hw_pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "Материал распространяется на условиях лицензии [Creative Commons CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). Можно использовать в любых целях (редактировать, поправлять и брать за основу), кроме коммерческих, но с обязательным упоминанием автора материала."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <center>Домашнее задание № 1 (демо).<br> Анализ данных по доходу населения UCI Adult</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "**В задании предлагается с помощью Pandas ответить на несколько вопросов по данным репозитория UCI [Adult](https://archive.ics.uci.edu/ml/datasets/Adult) (качать данные не надо – они уже есть в репозитории).**"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Уникальные значения признаков (больше информации по ссылке выше):\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": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T07:42:07.490245553Z",
     "start_time": "2023-06-23T07:42:06.991469506Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-23T07:42:07.543500211Z",
     "start_time": "2023-06-23T07:42:07.530259322Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "   age         workclass  fnlwgt  education  education-num  \\\n0   39         State-gov   77516  Bachelors             13   \n1   50  Self-emp-not-inc   83311  Bachelors             13   \n2   38           Private  215646    HS-grad              9   \n3   53           Private  234721       11th              7   \n4   28           Private  338409  Bachelors             13   \n\n       marital-status         occupation   relationship   race     sex  \\\n0       Never-married       Adm-clerical  Not-in-family  White    Male   \n1  Married-civ-spouse    Exec-managerial        Husband  White    Male   \n2            Divorced  Handlers-cleaners  Not-in-family  White    Male   \n3  Married-civ-spouse  Handlers-cleaners        Husband  Black    Male   \n4  Married-civ-spouse     Prof-specialty           Wife  Black  Female   \n\n   capital-gain  capital-loss  hours-per-week native-country salary  \n0          2174             0              40  United-States  <=50K  \n1             0             0              13  United-States  <=50K  \n2             0             0              40  United-States  <=50K  \n3             0             0              40  United-States  <=50K  \n4             0             0              40           Cuba  <=50K  ",
      "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>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>"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"datasets/adult.data.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. Сколько мужчин и женщин (признак *sex*) представлено в этом наборе данных?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sex\n",
      "Male      21790\n",
      "Female    10771\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "sex_count = data[\"sex\"].value_counts()\n",
    "print(sex_count)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-23T07:42:07.552864880Z",
     "start_time": "2023-06-23T07:42:07.545644564Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. Каков средний возраст (признак *age*) женщин?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-06-23T07:42:07.562703705Z",
     "start_time": "2023-06-23T07:42:07.553994781Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Average age of women: 36.86\n"
     ]
    }
   ],
   "source": [
    "mean_age_women = data[data[\"sex\"] == \"Female\"][\"age\"].mean()\n",
    "print(f\" Average age of women: {round(mean_age_women, 2)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3. Какова доля граждан Германии (признак *native-country*)?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-06-23T07:42:07.645454376Z",
     "start_time": "2023-06-23T07:42:07.563927404Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of German citizens in data: 0.42%\n"
     ]
    }
   ],
   "source": [
    "german_citizens_percentage = (data[\"native-country\"] == \"Germany\").mean()\n",
    "print(\n",
    "    f\"Percentage of German citizens in data: {round(german_citizens_percentage * 100, 2)}%\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4-5. Каковы средние значения и среднеквадратичные отклонения возраста тех, кто получает более 50K в год (признак *salary*) и тех, кто получает менее 50K в год? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-06-23T07:42:07.645994393Z",
     "start_time": "2023-06-23T07:42:07.606020224Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             mean        std\n",
      "salary                      \n",
      "<=50K   36.783738  14.020088\n",
      ">50K    44.249841  10.519028\n"
     ]
    }
   ],
   "source": [
    "age_stats = data.groupby(\"salary\")[\"age\"].agg([\"mean\", \"std\"])\n",
    "print(age_stats)"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "**6. Правда ли, что люди, которые получают больше 50k, имеют как минимум высшее образование? (признак *education – Bachelors, Prof-school, Assoc-acdm, Assoc-voc, Masters* или *Doctorate*)**"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-06-23T07:42:07.646536645Z",
     "start_time": "2023-06-23T07:42:07.606480202Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No, not all people with salary over 50K have at least higher education.\n"
     ]
    }
   ],
   "source": [
    "high_education = [\n",
    "    \"Bachelors\",\n",
    "    \"Prof-school\",\n",
    "    \"Assoc-acdm\",\n",
    "    \"Assoc-voc\",\n",
    "    \"Masters\",\n",
    "    \"Doctorate\",\n",
    "]\n",
    "has_high_education = (\n",
    "    data[data[\"salary\"] == \">50K\"][\"education\"].isin(high_education).all()\n",
    ")\n",
    "\n",
    "if has_high_education:\n",
