Yes   \n",
       "1090                        No                      No  Some of them   \n",
       "282                        Yes                      No            No   \n",
       "287                         No                      No  Some of them   \n",
       "43                         Yes                   Maybe            No   \n",
       "482                      Maybe                   Maybe  Some of them   \n",
       "\n",
       "        supervisor mental_health_interview phys_health_interview  \\\n",
       "715             No                      No                 Maybe   \n",
       "143            Yes                      No                    No   \n",
       "1127           Yes                     Yes                   Yes   \n",
       "734            Yes                      No                    No   \n",
       "989            Yes                     Yes                   Yes   \n",
       "1090  Some of them                      No                 Maybe   \n",
       "282             No                      No                 Maybe   \n",
       "287   Some of them                      No                 Maybe   \n",
       "43    Some of them                      No                    No   \n",
       "482   Some of them                   Maybe                 Maybe   \n",
       "\n",
       "     mental_vs_physical obs_consequence  \\\n",
       "715          Don't know              No   \n",
       "143          Don't know              No   \n",
       "1127                Yes             Yes   \n",
       "734                 Yes              No   \n",
       "989                 Yes             Yes   \n",
       "1090                Yes              No   \n",
       "282          Don't know              No   \n",
       "287                 Yes              No   \n",
       "43                   No              No   \n",
       "482          Don't know              No   \n",
       "\n",
       "                                               comments  \n",
       "715                                                 NaN  \n",
       "143                                                 NaN  \n",
       "1127                                 password: testered  \n",
       "734   We had a developer suffer from depression and ...  \n",
       "989                                                      \n",
       "1090                                                NaN  \n",
       "282                                                 NaN  \n",
       "287                                                 NaN  \n",
       "43                                                  NaN  \n",
       "482                               I don't have a job :D  \n",
       "\n",
       "[10 rows x 27 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sorted = df.sort_values('Age') # sort_values is increasing series\n",
    "df_sorted.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1Kx8cgRcVZ-n"
   },
   "source": [
    "## Filtering using Boolean Masking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       37\n",
       "1       44\n",
       "2       32\n",
       "3       31\n",
       "4       31\n",
       "5       33\n",
       "6       35\n",
       "7       39\n",
       "8       42\n",
       "9       23\n",
       "10      31\n",
       "11      29\n",
       "12      42\n",
       "13      36\n",
       "14      27\n",
       "15      29\n",
       "16      23\n",
       "17      32\n",
       "18      46\n",
       "19      36\n",
       "20      29\n",
       "21      31\n",
       "22      46\n",
       "23      41\n",
       "24      33\n",
       "25      35\n",
       "26      33\n",
       "27      35\n",
       "28      34\n",
       "29      37\n",
       "        ..\n",
       "1229    39\n",
       "1230    23\n",
       "1231    24\n",
       "1232    25\n",
       "1233    23\n",
       "1234    24\n",
       "1235    23\n",
       "1236    60\n",
       "1237    28\n",
       "1238    28\n",
       "1239    30\n",
       "1240    31\n",
       "1241    31\n",
       "1242    28\n",
       "1243    43\n",
       "1244    32\n",
       "1245    22\n",
       "1246    32\n",
       "1247    36\n",
       "1248    41\n",
       "1249    30\n",
       "1250    30\n",
       "1251    36\n",
       "1252    29\n",
       "1253    36\n",
       "1254    26\n",
       "1255    32\n",
       "1256    34\n",
       "1257    46\n",
       "1258    25\n",
       "Name: Age, Length: 1259, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Age # same as df['Age'] if there is black in column name: canot use df.Country education "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       True\n",
       "1       True\n",
       "2       True\n",
       "3       True\n",
       "4       True\n",
       "5       True\n",
       "6       True\n",
       "7       True\n",
       "8       True\n",
       "9       True\n",
       "10      True\n",
       "11      True\n",
       "12      True\n",
       "13      True\n",
       "14      True\n",
       "15      True\n",
       "16      True\n",
       "17      True\n",
       "18      True\n",
       "19      True\n",
       "20      True\n",
       "21      True\n",
       "22      True\n",
       "23      True\n",
       "24      True\n",
       "25      True\n",
       "26      True\n",
       "27      True\n",
       "28      True\n",
       "29      True\n",
       "        ... \n",
       "1229    True\n",
       "1230    True\n",
       "1231    True\n",
       "1232    True\n",
       "1233    True\n",
       "1234    True\n",
       "1235    True\n",
       "1236    True\n",
       "1237    True\n",
       "1238    True\n",
       "1239    True\n",
       "1240    True\n",
       "1241    True\n",
       "1242    True\n",
       "1243    True\n",
       "1244    True\n",
       "1245    True\n",
       "1246    True\n",
       "1247    True\n",
       "1248    True\n",
       "1249    True\n",
       "1250    True\n",
       "1251    True\n",
       "1252    True\n",
       "1253    True\n",
       "1254    True\n",
       "1255    True\n",
       "1256    True\n",
       "1257    True\n",
       "1258    True\n",
       "Name: Age, Length: 1259, dtype: bool"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'] > 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>Timestamp</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Country</th>\n",
       "      <th>state</th>\n",
       "      <th>self_employed</th>\n",
       "      <th>family_history</th>\n",
       "      <th>treatment</th>\n",
       "      <th>work_interfere</th>\n",
       "      <th>no_employees</th>\n",
       "      <th>...</th>\n",
       "      <th>leave</th>\n",
       "      <th>mental_health_consequence</th>\n",
       "      <th>phys_health_consequence</th>\n",
       "      <th>coworkers</th>\n",
       "      <th>supervisor</th>\n",
       "      <th>mental_health_interview</th>\n",
       "      <th>phys_health_interview</th>\n",
       "      <th>mental_vs_physical</th>\n",
       "      <th>obs_consequence</th>\n",
       "      <th>comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-08-27 11:29:31</td>\n",
       "      <td>37</td>\n",
       "      <td>Female</td>\n",
       "      <td>United States</td>\n",
       "      <td>IL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Often</td>\n",
       "      <td>6-25</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat easy</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-08-27 11:29:37</td>\n",
       "      <td>44</td>\n",
       "      <td>M</td>\n",
       "      <td>United States</td>\n",
       "      <td>IN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Rarely</td>\n",
       "      <td>More than 1000</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-08-27 11:29:44</td>\n",
       "      <td>32</td>\n",
       "      <td>Male</td>\n",
       "      <td>Canada</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Rarely</td>\n",
       "      <td>6-25</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat difficult</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-08-27 11:29:46</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Often</td>\n",
       "      <td>26-100</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat difficult</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-08-27 11:30:22</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>United States</td>\n",
       "      <td>TX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Never</td>\n",
       "      <td>100-500</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2014-08-27 11:31:22</td>\n",
       "      <td>33</td>\n",
       "      <td>Male</td>\n",
       "      <td>United States</td>\n",
       "      <td>TN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>6-25</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2014-08-27 11:31:50</td>\n",
       "      <td>35</td>\n",
       "      <td>Female</td>\n",
       "      <td>United States</td>\n",
       "      <td>MI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>1-5</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat difficult</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014-08-27 11:32:05</td>\n",
       "      <td>39</td>\n",
       "      <td>M</td>\n",
       "      <td>Canada</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Never</td>\n",
       "      <td>1-5</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2014-08-27 11:32:39</td>\n",
       "      <td>42</td>\n",
       "      <td>Female</td>\n",
       "      <td>United States</td>\n",
       "      <td>IL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>100-500</td>\n",
       "      <td>...</td>\n",
       "      <td>Very difficult</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2014-08-27 11:32:43</td>\n",
       "      <td>23</td>\n",
       "      <td>Male</td>\n",
       "      <td>Canada</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Never</td>\n",
       "      <td>26-100</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2014-08-27 11:32:44</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>United States</td>\n",
       "      <td>OH</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>6-25</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2014-08-27 11:32:49</td>\n",
       "      <td>29</td>\n",
       "      <td>male</td>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Never</td>\n",
       "      <td>100-500</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014-08-27 11:33:23</td>\n",
       "      <td>42</td>\n",
       "      <td>female</td>\n",
       "      <td>United States</td>\n",
       "      <td>CA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>26-100</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat difficult</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2014-08-27 11:33:26</td>\n",
       "      <td>36</td>\n",
       "      <td>Male</td>\n",
       "      <td>United States</td>\n",
       "      <td>CT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Never</td>\n",
       "      <td>500-1000</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>I'm not on my company's health insurance which...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014-08-27 11:33:57</td>\n",
       "      <td>27</td>\n",
       "      <td>Male</td>\n",
       "      <td>Canada</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Never</td>\n",
       "      <td>6-25</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat easy</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2014-08-27 11:34:00</td>\n",
       "      <td>29</td>\n",
       "      <td>female</td>\n",
       "      <td>United States</td>\n",
       "      <td>IL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Rarely</td>\n",
       "      <td>26-100</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat easy</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>I have chronic low-level neurological issues t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2014-08-27 11:34:20</td>\n",
       "      <td>23</td>\n",
       "      <td>Male</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>26-100</td>\n",
       "      <td>...</td>\n",
       "      <td>Very easy</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>My company does provide healthcare but not to ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2014-08-27 11:34:37</td>\n",
       "      <td>32</td>\n",
       "      <td>Male</td>\n",
       "      <td>United States</td>\n",
       "      <td>TN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>6-25</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2014-08-27 11:34:53</td>\n",
       "      <td>46</td>\n",
       "      <td>male</td>\n",
       "      <td>United States</td>\n",
       "      <td>MD</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>1-5</td>\n",
       "      <td>...</td>\n",
       "      <td>Very easy</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2014-08-27 11:35:08</td>\n",
       "      <td>36</td>\n",
       "      <td>Male</td>\n",
       "      <td>France</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6-25</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat easy</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2014-08-27 11:35:12</td>\n",
       "      <td>29</td>\n",
       "      <td>Male</td>\n",
       "      <td>United States</td>\n",
       "      <td>NY</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>100-500</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat difficult</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2014-08-27 11:35:24</td>\n",
       "      <td>31</td>\n",
       "      <td>male</td>\n",
       "      <td>United States</td>\n",
       "      <td>NC</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Never</td>\n",
       "      <td>1-5</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat difficult</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2014-08-27 11:35:48</td>\n",
       "      <td>46</td>\n",
       "      <td>Male</td>\n",
       "      <td>United States</td>\n",
       "      <td>MA</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Often</td>\n",
       "      <td>26-100</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2014-08-27 11:36:24</td>\n",
       "      <td>41</td>\n",
       "      <td>Male</td>\n",
       "      <td>United States</td>\n",
       "      <td>IA</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Never</td>\n",
       "      <td>More than 1000</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2014-08-27 11:36:48</td>\n",
       "      <td>33</td>\n",
       "      <td>male</td>\n",
       "      <td>United States</td>\n",
       "      <td>CA</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Rarely</td>\n",
       "      <td>26-100</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>Relatively new job. Ask again later</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2014-08-27 11:37:08</td>\n",
       "      <td>35</td>\n",
       "      <td>male</td>\n",
       "      <td>United States</td>\n",
       "      <td>TN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>More than 1000</td>\n",
       "      <td>...</td>\n",
       "      <td>Very easy</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Sometimes I think  about using drugs for my me...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2014-08-27 11:37:23</td>\n",
