3. Определение перспективного тарифа для телеком-компании.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Определение перспективного тарифа для телеком компании"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Клиентам предлагают два тарифных плана: «Смарт» и «Ультра». Чтобы скорректировать рекламный бюджет, коммерческий департамент федерального оператора сотовой связи хочет понять, какой тариф приносит больше денег.  \n",
    "    Необходимо сделать предварительный анализ тарифов на небольшой выборке клиентов. В распоряжении данные 500 пользователей «Мегалайна»: кто они, откуда, каким тарифом пользуются, сколько звонков и сообщений каждый отправил за 2018 год. Нужно проанализировать поведение клиентов и сделать вывод — какой тариф лучше.\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from IPython.display import display, HTML\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as st\n",
    "from scipy import stats as st\n",
    "\n",
    "sns.set_style(\"darkgrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Шаг 1. Откройте файл с данными и изучите общую информацию"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "calls = pd.read_csv('/datasets/calls.csv')\n",
    "internet = pd.read_csv('/datasets/internet.csv', index_col=0)\n",
    "messages = pd.read_csv('/datasets/messages.csv')\n",
    "tariffs = pd.read_csv('/datasets/tariffs.csv')\n",
    "users = pd.read_csv('/datasets/users.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Шаг 2. Подготовьте данные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_dfs = [calls, internet, messages, tariffs, users]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_info(df):\n",
    "    display(df.head(3))\n",
    "    print('***'*10)\n",
    "    display(df.info())\n",
    "    print('***'*20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "       id   call_date  duration  user_id\n",
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      "******************************\n",
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      " 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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      "************************************************************\n"
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       "      <td>112.95</td>\n",
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       "      <td>1000</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
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       "       id  mb_used session_date  user_id\n",
       "0  1000_0   112.95   2018-11-25     1000\n",
       "1  1000_1  1052.81   2018-09-07     1000\n",
       "2  1000_2  1197.26   2018-06-25     1000"
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      " 3   user_id       149396 non-null  int64  \n",
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      "memory usage: 5.7+ MB\n"
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       "       id message_date  user_id\n",
       "0  1000_0   2018-06-27     1000\n",
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       "2  1000_2   2018-08-04     1000"
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      " 2   user_id       123036 non-null  int64 \n",
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      "memory usage: 2.8+ MB\n"
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       "    <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",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>15360</td>\n",
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       "      <td>550</td>\n",
       "      <td>200</td>\n",
       "      <td>3</td>\n",
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       "      <td>smart</td>\n",
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       "   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  "
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      " 7   tariff_name            2 non-null      object\n",
      "dtypes: int64(7), object(1)\n",
      "memory usage: 256.0+ bytes\n"
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       "  <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",
       "      <th>tariff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Краснодар</td>\n",
       "      <td>Рафаил</td>\n",
       "      <td>Верещагин</td>\n",
       "      <td>2018-05-25</td>\n",
       "      <td>ultra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Москва</td>\n",
       "      <td>Иван</td>\n",
       "      <td>Ежов</td>\n",
       "      <td>2018-11-01</td>\n",
       "      <td>smart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1002</td>\n",
       "      <td>59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Стерлитамак</td>\n",
       "      <td>Евгений</td>\n",
       "      <td>Абрамович</td>\n",
       "      <td>2018-06-17</td>\n",
       "      <td>smart</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   user_id  age churn_date         city first_name  last_name    reg_date  \\\n",
       "0     1000   52        NaN    Краснодар     Рафаил  Верещагин  2018-05-25   \n",
       "1     1001   41        NaN       Москва       Иван       Ежов  2018-11-01   \n",
       "2     1002   59        NaN  Стерлитамак    Евгений  Абрамович  2018-06-17   \n",
       "\n",
       "  tariff  \n",
       "0  ultra  \n",
       "1  smart  \n",
       "2  smart  "
      ]
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     "metadata": {},
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      "******************************\n",
      "<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": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "************************************************************\n"
     ]
    }
   ],
   "source": [
    "for i in all_dfs:\n",
    "    get_info(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Неправильный тип данных у дат. Ниже исправим это.  \n",
    "Имеем пропуски значений в users.churn_date. Из описания задания это говорит о том, что договор действующий, то есть заполнять пропуски не нужно."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Приведите данные к нужным типам; найдите и исправьте ошибки в данных, если они есть."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Исправляем тип данных у дат."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def conv_to_date(df_col):  # почему то эта функция не отрабатывает должным образом, тип данных не меняется. Почему? \n",
    "    df_col = pd.to_datetime(df_col, format='%Y-%m-%d')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def conv_to_date(df, col): \n",
    "    df[col] = pd.to_datetime(df[col], format='%Y-%m-%d')\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ну что же, будем менять тип столбцов без функции:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "for df, col in zip((calls, internet, messages, users, users),\n",
    "                  ('call_date', 'session_date', 'message_date', 'churn_date', 'reg_date')):\n",
    "    conv_to_date(df, col)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#calls.info() # проверил, сработало ли."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### количество сделанных звонков и израсходованных минут разговора по месяцам(Для каждого пользователя)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Добавим колонку со значением месяца и вспомним, что оператор округляет в большую сторону до минуты каждый звонок. Внесем соотв-щие изменения"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "calls['duration'] = calls['duration'].apply(np.ceil)\n",
    "calls['duration'] = calls['duration'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>call_date</th>\n",
       "      <th>duration</th>\n",
       "      <th>user_id</th>\n",
       "      <th>month</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",
       "      <th>tariff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000_0</td>\n",
       "      <td>2018-07-25</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>7</td>\n",
