cell_phone_plan/yandex_megaline_karpova_new.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cb5492fa",
   "metadata": {},
   "source": [
    "# Определение перспективного тарифа для телеком-компании"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b4e62cb",
   "metadata": {},
   "source": [
    "**Заказчик** \n",
    "\n",
    "Комерческий департамент федерального оператора сотовой связи «Мегалайн».\n",
    "\n",
    "\n",
    "**Цель проекта**\n",
    "\n",
    "Предварительный анализ  поведения клиентов, пользующихся тарифными планами \"Смарт\" и \"Ультра\". \n",
    "\n",
    "Необходимо определить, какой тариф прибыльнее с точки зрения компании. Информация будет учтена при корректировке рекламного бюджета. Для ответа на этот вопрос будут проверены две гипотезы:\n",
    "\n",
    "1. средняя выручка пользователей тарифов «Ультра» и «Смарт» различаются;\n",
    "2. средняя выручка пользователей из Москвы отличается от выручки пользователей из других регионов.\n",
    "\n",
    "\n",
    "**Входные данные**\n",
    "\n",
    "Данные 500 пользователей «Мегалайна»: кто они, откуда, каким тарифом пользуются, сколько звонков и сообщений каждый отправил за 2018 год. Файлы `users`, `tariffs`, `calls`, `internet` и `messeges`. \n",
    "\n",
    "\n",
    "**Описание тарифов**\n",
    "\n",
    "*Тариф «Смарт»*\n",
    "\n",
    "1. Ежемесячная плата: 550 рублей\n",
    "2. Включено 500 минут разговора, 50 сообщений и 15 Гб интернет-трафика\n",
    "3. Стоимость услуг сверх тарифного пакета:\n",
    " - минута разговора: 3 рубля\n",
    " - сообщение: 3 рубля\n",
    " - 1 Гб интернет-трафика: 200 рублей\n",
    "\n",
    "*Тариф «Ультра»*\n",
    "\n",
    "1. Ежемесячная плата: 1950 рублей\n",
    "2. Включено 3000 минут разговора, 1000 сообщений и 30 Гб интернет-трафика\n",
    "3. Стоимость услуг сверх тарифного пакета:\n",
    " - минута разговора: 1 рубль\n",
    " - сообщение: 1 рубль\n",
    " - 1 Гб интернет-трафика: 150 рублей\n",
    "\n",
    "*Примечание:*\n",
    "\n",
    "*«Мегалайн» всегда округляет секунды до минут, а мегабайты — до гигабайт. Каждый звонок округляется отдельно: даже если он длился всего 1 секунду, будет засчитан как 1 минута.\n",
    "Для веб-трафика отдельные сессии не считаются. Вместо этого общая сумма за месяц округляется в бо́льшую сторону. Если абонент использует 1025 мегабайт в этом месяце, с него возьмут плату за 2 гигабайта.*\n",
    "\n",
    "\n",
    "**Ход исследования**\n",
    "\n",
    "Исследование пройдёт в пять этапов:\n",
    "\n",
    "*    Обзор данных;\n",
    "*    Предобработка данных;\n",
    "*    Расчеты;\n",
    "*    Анализ данных;\n",
    "*    Проверка гипотез."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a34f4b6",
   "metadata": {},
   "source": [
    "## Обзор данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1af0acd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# импорт библиотек\n",
    "\n",
    "from scipy import stats as st\n",
    "from math import factorial\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(action='ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "84bfdffd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# чтение файла с данными и сохранение в df\n",
    "\n",
    "try:\n",
    "    calls = pd.read_csv('/datasets/calls.csv')\n",
    "    messages = pd.read_csv('/datasets/messages.csv')\n",
    "    internet = pd.read_csv('/datasets/internet.csv')\n",
    "    tariffs = pd.read_csv('/datasets/tariffs.csv')\n",
    "    users = pd.read_csv('/datasets/users.csv')\n",
    "\n",
    "except:\n",
    "    calls = pd.read_csv('calls.csv')\n",
    "    messages = pd.read_csv('messages.csv')\n",
    "    internet = pd.read_csv('internet.csv')\n",
    "    tariffs = pd.read_csv('tariffs.csv')\n",
    "    users = pd.read_csv('users.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "923a4da2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2 entries, 0 to 1\n",
      "Data columns (total 8 columns):\n",
      " #   Column                 Non-Null Count  Dtype \n",
      "---  ------                 --------------  ----- \n",
      " 0   messages_included      2 non-null      int64 \n",
      " 1   mb_per_month_included  2 non-null      int64 \n",
      " 2   minutes_included       2 non-null      int64 \n",
      " 3   rub_monthly_fee        2 non-null      int64 \n",
      " 4   rub_per_gb             2 non-null      int64 \n",
      " 5   rub_per_message        2 non-null      int64 \n",
      " 6   rub_per_minute         2 non-null      int64 \n",
      " 7   tariff_name            2 non-null      object\n",
      "dtypes: int64(7), object(1)\n",
      "memory usage: 256.0+ bytes\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>messages_included</th>\n",
       "      <th>mb_per_month_included</th>\n",
       "      <th>minutes_included</th>\n",
       "      <th>rub_monthly_fee</th>\n",
       "      <th>rub_per_gb</th>\n",
       "      <th>rub_per_message</th>\n",
       "      <th>rub_per_minute</th>\n",
       "      <th>tariff_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
       "      <td>500</td>\n",
       "      <td>550</td>\n",
       "      <td>200</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>smart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>30720</td>\n",
       "      <td>3000</td>\n",
       "      <td>1950</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ultra</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   messages_included  mb_per_month_included  minutes_included  \\\n",
       "0                 50                  15360               500   \n",
       "1               1000                  30720              3000   \n",
       "\n",
       "   rub_monthly_fee  rub_per_gb  rub_per_message  rub_per_minute tariff_name  \n",
       "0              550         200                3               3       smart  \n",
       "1             1950         150                1               1       ultra  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# обзор датафрейма tariff\n",
    "\n",
    "tariffs.info()\n",
    "display(tariffs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ad7a974",
   "metadata": {},
   "source": [
    "В таблице восемь столбцов:\n",
    "\n",
    "- `messages_included` - количество включенных в тарифный пакет СМС\n",
    "- `mb_per_month_included` - количество включенных в тарифный пакет Гб интернет-трафика\n",
    "- `minutes_included` - количество включенных в тарифный пакет минут\n",
    "- `rub_monthly_fee` - абоненская плата\n",
    "- `rub_per_gb` - стоимость 1 Гб интернет-трафика сверх тарифного пакета\n",
    "- `rub_per_message` - стоимость 1 СМС сверх тарифного пакета\n",
    "- `rub_per_minute` - стоимость 1 минуты сверх тарифного пакета\n",
    "- `tariff_name` - наименование тарифа"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ecb0e5ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# функция для первичного ознакомления с остальными датафреймами\n",
    "\n",
    "def data_info(data):\n",
    "    data.info()\n",
    "    display(data.sample(7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4c9c49b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 202607 entries, 0 to 202606\n",
      "Data columns (total 4 columns):\n",
      " #   Column     Non-Null Count   Dtype  \n",
      "---  ------     --------------   -----  \n",
      " 0   id         202607 non-null  object \n",
      " 1   call_date  202607 non-null  object \n",
      " 2   duration   202607 non-null  float64\n",
      " 3   user_id    202607 non-null  int64  \n",
      "dtypes: float64(1), int64(1), object(2)\n",
      "memory usage: 6.2+ MB\n"
     ]
    },
    {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>189903</th>\n",
       "      <td>1467_170</td>\n",
       "      <td>2018-12-15</td>\n",
       "      <td>4.64</td>\n",
       "      <td>1467</td>\n",
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       "    <tr>\n",
       "      <th>152119</th>\n",
       "      <td>1379_354</td>\n",
       "      <td>2018-09-23</td>\n",
       "      <td>15.37</td>\n",
       "      <td>1379</td>\n",
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       "    <tr>\n",
       "      <th>81620</th>\n",
       "      <td>1203_193</td>\n",
       "      <td>2018-06-29</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1203</td>\n",
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       "    <tr>\n",
       "      <th>58521</th>\n",
       "      <td>1147_236</td>\n",
       "      <td>2018-07-11</td>\n",
       "      <td>6.11</td>\n",
       "      <td>1147</td>\n",
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       "    <tr>\n",
       "      <th>155449</th>\n",
       "      <td>1384_275</td>\n",
       "      <td>2018-11-28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1384</td>\n",
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       "    <tr>\n",
       "      <th>180102</th>\n",
       "      <td>1450_120</td>\n",
       "      <td>2018-12-14</td>\n",
       "      <td>3.88</td>\n",
       "      <td>1450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156645</th>\n",
       "      <td>1386_160</td>\n",
       "      <td>2018-11-14</td>\n",
       "      <td>17.27</td>\n",
       "      <td>1386</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              id   call_date  duration  user_id\n",
       "189903  1467_170  2018-12-15      4.64     1467\n",
       "152119  1379_354  2018-09-23     15.37     1379\n",
       "81620   1203_193  2018-06-29      0.00     1203\n",
       "58521   1147_236  2018-07-11      6.11     1147\n",
       "155449  1384_275  2018-11-28      0.00     1384\n",
       "180102  1450_120  2018-12-14      3.88     1450\n",
       "156645  1386_160  2018-11-14     17.27     1386"
      ]
     },
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   ],
   "source": [
    "data_info(calls)    # обзор датафрейма calls"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c080bbe",
   "metadata": {},
   "source": [
    "В таблице четыре столбца:\n",
    "\n",
    "- `id` - идентификатор звонка\n",
    "- `call_date` - дата звонка\n",
    "- `duration` - продолжительность разговора (в минутах)\n",
    "- `user_id` - уникальный идентификатор пользователя"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c6b4d071",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 123036 entries, 0 to 123035\n",
      "Data columns (total 3 columns):\n",
      " #   Column        Non-Null Count   Dtype \n",
      "---  ------        --------------   ----- \n",
      " 0   id            123036 non-null  object\n",
      " 1   message_date  123036 non-null  object\n",
      " 2   user_id       123036 non-null  int64 \n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 2.8+ MB\n"
     ]
    },
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>message_date</th>\n",
       "      <th>user_id</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>108749</th>\n",
       "      <td>1436_243</td>\n",
       "      <td>2018-07-07</td>\n",
       "      <td>1436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34764</th>\n",
       "      <td>1149_576</td>\n",
       "      <td>2018-09-06</td>\n",
       "      <td>1149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34604</th>\n",
       "      <td>1149_416</td>\n",
       "      <td>2018-05-20</td>\n",
       "      <td>1149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86051</th>\n",
       "      <td>1356_1068</td>\n",
       "      <td>2018-11-28</td>\n",
       "      <td>1356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118616</th>\n",
       "      <td>1482_178</td>\n",
       "      <td>2018-07-28</td>\n",
       "      <td>1482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102978</th>\n",
       "      <td>1419_267</td>\n",
       "      <td>2018-10-05</td>\n",
       "      <td>1419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92573</th>\n",
       "      <td>1382_34</td>\n",
       "      <td>2018-12-30</td>\n",
       "      <td>1382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               id message_date  user_id\n",
       "108749   1436_243   2018-07-07     1436\n",
       "34764    1149_576   2018-09-06     1149\n",
       "34604    1149_416   2018-05-20     1149\n",
       "86051   1356_1068   2018-11-28     1356\n",
       "118616   1482_178   2018-07-28     1482\n",
       "102978   1419_267   2018-10-05     1419\n",
       "92573     1382_34   2018-12-30     1382"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_info(messages)    # обзор датафрейма messages"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87fcbaaa",
   "metadata": {},
   "source": [
    "В таблице три столбца:\n",