       "      <td>33</td>\n",
       "      <td>male</td>\n",
       "      <td>United States</td>\n",
       "      <td>TN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1-5</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2014-08-27 11:37:59</td>\n",
       "      <td>35</td>\n",
       "      <td>Female</td>\n",
       "      <td>United States</td>\n",
       "      <td>CA</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Rarely</td>\n",
       "      <td>6-25</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2014-08-27 11:38:12</td>\n",
       "      <td>34</td>\n",
       "      <td>male</td>\n",
       "      <td>United States</td>\n",
       "      <td>OH</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>26-100</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat difficult</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2014-08-27 11:38:18</td>\n",
       "      <td>37</td>\n",
       "      <td>Male</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>6-25</td>\n",
       "      <td>...</td>\n",
       "      <td>Very difficult</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>No</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1229</th>\n",
       "      <td>2015-02-22 02:40:32</td>\n",
       "      <td>39</td>\n",
       "      <td>Male</td>\n",
       "      <td>Greece</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6-25</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1230</th>\n",
       "      <td>2015-02-24 08:54:35</td>\n",
       "      <td>23</td>\n",
       "      <td>Female</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>6-25</td>\n",
       "      <td>...</td>\n",
       "      <td>Very easy</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1231</th>\n",
       "      <td>2015-02-24 08:58:08</td>\n",
       "      <td>24</td>\n",
       "      <td>Cis Man</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>6-25</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1232</th>\n",
       "      <td>2015-02-24 09:00:56</td>\n",
       "      <td>25</td>\n",
       "      <td>Male</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>More than 1000</td>\n",
       "      <td>...</td>\n",
       "      <td>Very easy</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>I work at a large university with a track reco...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1233</th>\n",
       "      <td>2015-02-24 09:13:49</td>\n",
       "      <td>23</td>\n",
       "      <td>Male</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Rarely</td>\n",
       "      <td>6-25</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234</th>\n",
       "      <td>2015-02-24 09:15:13</td>\n",
       "      <td>24</td>\n",
       "      <td>ostensibly male, unsure what that really means</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>6-25</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>i'm in a country with social health care so my...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1235</th>\n",
       "      <td>2015-02-24 09:18:25</td>\n",
       "      <td>23</td>\n",
       "      <td>Male</td>\n",
       "      <td>Canada</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Often</td>\n",
       "      <td>26-100</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1236</th>\n",
       "      <td>2015-02-24 10:32:32</td>\n",
       "      <td>60</td>\n",
       "      <td>Male</td>\n",
       "      <td>United States</td>\n",
       "      <td>CA</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Often</td>\n",
       "      <td>More than 1000</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat easy</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1237</th>\n",
       "      <td>2015-02-26 05:44:16</td>\n",
       "      <td>28</td>\n",
       "      <td>male</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>26-100</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1238</th>\n",
       "      <td>2015-04-02 15:47:43</td>\n",
       "      <td>28</td>\n",
       "      <td>Male</td>\n",
       "      <td>United States</td>\n",
       "      <td>TN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Often</td>\n",
       "      <td>More than 1000</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat easy</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1239</th>\n",
       "      <td>2015-04-04 11:22:05</td>\n",
       "      <td>30</td>\n",
       "      <td>male</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>500-1000</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1240</th>\n",
       "      <td>2015-04-06 14:58:29</td>\n",
       "      <td>31</td>\n",
       "      <td>male</td>\n",
       "      <td>Germany</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>100-500</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat easy</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1241</th>\n",
       "      <td>2015-04-11 14:35:22</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>Poland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Often</td>\n",
       "      <td>6-25</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat easy</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1242</th>\n",
       "      <td>2015-04-23 14:03:51</td>\n",
       "      <td>28</td>\n",
       "      <td>Male</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Rarely</td>\n",
       "      <td>26-100</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1243</th>\n",
       "      <td>2015-05-05 14:22:18</td>\n",
       "      <td>43</td>\n",
       "      <td>f</td>\n",
       "      <td>United States</td>\n",
       "      <td>FL</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Rarely</td>\n",
       "      <td>More than 1000</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1244</th>\n",
       "      <td>2015-05-05 15:16:25</td>\n",
       "      <td>32</td>\n",
       "      <td>female</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>More than 1000</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1245</th>\n",
       "      <td>2015-05-06 10:14:50</td>\n",
       "      <td>22</td>\n",
       "      <td>Male</td>\n",
       "      <td>Australia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Often</td>\n",
       "      <td>100-500</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Yes</td>\n",
       "      <td>In australia all organisations of a certain si...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1246</th>\n",
       "      <td>2015-05-06 16:55:58</td>\n",
       "      <td>32</td>\n",
       "      <td>Male</td>\n",
       "      <td>United States</td>\n",
       "      <td>OR</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Never</td>\n",
       "      <td>100-500</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat easy</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1247</th>\n",
       "      <td>2015-05-07 10:08:50</td>\n",
       "      <td>36</td>\n",
       "      <td>male</td>\n",
       "      <td>Finland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Often</td>\n",
       "      <td>6-25</td>\n",
       "      <td>...</td>\n",
       "      <td>Very difficult</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1248</th>\n",
       "      <td>2015-06-25 12:24:31</td>\n",
       "      <td>41</td>\n",
       "      <td>Female</td>\n",
       "      <td>United States</td>\n",
       "      <td>WA</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>26-100</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1249</th>\n",
       "      <td>2015-07-22 18:57:54</td>\n",
       "      <td>30</td>\n",
       "      <td>M</td>\n",
       "      <td>United States</td>\n",
       "      <td>CA</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>26-100</td>\n",
       "      <td>...</td>\n",
       "      <td>Very easy</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Bipolar disorder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1250</th>\n",
       "      <td>2015-07-27 23:25:34</td>\n",
       "      <td>30</td>\n",
       "      <td>Male</td>\n",
       "      <td>United States</td>\n",
       "      <td>CA</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Often</td>\n",
       "      <td>26-100</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1251</th>\n",
       "      <td>2015-08-17 09:38:35</td>\n",
       "      <td>36</td>\n",
       "      <td>Male</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Often</td>\n",
       "      <td>100-500</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat easy</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1252</th>\n",
       "      <td>2015-08-20 16:52:09</td>\n",
       "      <td>29</td>\n",
       "      <td>male</td>\n",
       "      <td>United States</td>\n",
       "      <td>NC</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>100-500</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1253</th>\n",
       "      <td>2015-08-25 19:59:38</td>\n",
       "      <td>36</td>\n",
       "      <td>Male</td>\n",
       "      <td>United States</td>\n",
       "      <td>UT</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Rarely</td>\n",
       "      <td>More than 1000</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat easy</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>2015-09-12 11:17:21</td>\n",
       "      <td>26</td>\n",
       "      <td>male</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26-100</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat easy</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>2015-09-26 01:07:35</td>\n",
       "      <td>32</td>\n",
       "      <td>Male</td>\n",
       "      <td>United States</td>\n",
       "      <td>IL</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Often</td>\n",
       "      <td>26-100</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat difficult</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1256</th>\n",
       "      <td>2015-11-07 12:36:58</td>\n",
       "      <td>34</td>\n",
       "      <td>male</td>\n",
       "      <td>United States</td>\n",
       "      <td>CA</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>More than 1000</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat difficult</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1257</th>\n",
       "      <td>2015-11-30 21:25:06</td>\n",
       "      <td>46</td>\n",
       "      <td>f</td>\n",
       "      <td>United States</td>\n",
       "      <td>NC</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100-500</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1258</th>\n",
       "      <td>2016-02-01 23:04:31</td>\n",
       "      <td>25</td>\n",
       "      <td>Male</td>\n",
       "      <td>United States</td>\n",
       "      <td>IL</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>26-100</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1256 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Timestamp  Age  \\\n",
       "0     2014-08-27 11:29:31   37   \n",
       "1     2014-08-27 11:29:37   44   \n",
       "2     2014-08-27 11:29:44   32   \n",
       "3     2014-08-27 11:29:46   31   \n",
       "4     2014-08-27 11:30:22   31   \n",
       "5     2014-08-27 11:31:22   33   \n",
       "6     2014-08-27 11:31:50   35   \n",
       "7     2014-08-27 11:32:05   39   \n",
       "8     2014-08-27 11:32:39   42   \n",
       "9     2014-08-27 11:32:43   23   \n",
       "10    2014-08-27 11:32:44   31   \n",
       "11    2014-08-27 11:32:49   29   \n",
       "12    2014-08-27 11:33:23   42   \n",
       "13    2014-08-27 11:33:26   36   \n",
       "14    2014-08-27 11:33:57   27   \n",
       "15    2014-08-27 11:34:00   29   \n",
       "16    2014-08-27 11:34:20   23   \n",
       "17    2014-08-27 11:34:37   32   \n",
       "18    2014-08-27 11:34:53   46   \n",
       "19    2014-08-27 11:35:08   36   \n",
       "20    2014-08-27 11:35:12   29   \n",
       "21    2014-08-27 11:35:24   31   \n",
       "22    2014-08-27 11:35:48   46   \n",
       "23    2014-08-27 11:36:24   41   \n",
       "24    2014-08-27 11:36:48   33   \n",
       "25    2014-08-27 11:37:08   35   \n",
       "26    2014-08-27 11:37:23   33   \n",
       "27    2014-08-27 11:37:59   35   \n",
       "28    2014-08-27 11:38:12   34   \n",
       "29    2014-08-27 11:38:18   37   \n",
       "...                   ...  ...   \n",
       "1229  2015-02-22 02:40:32   39   \n",
       "1230  2015-02-24 08:54:35   23   \n",
       "1231  2015-02-24 08:58:08   24   \n",
       "1232  2015-02-24 09:00:56   25   \n",
       "1233  2015-02-24 09:13:49   23   \n",
       "1234  2015-02-24 09:15:13   24   \n",
       "1235  2015-02-24 09:18:25   23   \n",
       "1236  2015-02-24 10:32:32   60   \n",
       "1237  2015-02-26 05:44:16   28   \n",
       "1238  2015-04-02 15:47:43   28   \n",
       "1239  2015-04-04 11:22:05   30   \n",
       "1240  2015-04-06 14:58:29   31   \n",
       "1241  2015-04-11 14:35:22   31   \n",
       "1242  2015-04-23 14:03:51   28   \n",
       "1243  2015-05-05 14:22:18   43   \n",
       "1244  2015-05-05 15:16:25   32   \n",
       "1245  2015-05-06 10:14:50   22   \n",
       "1246  2015-05-06 16:55:58   32   \n",
       "1247  2015-05-07 10:08:50   36   \n",
       "1248  2015-06-25 12:24:31   41   \n",
       "1249  2015-07-22 18:57:54   30   \n",
       "1250  2015-07-27 23:25:34   30   \n",
       "1251  2015-08-17 09:38:35   36   \n",
       "1252  2015-08-20 16:52:09   29   \n",
       "1253  2015-08-25 19:59:38   36   \n",
       "1254  2015-09-12 11:17:21   26   \n",
       "1255  2015-09-26 01:07:35   32   \n",
       "1256  2015-11-07 12:36:58   34   \n",
       "1257  2015-11-30 21:25:06   46   \n",
       "1258  2016-02-01 23:04:31   25   \n",
       "\n",
       "                                              Gender         Country state  \\\n",
       "0                                             Female   United States    IL   \n",
       "1                                                  M   United States    IN   \n",
       "2                                               Male          Canada   NaN   \n",
       "3                                               Male  United Kingdom   NaN   \n",
       "4                                               Male   United States    TX   \n",
       "5                                               Male   United States    TN   \n",
       "6                                             Female   United States    MI   \n",
       "7                                                  M          Canada   NaN   \n",
       "8                                             Female   United States    IL   \n",
       "9                                               Male          Canada   NaN   \n",
       "10                                              Male   United States    OH   \n",
       "11                                              male        Bulgaria   NaN   \n",
       "12                                            female   United States    CA   \n",
       "13                                              Male   United States    CT   \n",
       "14                                              Male          Canada   NaN   \n",
       "15                                            female   United States    IL   \n",
       "16                                              Male  United Kingdom   NaN   \n",