       "      <td>52</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Краснодар</td>\n",
       "      <td>Рафаил</td>\n",
       "      <td>Верещагин</td>\n",
       "      <td>2018-05-25</td>\n",
       "      <td>ultra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000_1</td>\n",
       "      <td>2018-08-17</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>8</td>\n",
       "      <td>52</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Краснодар</td>\n",
       "      <td>Рафаил</td>\n",
       "      <td>Верещагин</td>\n",
       "      <td>2018-05-25</td>\n",
       "      <td>ultra</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  call_date  duration  user_id  month  age churn_date       city  \\\n",
       "0  1000_0 2018-07-25         0     1000      7   52        NaT  Краснодар   \n",
       "1  1000_1 2018-08-17         0     1000      8   52        NaT  Краснодар   \n",
       "\n",
       "  first_name  last_name   reg_date tariff  \n",
       "0     Рафаил  Верещагин 2018-05-25  ultra  \n",
       "1     Рафаил  Верещагин 2018-05-25  ultra  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls['month'] = calls['call_date'].dt.month\n",
    "calls_temp = calls.merge(users, on='user_id', how='inner')\n",
    "calls_temp.head(2) # посмотрим что получилось"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>duration</th>\n",
       "      <th>call_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">1000</th>\n",
       "      <th>5</th>\n",
       "      <td>159</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>172</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>340</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>408</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>466</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>350</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>338</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>333</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1001</th>\n",
       "      <th>11</th>\n",
       "      <td>430</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>414</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               duration  call_count\n",
       "user_id month                      \n",
       "1000    5           159          22\n",
       "        6           172          43\n",
       "        7           340          47\n",
       "        8           408          52\n",
       "        9           466          58\n",
       "        10          350          57\n",
       "        11          338          43\n",
       "        12          333          46\n",
       "1001    11          430          59\n",
       "        12          414          63"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls_month = calls_temp.pivot_table(index=['user_id', 'month'], \n",
    "                       aggfunc={'user_id': 'count', 'duration': 'sum'}).\\\n",
    "                        rename(columns={'user_id':'call_count'})\n",
    "calls_month.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### количество отправленных сообщений по месяцам\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>message_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">1000</th>\n",
       "      <th>5</th>\n",
       "      <td>22</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1002</th>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               message_count\n",
       "user_id month               \n",
       "1000    5                 22\n",
       "        6                 60\n",
       "        7                 75\n",
       "        8                 81\n",
       "        9                 57\n",
       "        10                73\n",
       "        11                58\n",
       "        12                70\n",
       "1002    6                  4\n",
       "        7                 11"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages['month'] = messages['message_date'].dt.month\n",
    "messages_temp = messages.merge(users, on='user_id', how='inner')\n",
    "messages_month = messages_temp.pivot_table(index=['user_id', 'month'], \n",
    "                       aggfunc={'user_id': 'count'}).\\\n",
    "                        rename(columns={'user_id':'message_count'})\n",
    "messages_month.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### объем израсходованного интернет-трафика по месяцам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>mb_used</th>\n",
       "      <th>session_date</th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000_0</td>\n",
       "      <td>112.95</td>\n",
       "      <td>2018-11-25</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000_1</td>\n",
       "      <td>1052.81</td>\n",
       "      <td>2018-09-07</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  mb_used session_date  user_id\n",
       "0  1000_0   112.95   2018-11-25     1000\n",
       "1  1000_1  1052.81   2018-09-07     1000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "internet.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "internet['month'] = internet['session_date'].dt.month\n",
    "internet_temp = internet.merge(users, on='user_id', how='inner')\n",
    "internet_month = internet_temp.pivot_table(index=['user_id', 'month'], \n",
    "                       aggfunc={'mb_used': 'sum'}).\\\n",
    "                        rename(columns={'mb_used':'mb_total'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Оператор округляет в конце месяца трафик, учтем это:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mb_total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th rowspan=\"5\" valign=\"top\">1000</th>\n",
       "      <th>5</th>\n",
       "      <td>2254.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>23234.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14004.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14056.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14569.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>1498</th>\n",
       "      <th>10</th>\n",
       "      <td>20580.0</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">1499</th>\n",
       "      <th>9</th>\n",
       "      <td>1846.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>17789.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>17964.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13056.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3203 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               mb_total\n",
       "user_id month          \n",
       "1000    5        2254.0\n",
       "        6       23234.0\n",
       "        7       14004.0\n",
       "        8       14056.0\n",
       "        9       14569.0\n",
       "...                 ...\n",
       "1498    10      20580.0\n",
       "1499    9        1846.0\n",
       "        10      17789.0\n",
       "        11      17964.0\n",
       "        12      13056.0\n",
       "\n",
       "[3203 rows x 1 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "internet_month['mb_total'] = internet_month['mb_total'].apply(np.ceil)\n",
    "internet_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "internet_month['mb_total'] = internet_month['mb_total'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>user_id</th>\n",
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       "      <th rowspan=\"8\" valign=\"top\">1000</th>\n",
       "      <th>5</th>\n",
       "      <td>2254</td>\n",
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       "      <th>6</th>\n",
       "      <td>23234</td>\n",
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       "      <th>7</th>\n",
       "      <td>14004</td>\n",
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       "      <th>8</th>\n",
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       "      <td>14569</td>\n",
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       "      <td>14703</td>\n",
