DataTalks_GroupBy II.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GroupBy\n",
    "import dataset from https://raw.githubusercontent.com/justmarkham/DAT8/master/data/u.user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>user_id</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>technician</td>\n",
       "      <td>85711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>53</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>94043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>M</td>\n",
       "      <td>writer</td>\n",
       "      <td>32067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>technician</td>\n",
       "      <td>43537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>15213</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  age gender  occupation zip_code\n",
       "0        1   24      M  technician    85711\n",
       "1        2   53      F       other    94043\n",
       "2        3   23      M      writer    32067\n",
       "3        4   24      M  technician    43537\n",
       "4        5   33      F       other    15213"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"https://raw.githubusercontent.com/justmarkham/DAT8/master/data/u.user\", sep = \"|\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What is the mean age per occupation?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "occupation\n",
       "administrator    38.746835\n",
       "artist           31.392857\n",
       "doctor           43.571429\n",
       "educator         42.010526\n",
       "engineer         36.388060\n",
       "entertainment    29.222222\n",
       "executive        38.718750\n",
       "healthcare       41.562500\n",
       "homemaker        32.571429\n",
       "lawyer           36.750000\n",
       "librarian        40.000000\n",
       "marketing        37.615385\n",
       "none             26.555556\n",
       "other            34.523810\n",
       "programmer       33.121212\n",
       "retired          63.071429\n",
       "salesman         35.666667\n",
       "scientist        35.548387\n",
       "student          22.081633\n",
       "technician       33.148148\n",
       "writer           36.311111\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"occupation\").age.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The Male ratio per occupation and sort it from the most to the least"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "homemaker         14.285714\n",
       "healthcare        31.250000\n",
       "librarian         43.137255\n",
       "artist            53.571429\n",
       "administrator     54.430380\n",
       "none              55.555556\n",
       "writer            57.777778\n",
       "marketing         61.538462\n",
       "other             65.714286\n",
       "student           69.387755\n",
       "educator          72.631579\n",
       "salesman          75.000000\n",
       "lawyer            83.333333\n",
       "entertainment     88.888889\n",
       "scientist         90.322581\n",
       "executive         90.625000\n",
       "programmer        90.909091\n",
       "retired           92.857143\n",
       "technician        96.296296\n",
       "engineer          97.014925\n",
       "doctor           100.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def male_ratio(x):\n",
    "    if x == \"M\": return 1\n",
    "    else: return 0\n",
    "# create a new column\n",
    "df[\"gender_M\"] = df[\"gender\"].apply(male_ratio)\n",
    "a = df.groupby(\"occupation\").gender_M.sum()/df.occupation.value_counts()*100\n",
    "a.sort_values(ascending = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For each occupation, calculate the minimum and maximum ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>occupation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>administrator</td>\n",
       "      <td>21</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>artist</td>\n",
       "      <td>19</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>doctor</td>\n",
       "      <td>28</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>educator</td>\n",
       "      <td>23</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>engineer</td>\n",
       "      <td>22</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>entertainment</td>\n",
       "      <td>15</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>executive</td>\n",
       "      <td>22</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>healthcare</td>\n",
       "      <td>22</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>homemaker</td>\n",
       "      <td>20</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lawyer</td>\n",
       "      <td>21</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>librarian</td>\n",
       "      <td>23</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>marketing</td>\n",
       "      <td>24</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>none</td>\n",
       "      <td>11</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>other</td>\n",
       "      <td>13</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>programmer</td>\n",
       "      <td>20</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>retired</td>\n",
       "      <td>51</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>salesman</td>\n",
       "      <td>18</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>scientist</td>\n",
       "      <td>23</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>student</td>\n",
       "      <td>7</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>technician</td>\n",
       "      <td>21</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>writer</td>\n",
       "      <td>18</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               min  max\n",
       "occupation             \n",
       "administrator   21   70\n",
       "artist          19   48\n",
       "doctor          28   64\n",
       "educator        23   63\n",
       "engineer        22   70\n",
       "entertainment   15   50\n",
       "executive       22   69\n",
       "healthcare      22   62\n",
       "homemaker       20   50\n",
       "lawyer          21   53\n",
       "librarian       23   69\n",
       "marketing       24   55\n",
       "none            11   55\n",
       "other           13   64\n",
       "programmer      20   63\n",
       "retired         51   73\n",
       "salesman        18   66\n",
       "scientist       23   55\n",
       "student          7   42\n",
       "technician      21   55\n",
       "writer          18   60"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"occupation\").age.agg([\"min\", \"max\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For each combination of occupation and gender, calculate the mean age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "occupation     gender\n",
       "administrator  F         40.638889\n",
       "               M         37.162791\n",
       "artist         F         30.307692\n",
       "               M         32.333333\n",
       "doctor         M         43.571429\n",
       "educator       F         39.115385\n",
       "               M         43.101449\n",
       "engineer       F         29.500000\n",
       "               M         36.600000\n",
       "entertainment  F         31.000000\n",