    "\n",
    "- `id` - идентификатор СМС\n",
    "- `message_date` - дата отправки СМС\n",
    "- `user_id` - уникальный идентификатор пользователя"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1e84a1ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 149396 entries, 0 to 149395\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   Unnamed: 0    149396 non-null  int64  \n",
      " 1   id            149396 non-null  object \n",
      " 2   mb_used       149396 non-null  float64\n",
      " 3   session_date  149396 non-null  object \n",
      " 4   user_id       149396 non-null  int64  \n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 5.7+ MB\n"
     ]
    },
    {
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       "      <th></th>\n",
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       "      <th>119720</th>\n",
       "      <td>119720</td>\n",
       "      <td>1403_275</td>\n",
       "      <td>248.93</td>\n",
       "      <td>2018-12-04</td>\n",
       "      <td>1403</td>\n",
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       "    <tr>\n",
       "      <th>52698</th>\n",
       "      <td>52698</td>\n",
       "      <td>1177_221</td>\n",
       "      <td>359.63</td>\n",
       "      <td>2018-08-19</td>\n",
       "      <td>1177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118638</th>\n",
       "      <td>118638</td>\n",
       "      <td>1401_59</td>\n",
       "      <td>569.12</td>\n",
       "      <td>2018-04-08</td>\n",
       "      <td>1401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61326</th>\n",
       "      <td>61326</td>\n",
       "      <td>1208_164</td>\n",
       "      <td>600.78</td>\n",
       "      <td>2018-08-08</td>\n",
       "      <td>1208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43685</th>\n",
       "      <td>43685</td>\n",
       "      <td>1150_115</td>\n",
       "      <td>246.86</td>\n",
       "      <td>2018-10-21</td>\n",
       "      <td>1150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46019</th>\n",
       "      <td>46019</td>\n",
       "      <td>1157_531</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2018-05-02</td>\n",
       "      <td>1157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1628</th>\n",
       "      <td>1628</td>\n",
       "      <td>1006_259</td>\n",
       "      <td>548.40</td>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>1006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0        id  mb_used session_date  user_id\n",
       "119720      119720  1403_275   248.93   2018-12-04     1403\n",
       "52698        52698  1177_221   359.63   2018-08-19     1177\n",
       "118638      118638   1401_59   569.12   2018-04-08     1401\n",
       "61326        61326  1208_164   600.78   2018-08-08     1208\n",
       "43685        43685  1150_115   246.86   2018-10-21     1150\n",
       "46019        46019  1157_531     0.00   2018-05-02     1157\n",
       "1628          1628  1006_259   548.40   2018-10-01     1006"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_info(internet)    # обзор датафрейма internet"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52096743",
   "metadata": {},
   "source": [
    "В таблице пять столбцов:\n",
    "\n",
    "- `id` - идентифиткатор интернет-сессии\n",
    "- `Unnamed: 0` - старые индексы таблицы\n",
    "- `mb_used` - количество интернет-трафика сессии ( в Мб)\n",
    "- `session_date` - дата интернет-сессии\n",
    "- `user_id` - уникальный идентификатор пользователя"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c13fb8a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 500 entries, 0 to 499\n",
      "Data columns (total 8 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   user_id     500 non-null    int64 \n",
      " 1   age         500 non-null    int64 \n",
      " 2   churn_date  38 non-null     object\n",
      " 3   city        500 non-null    object\n",
      " 4   first_name  500 non-null    object\n",
      " 5   last_name   500 non-null    object\n",
      " 6   reg_date    500 non-null    object\n",
      " 7   tariff      500 non-null    object\n",
      "dtypes: int64(2), object(6)\n",
      "memory usage: 31.4+ KB\n"
     ]
    },
    {
     "data": {
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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>user_id</th>\n",
       "      <th>age</th>\n",
       "      <th>churn_date</th>\n",
       "      <th>city</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>reg_date</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>1357</td>\n",
       "      <td>37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Томск</td>\n",
       "      <td>Касьян</td>\n",
       "      <td>Ермилов</td>\n",
       "      <td>2018-09-02</td>\n",
       "      <td>smart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>1422</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-12-21</td>\n",
       "      <td>Москва</td>\n",
       "      <td>Клара</td>\n",
       "      <td>Устинова</td>\n",
       "      <td>2018-10-12</td>\n",
       "      <td>smart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>1317</td>\n",
       "      <td>45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Москва</td>\n",
       "      <td>Всеволод</td>\n",
       "      <td>Зыков</td>\n",
       "      <td>2018-07-09</td>\n",
       "      <td>smart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>1115</td>\n",
       "      <td>71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Москва</td>\n",
       "      <td>Рашид</td>\n",
       "      <td>Толкачев</td>\n",
       "      <td>2018-07-28</td>\n",
       "      <td>smart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1013</td>\n",
       "      <td>36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Иваново</td>\n",
       "      <td>Борис</td>\n",
       "      <td>Андреенко</td>\n",
       "      <td>2018-11-14</td>\n",
       "      <td>ultra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>1387</td>\n",
       "      <td>74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Краснодар</td>\n",
       "      <td>Габриель</td>\n",
       "      <td>Зуев</td>\n",
       "      <td>2018-12-21</td>\n",
       "      <td>smart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1417</td>\n",
       "      <td>34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Тольятти</td>\n",
       "      <td>Исак</td>\n",
       "      <td>Макаров</td>\n",
       "      <td>2018-07-19</td>\n",
       "      <td>smart</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     user_id  age  churn_date       city first_name  last_name    reg_date  \\\n",
       "357     1357   37         NaN      Томск     Касьян    Ермилов  2018-09-02   \n",
       "422     1422   60  2018-12-21     Москва      Клара   Устинова  2018-10-12   \n",
       "317     1317   45         NaN     Москва   Всеволод      Зыков  2018-07-09   \n",
       "115     1115   71         NaN     Москва      Рашид   Толкачев  2018-07-28   \n",
       "13      1013   36         NaN    Иваново      Борис  Андреенко  2018-11-14   \n",
       "387     1387   74         NaN  Краснодар   Габриель       Зуев  2018-12-21   \n",
       "417     1417   34         NaN   Тольятти       Исак    Макаров  2018-07-19   \n",
       "\n",
       "    tariff  \n",
       "357  smart  \n",
       "422  smart  \n",
       "317  smart  \n",
       "115  smart  \n",
       "13   ultra  \n",
       "387  smart  \n",
       "417  smart  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_info(users)    # обзор датафрейма users"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1d15bdd",
   "metadata": {},
   "source": [
    "В таблице восемь столбцов:\n",
    "\n",
    "- `user_id` - уникальный идентификатор пользователя\n",
    "- `age` - возраст пользователя\n",
    "- `reg_date` - дата заключения договора\n",
    "- `churn_date` - дата расторжения договора\n",
    "- `city` - город проживания пользовтеля\n",
    "- `first_name` - имя пользователя\n",
    "- `last_name` - фамилия пользователя\n",
    "- `tariff` - тарифный план"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd6256d1",
   "metadata": {},
   "source": [
    "**Выводы**\n",
    "\n",
    "Мы ознакомились со всеми таблицами. Заметно, что тип данных в столбцах не всегда корректный (например, дата с типом `object`), есть столбцы, одинаковые по смыслу, но с разными названиями (`tariff`  и `tariff_name`). Так же нужно проверить данные на наличие дубликатов (например, что таблица с пользователями их не содержит, и `user_id` -  действительно уникальный идентификатор). Хорошо, что в данных нет пропусков (кроме информации о дате окончания действия договора с Оператором).\n",
    "\n",
    "\n",
    "Так же необходимо сгруппировать данные помесячно, перевести интернет-трафик в Гб, а секунды - в минуты, округлив значения согласно примечанию выше.\n",
    "\n",
    "Приступаем к предобработке данных."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9349e04c",
   "metadata": {},
   "source": [
    "## Предобработка данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "36d72a64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>messages_included</th>\n",
       "      <th>gb_per_month_included</th>\n",
       "      <th>minutes_included</th>\n",
       "      <th>rub_monthly_fee</th>\n",
       "      <th>rub_per_gb</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>15.0</td>\n",
       "      <td>500</td>\n",
       "      <td>550</td>\n",
       "      <td>200</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>smart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>30.0</td>\n",
       "      <td>3000</td>\n",
       "      <td>1950</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ultra</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "   messages_included  gb_per_month_included  minutes_included  \\\n",
       "0                 50                   15.0               500   \n",
       "1               1000                   30.0              3000   \n",
       "\n",
       "   rub_monthly_fee  rub_per_gb  rub_per_message  rub_per_minute tariff  \n",
       "0              550         200                3               3  smart  \n",
       "1             1950         150                1               1  ultra  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tariffs['mb_per_month_included'] = tariffs['mb_per_month_included'] / 1024    # переводим Мб в Гб\n",
    "\n",
    "tariffs = tariffs.rename(columns={'tariff_name': 'tariff',\n",
    "                                 'mb_per_month_included' : 'gb_per_month_included'})\n",
    "tariffs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da15e4d9",
   "metadata": {},
   "source": [
    "Столбцы `call_date`, `message_date`, `session_date`,`reg_date` и `churn_date` имееют тип данных `object`, поэтому меняем его на `datetime64`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1af61664",
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_type(df, column):\n",
    "    df[column] = pd.to_datetime(df[column], format='%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "84da92e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_type(calls, 'call_date')\n",
    "data_type(messages, 'message_date')\n",
    "data_type(internet, 'session_date')\n",
    "data_type(users, 'reg_date')\n",
    "data_type(users, 'churn_date')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f54a3837",
   "metadata": {},
   "source": [
    "Выделяем отдельную колонку с месяцем звонка, СМС и интернет-сесcий в датафреймах."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9ed831e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "calls['call_month'] = calls['call_date'].dt.month\n",
    "messages['message_month'] = messages['message_date'].dt.month\n",
    "internet['session_month'] = internet['session_date'].dt.month\n",
    "users['reg_date'] = users['reg_date'].dt.month\n",
    "users['churn_date'] = users['churn_date'].dt.month"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1665727a",
   "metadata": {},
   "source": [
    "*Про месяц: закладываемся на тот факт, что по заданию выборка была за один год. Если бы информация содержала данные за несколько лет, мы бы воспользовались переводом в datetime (метод astype('datetime64[M]'). Он приводит дату к формату первое число - месяц – год.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6bc2bfa",
   "metadata": {},
   "source": [
    "## Расчеты\n",
    "\n",
    "###  * calls, calls_pivot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c68b4796",
   "metadata": {},
   "source": [
    "В данных мы находим звонки с нулевой продолжительностью. Это не ошибка: нулями обозначены пропущенные звонки.\n",