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       "      <th>12</th>\n",
       "      <td>9818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1001</th>\n",
       "      <th>11</th>\n",
       "      <td>18430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>14037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               mb_total\n",
       "user_id month          \n",
       "1000    5          2254\n",
       "        6         23234\n",
       "        7         14004\n",
       "        8         14056\n",
       "        9         14569\n",
       "        10        14703\n",
       "        11        14757\n",
       "        12         9818\n",
       "1001    11        18430\n",
       "        12        14037"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "internet_month.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3174, 2) (2717, 1) (3203, 1)\n"
     ]
    }
   ],
   "source": [
    "print(calls_month.shape, messages_month.shape, internet_month.shape) # глянем на форму датафреймов"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ожидаемо размер таблиц различается: кто то из клиентов может  не звонить, не писать смс, не использовать интернет, в разных комбинациях. Будем использовать внешнее соединение, чтобы не потерять данные, там где будут пропуски в значения, позднее заполним их."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.merge(calls_month, messages_month, how='outer', \n",
    "                   left_on=['user_id', 'month'], right_on=['user_id', 'month'])\n",
    "df = pd.merge(df, internet_month, how='outer',\n",
    "                  left_on=['user_id', 'month'], right_on=['user_id', 'month']).reset_index() # и сбросим индекс\n",
    "df = pd.merge(df, users[['user_id', 'tariff', 'city']], how='left',\n",
    "                  left_on=['user_id'], right_on=['user_id'])\n",
    "df.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>call_count</th>\n",
       "      <th>message_count</th>\n",
       "      <th>mb_total</th>\n",
       "      <th>tariff</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>5</td>\n",
       "      <td>159.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2254.0</td>\n",
       "      <td>ultra</td>\n",
       "      <td>Краснодар</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>6</td>\n",
       "      <td>172.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>23234.0</td>\n",
       "      <td>ultra</td>\n",
       "      <td>Краснодар</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000</td>\n",
       "      <td>7</td>\n",
       "      <td>340.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>14004.0</td>\n",
       "      <td>ultra</td>\n",
       "      <td>Краснодар</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000</td>\n",
       "      <td>8</td>\n",
       "      <td>408.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14056.0</td>\n",
       "      <td>ultra</td>\n",
       "      <td>Краснодар</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000</td>\n",
       "      <td>9</td>\n",
       "      <td>466.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>14569.0</td>\n",
       "      <td>ultra</td>\n",
       "      <td>Краснодар</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1000</td>\n",
       "      <td>10</td>\n",
       "      <td>350.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>14703.0</td>\n",
       "      <td>ultra</td>\n",
       "      <td>Краснодар</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1000</td>\n",
       "      <td>11</td>\n",
       "      <td>338.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>14757.0</td>\n",
       "      <td>ultra</td>\n",
       "      <td>Краснодар</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1000</td>\n",
       "      <td>12</td>\n",
       "      <td>333.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>9818.0</td>\n",
       "      <td>ultra</td>\n",
       "      <td>Краснодар</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1001</td>\n",
       "      <td>11</td>\n",
       "      <td>430.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18430.0</td>\n",
       "      <td>smart</td>\n",
       "      <td>Москва</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1001</td>\n",
       "      <td>12</td>\n",
       "      <td>414.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14037.0</td>\n",
       "      <td>smart</td>\n",
       "      <td>Москва</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1002</td>\n",
       "      <td>6</td>\n",
       "      <td>117.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10857.0</td>\n",
       "      <td>smart</td>\n",
       "      <td>Стерлитамак</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1002</td>\n",
       "      <td>7</td>\n",
       "      <td>214.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>17581.0</td>\n",
       "      <td>smart</td>\n",
       "      <td>Стерлитамак</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1002</td>\n",
       "      <td>8</td>\n",
       "      <td>289.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>20320.0</td>\n",
       "      <td>smart</td>\n",
       "      <td>Стерлитамак</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1002</td>\n",
       "      <td>9</td>\n",
       "      <td>206.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16692.0</td>\n",
       "      <td>smart</td>\n",
       "      <td>Стерлитамак</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1002</td>\n",
       "      <td>10</td>\n",
       "      <td>212.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>13889.0</td>\n",
       "      <td>smart</td>\n",
       "      <td>Стерлитамак</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1002</td>\n",
       "      <td>11</td>\n",
       "      <td>243.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>18588.0</td>\n",
       "      <td>smart</td>\n",
       "      <td>Стерлитамак</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1002</td>\n",
       "      <td>12</td>\n",
       "      <td>236.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18114.0</td>\n",
       "      <td>smart</td>\n",
       "      <td>Стерлитамак</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1003</td>\n",
       "      <td>8</td>\n",
       "      <td>380.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>8566.0</td>\n",
       "      <td>ultra</td>\n",
       "      <td>Москва</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1003</td>\n",
       "      <td>9</td>\n",
       "      <td>961.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>12469.0</td>\n",
       "      <td>ultra</td>\n",
       "      <td>Москва</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1003</td>\n",
       "      <td>10</td>\n",
       "      <td>855.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>14769.0</td>\n",
       "      <td>ultra</td>\n",
       "      <td>Москва</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id  month  duration  call_count  message_count  mb_total tariff  \\\n",
       "0      1000      5     159.0        22.0           22.0    2254.0  ultra   \n",
       "1      1000      6     172.0        43.0           60.0   23234.0  ultra   \n",
       "2      1000      7     340.0        47.0           75.0   14004.0  ultra   \n",
       "3      1000      8     408.0        52.0           81.0   14056.0  ultra   \n",
       "4      1000      9     466.0        58.0           57.0   14569.0  ultra   \n",
       "5      1000     10     350.0        57.0           73.0   14703.0  ultra   \n",
       "6      1000     11     338.0        43.0           58.0   14757.0  ultra   \n",
       "7      1000     12     333.0        46.0           70.0    9818.0  ultra   \n",
       "8      1001     11     430.0        59.0            0.0   18430.0  smart   \n",
       "9      1001     12     414.0        63.0            0.0   14037.0  smart   \n",