       "17                                              Male   United States    TN   \n",
       "18                                              male   United States    MD   \n",
       "19                                              Male          France   NaN   \n",
       "20                                              Male   United States    NY   \n",
       "21                                              male   United States    NC   \n",
       "22                                              Male   United States    MA   \n",
       "23                                              Male   United States    IA   \n",
       "24                                              male   United States    CA   \n",
       "25                                              male   United States    TN   \n",
       "26                                              male   United States    TN   \n",
       "27                                            Female   United States    CA   \n",
       "28                                              male   United States    OH   \n",
       "29                                              Male  United Kingdom   NaN   \n",
       "...                                              ...             ...   ...   \n",
       "1229                                            Male          Greece   NaN   \n",
       "1230                                          Female  United Kingdom   NaN   \n",
       "1231                                         Cis Man  United Kingdom   NaN   \n",
       "1232                                            Male  United Kingdom   NaN   \n",
       "1233                                            Male  United Kingdom   NaN   \n",
       "1234  ostensibly male, unsure what that really means  United Kingdom   NaN   \n",
       "1235                                            Male          Canada   NaN   \n",
       "1236                                            Male   United States    CA   \n",
       "1237                                            male         Ireland   NaN   \n",
       "1238                                            Male   United States    TN   \n",
       "1239                                            male     Netherlands   NaN   \n",
       "1240                                            male         Germany   NaN   \n",
       "1241                                            Male          Poland   NaN   \n",
       "1242                                            Male         Ireland   NaN   \n",
       "1243                                               f   United States    FL   \n",
       "1244                                          female  United Kingdom   NaN   \n",
       "1245                                            Male       Australia   NaN   \n",
       "1246                                            Male   United States    OR   \n",
       "1247                                            male         Finland   NaN   \n",
       "1248                                          Female   United States    WA   \n",
       "1249                                               M   United States    CA   \n",
       "1250                                            Male   United States    CA   \n",
       "1251                                            Male    South Africa   NaN   \n",
       "1252                                            male   United States    NC   \n",
       "1253                                            Male   United States    UT   \n",
       "1254                                            male  United Kingdom   NaN   \n",
       "1255                                            Male   United States    IL   \n",
       "1256                                            male   United States    CA   \n",
       "1257                                               f   United States    NC   \n",
       "1258                                            Male   United States    IL   \n",
       "\n",
       "     self_employed family_history treatment work_interfere    no_employees  \\\n",
       "0              NaN             No       Yes          Often            6-25   \n",
       "1              NaN             No        No         Rarely  More than 1000   \n",
       "2              NaN             No        No         Rarely            6-25   \n",
       "3              NaN            Yes       Yes          Often          26-100   \n",
       "4              NaN             No        No          Never         100-500   \n",
       "5              NaN            Yes        No      Sometimes            6-25   \n",
       "6              NaN            Yes       Yes      Sometimes             1-5   \n",
       "7              NaN             No        No          Never             1-5   \n",
       "8              NaN            Yes       Yes      Sometimes         100-500   \n",
       "9              NaN             No        No          Never          26-100   \n",
       "10             NaN             No       Yes      Sometimes            6-25   \n",
       "11             NaN             No        No          Never         100-500   \n",
       "12             NaN            Yes       Yes      Sometimes          26-100   \n",
       "13             NaN            Yes        No          Never        500-1000   \n",
       "14             NaN             No        No          Never            6-25   \n",
       "15             NaN            Yes       Yes         Rarely          26-100   \n",
       "16             NaN             No       Yes      Sometimes          26-100   \n",
       "17             NaN             No       Yes      Sometimes            6-25   \n",
       "18             Yes            Yes        No      Sometimes             1-5   \n",
       "19             Yes            Yes        No            NaN            6-25   \n",
       "20              No            Yes       Yes      Sometimes         100-500   \n",
       "21             Yes             No        No          Never             1-5   \n",
       "22              No             No       Yes          Often          26-100   \n",
       "23              No             No       Yes          Never  More than 1000   \n",
       "24              No            Yes       Yes         Rarely          26-100   \n",
       "25              No            Yes       Yes      Sometimes  More than 1000   \n",
       "26              No             No        No            NaN             1-5   \n",
       "27              No            Yes       Yes         Rarely            6-25   \n",
       "28              No             No       Yes      Sometimes          26-100   \n",
       "29              No             No        No      Sometimes            6-25   \n",
       "...            ...            ...       ...            ...             ...   \n",
       "1229            No             No        No            NaN            6-25   \n",
       "1230            No            Yes       Yes      Sometimes            6-25   \n",
       "1231           Yes             No       Yes      Sometimes            6-25   \n",
       "1232            No            Yes       Yes      Sometimes  More than 1000   \n",
       "1233            No             No       Yes         Rarely            6-25   \n",
       "1234            No             No       Yes      Sometimes            6-25   \n",
       "1235            No             No       Yes          Often          26-100   \n",
       "1236            No             No       Yes          Often  More than 1000   \n",
       "1237            No             No        No      Sometimes          26-100   \n",
       "1238            No            Yes       Yes          Often  More than 1000   \n",
       "1239            No             No        No      Sometimes        500-1000   \n",
       "1240            No            Yes       Yes      Sometimes         100-500   \n",
       "1241           Yes             No       Yes          Often            6-25   \n",
       "1242            No             No       Yes         Rarely          26-100   \n",
       "1243            No            Yes       Yes         Rarely  More than 1000   \n",
       "1244            No             No        No            NaN  More than 1000   \n",
       "1245            No            Yes       Yes          Often         100-500   \n",
       "1246            No             No        No          Never         100-500   \n",
       "1247            No             No       Yes          Often            6-25   \n",
       "1248            No            Yes       Yes      Sometimes          26-100   \n",
       "1249            No            Yes       Yes      Sometimes          26-100   \n",
       "1250           Yes            Yes       Yes          Often          26-100   \n",
       "1251            No            Yes       Yes          Often         100-500   \n",
       "1252            No            Yes       Yes      Sometimes         100-500   \n",
       "1253            No            Yes        No         Rarely  More than 1000   \n",
       "1254            No             No       Yes            NaN          26-100   \n",
       "1255            No            Yes       Yes          Often          26-100   \n",
       "1256            No            Yes       Yes      Sometimes  More than 1000   \n",
       "1257            No             No        No            NaN         100-500   \n",
       "1258            No            Yes       Yes      Sometimes          26-100   \n",
       "\n",
       "                            ...                                       leave  \\\n",
       "0                           ...                               Somewhat easy   \n",
       "1                           ...                                  Don't know   \n",
       "2                           ...                          Somewhat difficult   \n",
       "3                           ...                          Somewhat difficult   \n",
       "4                           ...                                  Don't know   \n",
       "5                           ...                                  Don't know   \n",
       "6                           ...                          Somewhat difficult   \n",
       "7                           ...                                  Don't know   \n",
       "8                           ...                              Very difficult   \n",
       "9                           ...                                  Don't know   \n",
       "10                          ...                                  Don't know   \n",
       "11                          ...                                  Don't know   \n",
       "12                          ...                          Somewhat difficult   \n",
       "13                          ...                                  Don't know   \n",
       "14                          ...                               Somewhat easy   \n",
       "15                          ...                               Somewhat easy   \n",
       "16                          ...                                   Very easy   \n",
       "17                          ...                                  Don't know   \n",
       "18                          ...                                   Very easy   \n",
       "19                          ...                               Somewhat easy   \n",
       "20                          ...                          Somewhat difficult   \n",
       "21                          ...                          Somewhat difficult   \n",
       "22                          ...                                  Don't know   \n",
       "23                          ...                                  Don't know   \n",
       "24                          ...                                  Don't know   \n",
       "25                          ...                                   Very easy   \n",
       "26                          ...                                  Don't know   \n",
       "27                          ...                                  Don't know   \n",
       "28                          ...                          Somewhat difficult   \n",
       "29                          ...                              Very difficult   \n",
       "...                         ...                                         ...   \n",
       "1229                        ...                                  Don't know   \n",
       "1230                        ...                                   Very easy   \n",
       "1231                        ...                                  Don't know   \n",
       "1232                        ...                                   Very easy   \n",
       "1233                        ...                                  Don't know   \n",
       "1234                        ...                                  Don't know   \n",
       "1235                        ...                                  Don't know   \n",
       "1236                        ...                               Somewhat easy   \n",
       "1237                        ...                                  Don't know   \n",
       "1238                        ...                               Somewhat easy   \n",
       "1239                        ...                                  Don't know   \n",
       "1240                        ...                               Somewhat easy   \n",
       "1241                        ...                               Somewhat easy   \n",
       "1242                        ...                                  Don't know   \n",
       "1243                        ...                                  Don't know   \n",
       "1244                        ...                                  Don't know   \n",
       "1245                        ...                                  Don't know   \n",
       "1246                        ...                               Somewhat easy   \n",
       "1247                        ...                              Very difficult   \n",
       "1248                        ...                                  Don't know   \n",
       "1249                        ...                                   Very easy   \n",
       "1250                        ...                                  Don't know   \n",
       "1251                        ...                               Somewhat easy   \n",
       "1252                        ...                                  Don't know   \n",
       "1253                        ...                               Somewhat easy   \n",
       "1254                        ...                               Somewhat easy   \n",
       "1255                        ...                          Somewhat difficult   \n",
       "1256                        ...                          Somewhat difficult   \n",
       "1257                        ...                                  Don't know   \n",
       "1258                        ...                                  Don't know   \n",
       "\n",
       "     mental_health_consequence phys_health_consequence     coworkers  \\\n",
       "0                           No                      No  Some of them   \n",
       "1                        Maybe                      No            No   \n",
       "2                           No                      No           Yes   \n",
