project_03.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5a961446",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "# Статистический анализ данных сервиса аренды самокатов GoFast\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "72576667",
   "metadata": {},
   "outputs": [],
   "source": [
    "# импортируем необходимые библиотеки \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as st\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c42cbe00",
   "metadata": {},
   "source": [
    "#### Шаг 1. Загрузка данных"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d926c9e",
   "metadata": {},
   "source": [
    "Считаем данные из csv-файлов: **'/datasets/users_go.csv' , '/datasets/rides_go.csv', '/datasets/subscriptions_go.csv'** и напечатем таблицы для ознокомления.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "eff9f206",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>city</th>\n",
       "      <th>subscription_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Кира</td>\n",
       "      <td>22</td>\n",
       "      <td>Тюмень</td>\n",
       "      <td>ultra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Станислав</td>\n",
       "      <td>31</td>\n",
       "      <td>Омск</td>\n",
       "      <td>ultra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Алексей</td>\n",
       "      <td>20</td>\n",
       "      <td>Москва</td>\n",
       "      <td>ultra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Константин</td>\n",
       "      <td>26</td>\n",
       "      <td>Ростов-на-Дону</td>\n",
       "      <td>ultra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Адель</td>\n",
       "      <td>28</td>\n",
       "      <td>Омск</td>\n",
       "      <td>ultra</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id        name  age            city subscription_type\n",
       "0        1        Кира   22          Тюмень             ultra\n",
       "1        2   Станислав   31            Омск             ultra\n",
       "2        3     Алексей   20          Москва             ultra\n",
       "3        4  Константин   26  Ростов-на-Дону             ultra\n",
       "4        5       Адель   28            Омск             ultra"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>distance</th>\n",
       "      <th>duration</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4409.919140</td>\n",
       "      <td>25.599769</td>\n",
       "      <td>2021-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2617.592153</td>\n",
       "      <td>15.816871</td>\n",
       "      <td>2021-01-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>754.159807</td>\n",
       "      <td>6.232113</td>\n",
       "      <td>2021-04-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2694.783254</td>\n",
       "      <td>18.511000</td>\n",
       "      <td>2021-08-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>4028.687306</td>\n",
       "      <td>26.265803</td>\n",
       "      <td>2021-08-28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   user_id     distance   duration        date\n",
       "0        1  4409.919140  25.599769  2021-01-01\n",
       "1        1  2617.592153  15.816871  2021-01-18\n",
       "2        1   754.159807   6.232113  2021-04-20\n",
       "3        1  2694.783254  18.511000  2021-08-11\n",
       "4        1  4028.687306  26.265803  2021-08-28"
      ]
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     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subscription_type</th>\n",
       "      <th>minute_price</th>\n",
       "      <th>start_ride_price</th>\n",
       "      <th>subscription_fee</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>free</td>\n",
       "      <td>8</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ultra</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  subscription_type  minute_price  start_ride_price  subscription_fee\n",
       "0              free             8                50                 0\n",
       "1             ultra             6                 0               199"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_users = pd.read_csv('users_go.csv')\n",
    "\n",
    "df_rides = pd.read_csv('rides_go.csv')\n",
    "\n",
    "df_subscriptions = pd.read_csv('subscriptions_go.csv')\n",
    "\n",
    "\n",
    "\n",
    "display(df_users.head(), df_rides.head(), df_subscriptions.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78b17574",
   "metadata": {},
   "source": [
    "Изучим общую информацию о датафрейме  df.users:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a865ccc5",
   "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>name</th>\n",
       "      <th>age</th>\n",
       "      <th>city</th>\n",
       "      <th>subscription_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1543</th>\n",
       "      <td>153</td>\n",
       "      <td>Герман</td>\n",
       "      <td>23</td>\n",
       "      <td>Москва</td>\n",
       "      <td>ultra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>327</td>\n",
       "      <td>Наташа</td>\n",
       "      <td>24</td>\n",
       "      <td>Краснодар</td>\n",
       "      <td>ultra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1172</th>\n",
       "      <td>1173</td>\n",
       "      <td>Алина</td>\n",
       "      <td>19</td>\n",
       "      <td>Пятигорск</td>\n",
       "      <td>free</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1287</th>\n",
       "      <td>1288</td>\n",
       "      <td>Матвей</td>\n",
       "      <td>33</td>\n",
       "      <td>Ростов-на-Дону</td>\n",
       "      <td>free</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1334</th>\n",
       "      <td>1335</td>\n",
       "      <td>Юлиана</td>\n",
       "      <td>27</td>\n",
       "      <td>Ростов-на-Дону</td>\n",
       "      <td>free</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1531</th>\n",
       "      <td>1532</td>\n",
       "      <td>Алексей</td>\n",
       "      <td>26</td>\n",
       "      <td>Тюмень</td>\n",
       "      <td>free</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>546</td>\n",
       "      <td>Милена</td>\n",
       "      <td>22</td>\n",
       "      <td>Ростов-на-Дону</td>\n",
       "      <td>ultra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1333</th>\n",
       "      <td>1334</td>\n",
       "      <td>Мария</td>\n",
       "      <td>16</td>\n",
       "      <td>Ростов-на-Дону</td>\n",
       "      <td>free</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>82</td>\n",
       "      <td>Максим</td>\n",
       "      <td>19</td>\n",
       "      <td>Ростов-на-Дону</td>\n",
       "      <td>ultra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>127</td>\n",
       "      <td>Павел</td>\n",
       "      <td>25</td>\n",
       "      <td>Пятигорск</td>\n",
       "      <td>ultra</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id     name  age            city subscription_type\n",
       "1543      153   Герман   23          Москва             ultra\n",
       "326       327   Наташа   24       Краснодар             ultra\n",
       "1172     1173    Алина   19       Пятигорск              free\n",
       "1287     1288   Матвей   33  Ростов-на-Дону              free\n",
       "1334     1335   Юлиана   27  Ростов-на-Дону              free\n",
       "1531     1532  Алексей   26          Тюмень              free\n",
       "545       546   Милена   22  Ростов-на-Дону             ultra\n",
       "1333     1334    Мария   16  Ростов-на-Дону              free\n",
       "81         82   Максим   19  Ростов-на-Дону             ultra\n",
       "126       127    Павел   25       Пятигорск             ultra"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1565 entries, 0 to 1564\n",
      "Data columns (total 5 columns):\n",
      " #   Column             Non-Null Count  Dtype \n",
      "---  ------             --------------  ----- \n",
      " 0   user_id            1565 non-null   int64 \n",
      " 1   name               1565 non-null   object\n",
      " 2   age                1565 non-null   int64 \n",
      " 3   city               1565 non-null   object\n",
      " 4   subscription_type  1565 non-null   object\n",
      "dtypes: int64(2), object(3)\n",
      "memory usage: 61.3+ KB\n"
     ]
    }
   ],
   "source": [
    "display(df_users.sample(10))\n",
    "df_users.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c2844dd",
   "metadata": {},
   "source": [
    "В датафрейме содержится 1565 строк, пропущенных значений нет. Датафрейм необходимо проверить на наличие  явных и неявных дубликатов. Типы данных не требуют изменений.\n",
    "\n",
    "Изучим общую информацию о датафрейме `df_rides`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0d877b7a",
   "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>distance</th>\n",
       "      <th>duration</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16031</th>\n",
       "      <td>1392</td>\n",
       "      <td>235.127167</td>\n",
       "      <td>5.282512</td>\n",
       "      <td>2021-03-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15084</th>\n",
       "      <td>1322</td>\n",
       "      <td>5285.797316</td>\n",
       "      <td>33.821740</td>\n",
       "      <td>2021-03-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7872</th>\n",
       "      <td>799</td>\n",
       "      <td>3274.136803</td>\n",
       "      <td>14.541226</td>\n",
       "      <td>2021-04-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>382</td>\n",
       "      <td>3493.109112</td>\n",
       "      <td>21.122042</td>\n",
       "      <td>2021-06-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13767</th>\n",
       "      <td>1229</td>\n",
       "      <td>1133.592229</td>\n",
       "      <td>7.429126</td>\n",
       "      <td>2021-01-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8716</th>\n",
       "      <td>866</td>\n",
       "      <td>2172.744938</td>\n",
       "      <td>6.828322</td>\n",
       "      <td>2021-07-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14845</th>\n",
       "      <td>1306</td>\n",
       "      <td>3619.320786</td>\n",
       "      <td>16.686370</td>\n",
       "      <td>2021-03-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8226</th>\n",