       "10     1002      6     117.0        15.0            4.0   10857.0  smart   \n",
       "11     1002      7     214.0        26.0           11.0   17581.0  smart   \n",
       "12     1002      8     289.0        42.0           13.0   20320.0  smart   \n",
       "13     1002      9     206.0        36.0            4.0   16692.0  smart   \n",
       "14     1002     10     212.0        33.0           10.0   13889.0  smart   \n",
       "15     1002     11     243.0        32.0           16.0   18588.0  smart   \n",
       "16     1002     12     236.0        33.0           12.0   18114.0  smart   \n",
       "17     1003      8     380.0        55.0           37.0    8566.0  ultra   \n",
       "18     1003      9     961.0       134.0           91.0   12469.0  ultra   \n",
       "19     1003     10     855.0       108.0           83.0   14769.0  ultra   \n",
       "\n",
       "           city  \n",
       "0     Краснодар  \n",
       "1     Краснодар  \n",
       "2     Краснодар  \n",
       "3     Краснодар  \n",
       "4     Краснодар  \n",
       "5     Краснодар  \n",
       "6     Краснодар  \n",
       "7     Краснодар  \n",
       "8        Москва  \n",
       "9        Москва  \n",
       "10  Стерлитамак  \n",
       "11  Стерлитамак  \n",
       "12  Стерлитамак  \n",
       "13  Стерлитамак  \n",
       "14  Стерлитамак  \n",
       "15  Стерлитамак  \n",
       "16  Стерлитамак  \n",
       "17       Москва  \n",
       "18       Москва  \n",
       "19       Москва  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(20) # и что же у нас получилось?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3214 entries, 0 to 3213\n",
      "Data columns (total 8 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   user_id        3214 non-null   int64  \n",
      " 1   month          3214 non-null   int64  \n",
      " 2   duration       3214 non-null   float64\n",
      " 3   call_count     3214 non-null   float64\n",
      " 4   message_count  3214 non-null   float64\n",
      " 5   mb_total       3214 non-null   float64\n",
      " 6   tariff         3214 non-null   object \n",
      " 7   city           3214 non-null   object \n",
      "dtypes: float64(4), int64(2), object(2)\n",
      "memory usage: 226.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info() # уплыли типы некоторых столбцов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['user_id', 'month', 'duration', 'call_count', 'message_count',\n",
       "       'mb_total', 'tariff', 'city'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns # чтобы скопипастить для цикла ниже"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in [ 'duration', 'call_count', 'message_count', 'mb_total']:\n",
    "    df[i] = df[i].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Функция для подчета затрат по пользователям по месяцам."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_month_pay(row):\n",
    "    \n",
    "    def get_over(cur, lim):\n",
    "        return 0 if lim>=cur else cur-lim # внутрення функция для учета бесплатного пакета\n",
    "    \n",
    "    pay = 0\n",
    "    \n",
    "    if row.tariff == 'smart':\n",
    "        pay += 550\n",
    "        pay += get_over(row.duration, 500) * 3    \n",
    "        pay += get_over(row.message_count, 50) *3\n",
    "        pay += np.ceil(get_over(row.mb_total, 15*1024)/1024) * 200\n",
    "    \n",
    "    if row.tariff == 'ultra':\n",
    "        pay += 1950\n",
    "        pay += get_over(row.duration, 3000) * 1    \n",
    "        pay += get_over(row.message_count, 1000) * 1\n",
    "        pay += np.ceil(get_over(row.mb_total, 30*1024)/1024) * 150\n",
    "    \n",
    "    return pay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>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",
       "</div>"
      ],
      "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  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tariffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# код ревьюера\n",
    "\n",
    "if row.tariff == 'smart':\n",
    "        pay += tariffs.loc[0, 'rub_monthly_fee']\n",
    "        pay += get_over(row.duration, tariffs.loc[0, 'minutes_included']) * tariffs.loc[0, 'rub_per_minute'] \n",
    "        pay += get_over(row.message_count, tariffs.loc[0, 'messages_included']) * tariffs.loc[0, 'rub_per_message']\n",
    "        \n",
    "# Соответсвенно, если тариф ультра, то индекс 0 меняется на 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['month_pay'] = df.apply(lambda x: calculate_month_pay(x), axis=1) # считаем затраты в отдельном столбце"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>call_count</th>\n",
       "      <th>message_count</th>\n",
       "      <th>mb_total</th>\n",
       "      <th>tariff</th>\n",
       "      <th>city</th>\n",
       "      <th>month_pay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>5</td>\n",
       "      <td>159</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>2254</td>\n",
       "      <td>ultra</td>\n",
       "      <td>Краснодар</td>\n",
       "      <td>1950.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>6</td>\n",
       "      <td>172</td>\n",
       "      <td>43</td>\n",
       "      <td>60</td>\n",
       "      <td>23234</td>\n",
       "      <td>ultra</td>\n",
       "      <td>Краснодар</td>\n",
       "      <td>1950.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000</td>\n",
       "      <td>7</td>\n",
       "      <td>340</td>\n",
       "      <td>47</td>\n",
       "      <td>75</td>\n",
       "      <td>14004</td>\n",
       "      <td>ultra</td>\n",
       "      <td>Краснодар</td>\n",
       "      <td>1950.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000</td>\n",
       "      <td>8</td>\n",
       "      <td>408</td>\n",
       "      <td>52</td>\n",
       "      <td>81</td>\n",
       "      <td>14056</td>\n",
       "      <td>ultra</td>\n",
       "      <td>Краснодар</td>\n",
       "      <td>1950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000</td>\n",
       "      <td>9</td>\n",
       "      <td>466</td>\n",
       "      <td>58</td>\n",
       "      <td>57</td>\n",
       "      <td>14569</td>\n",
       "      <td>ultra</td>\n",
       "      <td>Краснодар</td>\n",
       "      <td>1950.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  month  duration  call_count  message_count  mb_total tariff  \\\n",
       "0     1000      5       159          22             22      2254  ultra   \n",
       "1     1000      6       172          43             60     23234  ultra   \n",
       "2     1000      7       340          47             75     14004  ultra   \n",
       "3     1000      8       408          52             81     14056  ultra   \n",
       "4     1000      9       466          58             57     14569  ultra   \n",
       "\n",
       "        city  month_pay  \n",
       "0  Краснодар     1950.0  \n",
       "1  Краснодар     1950.0  \n",
       "2  Краснодар     1950.0  \n",
       "3  Краснодар     1950.0  \n",
       "4  Краснодар     1950.0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Проанализируйте данные  \n",
    "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">duration</th>\n",
       "      <th colspan=\"3\" halign=\"left\">mb_total</th>\n",
       "      <th colspan=\"3\" halign=\"left\">message_count</th>\n",
       "      <th>month_pay</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>var</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>var</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>var</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tariff</th>\n",
       "      <th>month</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",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">smart</th>\n",
       "      <th>1</th>\n",
       "      <td>203.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>23787.0</td>\n",
       "      <td>8514.0</td>\n",
       "      <td>6444.0</td>\n",