    "\n",
    "\n",
    "Согласно Примечанию, Оператор округляет каждый звонок до минут в бОльшую сторону."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1c8c6d81",
   "metadata": {},
   "outputs": [],
   "source": [
    "calls['duration'] = np.ceil(calls['duration'])    # округляем каждый звонок в большую сторону"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b000bada",
   "metadata": {},
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>449</td>\n",
       "      <td>612</td>\n",
       "      <td>492</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     user_id   1    2    3    4    5    6    7    8    9    10   11    12\n",
       "487     1495   0    0    0    0    0    0    0   92  813  1030  954  1031\n",
       "488     1496  48  594  557  521  484  555  582  527  486   450  662   518\n",
       "489     1497   0    0    0    0    0    0    0    0    0   490  371   545\n",
       "490     1498   0    0    0    0    0    0  175  400  471   247    0     0\n",
       "491     1499   0    0    0    0    0    0    0    0   70   449  612   492"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls_pivot = calls.pivot_table(\n",
    "    index = 'user_id', \n",
    "    columns = 'call_month', \n",
    "    values = 'duration',\n",
    "    aggfunc = 'sum',\n",
    "    fill_value = 0).reset_index().rename_axis(None, axis=1) \n",
    "\n",
    "calls_pivot.tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a777c268",
   "metadata": {},
   "source": [
    "Так же отдельным фреймом сделаем еще одну сводную таблицу - по количеству звонков."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b31440e7",
   "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>user_id</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>1495</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>112</td>\n",
       "      <td>152</td>\n",
       "      <td>129</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>1496</td>\n",
       "      <td>7</td>\n",
       "      <td>77</td>\n",
       "      <td>79</td>\n",
       "      <td>67</td>\n",
       "      <td>63</td>\n",
       "      <td>74</td>\n",
       "      <td>73</td>\n",
       "      <td>88</td>\n",
       "      <td>68</td>\n",
       "      <td>72</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>1497</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "      <td>64</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>1498</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>57</td>\n",
       "      <td>59</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>1499</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>68</td>\n",
       "      <td>74</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     user_id  1   2   3   4   5   6   7   8    9   10   11   12\n",
       "487     1495  0   0   0   0   0   0   0  17  112  152  129  155\n",
       "488     1496  7  77  79  67  63  74  73  88   68   72   80   80\n",
       "489     1497  0   0   0   0   0   0   0   0    0   66   64   71\n",
       "490     1498  0   0   0   0   0   0  23  57   59   41    0    0\n",
       "491     1499  0   0   0   0   0   0   0   0    9   68   74   69"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls_count_pivot = calls.pivot_table(\n",
    "    index = 'user_id', \n",
    "    columns = 'call_month', \n",
    "    values = 'duration',\n",
    "    aggfunc = 'count',\n",
    "    fill_value = 0).reset_index().rename_axis(None, axis=1)\n",
    "\n",
    "calls_count_pivot.tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86e95aa1",
   "metadata": {},
   "source": [
    "### * messages, messages_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2be18cef",
   "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>user_id</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>1495</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>82</td>\n",
       "      <td>82</td>\n",
       "      <td>79</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>1496</td>\n",
       "      <td>9</td>\n",
       "      <td>45</td>\n",
       "      <td>46</td>\n",
       "      <td>49</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>42</td>\n",
       "      <td>41</td>\n",
       "      <td>29</td>\n",
       "      <td>35</td>\n",
       "      <td>46</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>1497</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>1498</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>62</td>\n",
       "      <td>44</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>1499</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>48</td>\n",
       "      <td>59</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     user_id  1   2   3   4   5   6   7   8   9  10  11  12\n",
       "421     1495  0   0   0   0   0   0   0  13  82  82  79  84\n",
       "422     1496  9  45  46  49  40  35  42  41  29  35  46  51\n",
       "423     1497  0   0   0   0   0   0   0   0   0  20  14  17\n",
       "424     1498  0   0   0   0   0   0  20  62  44  42   0   0\n",
       "425     1499  0   0   0   0   0   0   0   0  11  48  59  66"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages_pivot = messages.pivot_table(\n",
    "    index = 'user_id', \n",
    "    columns = 'message_month', \n",
    "    values = 'id',\n",
    "    aggfunc = 'count',\n",
    "    fill_value = 0).reset_index().rename_axis(None, axis=1)\n",
    "\n",
    "messages_pivot.tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1a4c55b",
   "metadata": {},
   "source": [
    "### * internet, internet_pivot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dde610b6",
   "metadata": {},
   "source": [
    "Для веб-трафика отдельные сессии не считаются. Вместо этого общая сумма за месяц округляется в бо́льшую сторону. Если абонент использует 1025 мегабайт в этом месяце, с него возьмут плату за 2 гигабайта.\n",
    "\n",
    "Сделаем сводную таблицу по месяцам, а затем округлим значение интернет-трафика в бо́льшую сторону."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0427f2fc",
   "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>user_id</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>1495</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>1496</td>\n",
       "      <td>4.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>1497</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>1498</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>1499</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     user_id    1     2     3     4     5     6     7     8     9    10    11  \\\n",
       "492     1495  0.0   0.0   0.0   0.0   0.0   0.0   0.0   3.0  33.0  29.0  39.0   \n",
       "493     1496  4.0  28.0  25.0  21.0  24.0  20.0  24.0  23.0  27.0  28.0  25.0   \n",
       "494     1497  0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  12.0  14.0   \n",
       "495     1498  0.0   0.0   0.0   0.0   0.0   0.0   8.0  24.0  22.0  21.0   0.0   \n",
       "496     1499  0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   2.0  18.0  18.0   \n",
       "\n",
       "       12  \n",
       "492  23.0  \n",
       "493  21.0  \n",
       "494  20.0  \n",
       "495   0.0  \n",
       "496  13.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "internet_pivot = internet.pivot_table(\n",
    "    index = 'user_id', \n",
    "    columns = 'session_month', \n",
    "    values = 'mb_used',\n",
    "    aggfunc = 'sum',\n",
    "    fill_value = 0).reset_index().rename_axis(None, axis=1)\n",
    "\n",
    "for i in range(1, 13):\n",
    "    internet_pivot[i] /= 1024\n",
    "    internet_pivot[i] = np.ceil(internet_pivot[i])\n",
    "    \n",
    "internet_pivot.tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e2de3ad",
   "metadata": {},
   "source": [
    "### * users"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6815f86",
   "metadata": {},
   "source": [
    "Проверим, что в датафрейме нет явных дубликатов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0cc5037b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a196021",
   "metadata": {},
   "source": [
    "Подсчитаем, сколько пользователей у каждого из тарифов:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "63095e5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tariff\n",
       "smart    351\n",
       "ultra    149\n",
       "Name: tariff, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.groupby('tariff')['tariff'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5fc7d62",
   "metadata": {},
   "source": [
    "### * помесячная выручка"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f87cbe6",
   "metadata": {},
   "source": [
    "Сделаем отдельную таблицу `users_dict`, которая будет содержать только id пользователей и их тарифный план и объединим ее с таблицей `tariffs`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "05ec0d94",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "     user_id tariff  reg_date  churn_date  messages_included  \\\n",
       "497     1497  smart        10          12                 50   \n",
       "498     1498  smart         7          10                 50   \n",
       "499     1499  smart         9          12                 50   \n",
       "\n",
       "     gb_per_month_included  minutes_included  rub_monthly_fee  rub_per_gb  \\\n",
       "497                   15.0               500              550         200   \n",
       "498                   15.0               500              550         200   \n",
       "499                   15.0               500              550         200   \n",
       "\n",
       "     rub_per_message  rub_per_minute  \n",
       "497                3               3  \n",
       "498                3               3  \n",
       "499                3               3  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_dict = users[['user_id','tariff', 'reg_date', 'churn_date']]\n",
    "users_dict['churn_date'] = users_dict['churn_date'].fillna(12).astype('int')\n",
    "\n",
    "users_tariffs_dict = users_dict.merge(tariffs, on='tariff', how='left')\n",
    "users_tariffs_dict.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e55a41c1",
   "metadata": {},
   "source": [
    "Далее, объединим новую таблицу со сводами по звонкам, сообщениям и интернет-трафику, пропуски заменим нулями. Посчитаем, сколько платных услуг было оказано в их разрезе по месяцам: вычтем бесплатный лимит из суммарного количества звонков, сообщений и интернет-трафика; остаток умножим на значение из тарифного плана; также, прибавим абонентскую плату, соответствующую тарифному плану, к одной из таблиц)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0411a4da",
   "metadata": {},
   "outputs": [
    {
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       "     user_id     1      2      3      4      5      6      7      8      9  \\\n",
       "495     1495   0.0    0.0    0.0    0.0    0.0    0.0    0.0   92.0  813.0   \n",
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       "\n",
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       "499                   15.0               500              550         200   \n",
       "\n",
       "     rub_per_message  rub_per_minute  \n",
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       "498                3               3  \n",
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       "\n",
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      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls_data = calls_pivot.merge(users_tariffs_dict, on='user_id', how='right').fillna(0)\n",
    "calls_data.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0f0b0a8e",
   "metadata": {},
   "outputs": [
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       "     user_id      1      2      3      4      5      6      7       8       9  \\\n",
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       "\n",
       "     ...  tariff  reg_date  churn_date messages_included  \\\n",
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       "497  ...   smart        10          12                50   \n",
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       "     gb_per_month_included  minutes_included  rub_monthly_fee  rub_per_gb  \\\n",
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       "497                   15.0               500              550         200   \n",
       "498                   15.0               500              550         200   \n",
       "499                   15.0               500              550         200   \n",
       "\n",
       "     rub_per_message  rub_per_minute  \n",
       "495                1               1  \n",
       "496                3               3  \n",
