uci_adult_dataset_pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "570fb89bb4f7838b1d0fdff70b2935790e5dbdbe"
   },
   "source": [
    "<center>\n",
    "<img src=\"https://habrastorage.org/files/fd4/502/43d/fd450243dd604b81b9713213a247aa20.jpg\">\n",
    "    \n",
    "## [mlcourse.ai](mlcourse.ai) – Open Machine Learning Course \n",
    "\n",
    "Author: [Yury Kashnitskiy](http://yorko.github.io)\n",
    "Translated and edited by [Sergey Isaev](https://www.linkedin.com/in/isvforall/), [Artem Trunov](https://www.linkedin.com/in/datamove/), [Anastasia Manokhina](https://www.linkedin.com/in/anastasiamanokhina/), and [Yuanyuan Pao](https://www.linkedin.com/in/yuanyuanpao/) All content is distributed under the [Creative Commons CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "998fc5f76227ab1df9460a7b05c508304b14b3a6"
   },
   "source": [
    "# <center> Assignment #1 (demo)\n",
    "## <center>  Exploratory data analysis with Pandas\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "89ef93231815456f4e0bc5dcc00b094aee800993"
   },
   "source": [
    "**In this task you should use Pandas to answer a few questions about the [Adult](https://archive.ics.uci.edu/ml/datasets/Adult) dataset. (You don't have to download the data – it's already here). Choose the answers in the [web-form](https://docs.google.com/forms/d/1uY7MpI2trKx6FLWZte0uVh3ULV4Cm_tDud0VDFGCOKg). This is a demo version of an assignment, so by submitting the form, you'll see a link to the solution .ipynb file.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "c322301ac858c748f8f80e63cf6f734fc70dde30"
   },
   "source": [
    "Unique values of all features (for more information, please see the links above):\n",
    "- `age`: continuous.\n",
    "- `workclass`: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.\n",
    "- `fnlwgt`: continuous.\n",
    "- `education`: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.\n",
    "- `education-num`: continuous.\n",
    "- `marital-status`: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.\n",
    "- `occupation`: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.\n",
    "- `relationship`: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.\n",
    "- `race`: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.\n",
    "- `sex`: Female, Male.\n",
    "- `capital-gain`: continuous.\n",
    "- `capital-loss`: continuous.\n",
    "- `hours-per-week`: continuous.\n",
    "- `native-country`: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands.   \n",
    "- `salary`: >50K,<=50K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "_uuid": "d3eb2411e23b7db11e4cbbb498a42752442a6f4c"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_uuid": "c5119d1b8151029b4ff57735c4279b795ae492a2"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age         workclass  fnlwgt  education  education-num  \\\n",
       "0   39         State-gov   77516  Bachelors             13   \n",
       "1   50  Self-emp-not-inc   83311  Bachelors             13   \n",
       "2   38           Private  215646    HS-grad              9   \n",
       "3   53           Private  234721       11th              7   \n",
       "4   28           Private  338409  Bachelors             13   \n",
       "\n",
       "       marital-status         occupation   relationship   race     sex  \\\n",
       "0       Never-married       Adm-clerical  Not-in-family  White    Male   \n",
       "1  Married-civ-spouse    Exec-managerial        Husband  White    Male   \n",
       "2            Divorced  Handlers-cleaners  Not-in-family  White    Male   \n",
       "3  Married-civ-spouse  Handlers-cleaners        Husband  Black    Male   \n",
       "4  Married-civ-spouse     Prof-specialty           Wife  Black  Female   \n",
       "\n",
       "   capital-gain  capital-loss  hours-per-week native-country salary  \n",
       "0          2174             0              40  United-States  <=50K  \n",
       "1             0             0              13  United-States  <=50K  \n",
       "2             0             0              40  United-States  <=50K  \n",
       "3             0             0              40  United-States  <=50K  \n",
       "4             0             0              40           Cuba  <=50K  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "source = 'https://raw.githubusercontent.com/Lenferdetroud/courses/main/\\\n",
    "Open%20Data%20Science%3A%20ML%20(course)/data/adult.data.csv'\n",
    "\n",
    "df = pd.read_csv(source)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "6b19853f936853d40971b56f4d298ab55ee78cac"
   },
   "source": [
    "**1. How many men and women (*sex* feature) are represented in this dataset?** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "_uuid": "d32cafcbe966dbdfa40c3017d16d6f8c96bfb3e2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      21790\n",
       "Female    10771\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['sex'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "4748793234a5d325ea60d13331ecf93e29774523"
   },
   "source": [
    "**2. What is the average age (*age* feature) of women?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "_uuid": "64f60d928855107d8980875656dcdf3587d91646"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36.85823043357163"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df[df['sex'] == 'Female'].mean())[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "80e87c421c348067e88769c64e554206c51727d1"
   },
   "source": [