       "      <td>41534011.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>721.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>298.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>36412.0</td>\n",
       "      <td>11597.0</td>\n",
       "      <td>6247.0</td>\n",
       "      <td>39029116.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>473.0</td>\n",
       "      <td>827.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>390.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>36823.0</td>\n",
       "      <td>15104.0</td>\n",
       "      <td>5828.0</td>\n",
       "      <td>33968406.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>718.0</td>\n",
       "      <td>1164.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>367.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>34778.0</td>\n",
       "      <td>13462.0</td>\n",
       "      <td>5698.0</td>\n",
       "      <td>32469724.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>602.0</td>\n",
       "      <td>963.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>387.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>34820.0</td>\n",
       "      <td>15805.0</td>\n",
       "      <td>5978.0</td>\n",
       "      <td>35739600.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>730.0</td>\n",
       "      <td>1197.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>395.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>30742.0</td>\n",
       "      <td>15795.0</td>\n",
       "      <td>5791.0</td>\n",
       "      <td>33537223.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>806.0</td>\n",
       "      <td>1204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>408.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>32250.0</td>\n",
       "      <td>15743.0</td>\n",
       "      <td>5897.0</td>\n",
       "      <td>34776835.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>799.0</td>\n",
       "      <td>1223.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>421.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>34793.0</td>\n",
       "      <td>16611.0</td>\n",
       "      <td>5593.0</td>\n",
       "      <td>31287251.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>838.0</td>\n",
       "      <td>1320.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>428.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>33535.0</td>\n",
       "      <td>16304.0</td>\n",
       "      <td>5196.0</td>\n",
       "      <td>26999781.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>790.0</td>\n",
       "      <td>1249.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>426.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>35392.0</td>\n",
       "      <td>16757.0</td>\n",
       "      <td>5859.0</td>\n",
       "      <td>34332382.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>816.0</td>\n",
       "      <td>1364.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>435.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>31605.0</td>\n",
       "      <td>16896.0</td>\n",
       "      <td>5340.0</td>\n",
       "      <td>28518716.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>805.0</td>\n",
       "      <td>1343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>478.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>39698.0</td>\n",
       "      <td>18114.0</td>\n",
       "      <td>5573.0</td>\n",
       "      <td>31065832.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>934.0</td>\n",
       "      <td>1578.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">ultra</th>\n",
       "      <th>1</th>\n",
       "      <td>428.0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>72769.0</td>\n",
       "      <td>13141.0</td>\n",
       "      <td>6865.0</td>\n",
       "      <td>47135896.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>940.0</td>\n",
       "      <td>1950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>297.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>87919.0</td>\n",
       "      <td>11776.0</td>\n",
       "      <td>10644.0</td>\n",
       "      <td>113312128.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>716.0</td>\n",
       "      <td>1975.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>489.0</td>\n",
       "      <td>333.0</td>\n",
       "      <td>111384.0</td>\n",
       "      <td>17536.0</td>\n",
       "      <td>10951.0</td>\n",
       "      <td>119943281.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1732.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>458.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>71653.0</td>\n",
       "      <td>16828.0</td>\n",
       "      <td>9718.0</td>\n",
       "      <td>94453559.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1407.0</td>\n",
       "      <td>2001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>510.0</td>\n",
       "      <td>289.0</td>\n",
       "      <td>83870.0</td>\n",
       "      <td>19363.0</td>\n",
       "      <td>10046.0</td>\n",
       "      <td>100924381.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1624.0</td>\n",
       "      <td>2059.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>479.0</td>\n",
       "      <td>302.0</td>\n",
       "      <td>91535.0</td>\n",
       "      <td>18925.0</td>\n",
       "      <td>9292.0</td>\n",
       "      <td>86356960.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>2039.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>545.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>94322.0</td>\n",
       "      <td>20496.0</td>\n",
       "      <td>10065.0</td>\n",
       "      <td>101308222.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>2372.0</td>\n",
       "      <td>2118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>510.0</td>\n",
       "      <td>312.0</td>\n",
       "      <td>97956.0</td>\n",
       "      <td>19576.0</td>\n",
       "      <td>10476.0</td>\n",
       "      <td>109758874.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>2171.0</td>\n",
       "      <td>2083.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>539.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>105716.0</td>\n",
       "      <td>19204.0</td>\n",
       "      <td>9486.0</td>\n",
       "      <td>89986689.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>2152.0</td>\n",
       "      <td>2024.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>558.0</td>\n",
       "      <td>326.0</td>\n",
       "      <td>106464.0</td>\n",
       "      <td>20045.0</td>\n",
       "      <td>9803.0</td>\n",
       "      <td>96100672.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>2697.0</td>\n",
       "      <td>2065.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>541.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>96570.0</td>\n",
       "      <td>19787.0</td>\n",
       "      <td>9907.0</td>\n",
       "      <td>98159284.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>2476.0</td>\n",
       "      <td>2092.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>592.0</td>\n",
       "      <td>342.0</td>\n",
       "      <td>117276.0</td>\n",
       "      <td>21793.0</td>\n",
       "      <td>10378.0</td>\n",
       "      <td>107711871.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>2681.0</td>\n",
       "      <td>2138.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             duration                  mb_total                        \\\n",
       "                 mean    std       var     mean      std          var   \n",
       "tariff month                                                            \n",
       "smart  1        203.0  154.0   23787.0   8514.0   6444.0   41534011.0   \n",
       "       2        298.0  190.0   36412.0  11597.0   6247.0   39029116.0   \n",
       "       3        390.0  191.0   36823.0  15104.0   5828.0   33968406.0   \n",
       "       4        367.0  186.0   34778.0  13462.0   5698.0   32469724.0   \n",