       "3                          Yes                     Yes  Some of them   \n",
       "4                           No                      No  Some of them   \n",
       "5                           No                      No           Yes   \n",
       "6                        Maybe                   Maybe  Some of them   \n",
       "7                           No                      No            No   \n",
       "8                        Maybe                      No           Yes   \n",
       "9                           No                      No           Yes   \n",
       "10                          No                      No  Some of them   \n",
       "11                          No                      No           Yes   \n",
       "12                         Yes                     Yes           Yes   \n",
       "13                          No                      No           Yes   \n",
       "14                          No                      No  Some of them   \n",
       "15                          No                      No           Yes   \n",
       "16                       Maybe                      No  Some of them   \n",
       "17                       Maybe                      No  Some of them   \n",
       "18                          No                      No           Yes   \n",
       "19                          No                      No  Some of them   \n",
       "20                       Maybe                      No  Some of them   \n",
       "21                          No                      No  Some of them   \n",
       "22                       Maybe                      No  Some of them   \n",
       "23                       Maybe                      No            No   \n",
       "24                          No                      No           Yes   \n",
       "25                         Yes                      No  Some of them   \n",
       "26                       Maybe                   Maybe  Some of them   \n",
       "27                          No                      No           Yes   \n",
       "28                          No                      No  Some of them   \n",
       "29                         Yes                   Maybe  Some of them   \n",
       "...                        ...                     ...           ...   \n",
       "1229                       Yes                      No            No   \n",
       "1230                        No                      No           Yes   \n",
       "1231                     Maybe                   Maybe  Some of them   \n",
       "1232                        No                      No           Yes   \n",
       "1233                        No                      No           Yes   \n",
       "1234                       Yes                   Maybe            No   \n",
       "1235                     Maybe                      No           Yes   \n",
       "1236                     Maybe                   Maybe  Some of them   \n",
       "1237                       Yes                   Maybe            No   \n",
       "1238                       Yes                   Maybe  Some of them   \n",
       "1239                     Maybe                      No           Yes   \n",
       "1240                        No                      No  Some of them   \n",
       "1241                     Maybe                      No  Some of them   \n",
       "1242                     Maybe                      No            No   \n",
       "1243                        No                      No  Some of them   \n",
       "1244                     Maybe                      No  Some of them   \n",
       "1245                     Maybe                   Maybe            No   \n",
       "1246                        No                      No            No   \n",
       "1247                       Yes                      No  Some of them   \n",
       "1248                       Yes                   Maybe            No   \n",
       "1249                        No                      No           Yes   \n",
       "1250                        No                      No  Some of them   \n",
       "1251                        No                      No  Some of them   \n",
       "1252                       Yes                      No  Some of them   \n",
       "1253                     Maybe                   Maybe  Some of them   \n",
       "1254                        No                      No  Some of them   \n",
       "1255                        No                      No  Some of them   \n",
       "1256                       Yes                     Yes            No   \n",
       "1257                       Yes                      No            No   \n",
       "1258                     Maybe                      No  Some of them   \n",
       "\n",
       "        supervisor mental_health_interview phys_health_interview  \\\n",
       "0              Yes                      No                 Maybe   \n",
       "1               No                      No                    No   \n",
       "2              Yes                     Yes                   Yes   \n",
       "3               No                   Maybe                 Maybe   \n",
       "4              Yes                     Yes                   Yes   \n",
       "5              Yes                      No                 Maybe   \n",
       "6               No                      No                    No   \n",
       "7               No                      No                    No   \n",
       "8              Yes                      No                 Maybe   \n",
       "9              Yes                   Maybe                 Maybe   \n",
       "10             Yes                      No                    No   \n",
       "11             Yes                     Yes                   Yes   \n",
       "12             Yes                   Maybe                 Maybe   \n",
       "13             Yes                      No                    No   \n",
       "14    Some of them                   Maybe                   Yes   \n",
       "15    Some of them                   Maybe                 Maybe   \n",
       "16              No                   Maybe                 Maybe   \n",
       "17             Yes                      No                    No   \n",
       "18             Yes                      No                   Yes   \n",
       "19    Some of them                   Maybe                 Maybe   \n",
       "20    Some of them                      No                    No   \n",
       "21    Some of them                      No                 Maybe   \n",
       "22             Yes                      No                 Maybe   \n",
       "23              No                      No                   Yes   \n",
       "24             Yes                      No                   Yes   \n",
       "25             Yes                      No                   Yes   \n",
       "26              No                      No                    No   \n",
       "27             Yes                   Maybe                 Maybe   \n",
       "28              No                      No                    No   \n",
       "29              No                      No                 Maybe   \n",
       "...            ...                     ...                   ...   \n",
       "1229            No                      No                    No   \n",
       "1230           Yes                   Maybe                 Maybe   \n",
       "1231  Some of them                      No                 Maybe   \n",
       "1232           Yes                     Yes                   Yes   \n",
       "1233           Yes                      No                 Maybe   \n",
       "1234            No                      No                 Maybe   \n",
       "1235  Some of them                      No                    No   \n",
       "1236            No                      No                 Maybe   \n",
       "1237            No                      No                    No   \n",
       "1238           Yes                      No                    No   \n",
       "1239           Yes                   Maybe                   Yes   \n",
       "1240            No                      No                    No   \n",
       "1241            No                      No                    No   \n",
       "1242            No                      No                 Maybe   \n",
       "1243           Yes                      No                    No   \n",
       "1244           Yes                      No                   Yes   \n",
       "1245           Yes                      No                 Maybe   \n",
       "1246  Some of them                   Maybe                   Yes   \n",
       "1247            No                      No                 Maybe   \n",
       "1248            No                      No                    No   \n",
       "1249           Yes                   Maybe                 Maybe   \n",
       "1250           Yes                   Maybe                 Maybe   \n",
       "1251           Yes                      No                   Yes   \n",
       "1252            No                      No                 Maybe   \n",
       "1253  Some of them                      No                    No   \n",
       "1254  Some of them                      No                    No   \n",
       "1255           Yes                      No                    No   \n",
       "1256            No                      No                    No   \n",
       "1257            No                      No                    No   \n",
       "1258            No                      No                    No   \n",
       "\n",
       "     mental_vs_physical obs_consequence  \\\n",
       "0                   Yes              No   \n",
       "1            Don't know              No   \n",
       "2                    No              No   \n",
       "3                    No             Yes   \n",
       "4            Don't know              No   \n",
       "5            Don't know              No   \n",
       "6            Don't know              No   \n",
       "7                    No              No   \n",
       "8                    No              No   \n",
       "9                   Yes              No   \n",
       "10           Don't know              No   \n",
       "11           Don't know              No   \n",
       "12                   No             Yes   \n",
       "13           Don't know              No   \n",
       "14                  Yes              No   \n",
       "15           Don't know              No   \n",
       "16                   No              No   \n",
       "17                   No              No   \n",
       "18                  Yes             Yes   \n",
       "19           Don't know              No   \n",
       "20                   No              No   \n",
       "21                  Yes              No   \n",
       "22                   No              No   \n",
       "23           Don't know              No   \n",
       "24           Don't know              No   \n",
       "25                   No              No   \n",
       "26           Don't know              No   \n",
       "27                  Yes              No   \n",
       "28                   No              No   \n",
       "29                   No              No   \n",
       "...                 ...             ...   \n",
       "1229         Don't know              No   \n",
       "1230                Yes              No   \n",
       "1231         Don't know             Yes   \n",
       "1232                Yes              No   \n",
       "1233         Don't know              No   \n",
       "1234         Don't know              No   \n",
       "1235         Don't know              No   \n",
       "1236         Don't know              No   \n",
       "1237                 No             Yes   \n",
       "1238                 No             Yes   \n",
       "1239         Don't know              No   \n",
       "1240         Don't know              No   \n",
       "1241         Don't know              No   \n",
       "1242         Don't know              No   \n",
       "1243         Don't know              No   \n",
       "1244                 No              No   \n",
       "1245         Don't know             Yes   \n",
       "1246         Don't know              No   \n",
       "1247         Don't know             Yes   \n",
       "1248         Don't know              No   \n",
       "1249                Yes              No   \n",
       "1250                Yes              No   \n",
       "1251                Yes              No   \n",
       "1252                 No              No   \n",
       "1253         Don't know              No   \n",
       "1254         Don't know              No   \n",
       "1255                Yes              No   \n",
       "1256                 No              No   \n",
       "1257                 No              No   \n",
       "1258         Don't know              No   \n",
       "\n",
       "                                               comments  \n",
       "0                                                   NaN  \n",
       "1                                                   NaN  \n",
       "2                                                   NaN  \n",
       "3                                                   NaN  \n",
       "4                                                   NaN  \n",
       "5                                                   NaN  \n",
       "6                                                   NaN  \n",
       "7                                                   NaN  \n",
       "8                                                   NaN  \n",
       "9                                                   NaN  \n",
       "10                                                  NaN  \n",
       "11                                                  NaN  \n",
       "12                                                  NaN  \n",
       "13    I'm not on my company's health insurance which...  \n",
       "14                                                  NaN  \n",
       "15    I have chronic low-level neurological issues t...  \n",
       "16    My company does provide healthcare but not to ...  \n",
       "17                                                  NaN  \n",
       "18                                                  NaN  \n",
       "19                                                  NaN  \n",
       "20                                                  NaN  \n",
       "21                                                  NaN  \n",
       "22                                                  NaN  \n",
       "23                                                  NaN  \n",
       "24                  Relatively new job. Ask again later  \n",
       "25    Sometimes I think  about using drugs for my me...  \n",
       "26                                                  NaN  \n",
       "27                                                  NaN  \n",
       "28                                                  NaN  \n",
       "29                                                  NaN  \n",
       "...                                                 ...  \n",