    "**3. What is the percentage of German citizens (*native-country* feature)?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "_uuid": "ca9807509692638bd945671ed7d8b4a5ad1f3626"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.42"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ger_cit_num = len((df[df['native-country'] == 'Germany']).value_counts(normalize=True))\n",
    "\n",
    "round(100*ger_cit_num/len(df), 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "be9911cf525df4fca2c1d14b8720bc8a82f462e7"
   },
   "source": [
    "**4-5. What are the mean and standard deviation of age for those who earn more than 50K per year (*salary* feature) and those who earn less than 50K per year?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "_uuid": "97948f0115f949913d211627151b5605bcd204fd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44.25 +- 10.52\n"
     ]
    }
   ],
   "source": [
    "A = df[(df['salary'] == '>50K')]\n",
    "\n",
    "print(round((A.mean())[0], 2), '+-', round((A.std())[0], 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36.78 +- 14.02\n"
     ]
    }
   ],
   "source": [
    "B = df[(df['salary'] == '<=50K')]\n",
    "\n",
    "print(round((B.mean())[0], 2), '+-', round((B.std())[0], 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "180c3fe6f8b250b877c1d86866067b80acabdb64"
   },
   "source": [
    "**6. Is it true that people who earn more than 50K have at least high school education? (*education – Bachelors, Prof-school, Assoc-acdm, Assoc-voc, Masters* or *Doctorate* feature)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "_uuid": "58f38ebfa2b0e1418a6a1a680d0743697f9876a2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No\n"
     ]
    }
   ],
   "source": [
    "unique_list = df.loc[df['salary'] == '>50K', 'education'].unique()\n",
    "\n",
    "if '7th-8th' or '12th' or '10th' or '11th' or '9th' or '5th-6th' or '1st-4th' in unique_list:\n",
    "    print('No')\n",
    "else:\n",
    "    print('Yes')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "87d6bc9ec1c230e66416ce94533ea250368009cc"
   },
   "source": [
    "**7. Display age statistics for each race (*race* feature) and each gender (*sex* feature). Use *groupby()* and *describe()*. Find the maximum age of men of *Amer-Indian-Eskimo* race.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "_uuid": "a4567b72200b583f7f2ee1583405d636c12169db"
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race</th>\n",
       "      <th>sex</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 rowspan=\"2\" valign=\"top\">Amer-Indian-Eskimo</th>\n",
       "      <th>Female</th>\n",
       "      <td>119.0</td>\n",
       "      <td>37.117647</td>\n",
       "      <td>13.114991</td>\n",
       "      <td>17.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>46.00</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>192.0</td>\n",
       "      <td>37.208333</td>\n",
       "      <td>12.049563</td>\n",
       "      <td>17.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>45.00</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Asian-Pac-Islander</th>\n",
       "      <th>Female</th>\n",
       "      <td>346.0</td>\n",
       "      <td>35.089595</td>\n",
       "      <td>12.300845</td>\n",
       "      <td>17.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>43.75</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>693.0</td>\n",
       "      <td>39.073593</td>\n",
       "      <td>12.883944</td>\n",
       "      <td>18.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>46.00</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Black</th>\n",
       "      <th>Female</th>\n",
       "      <td>1555.0</td>\n",
       "      <td>37.854019</td>\n",
       "      <td>12.637197</td>\n",
       "      <td>17.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>46.00</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>1569.0</td>\n",
       "      <td>37.682600</td>\n",
       "      <td>12.882612</td>\n",
       "      <td>17.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>46.00</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Other</th>\n",
       "      <th>Female</th>\n",
       "      <td>109.0</td>\n",
       "      <td>31.678899</td>\n",
       "      <td>11.631599</td>\n",
       "      <td>17.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>39.00</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>162.0</td>\n",
       "      <td>34.654321</td>\n",
       "      <td>11.355531</td>\n",
       "      <td>17.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>42.00</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">White</th>\n",
       "      <th>Female</th>\n",
       "      <td>8642.0</td>\n",
       "      <td>36.811618</td>\n",
       "      <td>14.329093</td>\n",
       "      <td>17.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>46.00</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>19174.0</td>\n",
       "      <td>39.652498</td>\n",
       "      <td>13.436029</td>\n",
       "      <td>17.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>49.00</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             count       mean        std   min   25%   50%  \\\n",
       "race               sex                                                       \n",
       "Amer-Indian-Eskimo Female    119.0  37.117647  13.114991  17.0  27.0  36.0   \n",
       "                   Male      192.0  37.208333  12.049563  17.0  28.0  35.0   \n",
       "Asian-Pac-Islander Female    346.0  35.089595  12.300845  17.0  25.0  33.0   \n",
       "                   Male      693.0  39.073593  12.883944  18.0  29.0  37.0   \n",
       "Black              Female   1555.0  37.854019  12.637197  17.0  28.0  37.0   \n",
       "                   Male     1569.0  37.682600  12.882612  17.0  27.0  36.0   \n",