       "       5        387.0  186.0   34820.0  15805.0   5978.0   35739600.0   \n",
       "       6        395.0  175.0   30742.0  15795.0   5791.0   33537223.0   \n",
       "       7        408.0  179.0   32250.0  15743.0   5897.0   34776835.0   \n",
       "       8        421.0  186.0   34793.0  16611.0   5593.0   31287251.0   \n",
       "       9        428.0  183.0   33535.0  16304.0   5196.0   26999781.0   \n",
       "       10       426.0  188.0   35392.0  16757.0   5859.0   34332382.0   \n",
       "       11       435.0  177.0   31605.0  16896.0   5340.0   28518716.0   \n",
       "       12       478.0  199.0   39698.0  18114.0   5573.0   31065832.0   \n",
       "ultra  1        428.0  269.0   72769.0  13141.0   6865.0   47135896.0   \n",
       "       2        297.0  296.0   87919.0  11776.0  10644.0  113312128.0   \n",
       "       3        489.0  333.0  111384.0  17536.0  10951.0  119943281.0   \n",
       "       4        458.0  267.0   71653.0  16828.0   9718.0   94453559.0   \n",
       "       5        510.0  289.0   83870.0  19363.0  10046.0  100924381.0   \n",
       "       6        479.0  302.0   91535.0  18925.0   9292.0   86356960.0   \n",
       "       7        545.0  307.0   94322.0  20496.0  10065.0  101308222.0   \n",
       "       8        510.0  312.0   97956.0  19576.0  10476.0  109758874.0   \n",
       "       9        539.0  325.0  105716.0  19204.0   9486.0   89986689.0   \n",
       "       10       558.0  326.0  106464.0  20045.0   9803.0   96100672.0   \n",
       "       11       541.0  310.0   96570.0  19787.0   9907.0   98159284.0   \n",
       "       12       592.0  342.0  117276.0  21793.0  10378.0  107711871.0   \n",
       "\n",
       "             message_count               month_pay  \n",
       "                      mean   std     var      mean  \n",
       "tariff month                                        \n",
       "smart  1              18.0  16.0   262.0     721.0  \n",
       "       2              24.0  21.0   473.0     827.0  \n",
       "       3              31.0  26.0   718.0    1164.0  \n",
       "       4              30.0  24.0   602.0     963.0  \n",
       "       5              33.0  27.0   730.0    1197.0  \n",
       "       6              33.0  28.0   806.0    1204.0  \n",
       "       7              33.0  28.0   799.0    1223.0  \n",
       "       8              33.0  28.0   838.0    1320.0  \n",
       "       9              33.0  28.0   790.0    1249.0  \n",
       "       10             33.0  28.0   816.0    1364.0  \n",
       "       11             34.0  28.0   805.0    1343.0  \n",
       "       12             35.0  30.0   934.0    1578.0  \n",
       "ultra  1              33.0  30.0   940.0    1950.0  \n",
       "       2              21.0  26.0   716.0    1975.0  \n",
       "       3              32.0  41.0  1732.0    2014.0  \n",
       "       4              31.0  37.0  1407.0    2001.0  \n",
       "       5              37.0  40.0  1624.0    2059.0  \n",
       "       6              41.0  44.0  1986.0    2039.0  \n",
       "       7              49.0  48.0  2372.0    2118.0  \n",
       "       8              48.0  46.0  2171.0    2083.0  \n",
       "       9              52.0  46.0  2152.0    2024.0  \n",
       "       10             58.0  51.0  2697.0    2065.0  \n",
       "       11             55.0  49.0  2476.0    2092.0  \n",
       "       12             60.0  51.0  2681.0    2138.0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distr = df.pivot_table(index=['tariff', 'month'], \n",
    "                          aggfunc={'duration':['mean', np.std, 'var'],\n",
    "                                  'message_count': ['mean', np.std, 'var'],\n",
    "                                  'mb_total': ['mean', np.std, 'var'],\n",
    "                                  'month_pay':'mean'}\n",
    "                      )\n",
    "                          #rename(columns={'user_id':'call_count'})\n",
    "distr.apply(np.floor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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></th>\n",
       "      <th>duration</th>\n",
       "      <th>mb_total</th>\n",
       "      <th>message_count</th>\n",
       "      <th>month_pay</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tariff</th>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">smart</th>\n",
       "      <th>1</th>\n",
       "      <td>203.0</td>\n",
       "      <td>8514.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>721.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>298.0</td>\n",
       "      <td>11597.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>827.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>390.0</td>\n",
       "      <td>15104.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1164.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>367.0</td>\n",
       "      <td>13462.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>963.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>387.0</td>\n",
       "      <td>15805.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1197.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>395.0</td>\n",
       "      <td>15795.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>408.0</td>\n",
       "      <td>15743.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1223.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>421.0</td>\n",
       "      <td>16611.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1320.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>428.0</td>\n",
       "      <td>16304.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1249.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>426.0</td>\n",
       "      <td>16757.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1364.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>435.0</td>\n",
       "      <td>16896.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>478.0</td>\n",
       "      <td>18114.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1578.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">ultra</th>\n",
       "      <th>1</th>\n",
       "      <td>428.0</td>\n",
       "      <td>13141.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>297.0</td>\n",
       "      <td>11776.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1975.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>489.0</td>\n",
       "      <td>17536.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>458.0</td>\n",
       "      <td>16828.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>510.0</td>\n",
       "      <td>19363.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>2059.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>479.0</td>\n",
       "      <td>18925.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2039.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>545.0</td>\n",
       "      <td>20496.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>2118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>510.0</td>\n",
       "      <td>19576.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>2083.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>539.0</td>\n",
       "      <td>19204.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2024.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>558.0</td>\n",
       "      <td>20045.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2065.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>541.0</td>\n",
       "      <td>19787.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2092.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>592.0</td>\n",
       "      <td>21793.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2138.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              duration  mb_total  message_count  month_pay\n",
       "tariff month                                              \n",