       "497                3               3  \n",
       "498                3               3  \n",
       "499                3               3  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(1, 13):\n",
    "    calls_data[i] -= calls_data['minutes_included']\n",
    "    calls_data.loc[calls_data[i] < 0, i] = 0\n",
    "    calls_data[i] *= calls_data['rub_per_minute']\n",
    "\n",
    "# добавляем абонентскую плату    \n",
    "for i in range(1, 13):\n",
    "    for j in range(0, len(calls_data)):\n",
    "        if i >= calls_data.iloc[j,14] and i <= calls_data.iloc[j,15]:\n",
    "            calls_data.iloc[j,i] += calls_data.iloc[j, 19]\n",
    "    \n",
    "calls_data.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "02626626",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36403.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls_data[1].sum()    # проверка: общая сумма за звонки + абонентская плата"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f47a116f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
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       "      <th>9</th>\n",
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       "      <th>12</th>\n",
       "      <th>tariff</th>\n",
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       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>ultra</td>\n",
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       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>1496</td>\n",
       "      <td>550.0</td>\n",
       "      <td>832.0</td>\n",
       "      <td>721.0</td>\n",
       "      <td>613.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>715.0</td>\n",
       "      <td>796.0</td>\n",
       "      <td>631.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>1036.0</td>\n",
       "      <td>604.0</td>\n",
       "      <td>smart</td>\n",
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       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>1497</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>550.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>685.0</td>\n",
       "      <td>smart</td>\n",
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       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>1498</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>smart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>1499</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>886.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>smart</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     user_id      1      2      3      4      5      6      7       8       9  \\\n",
       "495     1495    0.0    0.0    0.0    0.0    0.0    0.0    0.0  1950.0  1950.0   \n",
       "496     1496  550.0  832.0  721.0  613.0  550.0  715.0  796.0   631.0   550.0   \n",
       "497     1497    0.0    0.0    0.0    0.0    0.0    0.0    0.0     0.0     0.0   \n",
       "498     1498    0.0    0.0    0.0    0.0    0.0    0.0  550.0   550.0   550.0   \n",
       "499     1499    0.0    0.0    0.0    0.0    0.0    0.0    0.0     0.0   550.0   \n",
       "\n",
       "         10      11      12 tariff  \n",
       "495  1950.0  1950.0  1950.0  ultra  \n",
       "496   550.0  1036.0   604.0  smart  \n",
       "497   550.0   550.0   685.0  smart  \n",
       "498   550.0     0.0     0.0  smart  \n",
       "499   550.0   886.0   550.0  smart  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls_data = calls_data.drop(columns = ['messages_included', 'gb_per_month_included', 'minutes_included', 'rub_monthly_fee',\n",
    "                             'rub_per_gb', 'rub_per_message', 'rub_per_minute', 'reg_date', 'churn_date'], axis=1)\n",
    "calls_data.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fd4d87ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
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       "      <th>minutes_included</th>\n",
       "      <th>rub_monthly_fee</th>\n",
       "      <th>rub_per_gb</th>\n",
       "      <th>rub_per_message</th>\n",
       "      <th>rub_per_minute</th>\n",
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       "      <th>497</th>\n",
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       "      <td>11.0</td>\n",
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       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>50</td>\n",
       "      <td>15.0</td>\n",
       "      <td>500</td>\n",
       "      <td>550</td>\n",
       "      <td>200</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     user_id    1    2    3    4    5    6     7     8     9  ...  tariff  \\\n",
       "497     1497  0.0  0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0  ...   smart   \n",
       "498     1498  0.0  0.0  0.0  0.0  0.0  0.0  20.0  62.0  44.0  ...   smart   \n",
       "499     1499  0.0  0.0  0.0  0.0  0.0  0.0   0.0   0.0  11.0  ...   smart   \n",
       "\n",
       "     reg_date  churn_date messages_included  gb_per_month_included  \\\n",
       "497        10          12                50                   15.0   \n",
       "498         7          10                50                   15.0   \n",
       "499         9          12                50                   15.0   \n",
       "\n",
       "     minutes_included  rub_monthly_fee  rub_per_gb  rub_per_message  \\\n",
       "497               500              550         200                3   \n",
       "498               500              550         200                3   \n",
       "499               500              550         200                3   \n",
       "\n",
       "     rub_per_minute  \n",
       "497               3  \n",
       "498               3  \n",
       "499               3  \n",
       "\n",
       "[3 rows x 23 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages_data = messages_pivot.merge(users_tariffs_dict, on='user_id', how='right').fillna(0)\n",
    "messages_data.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0fcf4ed5",
   "metadata": {},
   "outputs": [
    {
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       "      <td>27.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>smart</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     user_id    1    2    3    4    5    6    7     8    9   10    11    12  \\\n",
       "495     1495  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0  0.0  0.0   0.0   0.0   \n",
       "496     1496  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0  0.0  0.0   0.0   3.0   \n",
       "497     1497  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0  0.0  0.0   0.0   0.0   \n",
       "498     1498  0.0  0.0  0.0  0.0  0.0  0.0  0.0  36.0  0.0  0.0   0.0   0.0   \n",
       "499     1499  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0  0.0  0.0  27.0  48.0   \n",
       "\n",
       "    tariff  \n",
       "495  ultra  \n",
       "496  smart  \n",
       "497  smart  \n",
       "498  smart  \n",
       "499  smart  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(1, 13):\n",
    "    messages_data[i] -= messages_data['messages_included']\n",
    "    messages_data.loc[messages_data[i] < 0, i] = 0\n",
    "    messages_data[i] *= messages_data['rub_per_message'] \n",
    "\n",
    "messages_data = messages_data.drop(columns = ['messages_included', 'gb_per_month_included', 'minutes_included', 'rub_monthly_fee',\n",
    "                             'rub_per_gb', 'rub_per_message', 'rub_per_minute','reg_date', 'churn_date'], axis=1)\n",
    "messages_data.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "da6def62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages_data[1].sum()    # проверка: общая сумма за СМС"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fba2cb75",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>tariff</th>\n",
       "      <th>reg_date</th>\n",
       "      <th>churn_date</th>\n",
       "      <th>messages_included</th>\n",
       "      <th>gb_per_month_included</th>\n",
       "      <th>minutes_included</th>\n",
       "      <th>rub_monthly_fee</th>\n",
       "      <th>rub_per_gb</th>\n",
       "      <th>rub_per_message</th>\n",
       "      <th>rub_per_minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>1495</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>...</td>\n",
       "      <td>ultra</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>1000</td>\n",
       "      <td>30.0</td>\n",
       "      <td>3000</td>\n",
       "      <td>1950</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>1496</td>\n",
       "      <td>4.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>...</td>\n",
       "      <td>smart</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>50</td>\n",
       "      <td>15.0</td>\n",
       "      <td>500</td>\n",
       "      <td>550</td>\n",
       "      <td>200</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>1497</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>smart</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>50</td>\n",
       "      <td>15.0</td>\n",
       "      <td>500</td>\n",
       "      <td>550</td>\n",
       "      <td>200</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>1498</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>...</td>\n",
       "      <td>smart</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>50</td>\n",
       "      <td>15.0</td>\n",
       "      <td>500</td>\n",
       "      <td>550</td>\n",
       "      <td>200</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>1499</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>smart</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>50</td>\n",
       "      <td>15.0</td>\n",
       "      <td>500</td>\n",
       "      <td>550</td>\n",
       "      <td>200</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     user_id    1     2     3     4     5     6     7     8     9  ...  \\\n",
       "495     1495  0.0   0.0   0.0   0.0   0.0   0.0   0.0   3.0  33.0  ...   \n",
       "496     1496  4.0  28.0  25.0  21.0  24.0  20.0  24.0  23.0  27.0  ...   \n",
       "497     1497  0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
       "498     1498  0.0   0.0   0.0   0.0   0.0   0.0   8.0  24.0  22.0  ...   \n",
       "499     1499  0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   2.0  ...   \n",
       "\n",
       "     tariff  reg_date  churn_date messages_included  gb_per_month_included  \\\n",
       "495   ultra         8          12              1000                   30.0   \n",
       "496   smart         1          12                50                   15.0   \n",
       "497   smart        10          12                50                   15.0   \n",
       "498   smart         7          10                50                   15.0   \n",
       "499   smart         9          12                50                   15.0   \n",
       "\n",
       "     minutes_included  rub_monthly_fee  rub_per_gb  rub_per_message  \\\n",
       "495              3000             1950         150                1   \n",
       "496               500              550         200                3   \n",
       "497               500              550         200                3   \n",
       "498               500              550         200                3   \n",
       "499               500              550         200                3   \n",
       "\n",
       "     rub_per_minute  \n",
       "495               1  \n",
       "496               3  \n",
       "497               3  \n",
       "498               3  \n",
       "499               3  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "internet_data = internet_pivot.merge(users_tariffs_dict, on='user_id', how='right').fillna(0)\n",
    "internet_data.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c42397eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>tariff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>1495</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ultra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>1496</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>smart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>1497</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>smart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>1498</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>smart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>1499</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>smart</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     user_id    1       2       3       4       5       6       7       8  \\\n",