       "1229                                                NaN  \n",
       "1230                                                NaN  \n",
       "1231                                                NaN  \n",
       "1232  I work at a large university with a track reco...  \n",
       "1233                                                NaN  \n",
       "1234  i'm in a country with social health care so my...  \n",
       "1235                                                NaN  \n",
       "1236                                                NaN  \n",
       "1237                                                NaN  \n",
       "1238                                                NaN  \n",
       "1239                                                NaN  \n",
       "1240                                                NaN  \n",
       "1241                                                NaN  \n",
       "1242                                                NaN  \n",
       "1243                                                NaN  \n",
       "1244                                                NaN  \n",
       "1245  In australia all organisations of a certain si...  \n",
       "1246                                                NaN  \n",
       "1247                                                NaN  \n",
       "1248                                                NaN  \n",
       "1249                                  Bipolar disorder   \n",
       "1250                                                NaN  \n",
       "1251                                                NaN  \n",
       "1252                                                NaN  \n",
       "1253                                                NaN  \n",
       "1254                                                NaN  \n",
       "1255                                                NaN  \n",
       "1256                                                NaN  \n",
       "1257                                                NaN  \n",
       "1258                                                NaN  \n",
       "\n",
       "[1256 rows x 27 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Age'] > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1        True\n",
       "2       False\n",
       "3       False\n",
       "4       False\n",
       "5       False\n",
       "6       False\n",
       "7       False\n",
       "8        True\n",
       "9       False\n",
       "10      False\n",
       "11      False\n",
       "12       True\n",
       "13      False\n",
       "14      False\n",
       "15      False\n",
       "16      False\n",
       "17      False\n",
       "18       True\n",
       "19      False\n",
       "20      False\n",
       "21      False\n",
       "22       True\n",
       "23       True\n",
       "24      False\n",
       "25      False\n",
       "26      False\n",
       "27      False\n",
       "28      False\n",
       "29      False\n",
       "        ...  \n",
       "1229    False\n",
       "1230    False\n",
       "1231    False\n",
       "1232    False\n",
       "1233    False\n",
       "1234    False\n",
       "1235    False\n",
       "1236     True\n",
       "1237    False\n",
       "1238    False\n",
       "1239    False\n",
       "1240    False\n",
       "1241    False\n",
       "1242    False\n",
       "1243     True\n",
       "1244    False\n",
       "1245    False\n",
       "1246    False\n",
       "1247    False\n",
       "1248     True\n",
       "1249    False\n",
       "1250    False\n",
       "1251    False\n",
       "1252    False\n",
       "1253    False\n",
       "1254    False\n",
       "1255    False\n",
       "1256    False\n",
       "1257     True\n",
       "1258    False\n",
       "Name: Age, Length: 1259, dtype: bool"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'] > 40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "150"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[df['Age'] > 40])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LiI5pI5HVZ-o"
   },
   "source": [
    "### Example: Find people who reported a family history of mental health conditions. </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Y9ny0e_TVZ-q"
   },
   "source": [
    "Solution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "T_Op5bxLVZ-q",
    "outputId": "58dd3e60-d095-497d-e7f3-887cda3bf94b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "492"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "family_history_mask = df.family_history=='Yes'\n",
    "df[family_history_mask].tail(5)\n",
    "len(df[df['family_history'] == 'Yes'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UwMZ0M4pVZ-t"
   },
   "source": [
    "You can use a simple expression like ```df[df['family_history'] == 'Yes']``` or you can make more complex boolean expressions using parentheses: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Z6ma1OzsVZ-v",
    "outputId": "5f7fe8c7-4133-4d9f-cb61-bbd7779f485c"
   },
   "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>Timestamp</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Country</th>\n",
       "      <th>state</th>\n",
       "      <th>self_employed</th>\n",
       "      <th>family_history</th>\n",
       "      <th>treatment</th>\n",
       "      <th>work_interfere</th>\n",
       "      <th>no_employees</th>\n",
       "      <th>...</th>\n",
       "      <th>leave</th>\n",
       "      <th>mental_health_consequence</th>\n",
       "      <th>phys_health_consequence</th>\n",
       "      <th>coworkers</th>\n",
       "      <th>supervisor</th>\n",
       "      <th>mental_health_interview</th>\n",
       "      <th>phys_health_interview</th>\n",
       "      <th>mental_vs_physical</th>\n",
       "      <th>obs_consequence</th>\n",
       "      <th>comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-08-27 11:29:46</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Often</td>\n",
       "      <td>26-100</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat difficult</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2014-08-27 11:31:50</td>\n",
       "      <td>35</td>\n",
       "      <td>Female</td>\n",
       "      <td>United States</td>\n",
       "      <td>MI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>1-5</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat difficult</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2014-08-27 11:32:39</td>\n",
       "      <td>42</td>\n",
       "      <td>Female</td>\n",
       "      <td>United States</td>\n",
       "      <td>IL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>100-500</td>\n",
       "      <td>...</td>\n",
       "      <td>Very difficult</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014-08-27 11:33:23</td>\n",
       "      <td>42</td>\n",
       "      <td>female</td>\n",
       "      <td>United States</td>\n",
       "      <td>CA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>26-100</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat difficult</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2014-08-27 11:34:00</td>\n",
       "      <td>29</td>\n",
       "      <td>female</td>\n",
       "      <td>United States</td>\n",
       "      <td>IL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Rarely</td>\n",
       "      <td>26-100</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat easy</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>I have chronic low-level neurological issues t...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Timestamp  Age  Gender         Country state self_employed  \\\n",
       "3   2014-08-27 11:29:46   31    Male  United Kingdom   NaN           NaN   \n",
       "6   2014-08-27 11:31:50   35  Female   United States    MI           NaN   \n",
       "8   2014-08-27 11:32:39   42  Female   United States    IL           NaN   \n",
       "12  2014-08-27 11:33:23   42  female   United States    CA           NaN   \n",
       "15  2014-08-27 11:34:00   29  female   United States    IL           NaN   \n",
       "\n",
       "   family_history treatment work_interfere no_employees  \\\n",
       "3             Yes       Yes          Often       26-100   \n",
       "6             Yes       Yes      Sometimes          1-5   \n",
       "8             Yes       Yes      Sometimes      100-500   \n",
       "12            Yes       Yes      Sometimes       26-100   \n",
       "15            Yes       Yes         Rarely       26-100   \n",
       "\n",
       "                          ...                                       leave  \\\n",
       "3                         ...                          Somewhat difficult   \n",
       "6                         ...                          Somewhat difficult   \n",
       "8                         ...                              Very difficult   \n",
       "12                        ...                          Somewhat difficult   \n",
       "15                        ...                               Somewhat easy   \n",
       "\n",
       "   mental_health_consequence phys_health_consequence     coworkers  \\\n",
       "3                        Yes                     Yes  Some of them   \n",
       "6                      Maybe                   Maybe  Some of them   \n",
       "8                      Maybe                      No           Yes   \n",
       "12                       Yes                     Yes           Yes   \n",
       "15                        No                      No           Yes   \n",
       "\n",
       "      supervisor mental_health_interview phys_health_interview  \\\n",
       "3             No                   Maybe                 Maybe   \n",
       "6             No                      No                    No   \n",
       "8            Yes                      No                 Maybe   \n",
       "12           Yes                   Maybe                 Maybe   \n",
       "15  Some of them                   Maybe                 Maybe   \n",
       "\n",
       "   mental_vs_physical obs_consequence  \\\n",
       "3                  No             Yes   \n",
       "6          Don't know              No   \n",
       "8                  No              No   \n",
       "12                 No             Yes   \n",
       "15         Don't know              No   \n",
       "\n",
       "                                             comments  \n",
       "3                                                 NaN  \n",
       "6                                                 NaN  \n",
       "8                                                 NaN  \n",
       "12                                                NaN  \n",
       "15  I have chronic low-level neurological issues t...  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered = df[(df['family_history'] == 'Yes') & (df['treatment'] == 'Yes')] # () for every condition\n",
    "df_filtered.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3UNhgIHjVZ-y"
   },
   "source": [
    "### <font color=\"red\">Q1: How many people are willing to discuss a mental health issue with their supervisor or their coworkers? </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2uanzG8zVZ-z",
    "outputId": "30f233de-79f0-402d-f4c4-24659b7e137b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1060"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[(df['supervisor'] != 'No') | (df['coworkers'] != 'No')])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "s5Wu-JcYVZ-3"
   },
   "source": [
    "### <font color=\"red\">Q2: Make a new DataFrame ```df_millenials``` with only millennials (born between 1976 and 1996). Make appropriate assumptions when constructing your filter. </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dgNzrGCnVZ-4"
   },
   "outputs": [],
   "source": [
    "df_millenials = df[(df['Age'] < 2015 - 1976) & (df['Age'] > 2015 - 1996)]\n",
    "# should assume the responses were done in the same year 2015"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "n8onap2fVZ-8"
   },
   "source": [
    "**NOTE: We will still use df for the following analysis**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2NM77kaZVZ-9"
   },
   "source": [
    "## Descriptive and Summary Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vGZSCd5tVZ_A"
   },
   "source": [
    "Example: What is the mean age of the survey sample?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9HI-vM2kVZ_B"
   },
   "source": [
    "Solution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2zrN3A_LVZ_B",
    "outputId": "92a53bd1-dc67-456c-f534-f696c81320ab"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79428148.31135821"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Does that look right?  What should we do?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1niH0x8fVZ_E"
   },
   "source": [
    "### <font color=\"red\">Q3: What is the _median_ age of the survey sample?  </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kbbISqYpVZ_F",
    "outputId": "1582ffd1-5bc4-4fb1-bc28-60108a5c3b1e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31.0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1neFC7WFVZ_H"
   },
   "source": [
    "### <font color=\"red\">Q4: Write one line of code to compute basic statistics (mean, standard deviation, min, 25% percentile, etc) about Age  </font>\n",
    "\n",
    "Hint: see the readings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1iIWxtHqVZ_I",
    "outputId": "29f693a3-4dbf-4111-cbe2-f8fa61718798"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1252.000000\n",
       "mean       32.059904\n",
       "std         7.309669\n",
       "min        11.000000\n",
       "25%        27.000000\n",
       "50%        31.000000\n",
       "75%        36.000000\n",
       "max        72.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nount = df[(df['Age'] < 100) & (df['Age'] > 10)]\n",
    "df_nount['Age'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gW_LQGwxVZ_K"
   },
   "source": [
    "## Unique Values, Counts, Membership"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3TTfX0gdVZ_K"
   },
   "source": [
    "Example: Write one line of code to check unique values of Gender</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KBPIDc2WVZ_L"
   },
   "source": [
    "Solution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Some of them', 'No', 'Yes'], dtype=object)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.coworkers.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "y2DMcfrsVZ_L",
    "outputId": "3ede4799-6d99-4223-b9b9-8da7e4be8bdb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Female', 'M', 'Male', 'male', 'female', 'm', 'Male-ish', 'maile',\n",
       "       'Trans-female', 'Cis Female', 'F', 'something kinda male?',\n",
       "       'Cis Male', 'Woman', 'f', 'Mal', 'Male (CIS)', 'queer/she/they',\n",
       "       'non-binary', 'Femake', 'woman', 'Make', 'Nah', 'All', 'Enby',\n",
       "       'fluid', 'Genderqueer', 'Female ', 'Androgyne', 'Agender',\n",
       "       'cis-female/femme', 'Guy (-ish) ^_^', 'male leaning androgynous',\n",
       "       'Male ', 'Man', 'Trans woman', 'msle', 'Neuter', 'Female (trans)',\n",
       "       'queer', 'Female (cis)', 'Mail', 'cis male', 'A little about you',\n",
       "       'Malr', 'p', 'femail', 'Cis Man',\n",