       "      <td>828</td>\n",
       "      <td>4315.378815</td>\n",
       "      <td>17.928370</td>\n",
       "      <td>2021-07-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13200</th>\n",
       "      <td>1190</td>\n",
       "      <td>2860.320847</td>\n",
       "      <td>17.720642</td>\n",
       "      <td>2021-09-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13703</th>\n",
       "      <td>1224</td>\n",
       "      <td>5179.707927</td>\n",
       "      <td>25.281322</td>\n",
       "      <td>2021-10-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       user_id     distance   duration        date\n",
       "16031     1392   235.127167   5.282512  2021-03-07\n",
       "15084     1322  5285.797316  33.821740  2021-03-23\n",
       "7872       799  3274.136803  14.541226  2021-04-18\n",
       "3555       382  3493.109112  21.122042  2021-06-23\n",
       "13767     1229  1133.592229   7.429126  2021-01-15\n",
       "8716       866  2172.744938   6.828322  2021-07-02\n",
       "14845     1306  3619.320786  16.686370  2021-03-03\n",
       "8226       828  4315.378815  17.928370  2021-07-31\n",
       "13200     1190  2860.320847  17.720642  2021-09-14\n",
       "13703     1224  5179.707927  25.281322  2021-10-05"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 18068 entries, 0 to 18067\n",
      "Data columns (total 4 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   user_id   18068 non-null  int64  \n",
      " 1   distance  18068 non-null  float64\n",
      " 2   duration  18068 non-null  float64\n",
      " 3   date      18068 non-null  object \n",
      "dtypes: float64(2), int64(1), object(1)\n",
      "memory usage: 564.8+ KB\n"
     ]
    }
   ],
   "source": [
    "display(df_rides.sample(10))\n",
    "df_rides.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06cd3c19",
   "metadata": {},
   "source": [
    "Датафрейм содержит 18068 строк, что достаточно для проведения  анализа в рамках проекта. Пропущенных \n",
    "значений нет. Проверим датафрейм на наличие явных  и неявных дубликатов, также преобразуем \n",
    " типы данных, придерживаясь следующих принципов:\n",
    "1. Там где возможно и целесообразно приведем данны к целочисленному типу.\n",
    "2. Данные с датами приведем к типу данных datetime.\n",
    "\n",
    "Изучим общую информацию о датафрейме `df_subscriptions`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "231b4f68",
   "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>subscription_type</th>\n",
       "      <th>minute_price</th>\n",
       "      <th>start_ride_price</th>\n",
       "      <th>subscription_fee</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>free</td>\n",
       "      <td>8</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ultra</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subscription_type  minute_price  start_ride_price  subscription_fee\n",
       "0              free             8                50                 0\n",
       "1             ultra             6                 0               199"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2 entries, 0 to 1\n",
      "Data columns (total 4 columns):\n",
      " #   Column             Non-Null Count  Dtype \n",
      "---  ------             --------------  ----- \n",
      " 0   subscription_type  2 non-null      object\n",
      " 1   minute_price       2 non-null      int64 \n",
      " 2   start_ride_price   2 non-null      int64 \n",
      " 3   subscription_fee   2 non-null      int64 \n",
      "dtypes: int64(3), object(1)\n",
      "memory usage: 192.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "display(df_subscriptions)\n",
    "df_subscriptions.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b158b115",
   "metadata": {},
   "source": [
    "Датафрейм с метаданными, дополняйщий информацию в датафрейме `df_users`, предобрабтка не требуется."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e95ebca7",
   "metadata": {},
   "source": [
    "#### Шаг 2. Предобработка данных"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91cf4bfd",
   "metadata": {},
   "source": [
    "##### Предобработка данных в таблице `df_users`\n",
    "\n",
    "Проверим наличие явных дубликатов в таблице и удалим найденные.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b2efa9b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_users.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "46a5550b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_users = df_users.drop_duplicates()\n",
    "df_users.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d6c1dbc",
   "metadata": {},
   "source": [
    "Проверим столбцы `name`,`city`на наличие неявных дубликтов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "36a3edb4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "количество уникальных имен: 194\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['Агата',\n",
       " 'Аделина',\n",
       " 'Адель',\n",
       " 'Айдар',\n",
       " 'Александр',\n",
       " 'Александра',\n",
       " 'Алексей',\n",
       " 'Алина',\n",
       " 'Алиса',\n",
       " 'Алия',\n",
       " 'Алла',\n",
       " 'Альберт',\n",
       " 'Альбина',\n",
       " 'Алёна',\n",
       " 'Амалия',\n",
       " 'Амина',\n",
       " 'Амир',\n",
       " 'Анастасия',\n",
       " 'Анатолий',\n",
       " 'Ангелина',\n",
       " 'Андрей',\n",
       " 'Анжелика',\n",
       " 'Анна',\n",
       " 'Антон',\n",
       " 'Антонина',\n",
       " 'Ариана',\n",
       " 'Арина',\n",
       " 'Арсен',\n",
       " 'Арсений',\n",
       " 'Артемий',\n",
       " 'Артур',\n",
       " 'Артём',\n",
       " 'Богдан',\n",
       " 'Борис',\n",
       " 'Вадим',\n",
       " 'Валентина',\n",
       " 'Валерий',\n",
       " 'Валерия',\n",
       " 'Варвара',\n",
       " 'Василий',\n",
       " 'Василина',\n",
       " 'Василиса',\n",
       " 'Вера',\n",
       " 'Вероника',\n",
       " 'Виктор',\n",
       " 'Виктория',\n",
       " 'Виолетта',\n",
       " 'Виталий',\n",
       " 'Виталина',\n",
       " 'Влад',\n",
       " 'Влада',\n",
       " 'Владимир',\n",
       " 'Владислав',\n",
       " 'Владислава',\n",
       " 'Всеволод',\n",
       " 'Вячеслав',\n",
       " 'Галина',\n",
       " 'Георгий',\n",
       " 'Герман',\n",
       " 'Глеб',\n",
       " 'Гордей',\n",
       " 'Григорий',\n",
       " 'Давид',\n",
       " 'Дамир',\n",
       " 'Даниил',\n",
       " 'Данил',\n",
       " 'Данила',\n",
       " 'Даниэль',\n",
       " 'Дарина',\n",
       " 'Дарья',\n",
       " 'Демид',\n",
       " 'Денис',\n",
       " 'Диана',\n",
       " 'Дмитрий',\n",
       " 'Ева',\n",
       " 'Евгений',\n",
       " 'Евгения',\n",
       " 'Егор',\n",
       " 'Екатерина',\n",
       " 'Елена',\n",
       " 'Елизавета',\n",
       " 'Елисей',\n",
       " 'Есения',\n",
       " 'Жанна',\n",
       " 'Зарина',\n",
       " 'Захар',\n",
       " 'Злата',\n",
       " 'Иван',\n",
       " 'Игнат',\n",
       " 'Игорь',\n",
       " 'Ильдар',\n",
       " 'Илья',\n",
       " 'Инна',\n",
       " 'Ирина',\n",
       " 'Камилла',\n",
       " 'Карина',\n",
       " 'Каролина',\n",
       " 'Кира',\n",
       " 'Кирилл',\n",
       " 'Константин',\n",
       " 'Кристина',\n",
       " 'Ксения',\n",
       " 'Лариса',\n",
       " 'Лев',\n",
       " 'Леонид',\n",
       " 'Лиана',\n",
       " 'Лидия',\n",
       " 'Лилия',\n",
       " 'Любовь',\n",
       " 'Людмила',\n",
       " 'Мадина',\n",
       " 'Майя',\n",
       " 'Макар',\n",
       " 'Максим',\n",
       " 'Марат',\n",
       " 'Маргарита',\n",
       " 'Марина',\n",
       " 'Мария',\n",
       " 'Марк',\n",
       " 'Марсель',\n",
       " 'Матвей',\n",
       " 'Мелания',\n",
       " 'Милана',\n",
       " 'Милена',\n",
       " 'Мирон',\n",
       " 'Мирослав',\n",
       " 'Мирослава',\n",
       " 'Михаил',\n",
       " 'Назар',\n",
       " 'Наталия',\n",
       " 'Наталья',\n",
       " 'Наташа',\n",
       " 'Нелли',\n",
       " 'Ника',\n",
       " 'Никита',\n",
       " 'Николай',\n",
       " 'Нина',\n",
       " 'Одиссей',\n",
       " 'Оксана',\n",
       " 'Олег',\n",
       " 'Олеся',\n",
       " 'Ольга',\n",
       " 'Павел',\n",
       " 'Петр',\n",
       " 'Платон',\n",
       " 'Полина',\n",
       " 'Радмир',\n",
       " 'Рамиль',\n",
       " 'Регина',\n",
       " 'Роберт',\n",
       " 'Родион',\n",
       " 'Роман',\n",
       " 'Ростислав',\n",
       " 'Руслан',\n",
       " 'Рустам',\n",
       " 'Савелий',\n",
       " 'Самир',\n",
       " 'Самира',\n",
       " 'Светлана',\n",
       " 'Святогор',\n",
       " 'Святослав',\n",
       " 'Семен',\n",
       " 'Сергей',\n",
       " 'Снежана',\n",
       " 'София',\n",
       " 'Софья',\n",
       " 'Станислав',\n",
       " 'Степан',\n",
       " 'Стефания',\n",
       " 'Таисия',\n",
       " 'Тамара',\n",
       " 'Тамерлан',\n",
       " 'Татьяна',\n",
       " 'Тимофей',\n",
       " 'Тимур',\n",
       " 'Тихон',\n",
       " 'Ульяна',\n",
       " 'Федор',\n",
       " 'Эвелина',\n",
       " 'Эдуард',\n",
       " 'Элина',\n",
       " 'Эльвира',\n",
       " 'Эльмира',\n",
       " 'Эмилия',\n",
       " 'Эмиль',\n",
       " 'Юлиана',\n",
       " 'Юлия',\n",
       " 'Юрий',\n",
       " 'Ян',\n",
       " 'Яна',\n",
       " 'Яромир',\n",
       " 'Ярослав',\n",
       " 'Ярослава',\n",
       " 'Ясмина']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f'количество уникальных имен: {len(df_users[\"name\"].unique())}')\n",
    "sorted(df_users['name'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5324cb0b",
   "metadata": {},
   "source": [
    " В столбце содержатся 194 уникальных  имени клиентов сервиса, и при визуальном осмотре  списка\n",
    "    неявных дубликатов не обнаружено.\n",
    "    \n",
    "Проверим столбец `cyty` на неявные дубликаты."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8aa15359",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "количество уникальных имен: 8\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['Екатеринбург',\n",
       " 'Краснодар',\n",
       " 'Москва',\n",
       " 'Омск',\n",
       " 'Пятигорск',\n",
       " 'Ростов-на-Дону',\n",
       " 'Сочи',\n",
       " 'Тюмень']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f'количество уникальных имен: {len(df_users[\"city\"].unique())}')\n",
