les3_practice.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Задание 1 \n",
    "Строка names содержит имена в формате 'Name1 Surname1, Name2 Surname2, …'.\n",
    "Поместите в переменную names_list список со строками имён – ['Name1 Surname1', 'Name2 Surname2', …]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "names = 'Name1 Surname1, Name2 Surname2, Name3 Surname3'\n",
    "\n",
    "names_list = names.split(', ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Name1 Surname1', 'Name2 Surname2', 'Name3 Surname3']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Задание 2\n",
    "В переменной df содержатся данные следующего вида:\n",
    "\n",
    "Отберите из них только те, где wealth принимает значение 'medium', а возраст составляет больше 35. Поместите результат в переменную medium_35."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# medium_35 = df.query(\"wealth == 'medium' and age > 35\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Задание 3\n",
    "Ваш коллега-новичок перепутал названия колонок в таблице df из предыдущих заданий и прислал вам такой вариант:\n",
    "Воспользуйтесь методом rename и приведите таблицу к более каноничному виду, сохранив её в ту же самую переменную df."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = df.rename(columns={'Клиент id': 'client_id', \n",
    "#                         'Пол': 'sex', \n",
    "#                         'Благосостояние': 'wealth', \n",
    "#                         'Возраст': 'age'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Задание 4 \n",
    "Добавьте в исследуемый нами датафрэйм df колонку older_than_30, в которой будет храниться информация о том, старше ли клиент 30 лет или нет (булево значение)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df['older_than_30'] = df['age'] > 30"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Задание 5 \n",
    "А теперь получите серию с числом уникальных значений для каждой колонки и присвойте её переменной unique_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# unique_num = df.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Задание 6\n",
    "У нас появились новые данные! Переменная data хранит в себе датафрейм следующего вида:\n",
    "\n",
    "Проведите inner join датафрейма data с df из предыдущего шага, объединив их по колонке client_id, и поместите результат в переменную full_data (да, придумывать названия переменных иногда бывает сложно)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# full_data = data.merge(df, on='client_id')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Задание 7\n",
    "💀 Сложное задание! 💀\n",
    "\n",
    "А теперь создадим свою собственную функцию! Напишите функцию read_n_agg, которая:\n",
    "\n",
    "принимает на вход один параметр – путь к файлу\n",
    "считывает переданный файл, в котором разделителем является ;\n",
    "считает средний заработок (income) по каждой компании (company), перенося колонку company в индекс (as_index=True)\n",
    "возвращает результат этих действий (агрегированный датафрэйм)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('C:/Users/olegi/Downloads/companies.csv', sep=';')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>company</th>\n",
       "      <th>income</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ЗАО «Доронин, Колобов и Калашникова»</td>\n",
       "      <td>1221331958</td>\n",
       "      <td>2019-09-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ООО «Авдеев»</td>\n",
       "      <td>21116804375</td>\n",
       "      <td>1972-12-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>РАО «Аксенов Сорокина»</td>\n",
       "      <td>79257158759</td>\n",
       "      <td>2007-03-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ларионов Инкорпорэйтед</td>\n",
       "      <td>36799366126</td>\n",
       "      <td>2011-08-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Григорьев Инкорпорэйтед</td>\n",
       "      <td>48201837498</td>\n",
       "      <td>1989-09-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                company       income        date\n",
       "0  ЗАО «Доронин, Колобов и Калашникова»   1221331958  2019-09-09\n",
       "1                          ООО «Авдеев»  21116804375  1972-12-21\n",
       "2                РАО «Аксенов Сорокина»  79257158759  2007-03-05\n",