       "495     1495  0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "496     1496  0.0  2600.0  2000.0  1200.0  1800.0  1000.0  1800.0  1600.0   \n",
       "497     1497  0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "498     1498  0.0     0.0     0.0     0.0     0.0     0.0     0.0  1800.0   \n",
       "499     1499  0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "          9      10      11      12 tariff  \n",
       "495   450.0     0.0  1350.0     0.0  ultra  \n",
       "496  2400.0  2600.0  2000.0  1200.0  smart  \n",
       "497     0.0     0.0     0.0  1000.0  smart  \n",
       "498  1400.0  1200.0     0.0     0.0  smart  \n",
       "499     0.0   600.0   600.0     0.0  smart  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(1, 13):\n",
    "    internet_data[i] -= internet_data['gb_per_month_included']\n",
    "    internet_data.loc[internet_data[i] < 0, i] = 0\n",
    "    internet_data[i] *= internet_data['rub_per_gb']\n",
    "\n",
    "internet_data= internet_data.drop(columns = ['messages_included', 'gb_per_month_included', 'minutes_included', 'rub_monthly_fee',\n",
    "                             'rub_per_gb', 'rub_per_message', 'rub_per_minute','reg_date', 'churn_date'], axis=1)\n",
    "internet_data.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "fb907696",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5600.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "internet_data[1].sum()    # проверка: общая сумма за интернет-трафик"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf293a09",
   "metadata": {},
   "source": [
    "Теперь у нас есть три таблицы с платными услугами:\n",
    " - calls_data\n",
    " - messages_data\n",
    " - internet_data\n",
    " \n",
    "Осталось объединить их в одну и сгруппировать данные по id пользователя и тарифу."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "479ad941",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tariff</th>\n",
       "      <th>user_id</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>ultra</td>\n",
       "      <td>1476</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
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       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
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       "      <th>496</th>\n",
       "      <td>ultra</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>ultra</td>\n",
       "      <td>1491</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>ultra</td>\n",
       "      <td>1493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>3750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>ultra</td>\n",
       "      <td>1495</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>1950.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    tariff  user_id    1    2    3       4       5       6       7       8  \\\n",
       "495  ultra     1476  0.0  0.0  0.0  1950.0  1950.0  1950.0  1950.0  1950.0   \n",
       "496  ultra     1485  0.0  0.0  0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "497  ultra     1491  0.0  0.0  0.0  1950.0  1950.0  1950.0  1950.0  1950.0   \n",
       "498  ultra     1493  0.0  0.0  0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "499  ultra     1495  0.0  0.0  0.0     0.0     0.0     0.0     0.0  1950.0   \n",
       "\n",
       "          9      10      11      12  \n",
       "495  1950.0  1950.0  1950.0  1950.0  \n",
       "496     0.0     0.0     0.0  1950.0  \n",
       "497  1950.0  1950.0  1950.0  1950.0  \n",
       "498     0.0     0.0  1950.0  3750.0  \n",
       "499  2400.0  1950.0  3300.0  1950.0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_money = pd.concat([calls_data, messages_data, internet_data], join='outer').reset_index(drop=True)\n",
    "\n",
    "df_money = df_money.groupby(['tariff','user_id'], as_index=False).sum()\n",
    "\n",
    "df_money.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c7ff48f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42075.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_money[1].sum()    # проверка: общая сумма столбца совпадает с суммой проверок выше"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9b8b3197",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "500"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_money[1].count()    # проверка: итоговое кол-во пользователей совпадает"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ac73061",
   "metadata": {},
   "source": [
    "Так же, объединяем таблицы по звонкам, СМС и интернет-трафиком с таблицей `user_dict` и выделим в отдельные таблицы уже в разрезе тарифов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1b41239a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_calls = calls_pivot.merge(users_dict, on='user_id', how='right').fillna(0)    # звонки в минутах\n",
    "\n",
    "df_calls_count = calls_count_pivot.merge(users_dict, on='user_id', how='right').fillna(0)    # кол-во звонков\n",
    "\n",
    "df_messages = messages_pivot.merge(users_dict, on='user_id', how='right').fillna(0)    # кол-во СМС\n",
    "\n",
    "df_internet = internet_pivot.merge(users_dict, on='user_id', how='right').fillna(0)    # интернет-трафик"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d564c953",
   "metadata": {},
   "outputs": [],
   "source": [
    "# функция для разделения датафрейма на два по тарифу \n",
    "\n",
    "df_money_ultra = df_money[df_money['tariff'] == 'ultra']    # потрачено рублей\n",
    "df_money_smart = df_money[df_money['tariff'] == 'smart']\n",
    "\n",
    "df_calls_ultra = df_calls[df_calls['tariff'] == 'ultra']    # звонки в минутах\n",
    "df_calls_smart = df_calls[df_calls['tariff'] == 'smart']\n",
    "\n",
    "df_calls_count_ultra = df_calls_count[df_calls_count['tariff'] == 'ultra']    # кол-во звонков\n",
    "df_calls_count_smart = df_calls_count[df_calls_count['tariff'] == 'smart']\n",
    "\n",
    "df_messages_ultra = df_messages[df_messages['tariff'] == 'ultra']    # кол-во СМС\n",
    "df_messages_smart = df_messages[df_messages['tariff'] == 'smart']\n",
    "\n",
    "df_internet_ultra = df_internet[df_internet['tariff'] == 'ultra']    # интернет-трафик\n",
    "df_internet_smart = df_internet[df_internet['tariff'] == 'smart']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5c1b4d1",
   "metadata": {},
   "source": [
    "## Анализ данных"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "418e6a38",
   "metadata": {},
   "source": [
    "Опишите поведение клиентов оператора, исходя из выборки. Сколько минут разговора, сколько сообщений и какой объём интернет-трафика требуется пользователям каждого тарифа в месяц? Посчитайте среднее количество, дисперсию и стандартное отклонение. Постройте гистограммы. Опишите распределения.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f57c50c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def analytics_bar(data1, data2, title):    # функция построения графика для математического среднего \n",
    "    index = [1,2,3,4,5,6,7,8,9,10,11,12]\n",
    "    data1 = data1[index].apply(np.mean)\n",
    "    data2 = data2[index].apply(np.mean)\n",
    "    smart = data1.values\n",
    "    ultra = data2.values\n",
    "    print(smart)\n",
    "    print(ultra)\n",
    "    df = pd.DataFrame({'smart': smart,\n",
    "                   'ultra': ultra},\n",
    "                 index = index)\n",
    "    ax = df.plot.bar(rot=0)\n",
    "    plt.grid(True) # сетка\n",
    "    plt.title(title, loc = 'left')\n",
    "    plt.legend(bbox_to_anchor = (1,1)) # положение легенды\n",
    "    plt.show()\n",
    "\n",
    "def analytics_hist(data1, data2, title):    # функция построения гистограммы матматического среднего\n",
    "    index = [1,2,3,4,5,6,7,8,9,10,11,12]\n",
    "    data1 = data1[index].apply(np.mean)\n",
    "    data2 = data2[index].apply(np.mean)\n",
    "    smart = data1.values\n",
    "    ultra = data2.values\n",
    "    print(smart)\n",
    "    print(ultra)\n",
    "    df = pd.DataFrame({'smart': smart,\n",
    "                   'ultra': ultra},\n",
    "                 index = index)\n",
    "    df.plot(kind='hist', alpha=0.5, bins=10)\n",
    "    plt.grid(True) # сетка\n",
    "    plt.title(title, loc = 'left')\n",
    "    plt.legend(bbox_to_anchor = (1,1)) # положение легенды\n",
    "    plt.show()\n",
    "\n",
    "def analytics_hist_add(data1, data2, func, title):    # функция построения гистограммы ст. отклонения и дисперсии\n",
    "    index = [1,2,3,4,5,6,7,8,9,10,11,12]\n",
    "    data1 = func(data1[index], ddof=1)\n",
    "    data2 = func(data2[index], ddof=1)\n",
    "    smart = data1.values\n",
    "    ultra = data2.values\n",
    "    print(smart)\n",
    "    print(ultra)\n",
    "    df = pd.DataFrame({'smart': smart,\n",
    "                   'ultra': ultra},\n",
    "                 index = index)\n",
    "    df.plot(kind='hist', alpha=0.5, bins=10)\n",
    "    plt.grid(True) # сетка\n",
    "    plt.title(title, loc = 'left')\n",
    "    plt.legend(bbox_to_anchor = (1,1)) # положение легенды\n",
    "    plt.show()\n",
    "    \n",
    "    # Создадим одну строку с двумя колонками\n",
    "fig, axes = plt.subplots(1, 2, figsize=(16, 5))\n",
    "\n",
    "sns.distplot(...)\n",
    "...\n",
    "sns.boxplot(...)\n",
    "\n",
    "..."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b67cadb4",
   "metadata": {},
   "source": [
    "### * звонки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "76253073",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 19.74643875  49.35612536  85.56695157 117.14814815 155.60683761\n",
      " 184.85754986 233.84330484 282.17948718 317.49002849 356.93447293\n",
      " 391.86324786 459.47293447]\n",
      "[ 25.8590604   47.8590604  121.59060403 169.06711409 229.47651007\n",
      " 270.43624161 333.33557047 366.79865772 416.34899329 460.69798658\n",
      " 486.87919463 553.02013423]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "analytics_bar(df_calls_smart, df_calls_ultra, 'Длительность разговора по месяцам, среднее арифметическое')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "df32f80f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2.68091168  6.64102564 11.91737892 16.37321937 22.19088319 25.95441595\n",
      " 32.48433048 39.48717949 44.10826211 50.22507123 55.37037037 63.83475783]\n",
      "[ 3.59060403  6.69127517 16.80536913 23.65771812 32.60402685 37.62416107\n",
      " 46.28187919 51.19463087 57.57718121 63.87248322 67.55704698 77.72483221]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "analytics_bar(df_calls_count_smart, df_calls_count_ultra, 'Количество звонков по месяцам, среднее арифметическое')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b0ea31e",
   "metadata": {},
   "source": [
    "Мы видим уверенный рост как самого количества звонков, так и их продолжительности от месяца к месяцу. Скорее всего, это связано с ростом абонентской базы.\n",
    "\n",
    "Средний пользователь тарифа \"Ультра\" звонят чаще и говорит дольше (за исключением февраля)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "438de76d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 19.74643875  49.35612536  85.56695157 117.14814815 155.60683761\n",
      " 184.85754986 233.84330484 282.17948718 317.49002849 356.93447293\n",
      " 391.86324786 459.47293447]\n",
      "[ 25.8590604   47.8590604  121.59060403 169.06711409 229.47651007\n",
      " 270.43624161 333.33557047 366.79865772 416.34899329 460.69798658\n",
      " 486.87919463 553.02013423]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "analytics_hist(df_calls_smart, df_calls_ultra, 'Распределение длительности разговора, среднее арифметическое')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a18f44d6",
   "metadata": {},
   "source": [
    "У пользователей тарифа \"Смарт\" есть несколько пиков, пользователи тарифа \"Ультра\" говорят или мало или много."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a3146fe6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 76.73748261 135.17136726 184.72730618 200.99428774 223.816224\n",
      " 231.07037195 243.62532347 250.3571032  245.35061626 233.23688444\n",
      " 213.31005894 216.57817785]\n",
      "[120.02342227 160.22931288 268.60455243 274.45616163 319.8217321\n",
      " 329.10466835 358.6860332  351.1772283  364.72872048 364.59399308\n",
      " 336.89381824 362.63365606]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "analytics_hist_add(df_calls_smart, df_calls_ultra, np.std, 'Распределение длительности разговора, стандартное отклонение')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94164e2b",
   "metadata": {},
   "source": [