       "smart  1         203.0    8514.0           18.0      721.0\n",
       "       2         298.0   11597.0           24.0      827.0\n",
       "       3         390.0   15104.0           31.0     1164.0\n",
       "       4         367.0   13462.0           30.0      963.0\n",
       "       5         387.0   15805.0           33.0     1197.0\n",
       "       6         395.0   15795.0           33.0     1204.0\n",
       "       7         408.0   15743.0           33.0     1223.0\n",
       "       8         421.0   16611.0           33.0     1320.0\n",
       "       9         428.0   16304.0           33.0     1249.0\n",
       "       10        426.0   16757.0           33.0     1364.0\n",
       "       11        435.0   16896.0           34.0     1343.0\n",
       "       12        478.0   18114.0           35.0     1578.0\n",
       "ultra  1         428.0   13141.0           33.0     1950.0\n",
       "       2         297.0   11776.0           21.0     1975.0\n",
       "       3         489.0   17536.0           32.0     2014.0\n",
       "       4         458.0   16828.0           31.0     2001.0\n",
       "       5         510.0   19363.0           37.0     2059.0\n",
       "       6         479.0   18925.0           41.0     2039.0\n",
       "       7         545.0   20496.0           49.0     2118.0\n",
       "       8         510.0   19576.0           48.0     2083.0\n",
       "       9         539.0   19204.0           52.0     2024.0\n",
       "       10        558.0   20045.0           58.0     2065.0\n",
       "       11        541.0   19787.0           55.0     2092.0\n",
       "       12        592.0   21793.0           60.0     2138.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distr_mean = df.pivot_table(index=['tariff', 'month'], \n",
    "                            aggfunc={'duration':'mean',\n",
    "                                  'message_count': 'mean',\n",
    "                                  'mb_total': 'mean',\n",
    "                                  'month_pay':'mean'}\n",
    "                      )\n",
    "                    \n",
    "distr_mean.apply(np.floor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "По средним значениям можно сделать следующие выводы: на тарифе \"смарт\" начиная с марта виден перерасход по всем параметрам, но перерасходы в оплате видны за весь год. Явная категория перерасхода - минуты разговоров. Интернет и смс-ки сверх лимита расходуют не так сильно. Видно что в декабре расходы приближаются к тарифу \"ультра\" и превышают стоимость пакета в 3 раза.\n",
    "На тарифе \"ультра\" абоненты явно не выбирают лимиты пакета, но некоторые пользователи, все же делают перерасход, за счет этого мы видим, что средние расходы выше стоимости пакета. В целом эти отклонения невелики. \n",
    "В декабре видим максимальные показатели потребления, перерасходов и соотв-но начислений."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_68/4211143422.py:4: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  df\n"
     ]
    },
    {
     "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>smart</th>\n",
       "      <th>ultra</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">duration</th>\n",
       "      <th>mean</th>\n",
       "      <td>418.0</td>\n",
       "      <td>527.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>422.0</td>\n",
       "      <td>518.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>var</th>\n",
       "      <td>36219.0</td>\n",
       "      <td>100874.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>190.0</td>\n",
       "      <td>318.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">message_count</th>\n",
       "      <th>mean</th>\n",
       "      <td>33.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>28.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>var</th>\n",
       "      <td>797.0</td>\n",
       "      <td>2285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>28.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">mb_total</th>\n",
       "      <th>mean</th>\n",
       "      <td>16209.0</td>\n",
       "      <td>19469.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>16507.0</td>\n",
       "      <td>19309.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>var</th>\n",
       "      <td>34462563.0</td>\n",
       "      <td>101751558.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5870.0</td>\n",
       "      <td>10087.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">month_pay</th>\n",
       "      <th>mean</th>\n",
       "      <td>1290.0</td>\n",
       "      <td>2070.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>1023.0</td>\n",
       "      <td>1950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>var</th>\n",
       "      <td>669786.0</td>\n",
       "      <td>141517.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>818.0</td>\n",
       "      <td>376.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "tariff                     smart        ultra\n",
       "duration      mean         418.0        527.0\n",
       "              median       422.0        518.0\n",
       "              var        36219.0     100874.0\n",
       "              std          190.0        318.0\n",
       "message_count mean          33.0         49.0\n",
       "              median        28.0         38.0\n",
       "              var          797.0       2285.0\n",
       "              std           28.0         48.0\n",
       "mb_total      mean       16209.0      19469.0\n",
       "              median     16507.0      19309.0\n",
       "              var     34462563.0  101751558.0\n",
       "              std         5870.0      10087.0\n",
       "month_pay     mean        1290.0       2070.0\n",
       "              median      1023.0       1950.0\n",
       "              var       669786.0     141517.0\n",
       "              std          818.0        376.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# код ревьюера\n",
    "\n",
    "(\n",
    "    df\n",
    "    .groupby('tariff')['duration', 'message_count', 'mb_total', 'month_pay']\n",
    "    .agg(['mean', 'median', 'var', 'std'])\n",
    "    .T\n",
    "    .round()\n",
    ")\n",
    "# не обращай внимания на ошибку"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['user_id', 'month', 'duration', 'call_count', 'message_count',\n",
       "       'mb_total', 'tariff', 'city', 'month_pay'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in ['duration', 'message_count', 'mb_total']:\n",
    "    sns.histplot(data=df, x=col, hue='tariff',  linewidth=.2)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">month_pay</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tariff</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>smart</th>\n",
       "      <td>1289.973531</td>\n",
       "      <td>1023.0</td>\n",
       "      <td>2875351.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ultra</th>\n",
       "      <td>2070.152284</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>2039100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          month_pay                   \n",
       "               mean  median        sum\n",
       "tariff                                \n",
       "smart   1289.973531  1023.0  2875351.0\n",
       "ultra   2070.152284  1950.0  2039100.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pay_mean_all = df.pivot_table(index=['tariff' ], \n",
    "                          aggfunc={'month_pay':['mean', 'median', 'sum']}\n",
    "                             )\n",
    "pay_mean_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tariff</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>smart</th>\n",
       "      <td>2229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ultra</th>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id\n",
       "tariff         \n",
       "smart      2229\n",
       "ultra       985"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_per_tariff = df.pivot_table(index=['tariff' ], \n",
