Exercises3_2.ipynb

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   "source": [
    "# Occupation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
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   "metadata": {
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   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/u.user). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called users."
   ]
  },
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       "<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",
       "      <th>0</th>\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",
       "      <th>1</th>\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",
       "      <th>2</th>\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",
       "      <th>3</th>\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",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>15213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>938</th>\n",
       "      <td>939</td>\n",
       "      <td>26</td>\n",
       "      <td>F</td>\n",
       "      <td>student</td>\n",
       "      <td>33319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>939</th>\n",
       "      <td>940</td>\n",
       "      <td>32</td>\n",
       "      <td>M</td>\n",
       "      <td>administrator</td>\n",
       "      <td>02215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>940</th>\n",
       "      <td>941</td>\n",
       "      <td>20</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>97229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>941</th>\n",
       "      <td>942</td>\n",
       "      <td>48</td>\n",
       "      <td>F</td>\n",
       "      <td>librarian</td>\n",
       "      <td>78209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>942</th>\n",
       "      <td>943</td>\n",
       "      <td>22</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>77841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>943 rows × 5 columns</p>\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\n",
       "..       ...  ...    ...            ...      ...\n",
       "938      939   26      F        student    33319\n",
       "939      940   32      M  administrator    02215\n",
       "940      941   20      M        student    97229\n",
       "941      942   48      F      librarian    78209\n",
       "942      943   22      M        student    77841\n",
       "\n",
       "[943 rows x 5 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users = pd.read_csv('https://raw.githubusercontent.com/justmarkham/DAT8/master/data/u.user', sep='|')\n",
    "users"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Discover what is the mean age per occupation"
   ]
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       "<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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>occupation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>administrator</th>\n",
       "      <td>430.949367</td>\n",
       "      <td>38.746835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>artist</th>\n",
       "      <td>451.892857</td>\n",
       "      <td>31.392857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>doctor</th>\n",
       "      <td>533.714286</td>\n",
       "      <td>43.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>educator</th>\n",
       "      <td>466.905263</td>\n",
       "      <td>42.010526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engineer</th>\n",
       "      <td>456.328358</td>\n",
       "      <td>36.388060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>entertainment</th>\n",
       "      <td>398.000000</td>\n",
       "      <td>29.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>executive</th>\n",
       "      <td>422.312500</td>\n",
       "      <td>38.718750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>healthcare</th>\n",
       "      <td>501.437500</td>\n",
       "      <td>41.562500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>homemaker</th>\n",
       "      <td>443.000000</td>\n",
       "      <td>32.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lawyer</th>\n",
       "      <td>359.083333</td>\n",
       "      <td>36.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>librarian</th>\n",
       "      <td>486.588235</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marketing</th>\n",
       "      <td>437.807692</td>\n",
       "      <td>37.615385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>none</th>\n",
       "      <td>368.666667</td>\n",
       "      <td>26.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other</th>\n",
       "      <td>542.733333</td>\n",
       "      <td>34.523810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>programmer</th>\n",
       "      <td>435.530303</td>\n",
       "      <td>33.121212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>retired</th>\n",
       "      <td>515.714286</td>\n",
       "      <td>63.071429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salesman</th>\n",
       "      <td>494.916667</td>\n",
       "      <td>35.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scientist</th>\n",
       "      <td>465.129032</td>\n",
       "      <td>35.548387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>student</th>\n",
       "      <td>484.954082</td>\n",
       "      <td>22.081633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>technician</th>\n",
       "      <td>497.629630</td>\n",
       "      <td>33.148148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>writer</th>\n",
       "      <td>495.711111</td>\n",
       "      <td>36.311111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  user_id        age\n",
       "occupation                          \n",
       "administrator  430.949367  38.746835\n",
       "artist         451.892857  31.392857\n",
       "doctor         533.714286  43.571429\n",