       "               M         29.000000\n",
       "executive      F         44.000000\n",
       "               M         38.172414\n",
       "healthcare     F         39.818182\n",
       "               M         45.400000\n",
       "homemaker      F         34.166667\n",
       "               M         23.000000\n",
       "lawyer         F         39.500000\n",
       "               M         36.200000\n",
       "librarian      F         40.000000\n",
       "               M         40.000000\n",
       "marketing      F         37.200000\n",
       "               M         37.875000\n",
       "none           F         36.500000\n",
       "               M         18.600000\n",
       "other          F         35.472222\n",
       "               M         34.028986\n",
       "programmer     F         32.166667\n",
       "               M         33.216667\n",
       "retired        F         70.000000\n",
       "               M         62.538462\n",
       "salesman       F         27.000000\n",
       "               M         38.555556\n",
       "scientist      F         28.333333\n",
       "               M         36.321429\n",
       "student        F         20.750000\n",
       "               M         22.669118\n",
       "technician     F         38.000000\n",
       "               M         32.961538\n",
       "writer         F         37.631579\n",
       "               M         35.346154\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"occupation\", \"gender\"]).age.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For each occupation present the percentage of women and men"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>occupation</th>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">administrator</td>\n",
       "      <td>F</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>M</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">artist</td>\n",
       "      <td>F</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>M</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>doctor</td>\n",
       "      <td>M</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">educator</td>\n",
       "      <td>F</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>M</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">engineer</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>M</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">entertainment</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>M</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">executive</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>M</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">healthcare</td>\n",
       "      <td>F</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">homemaker</td>\n",
       "      <td>F</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">lawyer</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>M</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">librarian</td>\n",
       "      <td>F</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>M</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">marketing</td>\n",
       "      <td>F</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>M</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">none</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">other</td>\n",
       "      <td>F</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>M</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">programmer</td>\n",
       "      <td>F</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>M</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">retired</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>M</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">salesman</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>M</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">scientist</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>M</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">student</td>\n",
       "      <td>F</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>M</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">technician</td>\n",
       "      <td>F</td>\n",
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       "      <td rowspan=\"2\" valign=\"top\">writer</td>\n",
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       "      <td>19</td>\n",
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       "      <td>M</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                      gender\n",
       "occupation    gender        \n",
       "administrator F           36\n",
       "              M           43\n",
       "artist        F           13\n",
       "              M           15\n",
       "doctor        M            7\n",
       "educator      F           26\n",
       "              M           69\n",
       "engineer      F            2\n",
       "              M           65\n",
       "entertainment F            2\n",
       "              M           16\n",
       "executive     F            3\n",
       "              M           29\n",
       "healthcare    F           11\n",
       "              M            5\n",
       "homemaker     F            6\n",
       "              M            1\n",
       "lawyer        F            2\n",
       "              M           10\n",
       "librarian     F           29\n",
       "              M           22\n",
       "marketing     F           10\n",
       "              M           16\n",
       "none          F            4\n",
       "              M            5\n",
       "other         F           36\n",
       "              M           69\n",
       "programmer    F            6\n",
       "              M           60\n",
       "retired       F            1\n",
       "              M           13\n",
       "salesman      F            3\n",
       "              M            9\n",
       "scientist     F            3\n",
       "              M           28\n",
       "student       F           60\n",
       "              M          136\n",
       "technician    F            1\n",
       "              M           26\n",
       "writer        F           19\n",
       "              M           26"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_group = df.groupby([\"occupation\", \"gender\"]).agg({\"gender\":\"count\"})\n",
    "gender_group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "               user_id  age  gender  zip_code  gender_M\n",
       "occupation                                             \n",
       "administrator       79   79      79        79        79\n",
       "artist              28   28      28        28        28\n",
       "doctor               7    7       7         7         7\n",
       "educator            95   95      95        95        95\n",
       "engineer            67   67      67        67        67\n",
       "entertainment       18   18      18        18        18\n",
       "executive           32   32      32        32        32\n",
       "healthcare          16   16      16        16        16\n",
       "homemaker            7    7       7         7         7\n",
       "lawyer              12   12      12        12        12\n",
       "librarian           51   51      51        51        51\n",
       "marketing           26   26      26        26        26\n",