       "3                Ларионов Инкорпорэйтед  36799366126  2011-08-11\n",
       "4               Григорьев Инкорпорэйтед  48201837498  1989-09-02"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_n_agg(path):\n",
    "    df = pd.read_csv(path, sep=';')\n",
    "    df_agg = df.groupby(['company'], as_index=True).agg({'income': 'mean'})\n",
    "    return df_agg\n",
    "\n",
    "# https://stepik.org/media/attachments/lesson/359209/companies.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Задание 8 \n",
    "\n",
    "Вернёмся к данным о поездках на такси. На этот раз – из Перу, с рейтингами водителей, пассажиров, координатами, и парой других деталей!\n",
    " - journey_id – уникальный id поездки\n",
    " - user_id – id пользователя\n",
    " - driver_id – id водителя\n",
    " - taxi_id – id машины\n",
    " - icon – тип поездки\n",
    " - start_type – тип заказа (asap, reserved, delayed)\n",
    " - start_at – время начала поездки\n",
    " - start_lat – исходное местоположение пользователя, широта\n",
    " - start_lon – исходное местоположение пользователя, долгота\n",
    " - end_at – время окончания поездки\n",
    " - end_lat – итоговое местоположение, широта\n",
    " - end_lon – итоговое местоположение, долгота\n",
    " - end_state – состояние заказа\n",
    " - driver_start_lat – исходное местоположение водителя, широта\n",
    " - driver_start_lon – исходное местоположение водителя, долгота\n",
    " - arrived_at – время прибытия водителя\n",
    " - source – платформа, с которой сделан заказ\n",
    " - driver_score – оценка водителя клиентом\n",
    " - rider_score – оценка клиента водителем\n",
    "\n",
    "Импортируйте pandas как pd. Загрузите датасет, используя ; в качестве разделителя. Также укажите, что столбцы start_at, end_at, arrived_at нужно прочитать как даты (параметр parse_dates). Результат запишите в taxi."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "taxi = pd.read_csv('https://stepik.org/media/attachments/lesson/359240/taxi_peru.csv', sep=';', parse_dates=['start_at', 'end_at', 'arrived_at'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>journey_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>driver_id</th>\n",
       "      <th>taxi_id</th>\n",
       "      <th>icon</th>\n",
       "      <th>start_type</th>\n",
       "      <th>start_at</th>\n",
       "      <th>start_lat</th>\n",
       "      <th>start_lon</th>\n",
       "      <th>end_at</th>\n",
       "      <th>end_lat</th>\n",
       "      <th>end_lon</th>\n",
       "      <th>end_state</th>\n",
       "      <th>driver_start_lat</th>\n",
       "      <th>driver_start_lon</th>\n",
       "      <th>arrived_at</th>\n",
       "      <th>source</th>\n",
       "      <th>driver_score</th>\n",
       "      <th>rider_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23a1406fc6a11d866e3c82f22eed4d4c</td>\n",
       "      <td>0e9af5bbf1edfe591b54ecdfd7e91e26</td>\n",
       "      <td>583949a89a9ee17d19e3ca4f137b6b4c</td>\n",
       "      <td>b12f4f09c783e29fe0d0ea624530db56</td>\n",
       "      <td>executive</td>\n",
       "      <td>asap</td>\n",
       "      <td>2010-11-16 16:44:00</td>\n",
       "      <td>-12,13983536</td>\n",
       "      <td>-77,02355957</td>\n",
       "      <td>2010-11-16 17:29:00</td>\n",
       "      <td>-12,05537033</td>\n",
       "      <td>-77,0413208</td>\n",
       "      <td>drop off</td>\n",
       "      <td>-12,10825481</td>\n",
       "      <td>-77,0272739</td>\n",
       "      <td>2010-11-16 17:02:00</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dd2af4715d0dc16eded53afc0e243577</td>\n",
       "      <td>a553c46e3a22fb9c326aeb3d72b3334e</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>executive</td>\n",
       "      <td>asap</td>\n",
       "      <td>2010-06-01 00:34:00</td>\n",
       "      <td>-12,13874817</td>\n",
       "      <td>-76,99536133</td>\n",
       "      <td>2010-06-01 00:37:00</td>\n",
       "      <td>-12,12465668</td>\n",
       "      <td>-77,02886963</td>\n",
       "      <td>not found</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dd91e131888064bf7df3ce08f3d4b4ad</td>\n",
       "      <td>a553c46e3a22fb9c326aeb3d72b3334e</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>executive</td>\n",
       "      <td>asap</td>\n",
       "      <td>2010-05-31 05:01:00</td>\n",