       "Other              Female    109.0  31.678899  11.631599  17.0  23.0  29.0   \n",
       "                   Male      162.0  34.654321  11.355531  17.0  26.0  32.0   \n",
       "White              Female   8642.0  36.811618  14.329093  17.0  25.0  35.0   \n",
       "                   Male    19174.0  39.652498  13.436029  17.0  29.0  38.0   \n",
       "\n",
       "                             75%   max  \n",
       "race               sex                  \n",
       "Amer-Indian-Eskimo Female  46.00  80.0  \n",
       "                   Male    45.00  82.0  \n",
       "Asian-Pac-Islander Female  43.75  75.0  \n",
       "                   Male    46.00  90.0  \n",
       "Black              Female  46.00  90.0  \n",
       "                   Male    46.00  90.0  \n",
       "Other              Female  39.00  74.0  \n",
       "                   Male    42.00  77.0  \n",
       "White              Female  46.00  90.0  \n",
       "                   Male    49.00  90.0  "
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['race', 'sex'])['age'].describe() # 82"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "19411d604b0d4337a4a048a2f0e898541f39ab76"
   },
   "source": [
    "**8. Among whom is the proportion of those who earn a lot (>50K) greater: married or single men (*marital-status* feature)? Consider as married those who have a *marital-status* starting with *Married* (Married-civ-spouse, Married-spouse-absent or Married-AF-spouse), the rest are considered bachelors.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "_uuid": "5bb3a8524fcbd794707075ccf6aaf8dd0f30a3dd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Among married\n"
     ]
    }
   ],
   "source": [
    "married = (df[df['marital-status'].apply(lambda l: l[0] == 'M')]['salary'] == '>50K').value_counts()[0]\n",
    "single = (df[df['marital-status'].apply(lambda l: l[0] == 'M')]['salary'] != '>50K').value_counts()[0]\n",
    "\n",
    "if married > single:\n",
    "    print('Among married')\n",
    "else:\n",
    "    print('Among single')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "34f2076e7d331b95a23b990372809b709d2c8183"
   },
   "source": [
    "**9. What is the maximum number of hours a person works per week (*hours-per-week* feature)? How many people work such a number of hours, and what is the percentage of those who earn a lot (>50K) among them?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "_uuid": "3571468172fc4bcf34e10b3d6ab358d4e41f7ef4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99 hours\n"
     ]
    }
   ],
   "source": [
    "print(df['hours-per-week'].max(), 'hours')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "85 people\n"
     ]
    }
   ],
   "source": [
    "no_life = (df['hours-per-week'] == 99).value_counts()[1]\n",
    "\n",
    "print(no_life, 'people')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29.41 %\n"
     ]
    }
   ],
   "source": [
    "rich_no_life = (((df['salary'] == '>50K') & (df['hours-per-week'] == 99)).value_counts())[1]\n",
    "\n",
    "print(round(100*rich_no_life/85, 2), '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "d539d4d137fac1c5ea25bc516188eb657d67e9b4"
   },
   "source": [
    "**10. Count the average time of work (*hours-per-week*) for those who earn a little and a lot (*salary*) for each country (*native-country*). What will these be for Japan?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "_uuid": "75a93411f1e3b519afcc27c026aaa0c67eb526e9"
   },
   "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>salary</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <th>&gt;50K</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>native-country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>?</th>\n",
       "      <td>40.164760</td>\n",
       "      <td>45.547945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cambodia</th>\n",
       "      <td>41.416667</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>37.914634</td>\n",
       "      <td>45.641026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>37.381818</td>\n",
       "      <td>38.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Columbia</th>\n",
       "      <td>38.684211</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cuba</th>\n",
       "      <td>37.985714</td>\n",
       "      <td>42.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dominican-Republic</th>\n",
       "      <td>42.338235</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ecuador</th>\n",
       "      <td>38.041667</td>\n",
       "      <td>48.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>El-Salvador</th>\n",
       "      <td>36.030928</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>England</th>\n",
       "      <td>40.483333</td>\n",
       "      <td>44.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>41.058824</td>\n",
       "      <td>50.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>39.139785</td>\n",
       "      <td>44.977273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>41.809524</td>\n",
       "      <td>50.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guatemala</th>\n",
       "      <td>39.360656</td>\n",
       "      <td>36.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Haiti</th>\n",
       "      <td>36.325000</td>\n",
       "      <td>42.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Holand-Netherlands</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Honduras</th>\n",
       "      <td>34.333333</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hong</th>\n",
       "      <td>39.142857</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hungary</th>\n",