    "Стандартное отклонение у пользователей \"Ультры\" больше."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "20049063",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 5888.64123728 18271.29852666 34124.17764754 40398.7037037\n",
      " 50093.70212454 53393.51679284 59353.29823362 62678.67912088\n",
      " 60196.92490028 54399.44426536 45501.18124542 46906.10712251]\n",
      "[ 14405.62189371  25673.43270452  72148.40558679  75326.18465445\n",
      " 102285.94032287 108309.88273173 128655.67041538 123325.44567386\n",
      " 133027.0395429  132928.77979322 113497.44476691 131503.16851079]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "analytics_hist_add(df_calls_smart, df_calls_ultra, np.var, 'Распределение длительности разговора, дисперсия')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fbd9f25",
   "metadata": {},
   "source": [
    "### * СМС"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8ff0956b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.76638177  3.98005698  6.98860399  9.80911681 13.56695157 15.60683761\n",
      " 19.32763533 22.52136752 24.91737892 28.28205128 30.67806268 34.55840456]\n",
      "[ 2.04026846  3.53691275  8.02013423 11.65100671 17.02013423 23.52348993\n",
      " 30.44295302 34.7852349  40.3557047  48.23489933 50.17449664 56.54362416]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "analytics_bar(df_messages_smart, df_messages_ultra, 'Количество СМС по месяцам, среднее арифметическое')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f7b3ca3",
   "metadata": {},
   "source": [
    "С количеством СМС все повторяется: уверенный рост от месяца к месяцу, пользователи \"Ультры\" опережают пользователей \"Смарта\", но только не в феврале."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "cf7d7ad3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.76638177  3.98005698  6.98860399  9.80911681 13.56695157 15.60683761\n",
      " 19.32763533 22.52136752 24.91737892 28.28205128 30.67806268 34.55840456]\n",
      "[ 2.04026846  3.53691275  8.02013423 11.65100671 17.02013423 23.52348993\n",
      " 30.44295302 34.7852349  40.3557047  48.23489933 50.17449664 56.54362416]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "analytics_hist(df_messages_smart, df_messages_ultra, 'Распределение кол-ва СМС, среднее арифметическое')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "669892a0",
   "metadata": {},
   "source": [
    "В среднем, пользователи обоих тарифов пишут менее 10 СМС в месяц. При этом, пользователи \"Смарта\" придерживаются месячного лимита, в то время как пользователи \"Ультры\" даже не приближаются к нему (имея возможность отправлять по 1000 СМС бесплатно на ежемесячной основе, в самый насыщенный месяц используют всего 1/20 своих возможностей)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6c151cfa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 7.34240975 12.54191378 18.17438971 19.921719   23.81837086 25.57865983\n",
      " 27.14128758 28.48716953 28.3369826  28.9607161  28.80766868 30.77840396]\n",
      "[10.77213572 13.30389269 24.84777161 27.33202413 32.88523652 39.30676028\n",
      " 45.1346907  45.09547885 46.29285069 52.13902793 50.09608761 52.27170223]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "analytics_hist_add(df_messages_smart, df_messages_ultra, np.std, 'Распределение кол-ва СМС, стандартное отклонение')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b6c48d00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 53.91098087 157.29960114 330.30844119 396.87488807 567.31479039\n",
      " 654.26783883 736.64949125 811.51882784 802.98458282 838.72307692\n",
      " 829.88177452 947.31015059]\n",
      "[ 116.03890804  176.99356067  617.41175404  747.0395429  1081.43878106\n",
      " 1545.02140395 2037.14030473 2033.60221295 2143.02802467 2718.47823327\n",
      " 2509.61799383 2732.33085434]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "analytics_hist_add(df_messages_smart, df_messages_ultra, np.var, 'Распределение кол-ва СМС, дисперсия')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84bc961b",
   "metadata": {},
   "source": [
    "### * интернет-трафик"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c9f4a2e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.84615385  1.95726496  3.34188034  4.34757835  6.39031339  7.45299145\n",
      "  9.09116809 11.20512821 12.14245014 14.13960114 15.3048433  17.47578348]\n",
      "[ 0.79865772  1.93288591  4.38255034  6.26174497  8.7114094  10.67785235\n",
      " 12.52348993 14.0738255  14.85234899 16.56375839 17.81208054 20.28187919]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "analytics_bar(df_internet_smart, df_internet_ultra, 'Использование интернет-трафика по месяцам, среднее арифметическое')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5b5e677",
   "metadata": {},
   "source": [
    "Тенденция роста и соотношения сохраняются. В ноябре и декабре пользователи тарифа \"Смарт\" превышали своей ежемесячный лимит в размере 15 Гб."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e0a6fb46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.84615385  1.95726496  3.34188034  4.34757835  6.39031339  7.45299145\n",
      "  9.09116809 11.20512821 12.14245014 14.13960114 15.3048433  17.47578348]\n",
      "[ 0.79865772  1.93288591  4.38255034  6.26174497  8.7114094  10.67785235\n",
      " 12.52348993 14.0738255  14.85234899 16.56375839 17.81208054 20.28187919]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "analytics_hist(df_internet_smart, df_internet_ultra, 'Распределение потребления интернет-трафика, среднее арифметическое')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00fcf8be",
   "metadata": {},
   "source": [
    "Похоже, что больше 20Гб требуется считанным  единицам. У пользователей \"Ультры\" не получается пока даже близко приблизится к лимитам тарифа."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "171c6fd0",
   "metadata": {},
   "source": [
    "### * стоимость услуг"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "62e7fcbc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  69.87179487  136.75498575  255.48717949  307.31054131  481.05128205\n",
      "  562.56125356  700.89458689  884.15954416  925.88603989 1143.21082621\n",
      " 1209.12535613 1517.13675214]\n",
      "[ 117.7852349   318.12080537  500.33557047  738.9261745   926.17449664\n",
      " 1149.66442953 1293.62416107 1495.97315436 1562.41610738 1705.36912752\n",
      " 1881.54362416 2008.38926174]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "analytics_bar(df_money_smart, df_money_ultra, 'Сумма платежей по месяцам, среднее арифметическое')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f704216",
   "metadata": {},
   "source": [
    "Пользователем \"Смарта\" начинает не хватать включенных в пакет услуг. Им приходится пользоваться платными, и с каждым месяцем все больше. \n",
    "\n",
    "Странно, но пользователям \"Ультры\" так же пришлось доплачивать  в конце года, хотя и не так много. Это говорит о том, что есть ну очень активные потребители."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "28481ac1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  69.87179487  136.75498575  255.48717949  307.31054131  481.05128205\n",
      "  562.56125356  700.89458689  884.15954416  925.88603989 1143.21082621\n",
      " 1209.12535613 1517.13675214]\n",
      "[ 117.7852349   318.12080537  500.33557047  738.9261745   926.17449664\n",
      " 1149.66442953 1293.62416107 1495.97315436 1562.41610738 1705.36912752\n",
      " 1881.54362416 2008.38926174]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "analytics_hist(df_money_smart, df_money_ultra, 'Распределение суммы платежа, среднее арифметическое')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3aa2a6ec",
   "metadata": {},
   "source": [
    "Здесь мы явно видим, сколько приходится доплачивать пользователям тарифов. Для \"Ультры\" это в пределах 50 рублей. А вот некоторые пользователи тарифа \"Смарт\" переплачивают в два, а то и в три раза."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b693b53a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 252.58598101  377.30571511  582.01095643  549.81286986  744.79157406\n",
      "  787.96277728  824.18213157  898.10362601  835.4935572   921.2507653\n",
      "  855.72103679 1058.13275083]\n",
      "[ 466.11801898  729.22151116  879.42888109  976.49479382 1062.76958253\n",
      " 1041.94945675 1093.05684353  999.40044152  879.5135718   861.56953217\n",
      "  741.33164044  691.51231362]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "analytics_hist_add(df_money_smart, df_money_ultra, np.std, 'Распределение суммы платежа, стандартное отклонение')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d91e55e6",
   "metadata": {},
   "source": [
    "analytics_hist_add(df_internet_smart, df_internet_ultra, np.var, 'Распределение суммы платежа, дисперсия')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47af0748",
   "metadata": {},
   "source": [
    "Итак, мы поняли, что не смотря на среднее количество потраченных минут, СМС и потребленного интернет-трафика (которые ниже лимитов тарифа \"Смарт\"), часть пользователей вынуждена пользоваться платными услугами; это вызывает переплату в разы, но все равно это меньше, чем цена пакета \"Ультра\".\n",
    "\n",
    "Пользователи \"Ультра\", в общем и целом, укладываются в предоплаченные услуги, а если нет, доплачиват немного, в районе 50-ти рублей."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "304aedb4",
   "metadata": {},
   "source": [
    "## Проверка гипотез"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9913cf0f",
   "metadata": {},
   "source": [
    "Итак, у нас есть две таблицы `df_money_ultra` и `df_money_smart` с выручкой пользователей помесячно. Добавим стобец `total`, чтобы получить выручку за год для каждого пользователя."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ebeba296",
   "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>tariff</th>\n",
       "      <th>user_id</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>ultra</td>\n",
       "      <td>1476</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>17550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>ultra</td>\n",
       "      <td>1485</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>ultra</td>\n",
       "      <td>1491</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>17550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>ultra</td>\n",
       "      <td>1493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>5700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>ultra</td>\n",
       "      <td>1495</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>11550.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    tariff  user_id    1    2    3       4       5       6       7       8  \\\n",
       "495  ultra     1476  0.0  0.0  0.0  1950.0  1950.0  1950.0  1950.0  1950.0   \n",
       "496  ultra     1485  0.0  0.0  0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "497  ultra     1491  0.0  0.0  0.0  1950.0  1950.0  1950.0  1950.0  1950.0   \n",
       "498  ultra     1493  0.0  0.0  0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "499  ultra     1495  0.0  0.0  0.0     0.0     0.0     0.0     0.0  1950.0   \n",
       "\n",
       "          9      10      11      12    total  \n",
       "495  1950.0  1950.0  1950.0  1950.0  17550.0  \n",
       "496     0.0     0.0     0.0  1950.0   1950.0  \n",
       "497  1950.0  1950.0  1950.0  1950.0  17550.0  \n",
       "498     0.0     0.0  1950.0  3750.0   5700.0  \n",
       "499  2400.0  1950.0  3300.0  1950.0  11550.0  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_money_ultra['total'] = df_money_ultra.iloc[:,2:14].sum(axis=1)\n",
    "df_money_ultra.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "173c6474",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tariff</th>\n",
       "      <th>user_id</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>smart</td>\n",
       "      <td>1494</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>953.0</td>\n",
       "      <td>1150.0</td>\n",
       "      <td>1150.0</td>\n",
       "      <td>1150.0</td>\n",
       "      <td>4953.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>smart</td>\n",
       "      <td>1496</td>\n",
       "      <td>550.0</td>\n",
       "      <td>3432.0</td>\n",
       "      <td>2721.0</td>\n",
       "      <td>1813.0</td>\n",
       "      <td>2350.0</td>\n",
       "      <td>1715.0</td>\n",
       "      <td>2596.0</td>\n",
       "      <td>2231.0</td>\n",
       "      <td>2950.0</td>\n",
       "      <td>3150.0</td>\n",
       "      <td>3036.0</td>\n",
       "      <td>1807.0</td>\n",
       "      <td>28351.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>smart</td>\n",