       "educator       466.905263  42.010526\n",
       "engineer       456.328358  36.388060\n",
       "entertainment  398.000000  29.222222\n",
       "executive      422.312500  38.718750\n",
       "healthcare     501.437500  41.562500\n",
       "homemaker      443.000000  32.571429\n",
       "lawyer         359.083333  36.750000\n",
       "librarian      486.588235  40.000000\n",
       "marketing      437.807692  37.615385\n",
       "none           368.666667  26.555556\n",
       "other          542.733333  34.523810\n",
       "programmer     435.530303  33.121212\n",
       "retired        515.714286  63.071429\n",
       "salesman       494.916667  35.666667\n",
       "scientist      465.129032  35.548387\n",
       "student        484.954082  22.081633\n",
       "technician     497.629630  33.148148\n",
       "writer         495.711111  36.311111"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.groupby('occupation').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Discover the Male ratio per occupation and sort it from the most to the least"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
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    {
     "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_gender_counts = users.groupby(by=['occupation', 'gender'])['gender'].count()\n",
    "users_per_occ = users.groupby('occupation')['gender'].count()\n",
    "ratio = all_gender_counts / users_per_occ * 100\n",
    "ratio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. For each occupation, calculate the minimum and maximum ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
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       "<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>zip_code</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>occupation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>administrator</th>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>F</td>\n",
       "      <td>02154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>artist</th>\n",
       "      <td>23</td>\n",
       "      <td>19</td>\n",
       "      <td>F</td>\n",
       "      <td>01945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>doctor</th>\n",
       "      <td>138</td>\n",
       "      <td>28</td>\n",
       "      <td>M</td>\n",
       "      <td>47401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>educator</th>\n",
       "      <td>13</td>\n",
       "      <td>23</td>\n",
       "      <td>F</td>\n",
       "      <td>01602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engineer</th>\n",
       "      <td>25</td>\n",
       "      <td>22</td>\n",
       "      <td>F</td>\n",
       "      <td>00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>entertainment</th>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>F</td>\n",
       "      <td>01040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>executive</th>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>F</td>\n",
       "      <td>00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>healthcare</th>\n",
       "      <td>60</td>\n",
       "      <td>22</td>\n",
       "      <td>F</td>\n",
       "      <td>02154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>homemaker</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>F</td>\n",
       "      <td>17331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lawyer</th>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>F</td>\n",
       "      <td>06371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>librarian</th>\n",
       "      <td>19</td>\n",
       "      <td>23</td>\n",
       "      <td>F</td>\n",
       "      <td>01080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marketing</th>\n",
       "      <td>46</td>\n",
       "      <td>24</td>\n",
       "      <td>F</td>\n",
       "      <td>01913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>none</th>\n",
       "      <td>57</td>\n",
       "      <td>11</td>\n",
       "      <td>F</td>\n",
       "      <td>21206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other</th>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>F</td>\n",
       "      <td>02110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>programmer</th>\n",
       "      <td>17</td>\n",
       "      <td>20</td>\n",
       "      <td>F</td>\n",
       "      <td>01331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>retired</th>\n",
       "      <td>106</td>\n",
       "      <td>51</td>\n",
       "      <td>F</td>\n",
       "      <td>02903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salesman</th>\n",
       "      <td>112</td>\n",
       "      <td>18</td>\n",
       "      <td>F</td>\n",
       "      <td>30011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scientist</th>\n",
       "      <td>14</td>\n",
       "      <td>23</td>\n",
       "      <td>F</td>\n",
       "      <td>02113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>student</th>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>F</td>\n",
       "      <td>01002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>technician</th>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>F</td>\n",
       "      <td>02139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>writer</th>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>F</td>\n",
       "      <td>08534</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               user_id  age gender zip_code\n",
       "occupation                                 \n",
       "administrator        7   21      F    02154\n",
       "artist              23   19      F    01945\n",
       "doctor             138   28      M    47401\n",
       "educator            13   23      F    01602\n",
       "engineer            25   22      F    00000\n",
       "entertainment       16   15      F    01040\n",
       "executive            6   22      F    00000\n",
       "healthcare          60   22      F    02154\n",