       "      <td>31.300000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>38.233333</td>\n",
       "      <td>46.475000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iran</th>\n",
       "      <td>41.440000</td>\n",
       "      <td>47.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ireland</th>\n",
       "      <td>40.947368</td>\n",
       "      <td>48.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>39.625000</td>\n",
       "      <td>45.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jamaica</th>\n",
       "      <td>38.239437</td>\n",
       "      <td>41.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>41.000000</td>\n",
       "      <td>47.958333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Laos</th>\n",
       "      <td>40.375000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>40.003279</td>\n",
       "      <td>46.575758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nicaragua</th>\n",
       "      <td>36.093750</td>\n",
       "      <td>37.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outlying-US(Guam-USVI-etc)</th>\n",
       "      <td>41.857143</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peru</th>\n",
       "      <td>35.068966</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philippines</th>\n",
       "      <td>38.065693</td>\n",
       "      <td>43.032787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>38.166667</td>\n",
       "      <td>39.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>41.939394</td>\n",
       "      <td>41.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puerto-Rico</th>\n",
       "      <td>38.470588</td>\n",
       "      <td>39.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scotland</th>\n",
       "      <td>39.444444</td>\n",
       "      <td>46.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South</th>\n",
       "      <td>40.156250</td>\n",
       "      <td>51.437500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taiwan</th>\n",
       "      <td>33.774194</td>\n",
       "      <td>46.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thailand</th>\n",
       "      <td>42.866667</td>\n",
       "      <td>58.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trinadad&amp;Tobago</th>\n",
       "      <td>37.058824</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United-States</th>\n",
       "      <td>38.799127</td>\n",
       "      <td>45.505369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vietnam</th>\n",
       "      <td>37.193548</td>\n",
       "      <td>39.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yugoslavia</th>\n",
       "      <td>41.600000</td>\n",
       "      <td>49.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "salary                          <=50K       >50K\n",
       "native-country                                  \n",
       "?                           40.164760  45.547945\n",
       "Cambodia                    41.416667  40.000000\n",
       "Canada                      37.914634  45.641026\n",
       "China                       37.381818  38.900000\n",
       "Columbia                    38.684211  50.000000\n",
       "Cuba                        37.985714  42.440000\n",
       "Dominican-Republic          42.338235  47.000000\n",
       "Ecuador                     38.041667  48.750000\n",
       "El-Salvador                 36.030928  45.000000\n",
       "England                     40.483333  44.533333\n",
       "France                      41.058824  50.750000\n",
       "Germany                     39.139785  44.977273\n",
       "Greece                      41.809524  50.625000\n",
       "Guatemala                   39.360656  36.666667\n",
       "Haiti                       36.325000  42.750000\n",
       "Holand-Netherlands          40.000000        NaN\n",
       "Honduras                    34.333333  60.000000\n",
       "Hong                        39.142857  45.000000\n",
       "Hungary                     31.300000  50.000000\n",
       "India                       38.233333  46.475000\n",
       "Iran                        41.440000  47.500000\n",
       "Ireland                     40.947368  48.000000\n",
       "Italy                       39.625000  45.400000\n",
       "Jamaica                     38.239437  41.100000\n",
       "Japan                       41.000000  47.958333\n",
       "Laos                        40.375000  40.000000\n",
       "Mexico                      40.003279  46.575758\n",
       "Nicaragua                   36.093750  37.500000\n",
       "Outlying-US(Guam-USVI-etc)  41.857143        NaN\n",
       "Peru                        35.068966  40.000000\n",
       "Philippines                 38.065693  43.032787\n",
       "Poland                      38.166667  39.000000\n",
       "Portugal                    41.939394  41.500000\n",
       "Puerto-Rico                 38.470588  39.416667\n",
       "Scotland                    39.444444  46.666667\n",
       "South                       40.156250  51.437500\n",
       "Taiwan                      33.774194  46.800000\n",
       "Thailand                    42.866667  58.333333\n",
       "Trinadad&Tobago             37.058824  40.000000\n",
       "United-States               38.799127  45.505369\n",
       "Vietnam                     37.193548  39.200000\n",
       "Yugoslavia                  41.600000  49.500000"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df['native-country'], df['salary'], aggfunc=np.mean, values=df['hours-per-week'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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