       "none                 9    9       9         9         9\n",
       "other              105  105     105       105       105\n",
       "programmer          66   66      66        66        66\n",
       "retired             14   14      14        14        14\n",
       "salesman            12   12      12        12        12\n",
       "scientist           31   31      31        31        31\n",
       "student            196  196     196       196       196\n",
       "technician          27   27      27        27        27\n",
       "writer              45   45      45        45        45"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "occup_count = df.groupby(\"occupation\").agg(\"count\")\n",
    "occup_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92.857143</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">salesman</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">scientist</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.677419</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.322581</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">student</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.612245</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69.387755</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">technician</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.703704</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.296296</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">writer</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.222222</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.777778</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      age      gender  gender_M  user_id  zip_code\n",
       "occupation    gender                                              \n",
       "administrator F       NaN   45.569620       NaN      NaN       NaN\n",
       "              M       NaN   54.430380       NaN      NaN       NaN\n",
       "artist        F       NaN   46.428571       NaN      NaN       NaN\n",
       "              M       NaN   53.571429       NaN      NaN       NaN\n",
       "doctor        M       NaN  100.000000       NaN      NaN       NaN\n",
       "educator      F       NaN   27.368421       NaN      NaN       NaN\n",
       "              M       NaN   72.631579       NaN      NaN       NaN\n",
       "engineer      F       NaN    2.985075       NaN      NaN       NaN\n",
       "              M       NaN   97.014925       NaN      NaN       NaN\n",
       "entertainment F       NaN   11.111111       NaN      NaN       NaN\n",
       "              M       NaN   88.888889       NaN      NaN       NaN\n",
       "executive     F       NaN    9.375000       NaN      NaN       NaN\n",
       "              M       NaN   90.625000       NaN      NaN       NaN\n",
       "healthcare    F       NaN   68.750000       NaN      NaN       NaN\n",
       "              M       NaN   31.250000       NaN      NaN       NaN\n",
       "homemaker     F       NaN   85.714286       NaN      NaN       NaN\n",
       "              M       NaN   14.285714       NaN      NaN       NaN\n",
       "lawyer        F       NaN   16.666667       NaN      NaN       NaN\n",
       "              M       NaN   83.333333       NaN      NaN       NaN\n",
       "librarian     F       NaN   56.862745       NaN      NaN       NaN\n",
       "              M       NaN   43.137255       NaN      NaN       NaN\n",
       "marketing     F       NaN   38.461538       NaN      NaN       NaN\n",
       "              M       NaN   61.538462       NaN      NaN       NaN\n",
       "none          F       NaN   44.444444       NaN      NaN       NaN\n",
       "              M       NaN   55.555556       NaN      NaN       NaN\n",
       "other         F       NaN   34.285714       NaN      NaN       NaN\n",
       "              M       NaN   65.714286       NaN      NaN       NaN\n",
       "programmer    F       NaN    9.090909       NaN      NaN       NaN\n",
       "              M       NaN   90.909091       NaN      NaN       NaN\n",
       "retired       F       NaN    7.142857       NaN      NaN       NaN\n",
       "              M       NaN   92.857143       NaN      NaN       NaN\n",
       "salesman      F       NaN   25.000000       NaN      NaN       NaN\n",
       "              M       NaN   75.000000       NaN      NaN       NaN\n",
       "scientist     F       NaN    9.677419       NaN      NaN       NaN\n",
       "              M       NaN   90.322581       NaN      NaN       NaN\n",
       "student       F       NaN   30.612245       NaN      NaN       NaN\n",
       "              M       NaN   69.387755       NaN      NaN       NaN\n",
       "technician    F       NaN    3.703704       NaN      NaN       NaN\n",
       "              M       NaN   96.296296       NaN      NaN       NaN\n",
       "writer        F       NaN   42.222222       NaN      NaN       NaN\n",
       "              M       NaN   57.777778       NaN      NaN       NaN"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "occup_gender = gender_group.div(occup_count, level = \"occupation\")*100\n",
    "occup_gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "occupation     gender\n",
       "administrator  F          45.569620\n",
       "               M          54.430380\n",
       "artist         F          46.428571\n",
       "               M          53.571429\n",
       "doctor         M         100.000000\n",
       "educator       F          27.368421\n",
       "               M          72.631579\n",
       "engineer       F           2.985075\n",
       "               M          97.014925\n",
       "entertainment  F          11.111111\n",
       "               M          88.888889\n",
       "executive      F           9.375000\n",
       "               M          90.625000\n",
       "healthcare     F          68.750000\n",
       "               M          31.250000\n",
       "homemaker      F          85.714286\n",
       "               M          14.285714\n",
       "lawyer         F          16.666667\n",
       "               M          83.333333\n",
       "librarian      F          56.862745\n",
       "               M          43.137255\n",
       "marketing      F          38.461538\n",
       "               M          61.538462\n",
       "none           F          44.444444\n",
       "               M          55.555556\n",
       "other          F          34.285714\n",
       "               M          65.714286\n",
       "programmer     F           9.090909\n",
       "               M          90.909091\n",
       "retired        F           7.142857\n",
       "               M          92.857143\n",
       "salesman       F          25.000000\n",
       "               M          75.000000\n",
       "scientist      F           9.677419\n",
       "               M          90.322581\n",
       "student        F          30.612245\n",
       "               M          69.387755\n",
       "technician     F           3.703704\n",
       "               M          96.296296\n",
       "writer         F          42.222222\n",
       "               M          57.777778\n",
       "Name: gender, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "occup_gender.loc[:, \"gender\"]"
   ]
  }
 ],
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