       "       'ostensibly male, unsure what that really means'], dtype=object)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Gender.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9MszMiO-VZ_O"
   },
   "source": [
    "Example: Write one line of code to count the occurrences of the countries and show the top 5 countries.  </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7vNBUsIMVZ_P"
   },
   "source": [
    "Solution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "D9SvONy5VZ_R",
    "outputId": "907cfc2e-a455-4f62-dd8f-2a2ba28aa944"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United States     751\n",
       "United Kingdom    185\n",
       "Canada             72\n",
       "Germany            45\n",
       "Ireland            27\n",
       "Netherlands        27\n",
       "Name: Country, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Country.value_counts().head(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Are you sure that's correct?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "s4X2vNrgVZ_Y"
   },
   "source": [
    "### <font color=\"red\">Q5: Find the unique categories of no_employees. What is the frequency of each category? </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4e4yFxHaVZ_Z",
    "outputId": "5e397ead-7a42-4a61-87fa-20df4b0e5045"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6-25              290\n",
       "26-100            289\n",
       "More than 1000    282\n",
       "100-500           176\n",
       "1-5               162\n",
       "500-1000           60\n",
       "Name: no_employees, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.no_employees.unique()\n",
    "df.no_employees.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SRRfpLm4VZ_c"
   },
   "source": [
    "### <font color=\"red\">Q6: How many Michiganders aged between 20 and 30 sought treatment for a mental health condition? How many did not? </font>\n",
    "Show your code and also include a markdown block with a complete sentence or two that answers these questions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zOfjr2vHVZ_d",
    "outputId": "9f5eec08-53e3-4473-83e4-4c795864324b"
   },
   "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>Timestamp</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Country</th>\n",
       "      <th>state</th>\n",
       "      <th>self_employed</th>\n",
       "      <th>family_history</th>\n",
       "      <th>treatment</th>\n",
       "      <th>work_interfere</th>\n",
       "      <th>no_employees</th>\n",
       "      <th>...</th>\n",
       "      <th>leave</th>\n",
       "      <th>mental_health_consequence</th>\n",
       "      <th>phys_health_consequence</th>\n",
       "      <th>coworkers</th>\n",
       "      <th>supervisor</th>\n",
       "      <th>mental_health_interview</th>\n",
       "      <th>phys_health_interview</th>\n",
       "      <th>mental_vs_physical</th>\n",
       "      <th>obs_consequence</th>\n",
       "      <th>comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>2014-08-27 12:40:06</td>\n",
       "      <td>29</td>\n",
       "      <td>Female</td>\n",
       "      <td>United States</td>\n",
       "      <td>MI</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100-500</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>2014-08-27 12:53:40</td>\n",
       "      <td>28</td>\n",
       "      <td>Male</td>\n",
       "      <td>United States</td>\n",
       "      <td>MI</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>26-100</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat easy</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>2014-08-27 13:07:46</td>\n",
       "      <td>27</td>\n",
       "      <td>female</td>\n",
       "      <td>United States</td>\n",
       "      <td>MI</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>1-5</td>\n",
       "      <td>...</td>\n",
       "      <td>Very difficult</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>Many of these questions were difficult to answ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>2014-08-27 13:15:19</td>\n",
       "      <td>28</td>\n",
       "      <td>Male</td>\n",
       "      <td>United States</td>\n",
       "      <td>MI</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>More than 1000</td>\n",
       "      <td>...</td>\n",
       "      <td>Very difficult</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>2014-08-27 21:55:28</td>\n",
       "      <td>29</td>\n",
       "      <td>f</td>\n",
       "      <td>United States</td>\n",
       "      <td>MI</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Never</td>\n",
       "      <td>26-100</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>629</th>\n",
       "      <td>2014-08-28 02:30:00</td>\n",
       "      <td>28</td>\n",
       "      <td>Male</td>\n",
       "      <td>United States</td>\n",
       "      <td>MI</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>26-100</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1129</th>\n",
       "      <td>2014-08-31 09:19:43</td>\n",
       "      <td>28</td>\n",
       "      <td>male</td>\n",
       "      <td>United States</td>\n",
       "      <td>MI</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>More than 1000</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat easy</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1176</th>\n",
       "      <td>2014-09-20 13:51:05</td>\n",
       "      <td>26</td>\n",
       "      <td>Female</td>\n",
       "      <td>United States</td>\n",
       "      <td>MI</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Rarely</td>\n",
       "      <td>100-500</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Timestamp  Age  Gender        Country state self_employed  \\\n",
       "149   2014-08-27 12:40:06   29  Female  United States    MI            No   \n",
       "191   2014-08-27 12:53:40   28    Male  United States    MI            No   \n",
       "215   2014-08-27 13:07:46   27  female  United States    MI           Yes   \n",
       "223   2014-08-27 13:15:19   28    Male  United States    MI            No   \n",
       "587   2014-08-27 21:55:28   29       f  United States    MI            No   \n",
       "629   2014-08-28 02:30:00   28    Male  United States    MI            No   \n",
       "1129  2014-08-31 09:19:43   28    male  United States    MI            No   \n",
       "1176  2014-09-20 13:51:05   26  Female  United States    MI            No   \n",
       "\n",
       "     family_history treatment work_interfere    no_employees  \\\n",
       "149              No        No            NaN         100-500   \n",
       "191             Yes       Yes      Sometimes          26-100   \n",
       "215             Yes       Yes      Sometimes             1-5   \n",
       "223              No       Yes      Sometimes  More than 1000   \n",
       "587              No        No          Never          26-100   \n",
       "629             Yes       Yes      Sometimes          26-100   \n",
       "1129             No        No            NaN  More than 1000   \n",
       "1176            Yes       Yes         Rarely         100-500   \n",
       "\n",
       "                            ...                                   leave  \\\n",
       "149                         ...                              Don't know   \n",
       "191                         ...                           Somewhat easy   \n",
       "215                         ...                          Very difficult   \n",
       "223                         ...                          Very difficult   \n",
       "587                         ...                              Don't know   \n",
       "629                         ...                              Don't know   \n",
       "1129                        ...                           Somewhat easy   \n",
       "1176                        ...                              Don't know   \n",
       "\n",
       "     mental_health_consequence phys_health_consequence     coworkers  \\\n",
       "149                      Maybe                      No            No   \n",
       "191                      Maybe                   Maybe  Some of them   \n",
       "215                         No                      No  Some of them   \n",
       "223                        Yes                      No            No   \n",
       "587                      Maybe                      No  Some of them   \n",
       "629                        Yes                      No  Some of them   \n",
       "1129                     Maybe                      No            No   \n",
       "1176                     Maybe                   Maybe            No   \n",
       "\n",
       "        supervisor mental_health_interview phys_health_interview  \\\n",
       "149   Some of them                      No                 Maybe   \n",
       "191             No                      No                 Maybe   \n",
       "215             No                      No                    No   \n",
       "223   Some of them                      No                    No   \n",
       "587   Some of them                      No                 Maybe   \n",
       "629   Some of them                      No                    No   \n",
       "1129            No                      No                   Yes   \n",
       "1176            No                      No                 Maybe   \n",
       "\n",
       "     mental_vs_physical obs_consequence  \\\n",
       "149          Don't know              No   \n",
       "191                  No              No   \n",
       "215          Don't know              No   \n",
       "223                  No              No   \n",
       "587          Don't know             Yes   \n",
       "629          Don't know             Yes   \n",
       "1129                 No              No   \n",
       "1176                 No              No   \n",
       "\n",
       "                                               comments  \n",
       "149                                                 NaN  \n",
       "191                                                 NaN  \n",
       "215   Many of these questions were difficult to answ...  \n",
       "223                                                 NaN  \n",
       "587                                                 NaN  \n",
       "629                                                 NaN  \n",
       "1129                                                NaN  \n",
       "1176                                                NaN  \n",
       "\n",
       "[8 rows x 27 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.Age >= 20) & (df.Age <= 30) & (df.state == 'MI')]\n",
    "# filtered['treatment'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BDXeB5sdVZ_j"
   },
   "source": [
    "Replace this with you answer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UK51a2wpVZ_k"
   },
   "source": [
    "### <font color=\"red\">Q7: Among the people from United States, how many repondents were there from each state?  </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ewBvXnp2VZ_l",
    "outputId": "acba1da4-1310-4317-a401-b7f725c69c12"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CA    138\n",
       "WA     70\n",
       "NY     56\n",
       "TN     45\n",
       "TX     44\n",
       "OH     30\n",
       "PA     29\n",
       "OR     29\n",
       "IL     28\n",
       "IN     27\n",
       "MI     22\n",
       "MN     21\n",
       "MA     20\n",
       "FL     15\n",
       "NC     14\n",
       "VA     14\n",
       "GA     12\n",
       "WI     12\n",
       "MO     12\n",
       "UT     10\n",
       "CO      9\n",
       "AL      8\n",
       "AZ      7\n",
       "MD      7\n",
       "NJ      6\n",
       "OK      6\n",
       "KY      5\n",
       "SC      5\n",
       "DC      4\n",
       "IA      4\n",
       "CT      4\n",
       "VT      3\n",
       "NV      3\n",
       "NH      3\n",
       "KS      3\n",
       "SD      3\n",
       "NM      2\n",
       "WY      2\n",
       "NE      2\n",
       "LA      1\n",
       "MS      1\n",
       "ME      1\n",
       "WV      1\n",
       "ID      1\n",
       "RI      1\n",
       "Name: state, dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Country == 'United States']['state'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Chh5GIJlVZ_n"
   },
   "source": [
    "## Basic Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7QwUQ7xdVZ_n"
   },
   "source": [
    "Example: Investigate the proportion (%) of people receiving health benefits from their employers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gXHPzifTVZ_o"
   },
   "source": [
    "Solution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "K5dDt8VTVZ_r",
    "outputId": "59d6a47b-e6d2-4125-d648-80db845840fe"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Yes           0.378872\n",
       "Don't know    0.324067\n",
       "No            0.297061\n",
       "Name: benefits, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1149dcf90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.benefits.value_counts(normalize=True).plot.bar()\n",
    "df.benefits.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example: Create a histogram of the distribution of Age values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3aYfJAfpVZ_w",
    "outputId": "5f6e7a2c-2353-4f98-b4ae-b13d184a9cc5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x114522350>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1S7d+DZNg8Bj6mWcAX6+qhar6MXAd8NusomNoLQWBU1fspjvXfSWwuaouX/TW9cAF3fIFwCemXduBoqreUFWPrqpZJsfMv1XVS4GbgT/oNmt2H1XVfwPfSHJq1/R04Et4DC12L/CkJId1f3O79tGqOYbW1ANlSZ7D5F9zu6aueMvIJY0qyZOBfwfu5Gfnvy9lcp3gI8CJTA7iF1XVt0cp8gCS5KnAn1XV85KczGSEcDRwO/BHVfWjMesbS5LTmVxI/yXga8DLmfwj0mOok+QvgRczuVPvduCVTK4JrIpjaE0FgSRp5dbSqSFJ0j4wCCSpcQaBJDXOIJCkxhkEktQ4g0CSGmcQSFLjDAJJatz/AyB3Ab9xp309AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1145282d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Age.value_counts().plot.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"red\">Q8: Experiment with the number of bins in the histogram of the Age distribution:</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fM3RDXkeVZ_v"
   },
   "source": [
    "Hint: use the bins= option to plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11928ced0>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1192464d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Age.value_counts().plot.hist(bins=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "G4W5ERXPVZ_y"
   },
   "source": [
    "## Discretization and Binning\n",
    "Discretization of continuous variables can potentially enhance the goodness-of-fit of a statistical model. Age, for example, is a common varaible to be categorized.\n",
    "\n",
    "### <font color=\"red\">Q9: </font> Discretize the age into bins (16,21], (21,25], (25,30], (30, 35], (35,40], (40,50], (50,60], (60,).  \n",
    "\n",
    "How many respondents are in each range? \n",
    "Hint: create a list of bin values\n",
    "Hint: use pd.cut()\n",
    "Hint: use value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Y4mlZZtSVZ_y"
   },
   "outputs": [],
   "source": [
    "bins = [15,20,25,30,35,40,50,60,200]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3-PEDPaJVZ_0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 30]     362\n",
       "(30, 35]     339\n",
       "(20, 25]     195\n",