       "      <td>-12,12453079</td>\n",
       "      <td>-77,02780151</td>\n",
       "      <td>2010-05-31 05:04:00</td>\n",
       "      <td>-12,13883209</td>\n",
       "      <td>-76,99530029</td>\n",
       "      <td>not found</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dd2af4715d0dc16eded53afc0e2466d0</td>\n",
       "      <td>a553c46e3a22fb9c326aeb3d72b3334e</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>executive</td>\n",
       "      <td>asap</td>\n",
       "      <td>2010-06-01 00:29:00</td>\n",
       "      <td>-12,13885117</td>\n",
       "      <td>-76,99530029</td>\n",
       "      <td>2010-06-01 00:32:00</td>\n",
       "      <td>-12,12458801</td>\n",
       "      <td>-77,02896118</td>\n",
       "      <td>not found</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>85b7eabcf5d84e42dc7629b7d27781af</td>\n",
       "      <td>56772d544fdfa589a020a1ff894a86f7</td>\n",
       "      <td>d665fb9f75ef5d9cd0fd89479380ba78</td>\n",
       "      <td>0accdd3aa5a322f4129fa20b53278c69</td>\n",
       "      <td>executive</td>\n",
       "      <td>reserved</td>\n",
       "      <td>2010-09-11 23:55:00</td>\n",
       "      <td>-12,08995438</td>\n",
       "      <td>-76,92626953</td>\n",
       "      <td>2010-09-12 01:07:00</td>\n",
       "      <td>-12,02223206</td>\n",
       "      <td>-77,10638428</td>\n",
       "      <td>drop off</td>\n",
       "      <td>-12,08311558</td>\n",
       "      <td>-76,9277072</td>\n",
       "      <td>2010-09-11 23:50:00</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         journey_id                           user_id  \\\n",
       "0  23a1406fc6a11d866e3c82f22eed4d4c  0e9af5bbf1edfe591b54ecdfd7e91e26   \n",
       "1  dd2af4715d0dc16eded53afc0e243577  a553c46e3a22fb9c326aeb3d72b3334e   \n",
       "2  dd91e131888064bf7df3ce08f3d4b4ad  a553c46e3a22fb9c326aeb3d72b3334e   \n",
       "3  dd2af4715d0dc16eded53afc0e2466d0  a553c46e3a22fb9c326aeb3d72b3334e   \n",
       "4  85b7eabcf5d84e42dc7629b7d27781af  56772d544fdfa589a020a1ff894a86f7   \n",
       "\n",
       "                          driver_id                           taxi_id  \\\n",
       "0  583949a89a9ee17d19e3ca4f137b6b4c  b12f4f09c783e29fe0d0ea624530db56   \n",
       "1                               NaN                               NaN   \n",
       "2                               NaN                               NaN   \n",
       "3                               NaN                               NaN   \n",
       "4  d665fb9f75ef5d9cd0fd89479380ba78  0accdd3aa5a322f4129fa20b53278c69   \n",
       "\n",
       "        icon start_type            start_at     start_lat     start_lon  \\\n",
       "0  executive       asap 2010-11-16 16:44:00  -12,13983536  -77,02355957   \n",
       "1  executive       asap 2010-06-01 00:34:00  -12,13874817  -76,99536133   \n",
       "2  executive       asap 2010-05-31 05:01:00  -12,12453079  -77,02780151   \n",
       "3  executive       asap 2010-06-01 00:29:00  -12,13885117  -76,99530029   \n",
       "4  executive   reserved 2010-09-11 23:55:00  -12,08995438  -76,92626953   \n",
       "\n",
       "               end_at       end_lat       end_lon  end_state driver_start_lat  \\\n",
       "0 2010-11-16 17:29:00  -12,05537033   -77,0413208   drop off     -12,10825481   \n",
       "1 2010-06-01 00:37:00  -12,12465668  -77,02886963  not found              NaN   \n",
       "2 2010-05-31 05:04:00  -12,13883209  -76,99530029  not found              NaN   \n",
       "3 2010-06-01 00:32:00  -12,12458801  -77,02896118  not found              NaN   \n",
       "4 2010-09-12 01:07:00  -12,02223206  -77,10638428   drop off     -12,08311558   \n",
       "\n",
       "  driver_start_lon          arrived_at  source  driver_score  rider_score  \n",
       "0      -77,0272739 2010-11-16 17:02:00  iPhone           5.0          5.0  \n",
       "1              NaN                 NaT  iPhone           NaN          NaN  \n",
       "2              NaN                 NaT  iPhone           NaN          NaN  \n",
       "3              NaN                 NaT  iPhone           NaN          NaN  \n",
       "4      -76,9277072 2010-09-11 23:50:00  iPhone           4.0          5.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверьте, с какой платформы было сделано больше всего заказов. В ответе укажите значение в %, округлённое до целого."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "platform = (taxi.groupby('source', as_index=False) \\\n",