       "      <td>1497</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>1685.0</td>\n",
       "      <td>2785.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>smart</td>\n",
       "      <td>1498</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>2386.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6636.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>smart</td>\n",
       "      <td>1499</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>1150.0</td>\n",
       "      <td>1513.0</td>\n",
       "      <td>598.0</td>\n",
       "      <td>3811.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    tariff  user_id      1       2       3       4       5       6       7  \\\n",
       "346  smart     1494    0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "347  smart     1496  550.0  3432.0  2721.0  1813.0  2350.0  1715.0  2596.0   \n",
       "348  smart     1497    0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "349  smart     1498    0.0     0.0     0.0     0.0     0.0     0.0   550.0   \n",
       "350  smart     1499    0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "          8       9      10      11      12    total  \n",
       "346   550.0   953.0  1150.0  1150.0  1150.0   4953.0  \n",
       "347  2231.0  2950.0  3150.0  3036.0  1807.0  28351.0  \n",
       "348     0.0     0.0   550.0   550.0  1685.0   2785.0  \n",
       "349  2386.0  1950.0  1750.0     0.0     0.0   6636.0  \n",
       "350     0.0   550.0  1150.0  1513.0   598.0   3811.0  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_money_smart['total'] = df_money_smart.iloc[:,2:14].sum(axis=1)\n",
    "df_money_smart.tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4abdbcb9",
   "metadata": {},
   "source": [
    "Смотрим выбросы: строим гисторамму  и смотрим распределения."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "5c00a87a",
   "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>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>tariff</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>smart</th>\n",
       "      <td>351.0</td>\n",
       "      <td>8193.0</td>\n",
       "      <td>5412.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>4188.0</td>\n",
       "      <td>7250.0</td>\n",
       "      <td>11199.0</td>\n",
       "      <td>28351.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        count    mean     std    min     25%     50%      75%      max\n",
       "tariff                                                                \n",
       "smart   351.0  8193.0  5412.0  550.0  4188.0  7250.0  11199.0  28351.0"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_money_smart.groupby('tariff')['total'].describe().round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "0d6d291f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_money_smart.total.hist(bins=100, range=(550,28500))\n",
    "plt.title('Распределение сумм выручки, тариф  Смарт, руб.', loc = 'left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69aaf1e0",
   "metadata": {},
   "source": [
    "График показывает, что потребление платных услуг в тарифе \"Смарт\" - обычное явление. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "00dc751e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df_money_smart['total'])    # проверяем распределение на нормальность\n",
    "plt.title('Проверка нормальности распределения (выручка Смарт, руб.)', loc = 'left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "bbf8455e",
   "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>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>tariff</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>ultra</th>\n",
       "      <td>149.0</td>\n",
       "      <td>13698.0</td>\n",
       "      <td>6912.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>7800.0</td>\n",
       "      <td>13650.0</td>\n",
       "      <td>19500.0</td>\n",
       "      <td>32400.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        count     mean     std     min     25%      50%      75%      max\n",
       "tariff                                                                   \n",
       "ultra   149.0  13698.0  6912.0  1950.0  7800.0  13650.0  19500.0  32400.0"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_money_ultra.groupby('tariff')['total'].describe().round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "deec73e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_money_ultra.total.hist(bins=100, range=(1950,32500))\n",
    "plt.title('Распределение сумм выручки, тариф  Ультра, руб.', loc = 'left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5c95b30",
   "metadata": {},
   "source": [
    "Мы видим пики, котрые кратны размеру абон. платы, что говорит о том, что предоплаченных услуг тарифа \"Ультра\" практически всегда достаточно."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "4e6d5a33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df_money_ultra['total'])\n",
    "plt.title('Проверка нормальности распределения (выручка Ультра, руб.)', loc = 'left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "15ed2667",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(143, 15)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_money_ultra = df_money_ultra.query('total <= 24000')\n",
    "df_money_ultra.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f265b017",
   "metadata": {},
   "source": [
    "### * проверка первой гипотезы"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfa592d1",
   "metadata": {},
   "source": [
    "<p>Введем основную и альтернативные гипотезы:</p>\n",
    "\n",
    "$\\begin{equation*}\n",
    " \\begin{cases}\n",
    "   H_0 :\\text{средняя выручка пользователей тарифов «Ультра» и «Смарт» одинакова}\\\\\n",
    "   H_1 :\\text{средняя выручка пользователей тарифов «Ультра» и «Смарт» различаются}\n",
    " \\end{cases}\n",
    "\\end{equation*}$\n",
    "\n",
    "Уровень значимости: $\\alpha = 0.05$ \n",
    "\n",
    "Посчитаем p-value, используя `ttest` из библиотеки `scipy` для сравнения средних двух выборок.\n",
    "\n",
    "У нас разные по размеру выборки (пользователей тарифа \"Смарт\" в два раза больше, чем пользователей тарифа \"Ультра\").\n",
    "Для подтверждения гипотезы о равенстве среднего двух генеральных совокупностей по взятым из них выборкам иcпользуется метод `st.stats.ttest_ind`, так же мы будем использовать параметр `equal_var=False`, т.к. есть сомнения по поводу нормального распределения дисперсий (если судить по гистограмме п.4.4).\n",
    "\n",
    "Если p-value будет меньше выбранного уровня значимости $\\alpha = 0.05$, то нулевую гипотезу о равенстве средних отвергнем. В противном случае делаем вывод, что данные не позволили нам отвергнуть нулевую гипотезу."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ec2c546d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-значение:  3.497011826267894e-14\n",
      "Отвергаем нулевую гипотезу\n"
     ]
    }
   ],
   "source": [
    "alpha = .05 # критический уровень статистической значимости\n",
    "\n",
    "results = st.stats.ttest_ind(df_money_ultra['total'], df_money_smart['total'], equal_var=False)\n",
    "\n",
    "print('p-значение: ', results.pvalue)    # тест двухсторонний\n",
    "if results.pvalue < alpha:\n",
    "    print(\"Отвергаем нулевую гипотезу\")\n",
    "else:\n",
    "    print(\"Не получилось отвергнуть нулевую гипотезу\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "559f5a2b",
   "metadata": {},
   "source": [
    "**Вывод:** \n",
    "\n",
    "p-value получилось меньше выбранного уровня 𝛼=0.05. Отвергаем нулевую гипотезу, т.к. средние выручки пользователей тарифов «Ультра» и «Смарт» различаются."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66ded753",
   "metadata": {},
   "source": [
    "_______\n",
    "Проверим еще одну теорию.\n",
    "\n",
    "<p>Введем основную и альтернативные гипотезы:</p>\n",
    "\n",
    "$\\begin{equation*}\n",
    " \\begin{cases}\n",
    "   H_0 :\\text{средняя выручка пользователей тарифов «Ультра» и «Смарт» одинакова}\\\\\n",
    "   H_1 :\\text{средняя выручка пользователей тарифа «Ультра» больше, чем средняя выручка пользователей тарифа «Смарт»}\n",
    " \\end{cases}\n",
    "\\end{equation*}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "0c740e7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-значение:  1.748505913133947e-14\n",
      "Отвергаем нулевую гипотезу\n"
     ]
    }
   ],
   "source": [
    "alpha = .05 # критический уровень статистической значимости\n",
    "\n",
    "results = st.stats.ttest_ind(df_money_ultra['total'], df_money_smart['total'], equal_var=False)\n",
    "\n",
    "# тест односторонний: p-value будет в два раза меньше\n",
    "print('p-значение: ', results.pvalue / 2)\n",
    "\n",
    "# тест односторонний влево:\n",
    "# отвергаем гипотезу только если выборочное среднее значимо меньше предполагаемого значения\n",
    "if (results.pvalue / 2 < alpha) and (df_money_ultra['total'].mean() > df_money_smart['total'].mean()):\n",
    "    print(\"Отвергаем нулевую гипотезу\")\n",
    "else:\n",
    "    print(\"Не получилось отвергнуть нулевую гипотезу\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57bb394a",
   "metadata": {},
   "source": [
    "**Вывод:** \n",
    "\n",
    "p-value получилось меньше выбранного уровня 𝛼=0.05. Отвергаем нулевую гипотезу, средняя выручка пользователей тарифа «Ультра» больше, чем средняя выручка пользователей тарифа «Смарт»."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce34c243",
   "metadata": {},
   "source": [
    "### * проверка второй гипотезы"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2f0f174",
   "metadata": {},
   "source": [
    "####  выручка по локациям"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81eb82b5",
   "metadata": {},
   "source": [
    "Сделаем отдельную таблицу `users_dict_city`, которая будет содержать только id пользователей и город их проживаня и объединим ее с таблицей `df_money`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "626037a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tariff</th>\n",
       "      <th>user_id</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "      <th>4</th>\n",
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       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>ultra</td>\n",
       "      <td>1491</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>Челябинск</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>ultra</td>\n",
       "      <td>1493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>Красноярск</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>ultra</td>\n",
       "      <td>1495</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>Иркутск</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    tariff  user_id    1    2    3       4       5       6       7       8  \\\n",
       "497  ultra     1491  0.0  0.0  0.0  1950.0  1950.0  1950.0  1950.0  1950.0   \n",
       "498  ultra     1493  0.0  0.0  0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "499  ultra     1495  0.0  0.0  0.0     0.0     0.0     0.0     0.0  1950.0   \n",
       "\n",
       "          9      10      11      12        city  \n",
       "497  1950.0  1950.0  1950.0  1950.0   Челябинск  \n",
       "498     0.0     0.0  1950.0  3750.0  Красноярск  \n",
       "499  2400.0  1950.0  3300.0  1950.0     Иркутск  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_dict_city = users[['user_id','city']]\n",
    "\n",
    "df_money_city = df_money.merge(users_dict_city, on='user_id', how='left')\n",
    "df_money_city.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "6c748445",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_money_city['total'] = df_money_city.iloc[:,2:14].sum(axis=1)    # добавляем столбец total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "d875f339",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>ultra</td>\n",
       "      <td>1453</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
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       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>Москва</td>\n",
       "      <td>23400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>ultra</td>\n",
       "      <td>1461</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>Москва</td>\n",
       "      <td>3900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>ultra</td>\n",
       "      <td>1476</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>Москва</td>\n",
       "      <td>17550.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    tariff  user_id       1       2       3       4       5       6       7  \\\n",
       "484  ultra     1453  1950.0  1950.0  1950.0  1950.0  1950.0  1950.0  1950.0   \n",