       "(35, 40]     185\n",
       "(40, 50]     122\n",
       "(50, 60]      22\n",
       "(15, 20]      22\n",
       "(60, 200]      4\n",
       "Name: Age_group, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age_group'] = pd.cut(df.Age, bins) # cut meaning: ? data type: category\n",
    "pd.cut(df.Age, bins)\n",
    "df['Age_group'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GDbfYtuEVZ_8"
   },
   "source": [
    "## Aggregation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "90l0AuGfVZ_9"
   },
   "source": [
    "Example: Find the size of each Age group."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LurKzxlWVZ_-"
   },
   "source": [
    "Solution:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5GQRlKaeVZ_-",
    "outputId": "f04c450c-e9af-4989-af28-3a0df2639a6a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age_group\n",
       "(15, 20]      22\n",
       "(20, 25]     195\n",
       "(25, 30]     362\n",
       "(30, 35]     339\n",
       "(35, 40]     185\n",
       "(40, 50]     122\n",
       "(50, 60]      22\n",
       "(60, 200]      4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Age_group').size() # same function as value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9Jh8LMd5VaAA"
   },
   "source": [
    "### <font color=\"red\">Q10: Find the median age of each state. </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Bk-Oeau1VaAB",
    "outputId": "84888e1e-42f5-4f45-e76c-d5defbcab20c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state\n",
       "AL    34.0\n",
       "AZ    33.0\n",
       "CA    31.0\n",
       "CO    31.0\n",
       "CT    37.5\n",
       "DC    37.5\n",
       "FL    34.0\n",
       "GA    30.0\n",
       "IA    40.0\n",
       "ID    55.0\n",
       "IL    30.0\n",
       "IN    34.0\n",
       "KS    39.0\n",
       "KY    24.0\n",
       "LA    35.0\n",
       "MA    32.0\n",
       "MD    29.0\n",
       "ME    40.0\n",
       "MI    34.0\n",
       "MN    30.0\n",
       "MO    33.5\n",
       "MS    33.0\n",
       "NC    32.0\n",
       "NE    26.0\n",
       "NH    34.0\n",
       "NJ    32.0\n",
       "NM    29.5\n",
       "NV    28.0\n",
       "NY    29.0\n",
       "OH    31.0\n",
       "OK    26.5\n",
       "OR    32.0\n",
       "PA    31.0\n",
       "RI    23.0\n",
       "SC    30.0\n",
       "SD    34.0\n",
       "TN    33.0\n",
       "TX    31.5\n",
       "UT    28.0\n",
       "VA    40.5\n",
       "VT    34.0\n",
       "WA    32.5\n",
       "WI    33.0\n",
       "WV    23.0\n",
       "WY    41.5\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('state')['Age'].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ala4108AVaAE"
   },
   "source": [
    "# Part 2 (on your own): Exploration of Movie Titles and Movie Cast"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3cJ_P6-OVaAI"
   },
   "source": [
    "## Time to load some data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8h74mSPfVaAL"
   },
   "outputs": [],
   "source": [
    "titles = pd.read_csv('data/titles.csv', index_col=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iltVCIzzVaAP"
   },
   "source": [
    "The titles DataFrame contains a list of movie titles and release year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sDtWZcdDVaAP"
   },
   "outputs": [],
   "source": [
    "cast = pd.read_csv('data/cast.zip', index_col=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QmRKuZ2lVaAQ"
   },
   "source": [
    "The ```cast``` DataFrame contains the following columns \n",
    "\n",
    "**title** = name of movie\n",
    "\n",
    "**year** = year of movie\n",
    "\n",
    "**name** = name of actor/actress\n",
    "\n",
    "**type** = actor or actress\n",
    "\n",
    "**character** = character name\n",
    "\n",
    "**n** = number in the credits (NaN when not available)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vUJbzigkVaAR",
    "outputId": "581af96c-82f1-461c-a08d-5a74b4875a6e"
   },
   "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>title</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Rising Son</td>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Thousand Plane Raid</td>\n",
       "      <td>1969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Crucea de piatra</td>\n",
       "      <td>1993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Country</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gaiking II</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     title  year\n",
       "0           The Rising Son  1990\n",
       "1  The Thousand Plane Raid  1969\n",
       "2         Crucea de piatra  1993\n",
       "3                  Country  2000\n",
       "4               Gaiking II  2011"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titles.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cTwkedBiVaAU",
    "outputId": "a3597a7a-f598-4fdd-f6b6-39be1c2d0953"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "      <th>name</th>\n",
       "      <th>type</th>\n",
       "      <th>character</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1318574</th>\n",
       "      <td>The Cherokee Flash</td>\n",
       "      <td>1945</td>\n",
       "      <td>Tom London</td>\n",
       "      <td>actor</td>\n",
       "      <td>Utah</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3030473</th>\n",
       "      <td>Kurbaan</td>\n",
       "      <td>1991</td>\n",
       "      <td>Kunika</td>\n",
       "      <td>actress</td>\n",
       "      <td>Mrs. Gayatri Prithvi Singh</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>834633</th>\n",
       "      <td>The Engagement Party</td>\n",
       "      <td>1999</td>\n",
       "      <td>Jacob Goren</td>\n",
       "      <td>actor</td>\n",
       "      <td>Mendel</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480606</th>\n",
       "      <td>Way of the Wicked</td>\n",
       "      <td>2014</td>\n",
       "      <td>Jake Croker</td>\n",
       "      <td>actor</td>\n",
       "      <td>Robbie Mueller</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2041819</th>\n",
       "      <td>Stateside</td>\n",
       "      <td>2004</td>\n",
       "      <td>Carl Matthew Simcox</td>\n",
       "      <td>actor</td>\n",
       "      <td>Platoon 1021</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        title  year                 name     type  \\\n",
       "1318574    The Cherokee Flash  1945           Tom London    actor   \n",
       "3030473               Kurbaan  1991               Kunika  actress   \n",
       "834633   The Engagement Party  1999          Jacob Goren    actor   \n",
       "480606      Way of the Wicked  2014          Jake Croker    actor   \n",
       "2041819             Stateside  2004  Carl Matthew Simcox    actor   \n",
       "\n",
       "                          character     n  \n",
       "1318574                        Utah   3.0  \n",
       "3030473  Mrs. Gayatri Prithvi Singh   9.0  \n",
       "834633                       Mendel  11.0  \n",
       "480606               Robbie Mueller   4.0  \n",
       "2041819                Platoon 1021  83.0  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "20egAUt2VaAW"
   },
   "source": [
    "### Q11: How many entries are there in the cast table?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title        3634467\n",
       "year         3634467\n",
       "name         3634467\n",
       "type         3634467\n",
       "character    3634467\n",
       "n            2244068\n",
       "dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cast)\n",
    "cast.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fDBtA5EPVaAa"
   },
   "source": [
    "### Q12: How many entries are there in the titles table?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JfdCBERDVaAb",
    "outputId": "a4e81b19-3449-48f9-8108-2e2cbecb954c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title    232330\n",
       "year     232330\n",
       "dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titles.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZV5eEDrtVaAf"
   },
   "source": [
    "## Some basic analyses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cGC68wHGVaAg"
   },
   "source": [
    "### Q13: What are the two earliest movies?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nZf39Ow4VaAg",
    "outputId": "a1a6285e-93af-46a3-eaf2-e0c61361b524"
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>177757</th>\n",
       "      <td>Miss Jerry</td>\n",
       "      <td>1894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215272</th>\n",
       "      <td>The Startled Lover</td>\n",
       "      <td>1898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124505</th>\n",
       "      <td>Reproduction of the Corbett and Jeffries Fight</td>\n",
       "      <td>1899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20657</th>\n",
       "      <td>Pierrot's Problem, or How to Make a Fat Wife O...</td>\n",
       "      <td>1900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13599</th>\n",
       "      <td>Family Troubles (II)</td>\n",
       "      <td>1900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94998</th>\n",
       "      <td>Trouble in Hogan's Alley</td>\n",
       "      <td>1900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183509</th>\n",
       "      <td>Soldiers of the Cross</td>\n",
       "      <td>1900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209080</th>\n",
       "      <td>The Divorce</td>\n",
       "      <td>1903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32701</th>\n",
       "      <td>Valsons</td>\n",
       "      <td>1905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68173</th>\n",
       "      <td>La malia dell'oro</td>\n",
       "      <td>1905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28222</th>\n",
       "      <td>The Joe Gans-Battling Nelson Fight</td>\n",
       "      <td>1906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214012</th>\n",
       "      <td>Impersonation of Nelson-McGovern Fight</td>\n",
       "      <td>1906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166226</th>\n",
       "      <td>The Story of the Kelly Gang</td>\n",
       "      <td>1906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193471</th>\n",
       "      <td>Highlights from The Mikado</td>\n",
       "      <td>1906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145153</th>\n",
       "      <td>Lika mot lika</td>\n",
       "      <td>1906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127691</th>\n",
       "      <td>El ciego de la aldea</td>\n",
       "      <td>1907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216665</th>\n",
       "      <td>Robbery Under Arms</td>\n",
       "      <td>1907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206190</th>\n",
       "      <td>Faust</td>\n",
       "      <td>1907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63550</th>\n",
       "      <td>Violante</td>\n",
       "      <td>1907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51425</th>\n",
       "      <td>Don Quijote</td>\n",
       "      <td>1908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130858</th>\n",
       "      <td>Os Guaranis</td>\n",
       "      <td>1908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225735</th>\n",
       "      <td>Gans-Nelson Fight</td>\n",
       "      <td>1908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172979</th>\n",
       "      <td>El pastorcito de Torrente</td>\n",
       "      <td>1908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230273</th>\n",
       "      <td>Amleto</td>\n",
       "      <td>1908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110913</th>\n",
       "      <td>The Fairylogue and Radio-Plays</td>\n",
       "      <td>1908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89045</th>\n",
       "      <td>Excelsior (II)</td>\n",
       "      <td>1909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147777</th>\n",
       "      <td>Yamato zakura</td>\n",
       "      <td>1909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76681</th>\n",
       "      <td>The Scottish Covenanters</td>\n",
       "      <td>1909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132520</th>\n",
       "      <td>Sonho de Valsa</td>\n",
       "      <td>1909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183046</th>\n",
       "      <td>Locura de amor</td>\n",
       "      <td>1909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24591</th>\n",
       "      <td>Silver from Water</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217755</th>\n",
       "      <td>Darkside: Moon</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43484</th>\n",
       "      <td>Rosie</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176427</th>\n",
       "      <td>Quatro Terra 2</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88423</th>\n",
       "      <td>Clutch Control</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67019</th>\n",
       "      <td>The Burning Skies</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142551</th>\n",
       "      <td>The Devil's Sea</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26576</th>\n",
       "      <td>The Cretaceous Period</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27491</th>\n",
       "      <td>Oracle: The Last Judgment</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160801</th>\n",
       "      <td>Berlin</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33204</th>\n",
       "      <td>Gnome Alone</td>\n",
       "      <td>2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124226</th>\n",
       "      <td>Battle in the Antarctica</td>\n",
       "      <td>2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106170</th>\n",
       "      <td>St. Cairo</td>\n",
       "      <td>2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130159</th>\n",
       "      <td>The Supreme Gentleman</td>\n",
       "      <td>2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57605</th>\n",
       "      <td>Inside Me</td>\n",
       "      <td>2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160134</th>\n",
       "      <td>Journey to the Space</td>\n",
       "      <td>2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171822</th>\n",
       "      <td>The Good Village</td>\n",
       "      <td>2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103630</th>\n",
       "      <td>Garriage</td>\n",
       "      <td>2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72581</th>\n",
       "      <td>The League</td>\n",
       "      <td>2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152227</th>\n",
       "      <td>Xenomorph</td>\n",
       "      <td>2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85417</th>\n",
       "      <td>Avatar 5</td>\n",
       "      <td>2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196677</th>\n",
       "      <td>Agartha</td>\n",
       "      <td>2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70051</th>\n",
       "      <td>Interplay</td>\n",
       "      <td>2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88936</th>\n",
       "      <td>The Snowman and the Falcon</td>\n",
       "      <td>2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72471</th>\n",