    "sorted(df_users['city'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c0289ca",
   "metadata": {},
   "source": [
    "В столбце содержится 8 уникальных имен, неявных дубликатов не обнаружено.\n",
    "\n",
    "Убедимся в том, что столбец `subscription_type`содержит только два уникальных названия, которые\n",
    "можно будет использовать в качестве маркеров категорий."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4d2eae56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subscription_type\n",
       "free                 835\n",
       "ultra                699\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_users[['subscription_type']].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e047937",
   "metadata": {},
   "source": [
    "##### Предобработка данных в таблице `df_rides`\n",
    "\n",
    "Проверим наличие явных дубликатов в таблице и удалим найденные."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8b5e411b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rides.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b48edaab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>distance</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>18068.000000</td>\n",
       "      <td>18068.000000</td>\n",
       "      <td>18068.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>842.869936</td>\n",
       "      <td>3070.659976</td>\n",
       "      <td>17.805011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>434.734317</td>\n",
       "      <td>1116.831209</td>\n",
       "      <td>6.091051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.855683</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>487.000000</td>\n",
       "      <td>2543.226360</td>\n",
       "      <td>13.597563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>889.000000</td>\n",
       "      <td>3133.609994</td>\n",
       "      <td>17.678395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1213.250000</td>\n",
       "      <td>3776.222735</td>\n",
       "      <td>21.724800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1534.000000</td>\n",
       "      <td>7211.007745</td>\n",
       "      <td>40.823963</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            user_id      distance      duration\n",
       "count  18068.000000  18068.000000  18068.000000\n",
       "mean     842.869936   3070.659976     17.805011\n",
       "std      434.734317   1116.831209      6.091051\n",
       "min        1.000000      0.855683      0.500000\n",
       "25%      487.000000   2543.226360     13.597563\n",
       "50%      889.000000   3133.609994     17.678395\n",
       "75%     1213.250000   3776.222735     21.724800\n",
       "max     1534.000000   7211.007745     40.823963"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rides.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67beb8f0",
   "metadata": {},
   "source": [
    "Приведем  столбец `date` к типу данных **datetime**.\n",
    "\n",
    "Данные в столбце `distance` округлим до целочисленного значения и приведем к **'int64'**.\n",
    "\n",
    "Данные  о продолжительносьти поездки из столбца `duration`округлим до следующего цлого и преведем к **'int64'**. \n",
    "\n",
    "Проверим результат методом .info(), также выведем несколько строк таблицы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "719c7c8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 18068 entries, 0 to 18067\n",
      "Data columns (total 4 columns):\n",
      " #   Column    Non-Null Count  Dtype         \n",
      "---  ------    --------------  -----         \n",
      " 0   user_id   18068 non-null  int64         \n",
      " 1   distance  18068 non-null  int32         \n",
      " 2   duration  18068 non-null  int32         \n",
      " 3   date      18068 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](1), int32(2), int64(1)\n",
      "memory usage: 423.6 KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>distance</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4410</td>\n",
       "      <td>26</td>\n",
       "      <td>2021-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2618</td>\n",
       "      <td>16</td>\n",
       "      <td>2021-01-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>754</td>\n",
       "      <td>7</td>\n",
       "      <td>2021-04-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2695</td>\n",
       "      <td>19</td>\n",
       "      <td>2021-08-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>4029</td>\n",
       "      <td>27</td>\n",
       "      <td>2021-08-28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  distance  duration       date\n",
       "0        1      4410        26 2021-01-01\n",
       "1        1      2618        16 2021-01-18\n",
       "2        1       754         7 2021-04-20\n",
       "3        1      2695        19 2021-08-11\n",
       "4        1      4029        27 2021-08-28"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_rides['date'] = pd.to_datetime( df_rides['date'], format = '%Y-%m-%d')\n",
    "df_rides['distance'] = df_rides['distance'].round().astype('int')\n",
    "df_rides['duration'] =np.ceil(df_rides['duration']).astype('int')\n",
    "display(df_rides.info(),df_rides.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10956269",
   "metadata": {},
   "source": [
    "Создадим столбец с номeром месяца `month_` на основе столбца `date`. Номер месяца возможно использовать для агрегации данных\n",
    "помесячно, при условии, что интервал времени охватывает полный год. В случае отсутствия такого соответствия требуется разбивка данных по годам."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f49e58a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2021    18068\n",
       "Name: date, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rides['date'].dt.year.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d5e9063",
   "metadata": {},
   "source": [
    "Убедились, что данные охватывают период  в один год."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4d6e20a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_rides['month'] = df_rides['date'].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "76a12bf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>distance</th>\n",
       "      <th>duration</th>\n",
       "      <th>date</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4410</td>\n",
       "      <td>26</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2618</td>\n",
       "      <td>16</td>\n",
       "      <td>2021-01-18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>754</td>\n",
       "      <td>7</td>\n",
       "      <td>2021-04-20</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2695</td>\n",
       "      <td>19</td>\n",
       "      <td>2021-08-11</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>4029</td>\n",
       "      <td>27</td>\n",
       "      <td>2021-08-28</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  distance  duration       date  month\n",
       "0        1      4410        26 2021-01-01      1\n",
       "1        1      2618        16 2021-01-18      1\n",
       "2        1       754         7 2021-04-20      4\n",
       "3        1      2695        19 2021-08-11      8\n",
       "4        1      4029        27 2021-08-28      8"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rides.head() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a32b912",
   "metadata": {},
   "source": [
    "Количество пользователей без подписки превышает общее число пользователей, за исключением Москвы и Омска, где оно одинаково."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47105e82",
   "metadata": {},
   "source": [
    "##### Оценка возраста пользователей"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "021d0b21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1534.000000</td>\n",
       "      <td>1534.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>767.500000</td>\n",
       "      <td>24.903520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>442.971971</td>\n",
       "      <td>4.572367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>384.250000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>767.500000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1150.750000</td>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1534.000000</td>\n",
       "      <td>43.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           user_id          age\n",
       "count  1534.000000  1534.000000\n",
       "mean    767.500000    24.903520\n",
       "std     442.971971     4.572367\n",
       "min       1.000000    12.000000\n",
       "25%     384.250000    22.000000\n",
       "50%     767.500000    25.000000\n",
       "75%    1150.750000    28.000000\n",
       "max    1534.000000    43.000000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_users.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9dfc0dbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_users.pivot_table(index = 'age',values = 'user_id', aggfunc ='count')\\\n",
    "                  .plot(kind = 'bar',figsize=(12,6),legend=False, grid=True)\n",
    "plt.title('Распределение возраста пользователей',fontsize = 14)\n",
    "plt.xlabel('Возраст',fontsize = 14)\n",
    "plt.ylabel('частота встречаемости',fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "585e830f",
   "metadata": {},
   "source": [
    "Распределение возраста пользователей близко к нормальному. Медианное и среднее значения составляют около 25 лет, что указывает на симметричность распределения. Максимальный возраст клиентов составляет 43 года, в то время как минимальный возраст — 12 лет.\n",
    "\n",
    " Отметим, что в представленных данных отсутствуют аномальные значения."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21e6519d",
   "metadata": {},
   "source": [
    "##### расстояние для одной поездки\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f89dd608",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>distance</th>\n",
       "      <th>duration</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>18068.000000</td>\n",
       "      <td>18068.000000</td>\n",
       "      <td>18068.000000</td>\n",
       "      <td>18068.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>842.869936</td>\n",
       "      <td>3070.659010</td>\n",
       "      <td>18.303520</td>\n",