       "487  ultra     1461     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "495  ultra     1476     0.0     0.0     0.0  1950.0  1950.0  1950.0  1950.0   \n",
       "\n",
       "          8       9      10      11      12    city    total  \n",
       "484  1950.0  1950.0  1950.0  1950.0  1950.0  Москва  23400.0  \n",
       "487     0.0     0.0     0.0  1950.0  1950.0  Москва   3900.0  \n",
       "495  1950.0  1950.0  1950.0  1950.0  1950.0  Москва  17550.0  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_money_moscow = df_money_city[df_money_city['city'] == 'Москва']     # выручка от пользователей Москвы\n",
    "df_money_moscow.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "3dc6dee0",
   "metadata": {},
   "outputs": [
    {
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       "      <th>497</th>\n",
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       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>ultra</td>\n",
       "      <td>1493</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1950.0</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>Красноярск</td>\n",
       "      <td>5700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>ultra</td>\n",
       "      <td>1495</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>Иркутск</td>\n",
       "      <td>11550.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    tariff  user_id    1    2    3       4       5       6       7       8  \\\n",
       "497  ultra     1491  0.0  0.0  0.0  1950.0  1950.0  1950.0  1950.0  1950.0   \n",
       "498  ultra     1493  0.0  0.0  0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "499  ultra     1495  0.0  0.0  0.0     0.0     0.0     0.0     0.0  1950.0   \n",
       "\n",
       "          9      10      11      12        city    total  \n",
       "497  1950.0  1950.0  1950.0  1950.0   Челябинск  17550.0  \n",
       "498     0.0     0.0  1950.0  3750.0  Красноярск   5700.0  \n",
       "499  2400.0  1950.0  3300.0  1950.0     Иркутск  11550.0  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_money_other = df_money_city[df_money_city['city'] != 'Москва']    # выручка от пользователей других городов\n",
    "df_money_other.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9376a382",
   "metadata": {},
   "source": [
    "Смотрим выбросы: строим гисторамму  и смотрим распределения."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "a400a4d3",
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\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>city</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>Москва</th>\n",
       "      <td>99.0</td>\n",
       "      <td>9546.0</td>\n",
       "      <td>6632.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>4378.0</td>\n",
       "      <td>7800.0</td>\n",
       "      <td>13776.0</td>\n",
       "      <td>28050.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        count    mean     std    min     25%     50%      75%      max\n",
       "city                                                                  \n",
       "Москва   99.0  9546.0  6632.0  550.0  4378.0  7800.0  13776.0  28050.0"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_money_moscow.groupby('city')['total'].describe().round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "cbfbd71f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_money_moscow.total.hist(bins=50, range=(550,28100))\n",
    "plt.title('Распределение сумм выручки от пользователей Москвы, руб.', loc = 'left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "bac66fe3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df_money_moscow['total'])\n",
    "plt.title('Проверка нормальности распределения (выручка от пользователей Москвы, руб.)', loc = 'left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a76715e2",
   "metadata": {},
   "source": [
    "Как видно на гистограммах, распределение не нормальное. Так же видим два пика (это абонентская плата по тарифом, остальное - дополнительные платные услуги. Пользователей тарифа \"Смарт\" больше."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "2b9f03f6",
   "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>user_id</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>401.0</td>\n",
       "      <td>401.0</td>\n",
       "      <td>401.0</td>\n",
       "      <td>401.0</td>\n",
       "      <td>401.0</td>\n",
       "      <td>401.0</td>\n",
       "      <td>401.0</td>\n",
       "      <td>401.0</td>\n",
       "      <td>401.0</td>\n",
       "      <td>401.0</td>\n",
       "      <td>401.0</td>\n",
       "      <td>401.0</td>\n",
       "      <td>401.0</td>\n",
       "      <td>401.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1248.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>327.0</td>\n",
       "      <td>435.0</td>\n",
       "      <td>623.0</td>\n",
       "      <td>744.0</td>\n",
       "      <td>892.0</td>\n",
       "      <td>1082.0</td>\n",
       "      <td>1128.0</td>\n",
       "      <td>1322.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>9905.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>146.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>522.0</td>\n",
       "      <td>686.0</td>\n",
       "      <td>718.0</td>\n",
       "      <td>874.0</td>\n",
       "      <td>916.0</td>\n",
       "      <td>967.0</td>\n",
       "      <td>980.0</td>\n",
       "      <td>889.0</td>\n",
       "      <td>943.0</td>\n",
       "      <td>891.0</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>6359.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1122.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>598.0</td>\n",
       "      <td>950.0</td>\n",
       "      <td>4947.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1243.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>950.0</td>\n",
       "      <td>950.0</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>1411.0</td>\n",
       "      <td>1798.0</td>\n",
       "      <td>8839.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1375.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>1150.0</td>\n",
       "      <td>1624.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>14134.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1499.0</td>\n",
       "      <td>2350.0</td>\n",
       "      <td>3432.0</td>\n",
       "      <td>2721.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3900.0</td>\n",
       "      <td>4473.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>4650.0</td>\n",
       "      <td>3759.0</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>5680.0</td>\n",
       "      <td>6770.0</td>\n",
       "      <td>32400.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id       1       2       3       4       5       6       7  \\\n",
       "count    401.0   401.0   401.0   401.0   401.0   401.0   401.0   401.0   \n",
       "mean    1248.0    75.0   197.0   327.0   435.0   623.0   744.0   892.0   \n",
       "std      146.0   296.0   522.0   686.0   718.0   874.0   916.0   967.0   \n",
       "min     1000.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "25%     1122.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "50%     1243.0     0.0     0.0     0.0     0.0     0.0   550.0   550.0   \n",
       "75%     1375.0     0.0     0.0     0.0   550.0  1150.0  1624.0  1950.0   \n",
       "max     1499.0  2350.0  3432.0  2721.0  3000.0  3900.0  4473.0  4200.0   \n",
       "\n",
       "            8       9      10      11      12    total  \n",
       "count   401.0   401.0   401.0   401.0   401.0    401.0  \n",
       "mean   1082.0  1128.0  1322.0  1400.0  1680.0   9905.0  \n",
       "std     980.0   889.0   943.0   891.0  1001.0   6359.0  \n",
       "min       0.0     0.0     0.0     0.0     0.0    550.0  \n",
       "25%       0.0   550.0   550.0   598.0   950.0   4947.0  \n",
       "50%     950.0   950.0  1350.0  1411.0  1798.0   8839.0  \n",
       "75%    1950.0  1950.0  1950.0  1950.0  1950.0  14134.0  \n",
       "max    4650.0  3759.0  4800.0  5680.0  6770.0  32400.0  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_money_other.describe().round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "693f4ea5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_money_other.total.hist(bins=50, range=(550,32500))\n",
    "plt.title('Распределение сумм выручки от пользователей других городов, руб.', loc = 'left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "30d383ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df_money_other['total'])\n",
    "plt.title('Проверка нормальности распределения (выручка от пользователей других городов, руб.)', loc = 'left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90c92f6c",
   "metadata": {},
   "source": [
    "Здесь картина такая же, но видно, что дополнительных платных услуг  пользователи других городов потребляют больше, чем москвичи."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afd71fd9",
   "metadata": {},
   "source": [
    "<p>Введем основную и альтернативные гипотезы:</p>\n",
    "\n",
    "$\\begin{equation*}\n",
    " \\begin{cases}\n",
    "   H_0 :\\text{средняя выручка пользователей из Москвы и регионов одинакова}\\\\\n",
    "   H_1 :\\text{средняя выручка пользователей из Москвы отличается от выручки пользователей из других регионов}\n",
    " \\end{cases}\n",
    "\\end{equation*}$\n",
    "\n",
    "Уровень значимости: $\\alpha = 0.05$ \n",
    "\n",
    "Посчитаем p-value, используя `ttest` из библиотеки `scipy` для сравнения средних двух выборок.\n",
    "\n",
    "У нас разные по размеру выборки (пользователей других городов в четыре раза больше, чем пользователей Москвы).\n",
    "Для подтверждения гипотезы о равенстве среднего двух генеральных совокупностей по взятым из них выборкам иcпользуется метод `st.stats.ttest_ind`, так же мы будем использовать параметр `equal_var=False`, т.к. есть сомнения по поводу нормального распределения дисперсий.\n",
    "\n",
    "Если p-value будет меньше выбранного уровня значимости $\\alpha = 0.05$, то нулевую гипотезу о равенстве средних отвергнем. В противном случае делаем вывод, что данные не позволили нам отвергнуть нулевую гипотезу."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "fbc89906",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-значение:  0.6273901360626399\n",
      "Не получилось отвергнуть нулевую гипотезу\n"
     ]
    }
   ],
   "source": [
    "alpha = .05   # критический уровень статистической значимости    \n",
    "results = st.stats.ttest_ind(df_money_moscow['total'], df_money_other['total'], equal_var=False)\n",
    "\n",
    "print('p-значение: ', results.pvalue)    # тест двухсторонний\n",
    "\n",
    "if results.pvalue < alpha:\n",
    "    print(\"Отвергаем нулевую гипотезу\")\n",
    "else:\n",
    "    print(\"Не получилось отвергнуть нулевую гипотезу\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e5e61e1",
   "metadata": {},
   "source": [
    "**Вывод:** \n",
    "\n",
    "p-value получилось больше выбранного уровня 𝛼=0.05. Отвергнуть нулевую гипотезу не получилось, средняя выручка пользователей из Москвы и регионов одинакова."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91046b3e",
   "metadata": {},
   "source": [
    "## Вывод"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21a4824f",
   "metadata": {},
   "source": [
    "Исследование проводилось на выборке из пятисот пользователей тарифов \"Ультра\" и \"Смарт\" как Москвы, так и других городов.\n",
    "\n",
    "При этом, пользователей из Москвы было меньше, а среди выбранного пользователями тарифа преобладал \"Смарт\".\n",
    "\n",
    "Было выявлено, что в среднем, пользователи тарифов укладываются в лимиты. Но в частных случаях, есть потребность в использовании дополнительных платных услуг; для Ультры переплаты составляют 50-100 рублей в месяц, в то время как некоторые пользователи тарифа \"Смарт\" могут тратить по 2-3 эквивалента абонентской платы в месяц.\n",
    "\n",
    "В ходе анализа нужно было подтвердить следующие гипотезы:\n",
    "\n",
    "- средняя выручка пользователей тарифов «Ультра» и «Смарт» различаются;\n",
    "- средняя выручка пользователей из Москвы отличается от выручки пользователей из других регионов.\n",
    "\n",
    "В первом случае мы опровергли нулевую гипотезу о равенстве средней выручки тарифов и не смогли опровергнуть гипотезу о том, что средняя выручка тарифа \"Ультра\" больше, чем средняя выручка тарифа \"Смарт\".\n",
    "\n",
    "Во втором случае нулевая гипотеза о равенстве средней выручке пользователей из Москвы и пользователей из других городов отвергнута не была. \n"
   ]
  }
 ],
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