       "      <td>Kennedy</td>\n",
       "      <td>2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10754</th>\n",
       "      <td>Hyperion: The Wrath of Kronos</td>\n",
       "      <td>2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229718</th>\n",
       "      <td>Gods Heart</td>\n",
       "      <td>2025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145689</th>\n",
       "      <td>Rise of Man</td>\n",
       "      <td>2025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102561</th>\n",
       "      <td>Settling of Score</td>\n",
       "      <td>2026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65488</th>\n",
       "      <td>War in Snow</td>\n",
       "      <td>2026</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>232330 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    title  year\n",
       "177757                                         Miss Jerry  1894\n",
       "215272                                 The Startled Lover  1898\n",
       "124505     Reproduction of the Corbett and Jeffries Fight  1899\n",
       "20657   Pierrot's Problem, or How to Make a Fat Wife O...  1900\n",
       "13599                                Family Troubles (II)  1900\n",
       "94998                            Trouble in Hogan's Alley  1900\n",
       "183509                              Soldiers of the Cross  1900\n",
       "209080                                        The Divorce  1903\n",
       "32701                                             Valsons  1905\n",
       "68173                                   La malia dell'oro  1905\n",
       "28222                  The Joe Gans-Battling Nelson Fight  1906\n",
       "214012             Impersonation of Nelson-McGovern Fight  1906\n",
       "166226                        The Story of the Kelly Gang  1906\n",
       "193471                         Highlights from The Mikado  1906\n",
       "145153                                      Lika mot lika  1906\n",
       "127691                               El ciego de la aldea  1907\n",
       "216665                                 Robbery Under Arms  1907\n",
       "206190                                              Faust  1907\n",
       "63550                                            Violante  1907\n",
       "51425                                         Don Quijote  1908\n",
       "130858                                        Os Guaranis  1908\n",
       "225735                                  Gans-Nelson Fight  1908\n",
       "172979                          El pastorcito de Torrente  1908\n",
       "230273                                             Amleto  1908\n",
       "110913                     The Fairylogue and Radio-Plays  1908\n",
       "89045                                      Excelsior (II)  1909\n",
       "147777                                      Yamato zakura  1909\n",
       "76681                            The Scottish Covenanters  1909\n",
       "132520                                     Sonho de Valsa  1909\n",
       "183046                                     Locura de amor  1909\n",
       "...                                                   ...   ...\n",
       "24591                                   Silver from Water  2022\n",
       "217755                                     Darkside: Moon  2022\n",
       "43484                                               Rosie  2022\n",
       "176427                                     Quatro Terra 2  2022\n",
       "88423                                      Clutch Control  2022\n",
       "67019                                   The Burning Skies  2022\n",
       "142551                                    The Devil's Sea  2022\n",
       "26576                               The Cretaceous Period  2022\n",
       "27491                           Oracle: The Last Judgment  2022\n",
       "160801                                             Berlin  2022\n",
       "33204                                         Gnome Alone  2023\n",
       "124226                           Battle in the Antarctica  2023\n",
       "106170                                          St. Cairo  2023\n",
       "130159                              The Supreme Gentleman  2023\n",
       "57605                                           Inside Me  2023\n",
       "160134                               Journey to the Space  2023\n",
       "171822                                   The Good Village  2023\n",
       "103630                                           Garriage  2023\n",
       "72581                                          The League  2023\n",
       "152227                                          Xenomorph  2023\n",
       "85417                                            Avatar 5  2023\n",
       "196677                                            Agartha  2023\n",
       "70051                                           Interplay  2023\n",
       "88936                          The Snowman and the Falcon  2024\n",
       "72471                                             Kennedy  2024\n",
       "10754                       Hyperion: The Wrath of Kronos  2024\n",
       "229718                                         Gods Heart  2025\n",
       "145689                                        Rise of Man  2025\n",
       "102561                                  Settling of Score  2026\n",
       "65488                                         War in Snow  2026\n",
       "\n",
       "[232330 rows x 2 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# insert code here\n",
    "titles[titles['year'] == min(titles['year'])]\n",
    "titles.sort_values('year')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vdacpNIqVaAj"
   },
   "source": [
    "### Q14: How many movies have the title \"Hamlet\"?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KRs9FLM9VaAj",
    "outputId": "7ce5b3b6-4848-402c-cca5-6b2d0861431e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hamlet    18\n",
       "Name: title, dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(titles)\n",
    "len(cast)\n",
    "titles[titles['title'] == 'Hamlet']['title'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4hNG1A9CVaAp"
   },
   "source": [
    "### Q15: List all of the \"Treasure Island\" movies from earliest to most recent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Rhf67jPPVaAp",
    "outputId": "ff83ba46-a80b-43ee-dbe1-4b2226f61f5f"
   },
   "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>title</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>206027</th>\n",
       "      <td>Treasure Island</td>\n",
       "      <td>1918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51287</th>\n",
       "      <td>Treasure Island</td>\n",
       "      <td>1920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191050</th>\n",
       "      <td>Treasure Island</td>\n",
       "      <td>1934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96934</th>\n",
       "      <td>Treasure Island</td>\n",
       "      <td>1950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89534</th>\n",
       "      <td>Treasure Island</td>\n",
       "      <td>1972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111343</th>\n",
       "      <td>Treasure Island</td>\n",
       "      <td>1973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205397</th>\n",
       "      <td>Treasure Island</td>\n",
       "      <td>1985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179354</th>\n",
       "      <td>Treasure Island</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  title  year\n",
       "206027  Treasure Island  1918\n",
       "51287   Treasure Island  1920\n",
       "191050  Treasure Island  1934\n",
       "96934   Treasure Island  1950\n",
       "89534   Treasure Island  1972\n",
       "111343  Treasure Island  1973\n",
       "205397  Treasure Island  1985\n",
       "179354  Treasure Island  1999"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titles[titles['title'] == 'Treasure Island'].sort_values('year')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "n6ekJFVmVaAu"
   },
   "source": [
    "### Q16: List the supporting roles (having n=2) played by Cary Grant in the 1940s,\n",
    "in order by year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6MgR83iQVaAu",
    "outputId": "b2c79b39-d14d-4d37-fd5e-7d1514885d15"
   },
   "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>title</th>\n",
       "      <th>year</th>\n",
       "      <th>name</th>\n",
       "      <th>type</th>\n",
       "      <th>character</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>845513</th>\n",
       "      <td>My Favorite Wife</td>\n",
       "      <td>1940</td>\n",
       "      <td>Cary Grant</td>\n",
       "      <td>actor</td>\n",
       "      <td>Nick Arden</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>845523</th>\n",
       "      <td>Penny Serenade</td>\n",
       "      <td>1941</td>\n",
       "      <td>Cary Grant</td>\n",
       "      <td>actor</td>\n",
       "      <td>Roger Adams</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   title  year        name   type    character    n\n",
       "845513  My Favorite Wife  1940  Cary Grant  actor   Nick Arden  2.0\n",
       "845523    Penny Serenade  1941  Cary Grant  actor  Roger Adams  2.0"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#(cast.year >= 1940) & (cast.year < 1950)\n",
    "#(cast.year == 1940) | (cast.year == 1941)... \n",
    "# cast.year // 10 == 194\n",
    "# insert code here\n",
    "cast[(cast['n'] == 2) & (cast['name'] == 'Cary Grant') & (cast.year // 10 == 194)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0nigWnfaVaAw"
   },
   "source": [
    "### Q17: What are the ten most common movie names of all time?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7vo-EPlZVaAw",
    "outputId": "30cd67a7-1f47-4a1f-ff9a-3060ed5ca7a4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hamlet                  18\n",
       "Carmen                  16\n",
       "Macbeth                 15\n",
       "The Outsider            12\n",
       "Maya                    12\n",
       "Temptation              11\n",
       "Othello                 11\n",
       "The Three Musketeers    11\n",
       "Freedom                 11\n",
       "Kismet                  11\n",
       "Name: title, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titles['title'].value_counts()[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "puDsuQMgVaAy"
   },
   "source": [
    "### Q18: Plot the number of \"Hamlet\" films made each decade\n",
    "\n",
    "Make sure your decades are in chronological order on the x-axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VW5tJBjCVaAz",
    "outputId": "bb560e2d-c531-4805-8563-a17af893f2ea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "189\n",
      "202\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1910    3\n",
       "1920    1\n",
       "1940    1\n",
       "1950    1\n",
       "1960    2\n",
       "1970    2\n",
       "1980    1\n",
       "1990    2\n",
       "2000    2\n",
       "2010    3\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11447ef50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(min(titles.year // 10))\n",
    "print(max(titles.year // 10))\n",
    "hamlet = titles[titles['title'] == 'Hamlet']\n",
    "# # titles[(titles['title'] == 'Hamlet') & (titles.year // 10 == 194)]\n",
    "hamlet['decades'] = hamlet['year'] // 10 * 10\n",
    "hamlet['decades'].value_counts().plot.bar()\n",
    "\n",
    "titles.head()\n",
    "t = titles\n",
    "t = t[t.title == 'Hamlet']\n",
    "(t.year // 10 * 10).value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0e8ojF-uVaA0"
   },
   "source": [
    "### Q19: Who are the 10 people most often credited as \"Herself\" in film history?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xZ3Ar2hPVaA1",
    "outputId": "2441de5e-b683-405d-9c7e-cd79ee07c138"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Laverne Andrews        15\n",
       "Maxene Andrews         15\n",
       "Patty Andrews          10\n",
       "Matutina                8\n",
       "The Andrews Sisters     7\n",
       "Lena Horne              7\n",
       "Zsa Zsa Gabor           7\n",
       "Hedda Hopper            6\n",
       "Dorothy Lamour          6\n",
       "Judy Canova             6\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cast[cast['character'] == 'Herself']['name'].value_counts() # have 'Himself'\n",
    "cast[(cast.name == cast.character) & (cast.type=='actress')].name.value_counts()[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nTo4QMqZVaA3"
   },
   "source": [
    "### Q20: What are the 10 most frequent roles that start with the word \"Science\"?\n",
    "Hint: read docs on str.startswith()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WP5VMIAqVaA4",
    "outputId": "aa267acd-6e2e-47de-c0c4-4e60084a5ca1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Science Teacher         60\n",
       "Science Fair Student     9\n",
       "Science Student          9\n",
       "Science Fair Judge       6\n",
       "Science Kid              5\n",
       "Science Club Member      5\n",
       "Science Reporter         5\n",
       "Science Promo Cadet      5\n",
       "Science                  4\n",
       "Science Officer          3\n",
       "Name: character, dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cast['character']\n",
    "# cast[cast['character'] re.match('Science?', )]\n",
    "cast[cast.character.str.startswith('Science')].character.value_counts()[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "eKpdayrYVaA7"
   },
   "source": [
    "### Q21: How many leading (n=1) roles were available to actors, and how many to actresses, in the 1950s?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1mYaTynCVaA8",
    "outputId": "7bf60f15-94e1-4a12-bcea-6f6e1e19e8b7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6616\n",
      "2965\n"
     ]
    }
   ],
   "source": [
    "new = cast[(cast.n==1) & (cast.year // 10 * 10 == 1950)]\n",
    "print(len(new[new.type==\"actor\"]))\n",
    "print(len(new[new.type==\"actress\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bfTRaAmFVaBB"
   },
   "source": [
    "### Q22: How many supporting (n=2) roles were available to actors,and how many to actresses, in the 1950s?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eT2piDNlVaBC",
    "outputId": "142cebf9-f2d8-42b1-d7f1-4215cb40e7d1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4564\n",
      "4556\n",
      "2.23136593592\n",
      "1.00175592625\n"
     ]
    }
   ],
   "source": [
    "new2 = cast[(cast.n==2) & (cast.year // 10 * 10 == 1950)]\n",
    "print(len(new2[new2.type==\"actor\"]))\n",
    "print(len(new2[new2.type==\"actress\"]))\n",
    "print(6616.0 / 2965)\n",
    "print(4564.0 / 4556)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wHN-cI-dVaBE"
   },
   "source": [
    "### Q23: Comment on the differences in gender ratios for leading vs. supporting roles in the 1950s.  Does there appear to be a bias?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "### For 1950s, the leading role has gender ratio (actor/actress) about 2.3 so there is some bias. \n",
    "### But the ratio for supporting roles is about 1 so there is no significant bias."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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