    "    print(\"Yes, all people with salary over 50K have at least higher education.\")\n",
    "else:\n",
    "    print(\"No, not all people with salary over 50K have at least higher education.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**7. Выведите статистику возраста для каждой расы (признак *race*) и каждого пола. Используйте *groupby* и *describe*. Найдите таким образом максимальный возраст мужчин расы *Amer-Indian-Eskimo*.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-06-23T07:42:07.713158187Z",
     "start_time": "2023-06-23T07:42:07.606855392Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             count   mean    std   min   25%   50%    75%  \\\n",
      "race               sex                                                      \n",
      "Amer-Indian-Eskimo Female    119.0  37.12  13.11  17.0  27.0  36.0  46.00   \n",
      "                   Male      192.0  37.21  12.05  17.0  28.0  35.0  45.00   \n",
      "Asian-Pac-Islander Female    346.0  35.09  12.30  17.0  25.0  33.0  43.75   \n",
      "                   Male      693.0  39.07  12.88  18.0  29.0  37.0  46.00   \n",
      "Black              Female   1555.0  37.85  12.64  17.0  28.0  37.0  46.00   \n",
      "                   Male     1569.0  37.68  12.88  17.0  27.0  36.0  46.00   \n",
      "Other              Female    109.0  31.68  11.63  17.0  23.0  29.0  39.00   \n",
      "                   Male      162.0  34.65  11.36  17.0  26.0  32.0  42.00   \n",
      "White              Female   8642.0  36.81  14.33  17.0  25.0  35.0  46.00   \n",
      "                   Male    19174.0  39.65  13.44  17.0  29.0  38.0  49.00   \n",
      "\n",
      "                            max  \n",
      "race               sex           \n",
      "Amer-Indian-Eskimo Female  80.0  \n",
      "                   Male    82.0  \n",
      "Asian-Pac-Islander Female  75.0  \n",
      "                   Male    90.0  \n",
      "Black              Female  90.0  \n",
      "                   Male    90.0  \n",
      "Other              Female  74.0  \n",
      "                   Male    77.0  \n",
      "White              Female  90.0  \n",
      "                   Male    90.0  \n",
      "Max Amer-Indian-Eskimo male age: 82.0\n"
     ]
    }
   ],
   "source": [
    "race_gender_stats = data.groupby([\"race\", \"sex\"])[\"age\"].describe()\n",
    "print(round(race_gender_stats, 2))\n",
    "\n",
    "max_AmIndEs_male_age = race_gender_stats.loc[(\"Amer-Indian-Eskimo\", \"Male\"), \"max\"]\n",
    "print(f\"Max Amer-Indian-Eskimo male age: {max_AmIndEs_male_age}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**8. Среди кого больше доля зарабатывающих много (>50K): среди женатых или холостых мужчин (признак *marital-status*)? Женатыми считаем тех, у кого *marital-status* начинается с *Married* (Married-civ-spouse, Married-spouse-absent или Married-AF-spouse), остальных считаем холостыми.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-06-23T07:48:08.048178744Z",
     "start_time": "2023-06-23T07:48:08.007343552Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The majority receiving high salary (>50K) are married men.\n",
      "Married 89.54% | Single 10.46%\n"
     ]
    }
   ],
   "source": [
    "data[\"marital_status_binary\"] = data[\"marital-status\"].apply(\n",
    "    lambda x: 1 if x.startswith(\"Married\") else 0\n",
    ")\n",
    "grouped = data[(data[\"salary\"] == \">50K\") & (data[\"sex\"] == \"Male\")].groupby(\n",
    "    \"marital_status_binary\"\n",
    ")\n",
    "percentage_high_earning = grouped.size() / grouped.size().sum()\n",
    "if percentage_high_earning[1] > percentage_high_earning[0]:\n",
    "    print(\"The majority receiving high salary (>50K) are married men.\")\n",
    "else:\n",
    "    print(\"The majority receiving high salary (>50K) are single men.\")\n",
    "print(\n",
    "    f\"Married {round(percentage_high_earning[1] * 100, 2)}% | Single {round(percentage_high_earning[0] * 100, 2)}%\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**9. Какое максимальное число часов человек работает в неделю (признак *hours-per-week*)? Сколько людей работают такое количество часов и каков среди них процент зарабатывающих много?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-06-23T07:47:54.653893988Z",
     "start_time": "2023-06-23T07:47:54.610579437Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Max working hours per week: 99\n",
      "People working max hours: 85\n",
      "Percentage of people with high salary working max hours: 29.41%\n"
     ]
    }
   ],
   "source": [
    "max_work_hours = data[\"hours-per-week\"].max()\n",
    "num_people = data[data[\"hours-per-week\"] == max_work_hours].shape[0]\n",
    "high_salary_percentage = (\n",
    "    data[(data[\"hours-per-week\"] == max_work_hours) & (data[\"salary\"] == \">50K\")].shape[\n",
    "        0\n",
    "    ]\n",
    "    / num_people\n",
    ") * 100\n",
    "print(\n",
    "    f\"\"\"\n",
    "Max working hours per week: {max_work_hours}\n",
    "People working max hours: {num_people}\n",
    "Percentage of people with high salary working max hours: {round(high_salary_percentage, 2)}%\n",
    "\"\"\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**10. Посчитайте среднее время работы (*hours-per-week*) зарабатывающих мало и много (*salary*) для каждой страны (*native-country*).**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-06-23T07:42:07.845740220Z",
     "start_time": "2023-06-23T07:42:07.653774910Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "salary  native-country            \n",
      "<=50K   ?                             40.16\n",
      "        Cambodia                      41.42\n",
      "        Canada                        37.91\n",
      "        China                         37.38\n",