    "    .agg({'journey_id': 'count'}) \\\n",
    "    .sort_values(['journey_id'], ascending=False))\n",
    "\n",
    "# taxi.source.value_counts() / taxi.shape[0]\n",
    "# (taxi.source.value_counts(normalize=True) * 100).idxmax()\n",
    "# taxi.source.value_counts(normalize=True).mul(100).max().round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>journey_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>iPhone</td>\n",
       "      <td>9741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>web</td>\n",
       "      <td>7631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Android</td>\n",
       "      <td>4909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>iPad</td>\n",
       "      <td>571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Wap</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    source  journey_id\n",
       "3   iPhone        9741\n",
       "4      web        7631\n",
       "0  Android        4909\n",
       "2     iPad         571\n",
       "1      Wap         136"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "platform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = ((platform / (platform.agg({'journey_id': 'sum'}))).round(2)) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>journey_id</th>\n",
       "      <th>source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   journey_id source\n",
       "3        42.0    NaN\n",
       "4        33.0    NaN\n",
       "0        21.0    NaN\n",
       "2         2.0    NaN\n",
       "1         1.0    NaN"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Чтобы показать разницу более наглядно, попробуем визуализировать показатели.\n",
    "\n",
    "Агрегируйте данные и постройте барчарт, где по оси x будет расположено название платформы (source), а по оси y – количество заказов (абсолютные значения).\n",
    "\n",
    "Какой график получился?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(x=\"source\", y=\"journey_id\", data=platform)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Альтернативный вариант построения распределения категориальной переменной – sns.countplot. В качестве единственного аргумента можно передать всего одну колонку: например, source, а количество значений для оси y функция посчитает самостоятельно.\n",
    "\n",
    "sns.countplot(taxi['source'])\n",
    "Используйте данный тип графика и проверьте, какой тип поездки (icon) встречался чаще всего."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = sns.countplot(x='icon', data=taxi)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь попробуем построить более сложный график!\n",
    "\n",
    "С помощью sns.countplot и параметра hue визуализируйте распределение переменной end_state (итоговое состояние заказа) в разбивке по платформам (source).\n",
    "\n",
    "Иными словами, по оси x расположите платформу, а в качестве цвета используйте состояние заказа. Далее выберите верные утверждения."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = sns.countplot(x='source', data=taxi, hue='end_state')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Задание 9\n",
    "Проверим, как распределены оценки водителей (driver_score). Для подготовки данных выполните следующие шаги:\n",
    "\n",
    " - Посчитайте частоту встречаемости каждой из оценок\n",
    " - Переведите в проценты и округлите до 2 знаков после точки (.mul(100).round(2))\n",
    " - Сбросьте индексы\n",
    " - Переименуйте колонки в driver_score и percentage\n",
    " - Отсортируйте по driver_score в порядке возрастания (от 0 до 5)\n",
    " - Запишите результат в driver_score_counts\n",
    "\n",
    "Полезные функции:\n",
    ".mul() – умножение, более удобный вариант для использования в цепочке методов \n",
    ".round() – округление\n",
    ".reset_index() – сбросить индекс\n",
    ".value_counts() – сколько раз встречается каждое значение переменной"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0    0.588105\n",
       "4.0    0.218824\n",
       "0.0    0.143007\n",
       "3.0    0.029673\n",
       "1.0    0.012288\n",
       "2.0    0.008105\n",
       "Name: driver_score, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi \\\n",