       "      <td>6.486772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>434.734317</td>\n",
       "      <td>1116.832209</td>\n",
       "      <td>6.094928</td>\n",
       "      <td>3.437410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>487.000000</td>\n",
       "      <td>2543.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>889.000000</td>\n",
       "      <td>3134.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1213.250000</td>\n",
       "      <td>3776.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1534.000000</td>\n",
       "      <td>7211.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            user_id      distance      duration         month\n",
       "count  18068.000000  18068.000000  18068.000000  18068.000000\n",
       "mean     842.869936   3070.659010     18.303520      6.486772\n",
       "std      434.734317   1116.832209      6.094928      3.437410\n",
       "min        1.000000      1.000000      1.000000      1.000000\n",
       "25%      487.000000   2543.000000     14.000000      4.000000\n",
       "50%      889.000000   3134.000000     18.000000      6.000000\n",
       "75%     1213.250000   3776.000000     22.000000      9.000000\n",
       "max     1534.000000   7211.000000     41.000000     12.000000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rides.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ca10aaf",
   "metadata": {},
   "source": [
    "Построим гистограммы с разным диапазоном значений и боксплот для оценки выбросов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "992dd200",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3695779W9994rSRoyZIjuvfdevfnmmzpx4oQWLVqk48ePq2bNmipRooR9+fXXX+37OHTokM6dO2d/ffz4cXXp0kWVKlVS586dVbRoUW3YsEHFixe/laECAADc0VyVlwEAAFhleabSt99+q2eeeUZdu3bV1q1blZiYKEmKjY3Vu+++q8WLF2d6X02aNJExJs316a1LcfjwYYfXs2fPzvTxAQAAcjJX5mUAAABWWZ6p9Pbbb2vq1Kn69NNPlTdvXnt7gwYNtHXrVpcGBwAAgLSRlwEAAHeyXFTav3+/GjVqlKrd399fMTExrogJAAAAmUBeBgAA3MlyUSkoKMjh5top1q1bp7Jly7okKAAAAGSMvAwAALiT5aJS3759NWjQIG3cuFE2m00nT57UF198oZdeeknPPfdcVsQIAAAAJ8jLAACAO1m+UfewYcOUnJysZs2a6dKlS2rUqJG8vb310ksv6d///ndWxAgAAAAnyMsAAIA7WS4q2Ww2vfbaaxo6dKgOHjyo+Ph4ValSRQUKFMiK+AAAAJAG8jIAAOBOlotKKby8vFSlShVXxgIAAIBbQF4GAADc4ZaKSps3b9Y333yjo0eP6urVqw7r5s2b55LAAAAAkDHyMgAA4C6Wb9Q9e/Zs1a9fX3v37tX8+fN17do1/f7771q5cqX8/f2zIkYAAAA4QV4GAADcyXJR6d1339W4ceP03XffycvLSxMmTNC+ffvUuXNnlSlTJitiBAAAgBPkZQAAwJ0sF5UOHTqkNm3aSPrn+v2EhATZbDa98MILmjZtmssDBAAAgHPkZQAAwJ0sF5UKFy6sixcvSpJKliyp3bt3S5JiYmJ06dIl10YHAACANJGXAQAAd7J8o+5GjRpp2bJlql69uh5//HENGjRIK1eu1LJly9SsWbOsiBEAAABOkJcBAAB3slxUmjRpkq5cuSJJeu2115Q3b179+uuvevTRR/X666+7PEAAAAA4R14GAADcyXJRqUiRIvZ/58mTR8OGDXNpQAAAAMgc8jIAAOBOlu+pFBcXl+4CAACA7OGqvGzKlCmqUaOG/Pz85Ofnp3r16unHH3/MwsgBAEBuYHmmUqFChWSz2VK1G2Nks9mUlJTkksAAAACQPlflZaVKldLo0aNVoUIFGWM0c+ZMtW/fXtu2bVPVqlVdHTYAAMglLBeVVq1aZf+3MUYPP/yw/vOf/6hkyZIuDQwAAADpc1Ve1rZtW4fX77zzjqZMmaINGzakWVRKTExUYmKi/TUz1gEAuPtYLio1btzY4bWHh4ceeOABlS1b1mVBAQAAIGNZkZclJSVpzpw5SkhIUL169dLsFxUVpREjRtzycVwtdNgPqdoOj25jebvMbHOnyQ1jAADkTJbvqXSjS5cuKTExUV5eXq6KBwAAALfgdvOyXbt2qUCBAvL29tazzz6r+fPnq0qVKmn2j4yMVGxsrH05duzYrYYOAAByKMszlT766CNJ0uXLl7Vo0SKVKVNGpUqVcnlgAAAASJ8r87JKlSpp+/btio2N1dy5c9W9e3f9/PPPaRaWvL295e3tfcuxAwCAnM9yUWncuHGSJF9fX1WrVk3Tp093eVAAAADImCvzMi8vL5UvX16SVKtWLf3222+aMGGCPvnkE5fECgAAch/LRaXo6OisiAMAAAAWZWVelpyc7HAjbgAAgJtZLipJ0vXr17V69WodOnRITz31lAoWLKiTJ0/Kz89PBQoUcHWMAAAASIMr8rLIyEhFRESoTJkyunjxor788kutXr1aS5cuzeLoAQBATma5qHTkyBG1bt1aR48eVWJiolq0aKGCBQtqzJgxSkxM1NSpU7MiTgAAANzEVXnZ2bNn1a1bN506dUr+/v6qUaOGli5dqhYtWmTxCAAAQE5muag0aNAg1a5dWzt27FDRokXt7R07dlTfvn1dGhwAAADS5qq87LPPPsuK8AAAQC6Xx+oGa9eu1euvv57qcbWhoaE6ceKEpX2tWbNGbdu2VXBwsGw2mxYsWOCw3hijN998UyVKlJCvr6+aN2+uAwcOZLjfyZMnKzQ0VD4+Pqpbt642bdpkKS4AAICcwJV5GQAAgFWWi0rJyclKSkpK1X78+HEVLFjQ0r4SEhIUHh6uyZMnO13/3nvv6aOPPtLUqVO1ceNG5c+fX61atdKVK1fS3OfXX3+tIUOGaPjw4dq6davCw8PVqlUrnT171lJsAAAAdzpX5mUAAABWWS4qtWzZUuPHj7e/ttlsio+P1/Dhw/Xwww9b2ldERITefvttdezYMdU6Y4zGjx+v119/Xe3bt1eNGjX03//+VydPnkw1o+lGH374ofr27auePXuqSpUqmjp1qvLly6fPP//cUmwAAAB3OlfmZQAAAFZZLip98MEH+uWXX1SlShVduXJFTz31lH2K9ZgxY1wWWHR0tE6fPq3mzZvb2/z9/VW3bl2tX7/e6TZXr17Vli1bHLbJkyePmjdvnuY2kpSYmKi4uDiHBQAA4E6XXXkZAACAM5Zv1F2qVCnt2LFDs2fP1s6dOxUfH6/evXura9eu8vX1dVlgp0+fliQFBgY6tAcGBtrX3ezcuXNKSkpyus2+ffvSPFZUVJRGjBhxmxEDAABkr+zKywAAAJyxXFSSJE9PTz399NOujsVtIiMjNWTIEPvruLg4lS5d2o0RAQAAZE5uy8sAAEDOcUtFpf3792vixInau3evJOmee+7RgAEDVLlyZZcFFhQUJEk6c+aMSpQoYW8/c+aMatas6XSbYsWKycPDQ2fOnHFoP3PmjH1/znh7e8vb2/v2gwYAAMhm2ZGXAQAAOGP5nkrffvutqlWrpi1btig8PFzh4eHaunWrqlevrm+//dZlgYWFhSkoKEgrVqywt8XFxWnjxo2qV6+e0228vLxUq1Yth22Sk5O1YsWKNLcBAADIqbIrLwMAAHDG8kyll19+WZGRkRo5cqRD+/Dhw/Xyyy/r0UcfzfS+4uPjdfDgQfvr6Ohobd++XUWKFFGZMmU0ePBgvf3226pQoYLCwsL0xhtvKDg4WB06dLBv06xZM3Xs2FEDBgyQJA0ZMkTdu3dX7dq1VadOHY0fP14JCQnq2bOn1aECAADc0VyZlwEAAFhluah06tQpdevWLVX7008/rbFjx1ra1+bNm/XQQw/ZX6fc16h79+6aMWOGXn75ZSUkJKhfv36KiYnRgw8+qCVLlsjHx8e+zaFDh3Tu3Dn76yeeeEJ//fWX3nzzTZ0+fVo1a9bUkiVLUt28GwAAIKdzZV4GAABgleWiUpMmTbR27VqVL1/eoX3dunVq2LCh5X0ZY9Jcb7PZNHLkyFS/vt3o8OHDqdoGDBhgn7kEAACQW7kyLwMAALDKclGpXbt2euWVV7RlyxY98MADkqQNGzZozpw5GjFihBYtWuTQFwAAAFmDvAwAALiT5aLS888/L0n6+OOP9fHHHztdJ/0zyygpKek2wwMAAEBayMsAAIA7WS4qJScnZ0UcAAAAsIi8DAAAuFMeqxv897//VWJiYlbEAgAAAAvIywAAgDtZLir17NlTsbGxWRELAAAALCAvAwAA7mS5qJTe09oAAACQfcjLAACAO1m+p5IkffPNN/Lz83O6rlu3brcVEAAAADKPvAwAALjLLRWV3nvvPXl4eKRqt9lsJC8AAADZiLwMAAC4yy0VlTZv3qyAgABXxwIAyAVCh/3g7hCAuwp5GQAAcBfL91QCAAAAAAAALBeVQkJCnE6xBgAAQPYiLwMAAO5k+fK36OjorIgDAAAAFpGXAQAAd7I8U2ngwIH66KOPUrVPmjRJgwcPdkVMAAAAyATyMgAA4E6Wi0rffvutGjRokKq9fv36mjt3rkuCAgAAQMbIywAAgDtZLiqdP39e/v7+qdr9/Px07tw5lwQFAACAjJGXAQAAd7JcVCpfvryWLFmSqv3HH39U2bJlXRIUAAAAMkZeBgAA3MnyjbqHDBmiAQMG6K+//lLTpk0lSStWrNAHH3yg8ePHuzo+AAAApIG8DAAAuJPlolKvXr2UmJiod955R6NGjZIkhYaGasqUKerWrZvLAwQAAIBz5GUAAMCdLBeVJOm5557Tc889p7/++ku+vr4qUKCAq+MCAABAJpCXAQAAd7F8T6UbFS9eXJK0cuVKHT161CUBAQAAwDryMgAAkN0sF5WWLl2qEiVK6J577tHGjRt1zz33qHnz5qpQoYK+/fbbrIgRAAAATpCXAQAAd7JcVBo2bJiaN2+uhx9+WO3atdNTTz2lixcv6rXXXtOIESOyIkYAAAA4QV4GAADcyXJRaf/+/Ro5cqTGjBmjv//+W927d1f+/PnVvXt3HThwICtiBAAAgBPkZQAAwJ0s36j7ypUrKlCggDw9PeXt7S1fX19Jko+Pj65everyAIG7ReiwH9wdgmWHR7dxdwjAXYvPDEjkZQAAwL1u6elvb7zxhvLly6erV6/q7bfflr+/vy5duuTq2AAAAJAB8jIAAOAulotKjRo10v79+yVJ9evX159//umwDgAAANmDvAwAALiT5aLS6tWrsyCMtIWGhurIkSOp2p9//nlNnjw5VfuMGTPUs2dPhzZvb29duXIly2IEAABwh+zOywAAAG50S5e/ZafffvtNSUlJ9te7d+9WixYt9Pjjj6e5jZ+fn/1XO0my2WxZGiMAAAAAAMDd5o4vKhUvXtzh9ejRo1WuXDk1btw4zW1sNpuCgoKyOjQAAAAAAIC7Vh53B2DF1atXNWvWLPXq1Svd2Ufx8fEKCQlR6dKl1b59e/3+++/p7jcxMVFxcXEOCwAAAAAAANKWo4pKCxYsUExMjHr06JFmn0qVKunzzz/XwoULNWvWLCUnJ6t+/fo6fvx4mttERUXJ39/fvpQuXToLogcAAAAAAMg9clRR6bPPPlNERISCg4PT7FOvXj1169ZNNWvWVOPGjTVv3jwVL15cn3zySZrbREZGKjY21r4cO3YsK8IHAAC4I0VFRen+++9XwYIFFRAQoA4dOjjcnxIAAMC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      "text/plain": [
       "<Figure size 1400x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# создадим фигуру  с двумя строками  идвумя столбцами.\n",
    "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(14, 8))\n",
    "fig.suptitle('Расстояние одной поездки', fontsize=16)\n",
    "\n",
    "axes[0,0].hist(df_rides['distance'],bins=100,)\n",
    "axes[0,0].set_title(' весь диапазон значений')\n",
    "axes[0,0].set_ylabel('частота встречаемости',fontsize=10)\n",
    "\n",
    "axes[0,1].hist(df_rides['distance'],bins=50,range = (0,1700))\n",
    "axes[0,1].set_title('диапазон от 0 до 1700м.')\n",
    "axes[0,1].set_ylabel('частота встречаемости',fontsize=10)\n",
    "\n",
    "axes[1,0].hist(df_rides['distance'],bins=10,range = (0,100))\n",
    "axes[1,0].set_title('диапазон поездок от 0 до 100м.')\n",
    "axes[1,0].set_ylabel('частота встречаемости',fontsize=10)\n",
    "\n",
    "axes[1,1].hist(df_rides['distance'],bins=100,range = (6000,7500))\n",
    "axes[1,1].set_title('длинные поездки. более 6000м.')\n",
    "axes[1,1].set_ylabel('частота встречаемости',fontsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e8555ede",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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i0xkAvO7M60T8+7//u+Li4rRnzx598MEHKi8v1+rVq7lOBNAB8ekMAO0mIyND77777jnbb775ZuXm5rb/ggA0i+tEAPArM2bM0LvvvqvIyEj95je/0cmTJ9W5c2e98cYbevfddzVjxgwuOAV0MByJAOB19fX16ty5s7p3764DBw7Isiz3dSICAgLUp08fHT16VCdPnlRoaKivlwv84nGdCAB+Y/HixWpsbNSTTz6pgIAAFRQUaNOmTSooKFBAQIDmzp2rxsZGLV682NdLBdAKRAQAr/vyyy8lSQEBAYqPj9eYMWP03HPPacyYMYqPj3dfgMo1B6BjICIAeF3//v0lSffee68SExNVWFio5cuXq7CwUImJibrvvvs85gB0DJwTAcDrTp8+rfDwcIWGhur48eMKCAhwnxNhWZYuuOAC1dfX69SpUwoLC/P1coFfPM6JAOA3tmzZIun7EyxjY2O1dOlSHTt2TEuXLlVsbKzq6+s95gB0DHzEE4DXub4T4+GHH9YLL7ygBx980L0vODhYDz/8sBYtWsR3ZwAdDEciAHid6zsxoqKi1KdPH499vXv3VmRkpMccgI6BcyIAeJ3T6VR0dLSqqqqUnp6u1NRUlZWV6ZJLLtG6deu0atUq9ezZUwcPHuTS14Af4IqVAPyK6/9XNmzYoFWrVrm3cyIl0HHxdgYAryssLNThw4eb3RcQECBJqqqqUmFhYXsuC8BPREQA8Lpvv/1WkjRkyBD16NHDY1/37t01ZMgQjzkAHQNvZwDwOtdRiH/+85/nvH1x5MgRffPNNx5zADoGIgKA13Xv3t39++uvv77ZEyvPngPg/4gIAF535hGG9evXe5xY2alTp2bnAPg/zokA4HVHjx51/762ttZj35m3z5wD4P+ICADtyvVpjB+6DaDjICIAeF3Xrl3dvz/z7QvJ8zoRZ84B8H+cEwHA644dO+b+/ahRo5SWluY+sXLt2rVavXr1OXMA/B8RAcDrDhw44P79hx9+6I4GSQoPD292DoD/4+0MAF4XGxsrSbr00kvdX7blEhkZqUsvvdRjDkDHQEQA8Lrrr79ekrRnzx5dfvnlWrRokaZMmaJFixYpISFBe/bs8ZgD0DHwLZ4AvO7Mb/G02Wyqq6tz7+vUqZNqa2v5Fk/Aj7T0NZQjEQC8LigoSEuWLJEk1dfXe+xzBcWSJUsICKCDISIAtJuAgADZbDaPbZ06deJaEUAHxdsZALzO6XQqPj5ePXr00JEjR7Rv3z73vn79+qlHjx46evSoysrKOBoB+AHezgDgNwoLC7Vv3z6VlJQoMTFRhYWFWr58uQoLC5WYmKiSkhKVl5ersLDQ10sF0ApEBACv+/bbbyVJN9xwg3JzczV8+HCFhYVp+PDhys3N1Q033OAxB6BjICIAeJ3r2zkzMzMVGOj5z05gYKAyMjI85gB0DEQEAK+76KKLJEk5OTmqra3V888/r5dfflnPP/+8amtrlZub6zEHoGPgstcAvK53796SpDVr1ig8PFyu87lXr16t6dOnu2+75gB0DByJAOB1KSkp7jO8z/5AmOu23W5XSkpKu68NgDkiAoDXOZ1OHT9+/Lwzx48fl9PpbKcVAWgLRAQAr1u0aJH7iENYWJjHPtdty7K0aNGidl8bAHNEBACvc5042adPn3NOnrzooovc50K45gB0DEQEAK9zOBySpAMHDuiKK67wuNjUFVdc4b4+hGsOQMdARADwukGDBkn6/ou4Vq5c6XGxqZUrV7ovde2aA9AxEBEAvG7o0KGSvj/BMiYmRkuXLtWxY8e0dOlSxcTEuE+odM0B6Bi4TgQAr4uOjnb/vqqqSg8++OCPzgHwfxyJAOB1Lb2IFBebAjoWjkQA8LqUlBT3V34fPnxYX3/9tXvfmV8FzsWmgI6FiADgdUFBQVqwYIHGjx+v9PR0ZWdnq6ysTJdccony8/O1atUqjxMsAXQMRASAdpGZmamVK1fqkUceUV5ennt7XFycVq5cqczMTB+uDoCJAOvsC9n/TDgcDkVERKimpsZ9zX4AvldfX6+//vWv2rBhg66//no99NBDCg0N9fWyAJyhpa+hnFgJoN3k5OTo0ksv1aOPPqrVq1fr0Ucf1aWXXqqcnBxfLw2AASICQLvIycnRuHHjVFVV5bG9qqpK48aNIySADoiIAOB1TqdTDzzwwHlnJk+ezLd4Ah0MEQHA6zZu3KjDhw9Lks4+Dct1u6qqShs3bmzvpQH4CYgIAF63YcMG9+9/KCLOngPg/4gIAF535sWlAgICPPadefvMOQD+j4gA4HVNTU3u39fV1XnsO/P2mXMA/B8RAaBdne/tDAAdCxEBwOtaeoSBIxFAx0JEAPA61ycz2moOgH8gIgB4XW1tbZvOAfAPRAQAr+vUqZP798HBnt/7FxIS0uwcAP9HRADwusjISPfvGxsbPfY1NDQ0OwfA/xERALwuLi6uTecA+AciAoDXJScnt+kcAP9ARADwulWrVrXpHAD/QEQA8LqysrI2nQPgH1odEd9++61++9vfqnv37goLC1NiYqI+/fRT937LsjR79mz16tVLYWFhGj169Dn/MBw7dkx33nmn7Ha7unbtqkmTJunEiRMeM//617+UkpKiTp06KSYmRvPnzzd8iAB87dSpU206B8A/tCoivvvuO11zzTUKCQnRmjVrtHPnTi1YsEAXXnihe2b+/Pl6/vnn9dJLL2nLli3q3Lmz0tLSPD7/feedd2rHjh3Kz89XXl6eNm3apPvvv9+93+FwKDU1VX379lVJSYmeeeYZPfHEE3r55Zfb4CEDaG9Op9P9+9TUVN1yyy1KTEzULbfcotTU1GbnAPi/AKsVF67/4x//qI8//liFhYXN7rcsS9HR0XrkkUf06KOPSpJqamoUGRmpZcuW6fbbb9cXX3yhhIQEbd26VVdddZUk6f3339fYsWN14MABRUdHa8mSJfqP//gPVVRUKDQ01P1n5+bmateuXc3+2XV1dR5f5ONwOBQTE6MjR47Ibre39CEC8II+ffqoqqrqR+d69uypAwcOtMOKAJyPw+FQjx49VFNTc97X0OAf3NOMf/zjH0pLS9Ott96qgoIC9e7dWw8++KDuu+8+SVJ5ebkqKio0evRo989ERERo+PDhKioq0u23366ioiJ17drVHRCSNHr0aAUGBmrLli265ZZbVFRUpBEjRrgDQpLS0tL05z//Wd99953HkQ+XefPmac6cOedsX7duncLDw1vzMAG0sbOvDXG+udWrV3t5NQB+TEvfWmxVRHz11VdasmSJsrOz9dhjj2nr1q36wx/+oNDQUE2cOFEVFRWSzr1gTGRkpHtfRUWFevbs6bmI4GB169bNY+bsz4u77rOioqLZiJg1a5ays7Pdt11HIlJTUzkSAfhYYmKiCgoKWjQ3duzYdlgRgPNxOBwtmmtVRDQ1Nemqq67SU089JUkaMmSISktL9dJLL2nixImtX2Ubstlsstls52wPCQnxuKwugPY3YMCAFkXEgAEDeL4CfqClz8NWnVjZq1cvJSQkeGy77LLLtH//fklSVFSUJKmystJjprKy0r0vKirqnPdGGxsbdezYMY+Z5u7jzD8DQMcRGNiyf2paOgfAP7TqGXvNNddo9+7dHtv27Nmjvn37Svr+krVRUVFav369e7/D4dCWLVvcV6JLTk5WdXW1SkpK3DMbNmxQU1OThg8f7p7ZtGmTxzX18/PzNWDAgGbfygAAAO2vVRExbdo0FRcX66mnntLevXv1xhtv6OWXX1ZWVpYkKSAgQFOnTtWTTz6pf/zjH9q+fbvuuusuRUdHKyMjQ9L3Ry5uuOEG3Xffffrkk0/08ccfa8qUKbr99tsVHR0tSfrNb36j0NBQTZo0STt27NCbb76pRYsWeZzzAKDjaOnJzZwEDXQwViu999571qBBgyybzWYNHDjQevnllz32NzU1Wf/n//wfKzIy0rLZbNavfvUra/fu3R4zR48ete644w6rS5cult1ut+6++27r+PHjHjOff/65de2111o2m83q3bu39fTTT7dqnTU1NZYkq6amprUPEUAbGzp0qCXpR38NHTrU10sFYLX8NbRV14noSBwOhyIiIn70M64AvG/YsGH67LPPfnRu6NChHm91AvCNlr6GturTGQBgIjj4f/+pCQwMVFxcnE6fPq2wsDCVl5erqanpnDkA/o9nLACvc10DRvr+o+Jffvnlj84B8H98ngqA13333XdtOgfAPxARALyupR/N5iPcQMfC2xkAvG7QoEHui9JJ0iWXXKKAgABZlqWysjKPOQAdBxEBwOv69OnjcfvMcDjfHAD/xtsZALyuJV8D3po5AP6BiADgdS39zhu+GwfoWIgIAF53ySWXtOkcAP9ARADwuv79+7fpHAD/QEQA8LrZs2d73I6NjdVVV12l2NjY884B8G98dwYArwsNDVVDQ4MkuT/a6XLm7ZCQENXX1/tkjQD+V0tfQzkSAcDrGhsbJX3/vRm9e/f22Ne7d28FBgZ6zAHoGIgIAF5ns9kkff+9GQcOHPDYd+DAAfcXcLnmAHQMRAQAr7vuuuvadA6AfyAiAHjd/fff36ZzAPwDEQHA695+++02nQPgH4gIAF735ZdftukcAP9ARADwujO/wTMgIMBj35m3z5wD4P+ICABed+ZHN8++NM2Zt/mIJ9CxEBEAvC4kJKRN5wD4ByICgNfFxMS06RwA/0BEAPC6Tp06tekcAP9ARADwuqNHj7bpHAD/QEQA8LpDhw616RwA/0BEAPC6ln4nBt+dAXQsRAQArzv7mzt/6hwA/0BEAPC6s68N8VPnAPgHIgKA17X0SpRcsRLoWIgIAF7X0NDQpnMA/AMRAcDrWno5ay57DXQsRAQAr6urq2vTOQD+gYgA4HVEBPDzREQAAAAjRAQAr+O7M4Cfp2BfLwBAx3Lq1Cnt2rWrVT9z8cUXa+fOnS2a++yzz1q9poEDByo8PLzVPwfgpyEiALTKrl27NGzYMK/c986dO43uu6SkREOHDvXCigCcDxEBoFUGDhyokpKSVv1McXGxsrKyfnTuxRdfVFJSktGaALS/AOtnep1Zh8OhiIgI1dTUyG63+3o5wC+a0+lUt27d5HA4FBAQ4HF5a9dtu92uY8eOKSgoyIcrBSC1/DWUEysBeF1QUJBeffXV8868+uqrBATQwRARANpFZmam3n77bcXGxnps79u3r95++21lZmb6aGUATPF2BoB25XQ69eqKPM1642PN+801uvvWX3MEAvAzLX0N5cRKAO0qKChIVyVfq87bgnVVchIBAXRgvJ0BAACMEBEAAMAIEQEAAIwQEQAAwAgRAQAAjBARAADACBEBAACMEBEAAMAIEQEAAIwQEQAAwAgRAQAAjBARAADACBEBAACMEBEAAMAIEQEAAIwQEQAAwAgRAQAAjBARAADACBEBAACMEBEAAMAIEQEAAIwQEQAAwAgRAQAAjBARAADACBEBAACMEBEAAMAIEQEAAIwQEQAAwAgRAQAAjBARAADACBEBAACMEBEAAMAIEQEAAIwQEQAAwAgRAQAAjBARAADACBEBAACMEBEAAMAIEQEAAIwQEQAAwAgRAQAAjBARAADACBEBAACMEBEAAMAIEQEAAIwQEQAAwAgRAQAAjBARAADACBEBAACMEBEAAMAIEQEAAIwQEQAAwAgRAQAAjBARAADACBEBAACMEBEAAMAIEQEAAIwQEQAAwAgRAQAAjBARAADACBEBAACMEBEAAMBIqyLiiSeeUEBAgMevgQMHuvfX1tYqKytL3bt3V5cuXTRu3DhVVlZ63Mf+/fuVnp6u8PBw9ezZU9OnT1djY6PHzMaNGzV06FDZbDbFx8dr2bJl5o8QAAB4RauPRFx++eU6dOiQ+9dHH33k3jdt2jS99957WrFihQoKCnTw4EFlZma69zudTqWnp6u+vl6bN2/Wa6+9pmXLlmn27NnumfLycqWnp2vUqFHatm2bpk6dqnvvvVdr1679iQ8VAAC0peBW/0BwsKKios7ZXlNTo1deeUVvvPGGrr/+eknSq6++qssuu0zFxcVKSkrSunXrtHPnTn3wwQeKjIzUlVdeqT/96U+aOXOmnnjiCYWGhuqll15SXFycFixYIEm67LLL9NFHH2nhwoVKS0v7iQ8XAAC0lVZHRFlZmaKjo9WpUyclJydr3rx5io2NVUlJiRoaGjR69Gj37MCBAxUbG6uioiIlJSWpqKhIiYmJioyMdM+kpaVp8uTJ2rFjh4YMGaKioiKP+3DNTJ069bzrqqurU11dnfu2w+GQJDU0NKihoaG1DxOAF7newmxsbOT5Cfihlj4vWxURw4cP17JlyzRgwAAdOnRIc+bMUUpKikpLS1VRUaHQ0FB17drV42ciIyNVUVEhSaqoqPAICNd+177zzTgcDp0+fVphYWHNrm3evHmaM2fOOdvXrVun8PDw1jxMAF72zQlJClZxcbG+LfX1agCc7dSpUy2aa1VE3Hjjje7fDx48WMOHD1ffvn311ltv/eCLe3uZNWuWsrOz3bcdDodiYmKUmpoqu93uw5UBONvn+49J2z9VUlKSrojt5uvlADiL62j+j2n12xln6tq1qy699FLt3btXY8aMUX19vaqrqz2ORlRWVrrPoYiKitInn3zicR+uT2+cOXP2JzoqKytlt9vPGyo2m002m+2c7SEhIQoJCTF6fAC8Izg42P1fnp+A/2np8/InXSfixIkT+vLLL9WrVy8NGzZMISEhWr9+vXv/7t27tX//fiUnJ0uSkpOTtX37dlVVVbln8vPzZbfblZCQ4J458z5cM677AAAA/qFVEfHoo4+qoKBA+/bt0+bNm3XLLbcoKChId9xxhyIiIjRp0iRlZ2frww8/VElJie6++24lJycrKSlJkpSamqqEhARNmDBBn3/+udauXavHH39cWVlZ7qMIDzzwgL766ivNmDFDu3bt0uLFi/XWW29p2rRpbf/oAQCAsVa9nXHgwAHdcccdOnr0qC666CJde+21Ki4u1kUXXSRJWrhwoQIDAzVu3DjV1dUpLS1Nixcvdv98UFCQ8vLyNHnyZCUnJ6tz586aOHGi5s6d656Ji4vTqlWrNG3aNC1atEh9+vTR0qVL+XgnAAB+JsCyLMvXi/AGh8OhiIgI1dTUcGIl4Ge2fX1UGUuKlTs5SVf27e7r5QA4S0tfQ/nuDAAAYISIAAAARogIAABghIgAAABGiAgAAGCEiAAAAEaICAAAYISIAAAARogIAABghIgAAABGiAgAAGCEiAAAAEaICAAAYISIAAAARogIAABghIgAAABGiAgAAGCEiAAAAEaICAAAYISIAAAARogIAABghIgAAABGiAgAAGCEiAAAAEaICAAAYISIAAAARogIAABghIgAAABGiAgAAGCEiAAAAEaICAAAYISIAAAARogIAABghIgAAABGiAgAAGCEiAAAAEaICAAAYISIAAAARogIAABghIgAAABGiAgAAGCEiAAAAEaICAAAYISIAAAARogIAABghIgAAABGiAgAAGCEiAAAAEaICAAAYISIAAAARogIAABghIgAAABGiAgAAGCEiAAAAEaICAAAYISIAAAARogIAABghIgAAABGiAgAAGCEiAAAAEaICAAAYISIAAAARogIAABghIgAAABGiAgAAGCEiAAAAEaICAAAYISIAAAARogIAABghIgAAABGiAgAAGCEiAAAAEaICAAAYISIAAAARogIAABghIgAAABGiAgAAGCEiAAAAEaICAAAYISIAAAARogIAABghIgAAABGiAgAAGCEiAAAAEaICAAAYISIAAAARogIAABghIgAAABGiAgAAGCEiAAAAEaICAAAYISIAAAARogIAABghIgAAABGiAgAAGCEiAAAAEaICAAAYISIAAAARogIAABghIgAAABGiAgAAGCEiAAAAEaICAAAYISIAAAARogIAABghIgAAABGflJEPP300woICNDUqVPd22pra5WVlaXu3burS5cuGjdunCorKz1+bv/+/UpPT1d4eLh69uyp6dOnq7Gx0WNm48aNGjp0qGw2m+Lj47Vs2bKfslQAANDGjCNi69at+tvf/qbBgwd7bJ82bZree+89rVixQgUFBTp48KAyMzPd+51Op9LT01VfX6/Nmzfrtdde07JlyzR79mz3THl5udLT0zVq1Cht27ZNU6dO1b333qu1a9eaLhcAALSxYJMfOnHihO688079/e9/15NPPuneXlNTo1deeUVvvPGGrr/+eknSq6++qssuu0zFxcVKSkrSunXrtHPnTn3wwQeKjIzUlVdeqT/96U+aOXOmnnjiCYWGhuqll15SXFycFixYIEm67LLL9NFHH2nhwoVKS0trdk11dXWqq6tz33Y4HJKkhoYGNTQ0mDxMAF7iOvLY2NjI8xPwQy19XhpFRFZWltLT0zV69GiPiCgpKVFDQ4NGjx7t3jZw4EDFxsaqqKhISUlJKioqUmJioiIjI90zaWlpmjx5snbs2KEhQ4aoqKjI4z5cM2e+bXK2efPmac6cOedsX7duncLDw00eJgAv+eaEJAWruLhY35b6ejUAznbq1KkWzbU6Iv7nf/5Hn332mbZu3XrOvoqKCoWGhqpr164e2yMjI1VRUeGeOTMgXPtd+84343A4dPr0aYWFhZ3zZ8+aNUvZ2dnu2w6HQzExMUpNTZXdbm/twwTgRZ/vPyZt/1RJSUm6Irabr5cD4Cyuo/k/plUR8c033+jhhx9Wfn6+OnXqZLQwb7HZbLLZbOdsDwkJUUhIiA9WBOCHBAcHu//L8xPwPy19XrbqxMqSkhJVVVVp6NChCg4OVnBwsAoKCvT8888rODhYkZGRqq+vV3V1tcfPVVZWKioqSpIUFRV1zqc1XLd/bMZutzd7FAIAALS/VkXEr371K23fvl3btm1z/7rqqqt05513un8fEhKi9evXu39m9+7d2r9/v5KTkyVJycnJ2r59u6qqqtwz+fn5stvtSkhIcM+ceR+uGdd9AAAA32vV2xkXXHCBBg0a5LGtc+fO6t69u3v7pEmTlJ2drW7duslut+uhhx5ScnKykpKSJEmpqalKSEjQhAkTNH/+fFVUVOjxxx9XVlaW++2IBx54QC+88IJmzJihe+65Rxs2bNBbb72lVatWtcVjBgAAbcDo0xnns3DhQgUGBmrcuHGqq6tTWlqaFi9e7N4fFBSkvLw8TZ48WcnJyercubMmTpyouXPnumfi4uK0atUqTZs2TYsWLVKfPn20dOnSH/x4JwAAaH8BlmVZvl6ENzgcDkVERKimpoZPZwB+ZtvXR5WxpFi5k5N0Zd/uvl4OgLO09DWU784AAABGiAgAAGCEiAAAAEaICAAAYISIAAAARogIAABghIgAAABGiAgAAGCEiAAAAEaICAAAYISIAAAARogIAABghIgAAABGiAgAAGCEiAAAAEaICAAAYISIAAAARoJ9vQAA7af8yEmdrGv09TL05eGT7v8GB/v+n6HOtmDF9ejs62UAHY7vn70A2kX5kZMa9exGXy/DwyMrt/t6CW4fPnodIQG0EhEB/EK4jkD85bYrFd+zi2/XcrpOeRuL9OvrktU5zObTteytOqGpb27ziyM0QEdDRAC/MPE9u2hQ7wifrqGhoUEVF0lD+16okJAQn64FgDlOrAQAAEaICAAAYISIAAAARogIAABghIgAAABGiAgAAGCEiAAAAEaICAAAYISIAAAARogIAABghIgAAABGiAgAAGCEiAAAAEaICAAAYISIAAAARogIAABghIgAAABGiAgAAGCEiAAAAEaICAAAYISIAAAARogIAABghIgAAABGiAgAAGCEiAAAAEaICAAAYISIAAAARogIAABghIgAAABGiAgAAGCEiAAAAEaICAAAYISIAAAARogIAABghIgAAABGiAgAAGCEiAAAAEaICAAAYISIAAAARogIAABghIgAAABGiAgAAGCEiAAAAEaICAAAYISIAAAARogIAABghIgAAABGiAgAAGCEiAAAAEaICAAAYISIAAAARogIAABghIgAAABGiAgAAGCEiAAAAEaICAAAYISIAAAARogIAABghIgAAABGiAgAAGCEiAAAAEaICAAAYISIAAAARogIAABghIgAAABGiAgAAGCEiAAAAEaICAAAYISIAAAARogIAABghIgAAABGgn29AADto85Zq8BO36rcsVuBnbr4dC2NjY062HhQXxz7QsHBvv1nqNxxQoGdvlWds1ZShE/XAnQ0RATwC3Hw5NfqHPdXPfaJr1fyvxa/v9jXS5AkdY6TDp68UsMU6eulAB0KEQH8QkR37quT5Q9p0W1Xqn9P3x+J+Pijj3XNtdf4/EjEl1Un9PCb2xQ9qq9P1wF0REQE8AthC+qkptreirMPUEJ33x62b2hoUHlwuS7rdplCQkJ8upam2ho11R6WLaiTT9cBdEScWAkAAIwQEQAAwAgRAQAAjBARAADACBEBAACMEBEAAMAIEQEAAIwQEQAAwAgRAQAAjLQqIpYsWaLBgwfLbrfLbrcrOTlZa9asce+vra1VVlaWunfvri5dumjcuHGqrKz0uI/9+/crPT1d4eHh6tmzp6ZPn67GxkaPmY0bN2ro0KGy2WyKj4/XsmXLzB8hAADwilZFRJ8+ffT000+rpKREn376qa6//nrdfPPN2rFjhyRp2rRpeu+997RixQoVFBTo4MGDyszMdP+80+lUenq66uvrtXnzZr322mtatmyZZs+e7Z4pLy9Xenq6Ro0apW3btmnq1Km69957tXbt2jZ6yAAAoE1YP9GFF15oLV261KqurrZCQkKsFStWuPd98cUXliSrqKjIsizLWr16tRUYGGhVVFS4Z5YsWWLZ7Xarrq7OsizLmjFjhnX55Zd7/Bm33XablZaW1qp11dTUWJKsmpoa04cG/KxsP1Bt9Z2ZZ20/UO3rpVj19fVWbm6uVV9f7+ul+NXfC+AvWvoaavwFXE6nUytWrNDJkyeVnJyskpISNTQ0aPTo0e6ZgQMHKjY2VkVFRUpKSlJRUZESExMVGfm/X7eblpamyZMna8eOHRoyZIiKioo87sM1M3Xq1POup66uTnV1de7bDodD0vdf9NPQ0GD6MIGfDdfbho2NjT5/Trj+fF+vQ/KvvxfAX7T0udDqiNi+fbuSk5NVW1urLl266J133lFCQoK2bdum0NBQde3a1WM+MjJSFRUVkqSKigqPgHDtd+0734zD4dDp06cVFhbW7LrmzZunOXPmnLN93bp1Cg8Pb+3DBH52vjkhScH66KOP9LVvvwncLT8/39dL8Mu/F8DXTp061aK5VkfEgAEDtG3bNtXU1GjlypWaOHGiCgoKWr3AtjZr1ixlZ2e7bzscDsXExCg1NVV2u92HKwP8w46DDj27vVjXXnutLo/27XOioaFB+fn5GjNmjM+/Ctyf/l4Af+E6mv9jWh0RoaGhio+PlyQNGzZMW7du1aJFi3Tbbbepvr5e1dXVHkcjKisrFRUVJUmKiorSJ5984nF/rk9vnDlz9ic6KisrZbfbf/AohCTZbDbZbLZztoeEhPj8HynAHwQHB7v/6y/PCX94fvrj3wvgay19Lvzk60Q0NTWprq5Ow4YNU0hIiNavX+/et3v3bu3fv1/JycmSpOTkZG3fvl1VVVXumfz8fNntdiUkJLhnzrwP14zrPgAAgH9o1ZGIWbNm6cYbb1RsbKyOHz+uN954Qxs3btTatWsVERGhSZMmKTs7W926dZPdbtdDDz2k5ORkJSUlSZJSU1OVkJCgCRMmaP78+aqoqNDjjz+urKws91GEBx54QC+88IJmzJihe+65Rxs2bNBbb72lVatWtf2jBwAAxloVEVVVVbrrrrt06NAhRUREaPDgwVq7dq3GjBkjSVq4cKECAwM1btw41dXVKS0tTYsXL3b/fFBQkPLy8jR58mQlJyerc+fOmjhxoubOneueiYuL06pVqzRt2jQtWrRIffr00dKlS5WWltZGDxkAALSFVkXEK6+8ct79nTp10osvvqgXX3zxB2f69u2r1atXn/d+rrvuOv3zn/9szdIAAEA747szAACAESICAAAYISIAAIARIgIAABghIgAAgBEiAgAAGCEiAACAESICAAAYISIAAIARIgIAABghIgAAgBEiAgAAGCEiAACAESICAAAYISIAAIARIgIAABghIgAAgBEiAgAAGCEiAACAESICAAAYISIAAIARIgIAABghIgAAgBEiAgAAGCEiAACAESICAAAYISIAAIARIgIAABghIgAAgBEiAgAAGCEiAACAESICAAAYISIAAIARIgIAABghIgAAgBEiAgAAGCEiAACAESICAAAYISIAAIARIgIAABghIgAAgBEiAgAAGCEiAACAESICAAAYISIAAICRYF8vAED7ON3glCSVflvj45VIJ0/X6dPDUtTX36lzmM2na9lbdcKnfz7QkRERwC/El//vxfKPOdt9vBKXYP3fvVt9vQi3zjb+OQRai2cN8AuRenmUJKl/zy4KCwny6Vp2H6rRIyu3a8H4RA3oFeHTtUjfB0Rcj86+XgbQ4RARwC9Et86huv3/i/X1MiRJjY2NkqT+F3XWoN6+jwgAZjixEgAAGCEiAACAESICAAAYISIAAIARIgIAABghIgAAgBEiAgAAGCEiAACAESICAAAYISIAAIARIgIAABghIgAAgBEiAgAAGCEiAACAESICAAAYISIAAIARIgIAABghIgAAgBEiAgAAGCEiAACAESICAAAYISIAAIARIgIAABghIgAAgBEiAgAAGCEiAACAESICAAAYISIAAIARIgIAABghIgAAgBEiAgAAGCEiAACAESICAAAYISIAAIARIgIAABghIgAAgBEiAgAAGCEiAACAESICAAAYISIAAIARIgIAABghIgAAgBEiAgAAGCEiAACAESICAAAYISIAAIARIgIAABghIgAAgBEiAgAAGCEiAACAESICAAAYISIAAIARIgIAABghIgAAgBEiAgAAGCEiAACAESICAAAYaVVEzJs3T1dffbUuuOAC9ezZUxkZGdq9e7fHTG1trbKystS9e3d16dJF48aNU2VlpcfM/v37lZ6ervDwcPXs2VPTp09XY2Ojx8zGjRs1dOhQ2Ww2xcfHa9myZWaPEAAAeEWrIqKgoEBZWVkqLi5Wfn6+GhoalJqaqpMnT7pnpk2bpvfee08rVqxQQUGBDh48qMzMTPd+p9Op9PR01dfXa/PmzXrttde0bNkyzZ492z1TXl6u9PR0jRo1Stu2bdPUqVN17733au3atW3wkAEAQFsIsCzLMv3hw4cPq2fPniooKNCIESNUU1Ojiy66SG+88YbGjx8vSdq1a5cuu+wyFRUVKSkpSWvWrNGvf/1rHTx4UJGRkZKkl156STNnztThw4cVGhqqmTNnatWqVSotLXX/Wbfffruqq6v1/vvvN7uWuro61dXVuW87HA7FxMToyJEjstvtpg8RgBd8vv+Yxv/9U6287ypdEdvN18sBcBaHw6EePXqopqbmvK+hwT/lD6mpqZEkdev2/T8CJSUlamho0OjRo90zAwcOVGxsrDsiioqKlJiY6A4ISUpLS9PkyZO1Y8cODRkyREVFRR734ZqZOnXqD65l3rx5mjNnzjnb161bp/Dw8J/yMAG0sW9OSFKwiouL9W3pj00DaG+nTp1q0ZxxRDQ1NWnq1Km65pprNGjQIElSRUWFQkND1bVrV4/ZyMhIVVRUuGfODAjXfte+8804HA6dPn1aYWFh56xn1qxZys7Odt92HYlITU3lSATgZz7ff0za/qmSkpI4EgH4IYfD0aI544jIyspSaWmpPvroI9O7aFM2m002m+2c7SEhIQoJCfHBigD8kODgYPd/eX4C/qelz0ujj3hOmTJFeXl5+vDDD9WnTx/39qioKNXX16u6utpjvrKyUlFRUe6Zsz+t4br9YzN2u73ZoxAAAKD9tSoiLMvSlClT9M4772jDhg2Ki4vz2D9s2DCFhIRo/fr17m27d+/W/v37lZycLElKTk7W9u3bVVVV5Z7Jz8+X3W5XQkKCe+bM+3DNuO4DAAD4XqvezsjKytIbb7yhd999VxdccIH7HIaIiAiFhYUpIiJCkyZNUnZ2trp16ya73a6HHnpIycnJSkpKkiSlpqYqISFBEyZM0Pz581VRUaHHH39cWVlZ7rcjHnjgAb3wwguaMWOG7rnnHm3YsEFvvfWWVq1a1cYPHwAAGLNaQVKzv1599VX3zOnTp60HH3zQuvDCC63w8HDrlltusQ4dOuRxP/v27bNuvPFGKywszOrRo4f1yCOPWA0NDR4zH374oXXllVdaoaGh1sUXX+zxZ7RETU2NJcmqqalp1c8B8L5/7jti9Z2ZZ/1z3xFfLwVAM1r6GvqTrhPhzxwOhyIiIn70M64A2t+2r48qY0mxcicn6cq+3X29HABnaelrKN+dAQAAjBARAADACBEBAACMEBEAAMAIEQEAAIwQEQAAwAgRAQAAjBARAADACBEBAACMEBEAAMAIEQEAAIwQEQAAwAgRAQAAjBARAADACBEBAACMEBEAAMAIEQEAAIwQEQAAwAgRAQAAjBARAADACBEBAACMEBEAAMAIEQEAAIwQEQAAwAgRAQAAjBARAADACBEBAACMEBEAAMAIEQEAAIwQEQAAwAgRAQAAjBARAADACBEBAACMEBEAAMAIEQEAAIwQEQAAwAgRAQAAjBARAADACBEBAACMEBEAAMAIEQEAAIwQEQAAwAgRAQAAjBARAADACBEBAACMEBEAAMAIEQEAAIwQEQAAwAgRAQAAjBARAADACBEBAACMEBEAAMAIEQEAAIwQEQAAwAgRAQAAjBARAADACBEBAACMEBEAAMAIEQEAAIwQEQAAwAgRAQAAjBARAADACBEBAACMEBEAAMAIEQEAAIwQEQAAwAgRAQAAjBARAADACBEBAACMEBEAAMAIEQEAAIwQEQAAwAgRAQAAjBARAADACBEBAACMEBEAAMAIEQEAAIwQEQAAwAgRAQAAjBARAADACBEBAACMEBEAAMAIEQEAAIwQEQAAwAgRAQAAjBARAADACBEBAACMEBEAAMAIEQEAAIwQEQAAwAgRAQAAjBARAADACBEBAACMEBEAAMAIEQEAAIwQEQAAwAgRAQAAjBARAADACBEBAACMEBEAAMAIEQEAAIwQEQAAwAgRAQAAjBARAADACBEBAACMtDoiNm3apJtuuknR0dEKCAhQbm6ux37LsjR79mz16tVLYWFhGj16tMrKyjxmjh07pjvvvFN2u11du3bVpEmTdOLECY+Zf/3rX0pJSVGnTp0UExOj+fPnt/7RAQAAr2l1RJw8eVJXXHGFXnzxxWb3z58/X88//7xeeuklbdmyRZ07d1ZaWppqa2vdM3feead27Nih/Px85eXladOmTbr//vvd+x0Oh1JTU9W3b1+VlJTomWee0RNPPKGXX37Z4CECAACvsH4CSdY777zjvt3U1GRFRUVZzzzzjHtbdXW1ZbPZrOXLl1uWZVk7d+60JFlbt251z6xZs8YKCAiwvv32W8uyLGvx4sXWhRdeaNXV1blnZs6caQ0YMKDFa6upqbEkWTU1NaYPD4CX/HPfEavvzDzrn/uO+HopAJrR0tfQ4LYMkvLyclVUVGj06NHubRERERo+fLiKiop0++23q6ioSF27dtVVV13lnhk9erQCAwO1ZcsW3XLLLSoqKtKIESMUGhrqnklLS9Of//xnfffdd7rwwgvP+bPr6upUV1fnvu1wOCRJDQ0NamhoaMuHCeAnamxsdP+X5yfgf1r6vGzTiKioqJAkRUZGemyPjIx076uoqFDPnj09FxEcrG7dunnMxMXFnXMfrn3NRcS8efM0Z86cc7avW7dO4eHhho8IgDd8c0KSglVcXKxvS329GgBnO3XqVIvm2jQifGnWrFnKzs5233Y4HIqJiVFqaqrsdrsPVwbgbJ/vPyZt/1RJSUm6Irabr5cD4Cyuo/k/pk0jIioqSpJUWVmpXr16ubdXVlbqyiuvdM9UVVV5/FxjY6OOHTvm/vmoqChVVlZ6zLhuu2bOZrPZZLPZztkeEhKikJAQswcEwCuCg4Pd/+X5Cfiflj4v2/Q6EXFxcYqKitL69evd2xwOh7Zs2aLk5GRJUnJysqqrq1VSUuKe2bBhg5qamjR8+HD3zKZNmzzek8nPz9eAAQOafSsDAAC0v1ZHxIkTJ7Rt2zZt27ZN0vcnU27btk379+9XQECApk6dqieffFL/+Mc/tH37dt11112Kjo5WRkaGJOmyyy7TDTfcoPvuu0+ffPKJPv74Y02ZMkW33367oqOjJUm/+c1vFBoaqkmTJmnHjh168803tWjRIo+3KwAAgG+1+u2MTz/9VKNGjXLfdr2wT5w4UcuWLdOMGTN08uRJ3X///aqurta1116r999/X506dXL/zH//939rypQp+tWvfqXAwECNGzdOzz//vHt/RESE1q1bp6ysLA0bNkw9evTQ7NmzPa4lAQAAfCvAsizL14vwBofDoYiICNXU1HBiJeBntn19VBlLipU7OUlX9u3u6+UAOEtLX0P57gwAAGCEiAAAAEaICAAAYISIAAAARogIAABghIgAAABGiAgAAGCEiAAAAEaICAAAYISIAAAARogIAABghIgAAABGiAgAAGCEiAAAAEaICAAAYISIAAAARogIAABghIgAAABGiAgAAGCEiAAAAEaICAAAYISIAAAARogIAABghIgAAABGiAgA7aqoqEhD+vXQ13/+tYb066GioiJfLwmAoWBfLwDAL0dAQMA52/7t3/5NkmRZVnsvB8BPxJEIAO2iuYBozX4A/oeIAOB1LX3Lgrc2gI6FtzMAtMqpU6e0a9euVv2M6y2LlsyVlJS0ek0DBw5UeHh4q38OwE9DRABolV27dmnYsGFeu3+T+y4pKdHQoUO9sBoA50NEAGiVgQMHtvpoQWvCwPRIBID2R0QAaJXw8HCv/l8/RxSAjoMTKwEAgBEiAgAAGCEiAACAESICAAAYISIAAIARIgIAABghIgAAgBEiAgAAGCEiAACAESICAAAYISIAAIARIgIAABghIgAAgBEiAgAAGCEiAACAESICAAAYISIAAIARIgIAABghIgAAgBEiAgAAGCEiAACAESICAAAYISIAAIARIgIAABghIgAAgBEiAgAAGCEiAACAESICAAAYISIAAIARIgIAABghIgAAgBEiAgAAGCEiAACAESICAAAYISIAAIARIgIAABghIgAAgBEiAgAAGCEiAACAESICAAAYISIAAIARIgIAABghIgAAgBEiAgAAGCEiAACAESICAAAYISIAAIARIgIAABghIgAAgBEiAgAAGCEiAACAESICAAAYISIAAIARIgIAABghIgAAgBEiAgAAGCEiAACAESICAAAYISIAAIARIgIAABghIgAAgBEiAgAAGCEiAACAESICAAAYISIAAIARIgIAABghIgAAgBEiAgAAGCEiAACAESICAAAYISIAAIARIgIAABghIgAAgBEiAgAAGPHriHjxxRfVr18/derUScOHD9cnn3zi6yUBAID/x28j4s0331R2drb+8z//U5999pmuuOIKpaWlqaqqytdLAwAAkgIsy7J8vYjmDB8+XFdffbVeeOEFSVJTU5NiYmL00EMP6Y9//OM583V1daqrq3PfdjgciomJ0ZEjR2S329tt3QDOFRoa2uLZ+vp6L64EQEs4HA716NFDNTU1530NDW7HNbVYfX29SkpKNGvWLPe2wMBAjR49WkVFRc3+zLx58zRnzpxztq9bt07h4eFeWyuAHxcQEKCW/P9KQECAVq9e3Q4rAnA+p06datGcX0bEkSNH5HQ6FRkZ6bE9MjJSu3btavZnZs2apezsbPdt15GI1NRUjkQAPnbVVVdp69atLZobO3ZsO6wIwPk4HI4WzfllRJiw2Wyy2WznbA8JCVFISIgPVgTAJT8/X127dm3RHM9XwPda+jz0yxMre/TooaCgIFVWVnpsr6ysVFRUlI9WBcBURESE+vfvf96Z/v37KyIiop1WBKAt+GVEhIaGatiwYVq/fr17W1NTk9avX6/k5GQfrgyAqb179/5gSPTv31979+5t5xUB+Kn8MiIkKTs7W3//+9/12muv6YsvvtDkyZN18uRJ3X333b5eGgBDe/fuVXV1tZKTk9WjRw8lJyerurqagAA6KL89J+K2227T4cOHNXv2bFVUVOjKK6/U+++/f87JlgA6loiICBUUFGj16tUaO3Ys50AAHZjfRoQkTZkyRVOmTPH1MgAAQDP89u0MAADg34gIAABghIgAAABGiAgAAGCEiAAAAEaICAAAYISIAAAARogIAABghIgAAABGiAgAAGCEiAAAAEaICAAAYISIAAAARogIAABghIgAAABGiAgAAGCEiAAAAEaICAAAYISIAAAARogIAABghIgAAABGiAgAAGCEiAAAAEaICAAAYISIAAAARogIAABghIgAAABGiAgAAGCEiAAAAEaICAAAYISIAAAARogIAABghIgAAABGgn29AG+xLEuS5HA4fLwSAGdraGjQqVOn5HA4FBIS4uvlADiL67XT9Vr6Q362EXH8+HFJUkxMjI9XAgBAx3T8+HFFRET84P4A68cyo4NqamrSwYMHdcEFFyggIMDXywFwBofDoZiYGH3zzTey2+2+Xg6As1iWpePHjys6OlqBgT985sPPNiIA+C+Hw6GIiAjV1NQQEUAHxomVAADACBEBAACMEBEA2p3NZtN//ud/ymaz+XopAH4CzokAAABGOBIBAACMEBEAAMAIEQEAAIwQEQAAwAgRAeC8rrvuOk2dOlWS1K9fP/3lL3/x6XoA+A8iAkCLbd26Vffff3+LZgkO4OfvZ/sFXADa3kUXXeTrJQDwIxyJAOB28uRJ3XXXXerSpYt69eqlBQsWeOw/8+iCZVl64oknFBsbK5vNpujoaP3hD3+Q9P1bIF9//bWmTZumgIAA95fgHT16VHfccYd69+6t8PBwJSYmavny5R5/xnXXXac//OEPmjFjhrp166aoqCg98cQTHjPV1dX6/e9/r8jISHXq1EmDBg1SXl6ee/9HH32klJQUhYWFKSYmRn/4wx908uTJNv7bAkBEAHCbPn26CgoK9O6772rdunXauHGjPvvss2Zn3377bS1cuFB/+9vfVFZWptzcXCUmJkqScnJy1KdPH82dO1eHDh3SoUOHJEm1tbUaNmyYVq1apdLSUt1///2aMGGCPvnkE4/7fu2119S5c2dt2bJF8+fP19y5c5Wfny/p+2/ovfHGG/Xxxx/r9ddf186dO/X0008rKChIkvTll1/qhhtu0Lhx4/Svf/1Lb775pj766CNNmTLFW39twC+XBQCWZR0/ftwKDQ213nrrLfe2o0ePWmFhYdbDDz9sWZZl9e3b11q4cKFlWZa1YMEC69JLL7Xq6+ubvb8zZ88nPT3deuSRR9y3R44caV177bUeM1dffbU1c+ZMy7Isa+3atVZgYKC1e/fuZu9v0qRJ1v333++xrbCw0AoMDLROnz79o+sB0HIciQAg6fv/g6+vr9fw4cPd27p166YBAwY0O3/rrbfq9OnTuvjii3XffffpnXfeUWNj43n/DKfTqT/96U9KTExUt27d1KVLF61du1b79+/3mBs8eLDH7V69eqmqqkqStG3bNvXp00eXXnpps3/G559/rmXLlqlLly7uX2lpaWpqalJ5efmP/j0AaDlOrARgJCYmRrt379YHH3yg/Px8Pfjgg3rmmWdUUFCgkJCQZn/mmWee0aJFi/SXv/xFiYmJ6ty5s6ZOnar6+nqPubN/PiAgQE1NTZKksLCw867rxIkT+v3vf+8+P+NMsbGxrXmIAH4EEQFAktS/f3+FhIRoy5Yt7hfb7777Tnv27NHIkSOb/ZmwsDDddNNNuummm5SVlaWBAwdq+/btGjp0qEJDQ+V0Oj3mP/74Y91888367W9/K+n78xv27NmjhISEFq9z8ODBOnDggPbs2dPs0YihQ4dq586dio+Pb/F9AjDD2xkAJEldunTRpEmTNH36dG3YsEGlpaX63e9+p8DA5v+ZWLZsmV555RWVlpbqq6++0uuvv66wsDD17dtX0vef5Ni0aZO+/fZbHTlyRJJ0ySWXKD8/X5s3b9YXX3yh3//+96qsrGzVOkeOHKkRI0Zo3Lhxys/PV3l5udasWaP3339fkjRz5kxt3rxZU6ZM0bZt21RWVqZ3332XEysBLyAiALg988wzSklJ0U033aTRo0fr2muv1bBhw5qd7dq1q/7+97/rmmuu0eDBg/XBBx/ovffeU/fu3SVJc+fO1b59+9S/f3/39SUef/xxDR06VGlpabruuusUFRWljIyMVq/z7bff1tVXX6077rhDCQkJmjFjhvuox+DBg1VQUKA9e/YoJSVFQ4YM0ezZsxUdHW32lwLgBwVYlmX5ehEAAKDj4UgEAAAwQkQAAAAjRAQAADBCRAAAACNEBAAAMEJEAAAAI0QEAAAwQkQAAAAjRAQAADBCRAAAACNEBAAAMPL/A9OIx+Srg5BzAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_rides[['distance']].boxplot(figsize=(6,12))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c31514d3",
   "metadata": {},
   "source": [
    "На гистограмме, охватывающей весь диапазон выделяются две основные области распределения расстояний по поездкам:\n",
    "1. Короткие поездки (0-1500 м): Наблюдается небольшая область коротких поездок с пиком, приходящимся  на 750 метров.\n",
    "2. Долгие поездки (примерно 3300 м): Основная доля поездок сосредотачивается в области длинных расстояний, с пиком, приходящимся\n",
    "    на 3300 метров.\n",
    "    \n",
    "Отделено стоит рассмотреть короткие поездки дальностью менее 100м. Решение об их удалении примем после исследования длительности поездок.\n",
    "\n",
    "Поездки на расстояние более 5700 метров, хотя относятся к выбросам согласно диаграмме размаха, оставим в датафрейме, так как они вписываются в логику работы сервиса и не кажутся слишком завышенными.\n",
    "\n",
    "Также оставим поездки с расстоянием менее 800 метров, несмотря на данные боксплота.\n",
    "\n",
    "Рассмотрим срез датафрейма с дистанциями менее 100 и оценим его размер.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1a1c787c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>distance</th>\n",
       "      <th>duration</th>\n",
       "      <th>date</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6611</th>\n",
       "      <td>707</td>\n",
       "      <td>29</td>\n",
       "      <td>15</td>\n",
       "      <td>2021-09-12</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6691</th>\n",
       "      <td>712</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2021-11-22</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6816</th>\n",
       "      <td>721</td>\n",
       "      <td>63</td>\n",
       "      <td>8</td>\n",
       "      <td>2021-09-25</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6850</th>\n",
       "      <td>724</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>2021-05-09</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7367</th>\n",
       "      <td>760</td>\n",
       "      <td>55</td>\n",
       "      <td>9</td>\n",
       "      <td>2021-10-13</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17414</th>\n",
       "      <td>1489</td>\n",
       "      <td>22</td>\n",
       "      <td>7</td>\n",
       "      <td>2021-10-06</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17502</th>\n",
       "      <td>1495</td>\n",
       "      <td>79</td>\n",
       "      <td>10</td>\n",
       "      <td>2021-05-16</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17646</th>\n",