      "        Columbia                      38.68\n",
      "        Cuba                          37.99\n",
      "        Dominican-Republic            42.34\n",
      "        Ecuador                       38.04\n",
      "        El-Salvador                   36.03\n",
      "        England                       40.48\n",
      "        France                        41.06\n",
      "        Germany                       39.14\n",
      "        Greece                        41.81\n",
      "        Guatemala                     39.36\n",
      "        Haiti                         36.33\n",
      "        Holand-Netherlands            40.00\n",
      "        Honduras                      34.33\n",
      "        Hong                          39.14\n",
      "        Hungary                       31.30\n",
      "        India                         38.23\n",
      "        Iran                          41.44\n",
      "        Ireland                       40.95\n",
      "        Italy                         39.62\n",
      "        Jamaica                       38.24\n",
      "        Japan                         41.00\n",
      "        Laos                          40.38\n",
      "        Mexico                        40.00\n",
      "        Nicaragua                     36.09\n",
      "        Outlying-US(Guam-USVI-etc)    41.86\n",
      "        Peru                          35.07\n",
      "        Philippines                   38.07\n",
      "        Poland                        38.17\n",
      "        Portugal                      41.94\n",
      "        Puerto-Rico                   38.47\n",
      "        Scotland                      39.44\n",
      "        South                         40.16\n",
      "        Taiwan                        33.77\n",
      "        Thailand                      42.87\n",
      "        Trinadad&Tobago               37.06\n",
      "        United-States                 38.80\n",
      "        Vietnam                       37.19\n",
      "        Yugoslavia                    41.60\n",
      ">50K    ?                             45.55\n",
      "        Cambodia                      40.00\n",
      "        Canada                        45.64\n",
      "        China                         38.90\n",
      "        Columbia                      50.00\n",
      "        Cuba                          42.44\n",
      "        Dominican-Republic            47.00\n",
      "        Ecuador                       48.75\n",
      "        El-Salvador                   45.00\n",
      "        England                       44.53\n",
      "        France                        50.75\n",
      "        Germany                       44.98\n",
      "        Greece                        50.62\n",
      "        Guatemala                     36.67\n",
      "        Haiti                         42.75\n",
      "        Honduras                      60.00\n",
      "        Hong                          45.00\n",
      "        Hungary                       50.00\n",
      "        India                         46.48\n",
      "        Iran                          47.50\n",
      "        Ireland                       48.00\n",
      "        Italy                         45.40\n",
      "        Jamaica                       41.10\n",
      "        Japan                         47.96\n",
      "        Laos                          40.00\n",
      "        Mexico                        46.58\n",
      "        Nicaragua                     37.50\n",
      "        Peru                          40.00\n",
      "        Philippines                   43.03\n",
      "        Poland                        39.00\n",
      "        Portugal                      41.50\n",
      "        Puerto-Rico                   39.42\n",
      "        Scotland                      46.67\n",
      "        South                         51.44\n",
      "        Taiwan                        46.80\n",
      "        Thailand                      58.33\n",
      "        Trinadad&Tobago               40.00\n",
      "        United-States                 45.51\n",
      "        Vietnam                       39.20\n",
      "        Yugoslavia                    49.50\n",
      "Name: hours-per-week, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "pd.set_option(\"display.max_rows\", None)\n",
    "pd.set_option(\"display.max_columns\", None)\n",
    "work_hours_mean__salary_country = data.groupby([\"salary\", \"native-country\"])[\n",
    "    \"hours-per-week\"\n",
    "].mean()\n",
    "print(round(work_hours_mean__salary_country, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1200x600 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# additional visualization for task 10\n",
    "work_hours_mean__salary_country = work_hours_mean__salary_country.unstack(level=0)\n",
    "sorted_countries = work_hours_mean__salary_country[\">50K\"].sort_values()\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.bar(range(len(sorted_countries)), sorted_countries, label=\">50K\", alpha=0.7)\n",
    "plt.bar(\n",
    "    range(len(sorted_countries)),\n",
    "    work_hours_mean__salary_country[\"<=50K\"][sorted_countries.index],\n",
    "    label=\"<=50K\",\n",
    "    alpha=0.7,\n",
    ")\n",
    "\n",
    "plt.xticks(range(len(sorted_countries)), sorted_countries.index, rotation=90)\n",
    "plt.xlabel(\"Country\")\n",
    "plt.ylabel(\"Weekly working hours (mean)\")\n",
    "plt.title(\"Weekly working hours and salary in countries\")\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-23T07:42:08.062603367Z",
     "start_time": "2023-06-23T07:42:07.683188159Z"
    }
   }
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
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