    "    .driver_score \\\n",
    "    .value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0    58.81\n",
       "4.0    21.88\n",
       "0.0    14.30\n",
       "3.0     2.97\n",
       "1.0     1.23\n",
       "2.0     0.81\n",
       "Name: driver_score, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi \\\n",
    "    .driver_score \\\n",
    "    .value_counts(normalize=True) \\\n",
    "    .mul(100) \\\n",
    "    .round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>driver_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>58.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>21.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>14.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  driver_score\n",
       "0    5.0         58.81\n",
       "1    4.0         21.88\n",
       "2    0.0         14.30\n",
       "3    3.0          2.97\n",
       "4    1.0          1.23\n",
       "5    2.0          0.81"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi \\\n",
    "    .driver_score \\\n",
    "    .value_counts(normalize=True) \\\n",
    "    .mul(100) \\\n",
    "    .round(2) \\\n",
    "    .reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>driver_score</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>58.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>21.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>14.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   driver_score  percentage\n",
       "0           5.0       58.81\n",
       "1           4.0       21.88\n",
       "2           0.0       14.30\n",
       "3           3.0        2.97\n",
       "4           1.0        1.23\n",
       "5           2.0        0.81"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi \\\n",
    "    .driver_score \\\n",
    "    .value_counts(normalize=True) \\\n",
    "    .mul(100).round(2) \\\n",
    "    .reset_index() \\\n",
    "    .rename(columns={'index': 'driver_score', 'driver_score': 'percentage'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "driver_score_counts = taxi \\\n",
    "    .driver_score \\\n",
    "    .value_counts(normalize=True) \\\n",
    "    .mul(100).round(2) \\\n",
    "    .reset_index() \\\n",
    "    .rename(columns={'index': 'driver_score', 'driver_score': 'percentage'}) \\\n",
    "    .sort_values('driver_score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>driver_score</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>14.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>21.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>58.81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   driver_score  percentage\n",
       "2           0.0       14.30\n",
       "4           1.0        1.23\n",
       "5           2.0        0.81\n",
       "3           3.0        2.97\n",
       "1           4.0       21.88\n",
       "0           5.0       58.81"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "driver_score_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь строим график! Сначала импортируем библиотеки:\n",
    "\n",
    " - import seaborn as sns\n",
    " - import matplotlib.pyplot as plt\n",
    "Укажите, какую из четырех строчек в нижепредставленном коде нужно исправить, чтобы получить следующий график:\n",
    "\n",
    " - ax = sns.barplot(x=driver_score, y=percentage, data=driver_score_counts, color='blue', alpha=0.5)\n",
    " - ax.set(xlabel='Driver score', ylabel='Percentage')\n",
    " - sns.despine()  # убрать часть рамки графика\n",
    " - plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(x='driver_score', y='percentage', data=driver_score_counts, color='blue', alpha=0.5)\n",
    "ax.set(xlabel='Driver score', ylabel='Percentage')\n",
    "sns.despine()  # убрать часть рамки графика\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "💀 Сложное задание! 💀\n",
    "\n",
    "Проделайте аналогичные шаги для rider_score (оценок клиентов водителями), не забыв построить график. В чем его отличие от распределения оценок водителей?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        5.0\n",
       "1        NaN\n",
       "2        NaN\n",
       "3        NaN\n",
       "4        5.0\n",
       "        ... \n",
       "23106    5.0\n",
       "23107    5.0\n",
       "23108    0.0\n",
       "23109    5.0\n",
       "23110    NaN\n",