    "                          aggfunc={'user_id':'count'}\n",
    "                                 )\n",
    "users_per_tariff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Основные выводы: т.к. пользователей тарифа \"Ультра\" меньше, имеем более \"низкое\" распределение. \n",
    "\n",
    "Пользователи тарифа \"Ультра\" реже выходят за лимиты стоимости пакета (медиана = 1950), в то время как у \"Смарт\" - перерасходы более частые.  \n",
    "\n",
    "Распределение у тарифа \"Ультра\" имеет более длинный хвост справа (дисперсия выше), т.к. абоненты имеют больший пакет и соотвестнно чаще выходят на повешенное потребление.Заметно, что в пользователи \"Смарт\", имея ограниченность в пакете и стараясь не превышать лимиты,  максимально приближаются к лимиту пакета минут, часто укладываются в лимит пакета СМС, и часто незначительно превышают лимит пакета интернета.  \n",
    "У тарифа \"Ультра\" пользователи даже близко не расходуют пакет минут, не часто расходую пакет СМС, но некоторые и расходуют больше, и также чаще не превышают лимит пакета Интернета. Неплохим рекламным ходом, возможно, было бы еще большее увеличение пакета минут(его все равно никто не расходует должным образом).\n",
    "\n",
    "<b>Средняя прибыль от пользователя тарифа \"Ультра\" - выше, при этом в выборке оказалось более чем в два раза меньше таких абонентов. А общая прибыль от абонентов \"Смарт\" - больше почти на треть.\n",
    "Итог: \"Мегалайну\" стоит сосредоточится на рекламной акции тарифа \"Ультра\" и привлечь большее количество его пользователей.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Проверьте гипотезы\n",
    "Cредняя выручка пользователей тарифов «Ультра» и «Смарт» различаются;\n",
    "средняя выручка пользователей из Москвы отличается от выручки пользователей из других регионов."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cредняя выручка пользователей тарифов «Ультра» и «Смарт» различаются"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сформулируем нулевую и альтернативную гипотезу.\n",
    "\n",
    "$H_0 $ — средняя выручка пользователей тарифа «Ультра» равна средней выручке пользователей тарифа «Смарт».\n",
    "\n",
    "$H_1 $ — средняя выручка пользователей тарифов «Ультра» и «Смарт» различаются."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-значение: 4.2606313931076085e-250\n",
      "Отвергаем нулевую гипотезу\n"
     ]
    }
   ],
   "source": [
    "ultra = df.query('tariff == \"ultra\"')['month_pay']\n",
    "smart = df.query('tariff == \"smart\"')['month_pay']\n",
    "\n",
    "alpha = .01\n",
    "\n",
    "results = st.ttest_ind(\n",
    "    ultra, \n",
    "    smart, \n",
    "    equal_var=False)\n",
    "\n",
    "print('p-значение:', results.pvalue)\n",
    "\n",
    "if (results.pvalue < alpha):\n",
    "    print(\"Отвергаем нулевую гипотезу\")\n",
    "else:\n",
    "    print(\"Не получилось отвергнуть нулевую гипотезу\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Без сомнений (значение значительно меньше alpha) отвегаем нулевую гипотезу."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ранее, мы получили среднее и медианные значения прибыли по тарифам, тариф \"Ультра\" больше почти в 2 раза.  \n",
    "<b>Вывод: с уверенностью 99% средняя выручка пользователей тарифов «Ультра» и «Смарт» различаются."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### средняя выручка пользователей из Москвы отличается от выручки пользователей из других регионов."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сформулируем гипотезы.\n",
    "\n",
    "$H_0 $ — средняя выручка пользователей из Москвы равна средней выручке пользователей из других регионов.\n",
    "\n",
    "$H_1 $ — средняя выручка пользователей из Москвы отличается от средней выручке пользователей из других регионов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-значение: 0.5257376663729298\n",
      "Не получилось отвергнуть нулевую гипотезу\n"
     ]
    }
   ],
   "source": [
    "msk_users = df.query('city == \"Москва\"')\n",
    "ather_users = df.query('city != \"Москва\"')\n",
    "\n",
    "alpha = .1\n",
    "\n",
    "results = st.ttest_ind(\n",
    "    msk_users['month_pay'], \n",
    "    ather_users['month_pay'], \n",
    "    equal_var=False)\n",
    "\n",
    "print('p-значение:', results.pvalue)\n",
    "\n",
    "if (results.pvalue < alpha):\n",
    "    print(\"Отвергаем нулевую гипотезу\")\n",
    "else:\n",
    "    print(\"Не получилось отвергнуть нулевую гипотезу\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month_pay</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tariff</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>smart</th>\n",
       "      <td>1239.357513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ultra</th>\n",
       "      <td>2074.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          month_pay\n",
       "tariff             \n",
       "smart   1239.357513\n",
       "ultra   2074.000000"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msk_users.pivot_table(index='tariff', aggfunc={'month_pay':'mean'}) # посмотрим среднюю выручку по тарифам в Москве"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>month_pay</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tariff</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>smart</th>\n",
       "      <td>1300.574607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ultra</th>\n",
       "      <td>2069.013158</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          month_pay\n",
       "tariff             \n",
       "smart   1300.574607\n",
       "ultra   2069.013158"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ather_users.pivot_table(index='tariff', aggfunc={'month_pay':'mean'}) # посмотрим среднюю выручку по другим городам"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Да, действительно, показатели почти равны."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Вывод: гипотезу о том, что доходы от пользователей из Москвы и из других регионов равны отвергнуть не удалось. \n",
    "\n",
    "Если бы мы отклонили нулевую гипотезу, вероятность ошибиться составила бы 50.3%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Напишите общий вывод"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>В ходе проекта мы импортировали данные: исправлены типы переменных столбцов, произведено объеденение таблиц, сформировано множество сводных таблиц, составлены гистрограммы, проверены две гипотезы.\n",
    "\n",
    "Из анализа гистограмм были сделаны выводы:\n",
    "По средним значениям можно сделать следующие выводы: на тарифе \"смарт\" начиная с марта виден перерасход по всем параметрам, но перерасходы в оплате видны за весь год. Явная категория перерасхода - минуты разговоров. Интернет и смс-ки сверх лимита расходуют не так сильно. Видно что в декабре расходы приближаются к тарифу \"ультра\" и превышают стоимость пакета в 3 раза.\n",
    "\n",
    "Общий вывод: <b>Средняя прибыль от пользователя тарифа \"Ультра\" - выше, при этом в выборке оказалось более чем в два раза меньше таких абонентов. А общая прибыль от абонентов \"Смарт\" - больше почти на треть.\n",
    "Итог: \"Мегалайну\" стоит сосредоточится на рекламной акции тарифа \"Ультра\" и привлечь большее количество его пользователей.\n",
    "На тарифе \"ультра\" абоненты явно не выбирают лимиты пакета, но некоторые пользователи, все же делают перерасход, за счет этого мы видим, что средние расходы выше стоимости пакета. В целом эти отклонения невелики. \n",
    "В декабре видим максимальные показатели потребления, перерасходов и соотв-но начислений.  \n",
    "\n",
    "    \n",
    "Были проверены две гипотезы:\n",
    "Результат первой: средняя выручка пользователей тарифов «Ультра» и «Смарт» различаются. \n",
    "Результат второй:  гипотезу о том, что доходы от пользователей из Москвы и из других регионов равны отвергнуть не удалось."
   ]
  }
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