       "homemaker           20   20      F    17331\n",
       "lawyer              10   21      F    06371\n",
       "librarian           19   23      F    01080\n",
       "marketing           46   24      F    01913\n",
       "none                57   11      F    21206\n",
       "other                2   13      F    02110\n",
       "programmer          17   20      F    01331\n",
       "retired            106   51      F    02903\n",
       "salesman           112   18      F    30011\n",
       "scientist           14   23      F    02113\n",
       "student              9    7      F    01002\n",
       "technician           1   21      F    02139\n",
       "writer               3   18      F    08534"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = users.groupby('occupation').max()\n",
    "y = users.groupby('occupation').min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. For each combination of occupation and gender, calculate the mean age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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>user_id</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>occupation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>administrator</th>\n",
       "      <td>430.949367</td>\n",
       "      <td>38.746835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>artist</th>\n",
       "      <td>451.892857</td>\n",
       "      <td>31.392857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>doctor</th>\n",
       "      <td>533.714286</td>\n",
       "      <td>43.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>educator</th>\n",
       "      <td>466.905263</td>\n",
       "      <td>42.010526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engineer</th>\n",
       "      <td>456.328358</td>\n",
       "      <td>36.388060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>entertainment</th>\n",
       "      <td>398.000000</td>\n",
       "      <td>29.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>executive</th>\n",
       "      <td>422.312500</td>\n",
       "      <td>38.718750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>healthcare</th>\n",
       "      <td>501.437500</td>\n",
       "      <td>41.562500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>homemaker</th>\n",
       "      <td>443.000000</td>\n",
       "      <td>32.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lawyer</th>\n",
       "      <td>359.083333</td>\n",
       "      <td>36.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>librarian</th>\n",
       "      <td>486.588235</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marketing</th>\n",
       "      <td>437.807692</td>\n",
       "      <td>37.615385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>none</th>\n",
       "      <td>368.666667</td>\n",
       "      <td>26.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other</th>\n",
       "      <td>542.733333</td>\n",
       "      <td>34.523810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>programmer</th>\n",
       "      <td>435.530303</td>\n",
       "      <td>33.121212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>retired</th>\n",
       "      <td>515.714286</td>\n",
       "      <td>63.071429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salesman</th>\n",
       "      <td>494.916667</td>\n",
       "      <td>35.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scientist</th>\n",
       "      <td>465.129032</td>\n",
       "      <td>35.548387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>student</th>\n",
       "      <td>484.954082</td>\n",
       "      <td>22.081633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>technician</th>\n",
       "      <td>497.629630</td>\n",
       "      <td>33.148148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>writer</th>\n",
       "      <td>495.711111</td>\n",
       "      <td>36.311111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  user_id        age\n",
       "occupation                          \n",
       "administrator  430.949367  38.746835\n",
       "artist         451.892857  31.392857\n",
       "doctor         533.714286  43.571429\n",
       "educator       466.905263  42.010526\n",
       "engineer       456.328358  36.388060\n",
       "entertainment  398.000000  29.222222\n",
       "executive      422.312500  38.718750\n",
       "healthcare     501.437500  41.562500\n",
       "homemaker      443.000000  32.571429\n",
       "lawyer         359.083333  36.750000\n",
       "librarian      486.588235  40.000000\n",
       "marketing      437.807692  37.615385\n",
       "none           368.666667  26.555556\n",
       "other          542.733333  34.523810\n",
       "programmer     435.530303  33.121212\n",
       "retired        515.714286  63.071429\n",
       "salesman       494.916667  35.666667\n",
       "scientist      465.129032  35.548387\n",
       "student        484.954082  22.081633\n",
       "technician     497.629630  33.148148\n",
       "writer         495.711111  36.311111"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.groupby('gender').mean()\n",
    "users.groupby('occupation').mean()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8.  For each occupation present the percentage of women and men"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# create a data frame and apply count to gender\n",
    "gender_ocup = users.groupby(['occupation', 'gender']).agg({'gender': 'count'})\n",
    "\n",
    "# create a DataFrame and apply count for each occupation\n",
    "occup_count = users.groupby(['occupation']).agg('count')\n",
    "\n",
    "# divide the gender_ocup per the occup_count and multiply per 100\n",
    "occup_gender = gender_ocup.div(occup_count, level = \"occupation\") * 100\n",
    "\n",
    "# present all rows from the 'gender column'\n",
    "occup_gender.loc[: , 'gender']"
   ]
  }
 ],
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