       "Name: rider_score, Length: 23111, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi \\\n",
    "    .rider_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0    0.870955\n",
       "4.0    0.088304\n",
       "0.0    0.022872\n",
       "3.0    0.013840\n",
       "1.0    0.002079\n",
       "2.0    0.001949\n",
       "Name: rider_score, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi \\\n",
    "    .rider_score \\\n",
    "    .value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0    87.10\n",
       "4.0     8.83\n",
       "0.0     2.29\n",
       "3.0     1.38\n",
       "1.0     0.21\n",
       "2.0     0.19\n",
       "Name: rider_score, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi \\\n",
    "    .rider_score \\\n",
    "    .value_counts(normalize=True) \\\n",
    "    .mul(100) \\\n",
    "    .round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>rider_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>87.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>8.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  rider_score\n",
       "0    5.0        87.10\n",
       "1    4.0         8.83\n",
       "2    0.0         2.29\n",
       "3    3.0         1.38\n",
       "4    1.0         0.21\n",
       "5    2.0         0.19"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi \\\n",
    "    .rider_score \\\n",
    "    .value_counts(normalize=True) \\\n",
    "    .mul(100) \\\n",
    "    .round(2) \\\n",
    "    .reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rider_score</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>87.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>8.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rider_score  percentage\n",
       "0          5.0       87.10\n",
       "1          4.0        8.83\n",
       "2          0.0        2.29\n",
       "3          3.0        1.38\n",
       "4          1.0        0.21\n",
       "5          2.0        0.19"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi \\\n",
    "    .rider_score \\\n",
    "    .value_counts(normalize=True) \\\n",
    "    .mul(100) \\\n",
    "    .round(2) \\\n",
    "    .reset_index() \\\n",
    "    .rename(columns={'index': 'rider_score', 'rider_score': 'percentage'})\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rider_score</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>8.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>87.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rider_score  percentage\n",
       "2          0.0        2.29\n",
       "4          1.0        0.21\n",
       "5          2.0        0.19\n",
       "3          3.0        1.38\n",
       "1          4.0        8.83\n",
       "0          5.0       87.10"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi \\\n",
    "    .rider_score \\\n",
    "    .value_counts(normalize=True) \\\n",
    "    .mul(100) \\\n",
    "    .round(2) \\\n",
    "    .reset_index() \\\n",
    "    .rename(columns={'index': 'rider_score', 'rider_score': 'percentage'}) \\\n",
    "    .sort_values('rider_score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "rider_score_counts = taxi \\\n",
    "    .rider_score \\\n",
    "    .value_counts(normalize=True) \\\n",
    "    .mul(100) \\\n",
    "    .round(2) \\\n",
    "    .reset_index() \\\n",
    "    .rename(columns={'index': 'rider_score', 'rider_score': 'percentage'}) \\\n",
    "    .sort_values('rider_score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rider_score</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>8.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>87.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rider_score  percentage\n",
       "2          0.0        2.29\n",
       "4          1.0        0.21\n",
       "5          2.0        0.19\n",
       "3          3.0        1.38\n",
       "1          4.0        8.83\n",
       "0          5.0       87.10"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rider_score_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(x='rider_score', y='percentage', data=rider_score_counts, color='green', alpha=0.5)\n",
    "ax.set(xlabel='Rider score', ylabel='Percentage')\n",
    "sns.despine()  # убрать часть рамки графика\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}