       "      <td>1506</td>\n",
       "      <td>37</td>\n",
       "      <td>5</td>\n",
       "      <td>2021-06-27</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17722</th>\n",
       "      <td>1511</td>\n",
       "      <td>87</td>\n",
       "      <td>4</td>\n",
       "      <td>2021-01-21</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17760</th>\n",
       "      <td>1514</td>\n",
       "      <td>76</td>\n",
       "      <td>6</td>\n",
       "      <td>2021-01-26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id  distance  duration       date  month\n",
       "6611       707        29        15 2021-09-12      9\n",
       "6691       712        24         3 2021-11-22     11\n",
       "6816       721        63         8 2021-09-25      9\n",
       "6850       724        30         4 2021-05-09      5\n",
       "7367       760        55         9 2021-10-13     10\n",
       "...        ...       ...       ...        ...    ...\n",
       "17414     1489        22         7 2021-10-06     10\n",
       "17502     1495        79        10 2021-05-16      5\n",
       "17646     1506        37         5 2021-06-27      6\n",
       "17722     1511        87         4 2021-01-21      1\n",
       "17760     1514        76         6 2021-01-26      1\n",
       "\n",
       "[77 rows x 5 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rides.query('distance<100')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30601a90",
   "metadata": {},
   "source": [
    "Количество таких поездок невилико и составляет менеее 0.5% от общего числа."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97c940cf",
   "metadata": {},
   "source": [
    "##### Исследование продолжительности поездки."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "829adc09",
   "metadata": {},
   "source": [
    "Построим гистограммы для всего диапазона и для диапазона  коротких поездок до 5 мин."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "25317e67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ru3dvtWrVStu3b9cDDzzg8p7Uv/76SwsWLNDZs2cd83Jzc/X444/r999/V4cOHUp1OXNBOTk52rhxoyQV+5vlnrCP8k+aNMlpxNwwDE2ePFl79uxRWFiYBg0a5Chr3ry5evfurb///lu9e/fW4cOHndo8e/asPvroI6/EV5Q1a9ZI8qwfSrOt0rnfTLdf0lzUfcbedtdddyk6OlpHjx7V8OHDnfatgwcP6qmnnpIkPfbYYwoKCiq0/IYNG7R69Wp1797dp0+wt8c5f/58bdiwwalsyZIl+uijjxQQEKAnnnjCMT8iIkKPPfaYsrKy1KtXL+3du7dQuzk5Ofroo4+cEutt27bp9OnTheq+8847jnU3btzYqWzNmjXavHmzwsPDdc8995RqG89PoqtVq6a33npLlSpV0kMPPVTkiaolS5bo66+/1sMPP3xBVxMBwMWCS9MBwA3/+te/9NZbb+n48eOSziXlRT0V2f5FeeXKlTp06JAeeugh3XTTTZo8ebJeeOEFpaenq3HjxpoxY4bHcRw7dswxolSaezvd4efnp5UrV6pHjx5aunSp3n//fTVr1kzR0dHKzc3Vzz//rL179yovL08DBgyQv7+/3njjDY0ZM0ZpaWmqW7duiZeouvLXX39p5MiRjuk//vhD69at02+//aY6derooYce8sr2/fOf/9QXX3yhN998Uy1btlT79u1Vu3Zt7d69WwcOHFBwcLCSk5MdI4p2ixcvVvfu3fXll1+qYcOGuvHGGxUVFaW0tDTt3btXv//+u8dXEBRl4cKFjie1nzlzRl999ZW2bt0qSRozZkyZbuu0adM0b948/frrr/Lz89OaNWscsdjZfzngu+++04ABAxz7+IWwWq16//331bVrV82fP19r1qxR27ZtderUKW3cuFHZ2dmKj4/X+PHjXS7/22+/KSgoSLNnz76gOC5Ut27dNGbMGE2ePFmdO3dWu3btFB0dre+++067d+9WpUqVtGDBgkK3bkybNk3Hjh1TcnKymjdvrmbNmunyyy+Xv7+/fvvtN+3Zs0dZWVn65JNPHPeJT506VSkpKWrWrJnq1q2rs2fP6ptvvtEPP/wg6dxPqF133XWSzn2eHn30Ua1YsUKGYah69ep69NFHC8X/2WefSZJef/11bd682a1L5WNjYzVu3DiNHz9eiYmJWr9+faER799++021atXSc88953GfAsBFyVc/YA4AFUn9+vUNSaV6LV682DAMw7jvvvuMVq1aGdOnTzdOnTrlcj0HDx40JBn169d3mr9p0yZHe/fff3+xMR48eLDI7Vi8eLEhyUhMTCxxm7Ozs40FCxYYHTt2NGrUqGH4+/sbtWvXNpo3b24MGTLEWLdunaPuM888YzRr1swYM2aMceLEiSLbtG+Dq20u+AoKCjKuvvpq4/HHHzeOHDniVlvnGz9+vCHJGD9+vMvy5ORko0OHDkZYWJgREBBg1KtXzxgwYIDx3XffFdlmTk6OMX/+fOPmm282wsLCjMDAQOOyyy4zOnfubMydO7fI5Yp6Xwtq3759oX6oVKmSERERYfTq1ctYv359scsXxZNtdRWDu/u4YZTc74ZR/Ht3+PBhY8iQIcbll19uBAYGGtWqVTNiY2ON+fPnGzabrVB9+z4tyRg3bpzH6ytKSZ+nkj5Ln3zyidG9e3fHZycyMtK46667jG3bthW73jVr1hh33HGHUbduXSMgIMAICwszGjVqZPTt29dITk42srKyHHXfffdd47bbbjOio6ON4OBgIyAgwIiKijJ69eplfPDBB07tFvU5K+nlbp+cPXvWaNeunSHJmDlzpmO+fX+QZCxatKjQcu5+NgDgYmMxDC+dvgeAi1iDBg30yy+/eDTimZSUpAkTJmjx4sVFjp4D5U2HDh106NAhHTp0qMS6mzdvVseOHdnHK4BDhw4pJiZG48ePL/IheucbMGCAli5d6rWrPAAAzrhHHAAAAAAAE3GPOAAAcBg5cqTLB4C5cs0112jx4sVeeUI+ylbNmjW1ePFit3+K7qGHHir0E3oAAO/h0nQAcMP777+v06dPe3T57Z49e7Rnzx7ddNNNuvLKK8suOAAAAFQoJOIAAAAAAJiIe8QBAAAAADARiTgAAAAAACYiEQcAAAAAwEQk4gAAAAAAmIhEHAAAAAAAE5GIAwAAAABgIhJxAAAAAABMRCIOAAAAAICJSMQBAAAAADARiTgAAAAAACYiEQcAAAAAwEQk4gAAAAAAmIhEHAAAAAAAE5GIAwAAAABgIhJxAAAAAABMRCIOAAAAAICJSMQBoAJZtWqV9uzZ45heuXKlvvnmG98FBAAAAI+RiANABbJ371498cQT+uGHH/Tll1/q4Ycf1qlTp3wdFgAAADxAIg4AFchDDz2ko0eP6qqrrlJsbKzatWuntm3b+josAMAlZP/+/eratauqVq2q8PBw/eMf/9Dvv//u67AKsVgsLl/Tpk3zdWiALIZhGL4OAgDgvpycHO3bt0+VK1dWo0aNfB0OAOAS8ttvv+n6669XaGioHn/8cZ0+fVrPP/+8oqOjtX37dgUGBvo6RAeLxaLOnTvr/vvvd5p//fXX69prr/VRVMA5jIjDZzZv3ux0dtJqteqqq67S1KlTVfD80JEjR/Tggw8qIiJCVqtV1157rRYtWlSozezsbCUlJemqq65SUFCQ6tSpozvuuEM//fSTJOnQoUOyWCx6/vnnSx33kiVLXJ5dbdCggVO9AQMGFJr366+/Kjg4WBaLRYcOHXLM/89//qMePXooKipKVqtVV1xxhSZNmqS8vDyn5Tt06FDk2d3Stvnwww+rYcOGqly5ssLDw3Xrrbfqv//9b6Htnjdvnq699lpZrVZFRUVpyJAhSk9PLxRfkyZNCi37/PPPF4rPlaSkJDVu3FhVq1ZVSEiI2rZtq5UrV5Z6He72gcViUVJSktM8+/tcMOZPPvlEN998s6pUqaJq1aqpR48ehe7RHjBggKpWrVooxvfff18Wi0WbN2922p4OHTo41duxY4fjPXUVp9VqVYsWLdSoUSPNnDlTFoulUBsAAJSFKVOmKCsrSxs3btTjjz+u0aNH67333tNXX32lJUuW+Dq8Qq666ir179/f6UUSjvKARBw+N3r0aL355pt65ZVXFB0drdGjRzsl2cePH1fbtm316aefaujQoXr55Zd15ZVXauDAgZo1a5ajXl5ennr27KkJEyaoRYsWeuGFF/TEE08oIyND+/bt83rcEydO1Jtvvqk333xTzZs3d2uZcePGKTs7u9D8JUuWqGrVqho+fLhefvlltWjRQuPGjdPIkSML1b3ssssc67W/7r333lK3mZubq/79+2vOnDkaNWqUDh8+rK5du+rw4cOOOklJSRoyZIiioqL0wgsvKCEhQa+++qq6dOkim83m1ra7IysrS7fffrteeuklTZ48Wbm5uUpISND27dtL1Z4n/eqON998Uz169FDVqlU1ffp0jR07Vt9++61uuummEk8yeOKZZ55xq156erqmTp3qtfUCgBmKOqlc8ISoq3muTj7m5uZq3LhxatGihUJDQ1WlShXdfPPN2rRpk9Oy9pPxRb1K0+aBAwd06623KjIyUlarVfXq1dPDDz+skydPOtU7ceKEBg4cqIiICAUFBalZs2ZaunSpy/hcDRY0adLErROuZ8+e1aRJk3TFFVfIarWqQYMGGj16tHJychx1GjRoUGw/FBxEKOiDDz5Qz549FR0d7ZgXFxenq666Su+9916JMUruD2pI0saNGx0nwMPCwtS7d2/t37/frfXY/f333y6/fxUnKSlJFotFtWvXLvRd59///rcj5j/++MMxv0GDBhowYIBT3eXLlxfaNm+816j4/H0dANC5c2fHP5z7779flStX1u7duzVw4EBJ0rPPPqu8vDzt3btXNWrUkHRuFPfee+9VUlKS/vnPfyo4OFjLli3Thg0b9OKLL+rJJ590tD9y5MhCI+wXwv7PuHv37mrRooUk6Z133tFff/1V7HLffPONli1bpm7duumTTz5xKktOTlZwcLBj+uGHH9bDDz+sefPmafLkybJarY6y0NBQ9e/f32n5tLQ0/fvf/y5VmwWvLOjQoYNat26tHTt2KDo6Wr///rumTp2qLl266JNPPpGf37nzd9dcc42GDh2qt956Sw888ECx2+6umTNnOk3fe++9ql27tv773/+qdevWHrfnbh9YLJYS95HTp0/r8ccf10MPPaSFCxc65icmJurqq6/WlClTnOaX1ieffKJNmzapa9euWrt2bbF1p06dqoCAAMd+CAAVxWWXXeY4kXj69Gk98sgjJS5T1MnHzMxMvf7667r33ns1aNAgnTp1Sm+88Ybi4+O1ffv2QifL7733XnXv3t1p3qhRo0rVZlZWli677DL16tVLISEh2rdvn+bOnasjR47o448/lnQuCezQoYN+/PFHDR06VDExMVq+fLkGDBig9PR0PfHEE+52W4keeughLV26VHfeeaeeeuopbdu2TVOnTtX+/fu1YsUKSdKsWbN0+vRpSefu9Z4yZYpGjx7tuNXJ1RVddkeOHNGJEyfUsmXLQmWtW7fWmjVrPIp34sSJiomJkSS98MILhb5Lffrpp+rWrZsuv/xyJSUl6e+//9acOXPUrl077d69u8STBtK5pH/evHkyDEONGjXSmDFjdN9997kd46lTp7Rq1SrdfvvtjnmLFy9WUFBQicn92bNn9eyzz7q9LlxaSMThcxkZGfrjjz905swZLV26VPn5+br11lslSYZh6IMPPtDdd98twzCczjrGx8frnXfe0e7du9WuXTt98MEHqlmzph577LFC6yh4ie+ZM2f0xx9/qFKlSgoLCytUXhz7P92goCCPtnPUqFG64YYbdNdddxVKxM9PFk+dOqWcnBzdfPPNevXVV/Xdd9+pWbNmHq3L0zazs7N1+vRp/f7773r55ZcVHBzsOMh++umnys3N1bBhwxxJuCQNGjRIo0eP1urVq50S8by8PKf3STrX3+6y2WzKyMhQRkaG5s2bJz8/P7Vr186pjrvrcLcPateurd9++63YuFJSUpSenq57773Xad2VKlVSmzZtCo2SSCoUY0lPNzcMQ6NGjVJCQoKaNGlSbCJ+5MgRzZkzRzNmzND7779fbLsAUJ7YbDaFhYU5Tir/8ccfbiXiRZ18rF69ug4dOuR0b/KgQYN0zTXXaM6cOXrjjTec6t9www2FTmgXfHiXu23ecMMNWrZsmdOyubm5TvMWLlyo/fv366233lK/fv0knTsx3L59e40ZM0YPPvigqlWrVuL2l+Srr77S0qVL9dBDD+m1116TJD366KOqXbu2nn/+eW3atEkdO3ZUnz59HMts3rxZU6ZMcRoUKc6xY8ckSXXq1ClUVqdOHZ08eVI5OTlOAwiuuDuo8fTTTys8PFypqakKDw+XJPXp00fXX3+9xo8fX+iqgoJuvPFG3X333YqJidHRo0c1d+5c9evXTxkZGW7tc5J0++23a9GiRY5E/PDhw9qwYYPuueeeQoMgBb322ms6fPiwOnbsqJ9//tmt9eHSQSIOnzv/gODn56cxY8YoISFBkvT7778rPT1dCxcuLHK08cSJE5Kkn376SVdffbX8/UvercePH6/x48dLOpdQ33rrrZo1a5YaNmxY4rL25Co0NLTEunafffaZPv74Y23YsMHpkm+7b775RmPGjNHGjRuVmZnpVJaRkeH2ekrb5pIlSxwHpMjISKWkpKh+/fqSpF9++UWSdPXVVzstExgYqMsvv9xRbvfdd9+pVq1apYpZkjZs2KBu3bpJkkJCQvT+++8Xeiq4u+twtw9uvPFGvf/++7r77rt1ww03yGKxOEYL7H744QdJcpwkKigkJMRpOisry+N+ePvtt/XNN9/ovffeU3JycrF1x48fr6ioKP3zn/8kEQdQoWRnZ3t8Mru4k4+VKlVSpUqVJEn5+flKT09Xfn6+WrZsqd27d5cqRk/bzMjIUHZ2tvbt26fVq1frlltucZStWbNGkZGRTreRBQQE6PHHH9e9996rLVu2qGfPno4y+2DB+Qo+28QV+2j08OHDneY/9dRTev7557V69Wp17NjRja0v2t9//y1JLhNt+3v6999/l5iIuzOocezYMe3Zs0cjRoxwJOGS1LRpU3Xu3Nmt0ffPP//cafrBBx9UixYtNHr0aA0YMMDphH1RHnzwQXXr1k1paWmKjIzU0qVLFRsbq6uuuqrY5c6cOaOJEydq6NCh+uOPP1wm4qV9r3FxIBGHzz3//PNq1qyZbDabduzYocmTJ8vf31/jx49Xfn6+JKl///5KTEx0uXzTpk09XufgwYN11113KS8vT/v371dSUpL69OlT6KFbrhw6dEgBAQGKiopye33PPPOM4uPjdeuttxZ6kEl6errat2+vkJAQTZw4UVdccYWCgoK0e/duPfPMM44+8ISnbfbq1UtXXnmlTpw4oQULFuiee+7RZ5995tYlXwU1aNDAcSbebvny5W5ftt2qVSulpKTor7/+0ltvvaUHH3xQ9erVc7oMzp11eNIHM2bMUPfu3dW1a9ci47LXf/PNNxUZGVmovOAJoKCgIMdliXb//e9/NXHiRJft5+bmauzYsRo4cGCJB/f9+/dryZIleuuttxQQEFBsXQAob/744w+3Tnyfr6STj0uXLtULL7yg7777zul+Xvtlz6XhSZvx8fHatm2bJKlr16569913HWW//PKLGjZs6HRVmSTHpeAFT2ifP1hwvoiIiGLj/eWXX+Tn56crr7zSaX5kZKTCwsIKrac07Inr+fec29mTa3eSW3cGNYoaCJDO9d26deuUlZWlKlWqlBz4/xcYGKihQ4fq4Ycf1q5du3TTTTeVuEzz5s3VpEkTLVu2TE8//bSWLFmi0aNH69dffy12uRdffFHZ2dkaPXp0oZMjdqV9r3FxIBGHz7Vo0cJxOVS3bt105MgRx4OwatWqpWrVqikvL09xcXHFtnPFFVdo27ZtstlsJSYnDRs2dLQXHx+vM2fO6Nlnn9Xhw4edHj7iys6dO3XDDTcUOqAWZeXKlUpNTS3yrPzmzZv1559/6sMPP3Q6g37w4EG32vdGm3Xr1lXdunUlSXfccYdq1qyp+fPna/r06Y6R8QMHDujyyy93LJObm6uDBw8Wel+qVKlSaN6ePXvcjr1GjRqO5RMSEnT11Vdr5syZTl9q3FmHJ31w5ZVX6ptvvtHevXsdD9hZv3690z3rV1xxhaRzl7GXtC9K50ZTCtYr+JT5882bN08nTpwo9GAiV0aNGqXmzZvrnnvuKbEuAJQnubm5OnbsmDp37uz2MiWdfHzrrbc0YMAA9enTR08//bRq166tSpUqaerUqY5fTfGUp23OmTNHf/zxh7799ltNnTpVDz/8sN56661Srds+WHC+QYMGub28J7fbecp+Sbr9EvXzHTt2TOHh4SWOhkulG9Twlnr16klSoQfqFefBBx/UvHnz1Lp1a6Wlpenuu+/WCy+8UGT9P/74QzNnztSoUaOcRvMLutD3GhUbiTjKnb///ltnz57V2bNnFRgYqISEBCUnJ2vfvn2Ffrbq999/d1z+m5CQoNWrV+uVV15xelibdO7e2+IOTPbRTvtlaEX59ttv9e2332rKlClubUteXp5Gjx6t++67r8gnq9vXef7DwnJzczVv3jy31uHtNjMyMpSbm+s42x0XF6fAwEDNnj1bXbt2dfTjG2+8oYyMDPXo0aPUcZYkOztbWVlZLs+8l8TTPggICNANN9zgmC54z3h8fLxCQkI0ZcoUdezYsdCXwfP3RU+dOnVKzz33nJ588kmXo+3nS01N1fr165WSklLsPv3dd9+pcuXKJZ5YAgAzffXVV7LZbC4f9lWUkk4+vv/++7r88sv14YcfOv1fdDXS6C5P22zVqpWkcwMKtWvX1v33369nn31WjRo1Uv369fX1118rPz/f6ST+d999J0mOE9525w8W2Lkz6lu/fn3l5+frhx9+cIy2S+d+fSY9Pb3Qekqjbt26qlWrlnbu3FmozNWD8YrizqDG+QMBBX333XeqWbOmR6PhdvZLxD05Zvfr109PP/20nnjiCd15550l3tM/efJkVatWrcQH8ZX2vcbFgUQcPpeSkqLffvvNcWn622+/rdtuu83xgJRp06Zp06ZNatOmjQYNGqTGjRvr5MmT2r17tz799FPHGc37779fy5Yt0/Dhw7V9+3bdfPPNysrK0qeffqpHH31UvXv3dqzzwIEDWrt2rfLz8/Xtt99q5syZatWqlWNU2JV169bpX//6l6Rzl12df6b7yJEjysrK0ltvveX0AJjffvtNgYGBxd7HdOONN6p69epKTEzU448/LovFojfffPOCnvTubpt79+7VU089pVtvvVW1a9fW0aNHtWjRIuXn5zvuZatVq5ZGjRqlCRMmqGvXrrrtttt04MABzZs3T61atSr0wJvSysjIULdu3dStWzdFRUXp5MmTevPNN3Xs2LFSrcPb/RoSEqL58+frH//4h2644Qb17dtXtWrV0uHDh7V69Wq1a9dOr7zySqna3r17t2rWrKkRI0aUWHf9+vXq3LlziaPyjRo1Uvv27Z1+sxwAfG358uWqVKmS0z3RxXHn5OP5J17tdbZt26bU1NRSn4y8kDbtl13bTyJ3795d69ev17vvvus4tp49e1Zz5sxR1apV1b59+1LFWFD37t01evRozZo1S6+++qpj/osvvihJXjtxnpCQoKVLl+rXX391jC5v2LBB33//faGBEFfcHdSoU6eOmjdvrqVLl2rUqFEKCwuTJO3bt0/r168v8buBqxPkp06d0qxZs1SzZk2PfnEkPDxcvXv31nvvvafZs2cXW/fQoUNKTU3V3Llz3bpM3x2cXL84kYjD5+z/iP39/VW3bl0NHTpUEyZMcJRHRERo+/btmjhxoj788EPNmzdPNWrU0LXXXqvp06c76lWqVElr1qzRc889p+TkZH3wwQeqUaOGbrrpJl133XVO63z99df1+uuvy8/PT1FRUbr99tv13HPPFRvn1KlTHb9HXtSB5h//+EehA8MjjzxS7L3WNWrU0KpVq/TUU09pzJgxql69uvr3769OnTopPj6+2JgutM2aNWsqODhYs2bN0smTJx0HpjfffFNt2rRx1EtKSlKtWrUcVxuEh4dr8ODBmjJlitfuUbZarYqOjtbChQt14sQJhYWF6dprr9XHH3/s9he20vSBJ+677z5FRUVp2rRpmjlzpnJyclS3bl3dfPPNF/wTbs8++2yhB765YrFYCj3dFwDKu6ysLM2dO1ezZ8/WVVdd5XSS0P5wzK+//lqpqamKjY11lLlz8rFnz5768MMPdfvtt6tHjx46ePCgFixYoMaNGxd68Ka73G1z4sSJOnLkiJo0aSKr1ardu3dr8eLFatq0qeMZNoMHD9arr76qAQMGaNeuXWrQoIHef/99ff7555o1a5ZXnpguSc2aNVNiYqIWLlzoeE7K9u3btXTpUvXp0+eCH9RmN3r0aC1fvlwdO3bUE088odOnT2vmzJm67rrrSjwWejqoMXPmTHXr1k2xsbEaOHCg4+fLQkNDS7yVa+7cuVq5cqV69eql6OhoHTt2TIsWLdLhw4f15ptvOj0R3x1LlizR3LlzVbNmzWLrbdmyRY0aNfLaT7tKnFy/aBkA3NK+fXtj/PjxRZYfPHjQ4CMFAEBh9mNkSa/ExETHMpIMi8Vi7Nq1y6mt9u3bG+3bt3dM5+fnG1OmTDHq169vWK1W4/rrrzdWrVplJCYmGvXr1y8Uw8yZMwvFd+2115aqzffff99o1aqVERISYgQHBxtXXnml8dRTTxm///67U/vHjx83HnjgAaNmzZpGYGCgcd111xmLFy922UfuxFcUm81mTJgwwYiJiTECAgKMevXqGaNGjTKys7Nd1t+0aZMhydi0aVOJbZ9v3759RpcuXYzKlSsbYWFhRr9+/Yy0tLQSl2vfvr1b+8H5Pv30U6Ndu3ZGcHCwERISYvTq1cv49ttvS1zX+vXrjc6dOxuRkZFGQECAERYWZnTp0sXYsGGDW9s4fvx4Q1Kh97K48vr16xuSjBUrVjjVvZB90TDOfRbcef9RsVgM4wKufwUuIR06dFCHDh2KPAN76NAhxcTEXNAl5QAAXIzsx8iDBw8WeZVYUlKSDh06VOjXRXDx4LsU8D9cmg64qXPnzk4PPymoatWq6tevn4kRAQAAAKiISMQBNz377LPFltesWbPUP1UCAMDFzH6yumrVqkXWadq0qU9+zgrmYVAD+B8uTQcAAB7bunWrZs6cqV27dunYsWNasWKF+vTp4yg3DEPjx4/Xa6+9pvT0dLVr107z589Xw4YNHXVOnjypxx57TB9//LH8/PyUkJCgl19+udhkDQCAi0HRP94HAABQhKysLDVr1kxz5851WT5jxgzNnj1bCxYs0LZt21SlShXFx8crOzvbUadfv3765ptvlJKSolWrVmnr1q0aPHiwWZsAAIDPMCIOAAAuiMVicRoRNwxDUVFReuqppxw/VZSRkaGIiAgtWbJEffv21f79+9W4cWPt2LFDLVu2lCStXbtW3bt312+//cYlygCAixoj4gAAwKsOHjyotLQ0p99+Dg0NVZs2bZSamipJSk1NVVhYmCMJl6S4uDj5+flp27ZtpscMAICZLtqHteXn5+vo0aOqVq2aLBaLr8MBAECGYejUqVOKioqSn9/Fey48LS1NkhQREeE0PyIiwlGWlpam2rVrO5X7+/srPDzcUaegnJwc5eTkOKbz8/N18uRJ1ahRg2M9AKBccPdYf9Em4kePHlW9evV8HQYAAIX8+uuvuuyyy3wdRoUzdepUTZgwwddhAABQopKO9RdtIl6tWjVJ5zogJCTEZR2bzab169erS5cuCggIMDM8r6jo8UsVfxuI37eI37eI33OZmZmqV6+e4xh1sYqMjJQkHT9+XHXq1HHMP378uJo3b+6oc+LECaflzp49q5MnTzqWL2jUqFEaPny4YzojI0PR0dE6ePDgBfepzWbTpk2b1LFjxwq5P/sCfeY5+sxz9Jnn6DPPebPPTp06pZiYmBKPSxdtIm6/RC0kJKTYRLxy5coKCQmpkDtpRY9fqvjbQPy+Rfy+Rfyld7FfRh0TE6PIyEht2LDBkXhnZmZq27ZteuSRRyRJsbGxSk9P165du9SiRQtJ0saNG5Wfn682bdq4bNdqtcpqtRaaHx4eXuSx3l32/aFGjRoVcn/2BfrMc/SZ5+gzz9FnnvNmn9mXL+lYf9Em4gAAoOycPn1aP/74o2P64MGD2rNnj8LDwxUdHa1hw4Zp8uTJatiwoWJiYjR27FhFRUU5nqzeqFEjde3aVYMGDdKCBQtks9k0dOhQ9e3blyemAwAueiTiAADAYzt37lTHjh0d0/ZLxhMTE7VkyRKNGDFCWVlZGjx4sNLT03XTTTdp7dq1CgoKcizz9ttva+jQoerUqZP8/PyUkJCg2bNnm74tAACYjUQcAAB4rEOHDjIMo8hyi8WiiRMnauLEiUXWCQ8PV3JyclmEBwBAuXbx/nYKAAAAAADlEIk4AAAAAAAmIhEHAAAAAMBEJOIAAAAAAJiIRBwAAAAAABORiAMAAAAAYCIScQAAAAAATEQiDgAAAACAiUjEAQAAAAAwEYk4AAAAAAAmIhEHAAAAAMBEHifiW7duVa9evRQVFSWLxaKVK1c6ymw2m5555hldd911qlKliqKionT//ffr6NGjTm2cPHlS/fr1U0hIiMLCwjRw4ECdPn3aqc7XX3+tm2++WUFBQapXr55mzJhRui0EAAAAAKAc8TgRz8rKUrNmzTR37txCZWfOnNHu3bs1duxY7d69Wx9++KEOHDig2267zalev3799M033yglJUWrVq3S1q1bNXjwYEd5ZmamunTpovr162vXrl2aOXOmkpKStHDhwlJsIgAAAAAA5Ye/pwt069ZN3bp1c1kWGhqqlJQUp3mvvPKKWrdurcOHDys6Olr79+/X2rVrtWPHDrVs2VKSNGfOHHXv3l3PP/+8oqKi9Pbbbys3N1eLFi1SYGCgrr32Wu3Zs0cvvviiU8IOAAAAAEBF43Ei7qmMjAxZLBaFhYVJklJTUxUWFuZIwiUpLi5Ofn5+2rZtm26//XalpqbqlltuUWBgoKNOfHy8pk+frr/++kvVq1cvtJ6cnBzl5OQ4pjMzMyWdu1zeZrO5jM0+v6jy8q6ixy9V/G0gft8ift8i/tKvEwAAXNrKNBHPzs7WM888o3vvvVchISGSpLS0NNWuXds5CH9/hYeHKy0tzVEnJibGqU5ERISjzFUiPnXqVE2YMKHQ/PXr16ty5crFxllwFL+iqejxSxV/G4jft4jft4jffWfOnDFtXQAAoPwqs0TcZrPp7rvvlmEYmj9/flmtxmHUqFEaPny4YzozM1P16tVTly5dHCcBXMWYkpKizp07KyAgoMxj9LaKHr9U8behPMffJGmd0/S+pPhCdcpz/O4gft8ifs/Zr9YCAACXtjJJxO1J+C+//KKNGzc6JcKRkZE6ceKEU/2zZ8/q5MmTioyMdNQ5fvy4Ux37tL1OQVarVVartdD8gICAEr9guVOnPKvo8UsVfxvKY/w5eRan6eLiK4/xe4L4fYv4PVsXcLFpkrSu0DHH7tC0HiZHAwCuNRi5usgyayVDM1qbGIzK4HfE7Un4Dz/8oE8//VQ1atRwKo+NjVV6erp27drlmLdx40bl5+erTZs2jjpbt251upcuJSVFV199tcvL0gEAAAAAqCg8HhE/ffq0fvzxR8f0wYMHtWfPHoWHh6tOnTq68847tXv3bq1atUp5eXmO+77Dw8MVGBioRo0aqWvXrho0aJAWLFggm82moUOHqm/fvoqKipIk3XfffZowYYIGDhyoZ555Rvv27dPLL7+sl156yUubDVQ8Bc/iMcoAAAAAVEweJ+I7d+5Ux44dHdP2+7ITExOVlJSkjz76SJLUvHlzp+U2bdqkDh06SJLefvttDR06VJ06dZKfn58SEhI0e/ZsR93Q0FCtX79eQ4YMUYsWLVSzZk2NGzeOny4DAAAAAFR4HifiHTp0kGEYRZYXV2YXHh6u5OTkYus0bdpU//3vfz0NDwAAAACAcs3r94gDAAAAAICikYgDAAAAAGAiEnEAAAAAAExEIg4AAAAAgIlIxAEAAAAAMBGJOAAAAAAAJiIRBwAAAADARCTiAAAAAACYiEQcAAAAAAATkYgDAAAAAGAiEnEAAAAAAExEIg4AAAAAgIlIxAEAAAAAMBGJOAAAAAAAJiIRBwAAAADARCTiAAAAAACYiEQcAAAAAAATkYgDAAAAAGAiEnEAAAAAAExEIg4AAAAAgIlIxAEAAAAAMBGJOAAAAAAAJvL3dQAAyr8GI1c7TR+a1sNHkQAAAAAVH4k4UIbKcwLbYORqWSsZmtFaapK0Tgee6+nrkAAAAIBLApemAwAAAABgIhJxAAAAAABMRCIOAAAAAICJSMQBAAAAADARiTgAAAAAACYiEQcAAAAAwEQk4gAAAAAAmIhEHAAAAAAAE/n7OgAA/9Ng5Gqn6UPTevgoEgAAAABlhRFxAAAAAABMRCIOAAAAAICJSMQBAAAAADARiTgAAAAAACYiEQe8oEnSukIPWgMAAAAAV0jEAQAAAAAwEYk4AAAAAAAmIhEHAAAAAMBEJOIAAAAAAJiIRBwAAAAAABORiAMAAAAAYCIScQAAAAAATEQiDgAAAACAiUjEAQAAAAAwEYk4AAAAAAAmIhEHAAAAAMBEJOIAAAAAAJiIRBwAAAAAABORiAMAAAAAYCIScQAAAAAATEQiDgAAAACAiUjEAQAAAAAwEYk4AAAAAAAmIhEHAAAAAMBEJOIAAAAAAJiIRBwAAAAAABORiAMAAAAAYCIScQAA4HV5eXkaO3asYmJiFBwcrCuuuEKTJk2SYRiOOoZhaNy4capTp46Cg4MVFxenH374wYdRAwBgDhJxAADgddOnT9f8+fP1yiuvaP/+/Zo+fbpmzJihOXPmOOrMmDFDs2fP1oIFC7Rt2zZVqVJF8fHxys7O9mHkAACUPY8T8a1bt6pXr16KioqSxWLRypUrncrdObt98uRJ9evXTyEhIQoLC9PAgQN1+vRppzpff/21br75ZgUFBalevXqaMWOG51sHAAB84osvvlDv3r3Vo0cPNWjQQHfeeae6dOmi7du3Szr3fWHWrFkaM2aMevfuraZNm2rZsmU6evRooe8WAABcbPw9XSArK0vNmjXTgw8+qDvuuKNQuf3s9tKlSxUTE6OxY8cqPj5e3377rYKCgiRJ/fr107Fjx5SSkiKbzaYHHnhAgwcPVnJysiQpMzNTXbp0UVxcnBYsWKC9e/fqwQcfVFhYmAYPHnyBmwwAAMrajTfeqIULF+r777/XVVddpa+++kqfffaZXnzxRUnSwYMHlZaWpri4OMcyoaGhatOmjVJTU9W3b99Cbebk5CgnJ8cxnZmZKUmy2Wyy2WwXFK99+Qtt51Ji7yurn1FiHZzDfuY5+sxz9Jlr1kpF/6+y/x/zRp+524bHiXi3bt3UrVs3l2UFz25L0rJlyxQREaGVK1eqb9++2r9/v9auXasdO3aoZcuWkqQ5c+aoe/fuev755xUVFaW3335bubm5WrRokQIDA3Xttddqz549evHFF0nEAQCoAEaOHKnMzExdc801qlSpkvLy8vTcc8+pX79+kqS0tDRJUkREhNNyERERjrKCpk6dqgkTJhSav379elWuXNkrcaekpHilnUvJpJb5RZatWbPGxEgqDvYzz9FnnqPPnM1oXXIdb/TZmTNn3KrncSJeHHfObqempiosLMyRhEtSXFyc/Pz8tG3bNt1+++1KTU3VLbfcosDAQEed+Ph4TZ8+XX/99ZeqV69eaN2lOUte0c8WVfT4pYq/DQVHAwpuR8EzbyVtZ3H1vdmWvdwet9XPKLY9T9dtlotl/yF+3/BF/BW1r0rjvffe09tvv63k5GTHCfVhw4YpKipKiYmJpWpz1KhRGj58uGM6MzNT9erVU5cuXRQSEnJB8dpsNqWkpKhz584KCAi4oLYuFfY+G7vTTzn5Fpd19iXFmxxV+cZ+5jn6zHP0mWtNktYVWWb1MzSpZb5X+syeh5bEq4m4O2e309LSVLt2becg/P0VHh7uVCcmJqZQG/YyV4n4hZwlr+hniyp6/FLF3wb7aEDBM/8Fz7yVNDJQXH1vtlWwfFLL/GLb83TdZqvo+w/x+5aZ8bt7lvxi8PTTT2vkyJGOS8yvu+46/fLLL5o6daoSExMVGRkpSTp+/Ljq1KnjWO748eNq3ry5yzatVqusVmuh+QEBAV77sunNti4VOfkW5eS5TsTpS9fYzzxHn3mOPnNW1P+p83mjz9xd3quJuC+V5ix5RT9bVNHjlyr+NhQcDSh45r/gmbeSRgaKq+/Ntuzl9rN/Y3f6ade4rqVuy1culv2H+H3DF/G7e5b8YnDmzBn5+Tk/E7ZSpUrKzz934jImJkaRkZHasGGDI/HOzMzUtm3b9Mgjj5gdLgAApvJqIu7O2e3IyEidOHHCabmzZ8/q5MmTjuUjIyN1/Phxpzr2aXudgi7kLHlFP1tU0eOXKv422EcDCm5DwTNvJW1jcfW92VbB8pz8wrF70pavVfT9h/h9y8z4K3I/eapXr1567rnnFB0drWuvvVb/93//pxdffFEPPvigJMlisWjYsGGaPHmyGjZs6HjAa1RUlPr06ePb4AEAKGNe/R3x889u29nPbsfGxkqSYmNjlZ6erl27djnqbNy4Ufn5+WrTpo2jztatW53upUtJSdHVV1/t8rJ0AABQvsyZM0d33nmnHn30UTVq1Ej/+te/9M9//lOTJk1y1BkxYoQee+wxDR48WK1atdLp06e1du1ax6+sAABwsfJ4RPz06dP68ccfHdMHDx7Unj17FB4erujo6BLPbjdq1Ehdu3bVoEGDtGDBAtlsNg0dOlR9+/ZVVFSUJOm+++7ThAkTNHDgQD3zzDPat2+fXn75Zb300kve2WqgGA1GrnaaPjSth48iAYCKq1q1apo1a5ZmzZpVZB2LxaKJEydq4sSJ5gUGAEA54HEivnPnTnXs2NExbb8vOzExUUuWLNGIESOUlZWlwYMHKz09XTfddFOhs9tvv/22hg4dqk6dOsnPz08JCQmaPXu2ozw0NFTr16/XkCFD1KJFC9WsWVPjxo3jp8sAAAAAABWex4l4hw4dZBhF/xi6O2e3w8PDlZycXOx6mjZtqv/+97+ehgcAAAAAQLnm1XvEAQAAAABA8UjEAQAAAAAwEYk4AAAAAAAmIhEHAAAAAMBEJOIAAAAAAJiIRBwAAAAAABORiAMAAAAAYCIScQAAAAAATEQiDgAAAACAifx9HQCAiq/ByNVO04em9fBRJAAAAED5x4g4AAAAAAAmIhEHAAAAAMBEJOIAAAAAAJiIRBwAAAAAABORiAMAAAAAYCIScQAAAAAATEQiDgAAAACAiUjEAQAAAAAwEYk4AAAAAAAmIhEHAAAAAMBEJOIAAAAAAJiIRBwAAAAAABP5+zoAwGwNRq52mj40rYePIgEAAABwKWJEHAAAAAAAE5GIAwAAAABgIhJxAAAAAABMRCIOAAAAAICJSMQBAAAAADARiTgAAAAAACYiEQcAAAAAwEQk4gAAAAAAmIhEHAAAAAAAE5GIAwAAAABgIhJxAAAAAABMRCIOAAAAAICJSMQBAAAAADARiTgAAAAAACYiEQcAAAAAwEQk4gAAAAAAmIhEHAAAAAAAE5GIAwAAAABgIhJxAAAAAABMRCIOAAAAAICJSMQBAAAAADARiTgAAAAAACYiEQcAAAAAwEQk4gAAAAAAmIhEHAAAAAAAE5GIAwAAAABgIhJxAAAAAABMRCIOAAAAAICJSMQBAAAAADARiTgAAAAAACYiEQcAAAAAwEQk4gAAAAAAmIhEHAAAAAAAE5GIAwAAAABgIhJxAAAAAABMRCIOAAAAAICJSMQBAAAAADARiTgAAAAAACby93UAAC4tDUaudpo+NK2HjyIBAAAAfIMRcQAAAAAATOT1RDwvL09jx45VTEyMgoODdcUVV2jSpEkyDMNRxzAMjRs3TnXq1FFwcLDi4uL0ww8/OLVz8uRJ9evXTyEhIQoLC9PAgQN1+vRpb4cLAAAAAICpvJ6IT58+XfPnz9crr7yi/fv3a/r06ZoxY4bmzJnjqDNjxgzNnj1bCxYs0LZt21SlShXFx8crOzvbUadfv3765ptvlJKSolWrVmnr1q0aPHiwt8MFAAAAAMBUXr9H/IsvvlDv3r3Vo8e5+z4bNGigf//739q+fbukc6Phs2bN0pgxY9S7d29J0rJlyxQREaGVK1eqb9++2r9/v9auXasdO3aoZcuWkqQ5c+aoe/fuev755xUVFeXtsAEAAAAAMIXXE/Ebb7xRCxcu1Pfff6+rrrpKX331lT777DO9+OKLkqSDBw8qLS1NcXFxjmVCQ0PVpk0bpaamqm/fvkpNTVVYWJgjCZekuLg4+fn5adu2bbr99tsLrTcnJ0c5OTmO6czMTEmSzWaTzWZzGat9flHl5V1Fj1/yzTZYKxlO0wXXXVK5qzKrn3HBbZVU35tt2cvtcVv9jGLb82afebodxanonwHi9y1fxF9R+woAAHiX1xPxkSNHKjMzU9dcc40qVaqkvLw8Pffcc+rXr58kKS0tTZIUERHhtFxERISjLC0tTbVr13YO1N9f4eHhjjoFTZ06VRMmTCg0f/369apcuXKxMaekpLi3ceVURY9fMncbZrR2nl6zZo1H5a5MapnvlbaKq+/NtgqWT2qZX2x73uyz0vRvSSr6Z4D4fcvM+M+cOWPaugAAQPnl9UT8vffe09tvv63k5GRde+212rNnj4YNG6aoqCglJiZ6e3UOo0aN0vDhwx3TmZmZqlevnrp06aKQkBCXy9hsNqWkpKhz584KCAgos9jKSkWPX/LNNjRJWuc0vS8p3qPy89njH7vTTzn5lgtqq6T63mzLXm71MzSpZb7G7vTTrnFdL6gtd2PzdDuKU9E/A8TvW76I3361FgAAuLR5PRF/+umnNXLkSPXt21eSdN111+mXX37R1KlTlZiYqMjISEnS8ePHVadOHcdyx48fV/PmzSVJkZGROnHihFO7Z8+e1cmTJx3LF2S1WmW1WgvNDwgIKPELljt1yrOKHr9k7jbk5FkKrduTcpdt5luUk2e54LaKq+/NtgqW5+QXjr20bZUUW2n6tyQV/TNA/L5lZvwVuZ8AAID3eP2p6WfOnJGfn3OzlSpVUn7+uUt3Y2JiFBkZqQ0bNjjKMzMztW3bNsXGxkqSYmNjlZ6erl27djnqbNy4Ufn5+WrTpo23QwYAAAAAwDReHxHv1auXnnvuOUVHR+vaa6/V//3f/+nFF1/Ugw8+KEmyWCwaNmyYJk+erIYNGyomJkZjx45VVFSU+vTpI0lq1KiRunbtqkGDBmnBggWy2WwaOnSo+vbtyxPTAQAAAAAVmtcT8Tlz5mjs2LF69NFHdeLECUVFRemf//ynxo0b56gzYsQIZWVlafDgwUpPT9dNN92ktWvXKigoyFHn7bff1tChQ9WpUyf5+fkpISFBs2fP9na4AAAAAACYyuuJeLVq1TRr1izNmjWryDoWi0UTJ07UxIkTi6wTHh6u5ORkb4cHAAAAAIBPef0ecQAAAAAAUDQScQAAAAAATEQiDgAAAACAiUjEAQAAAAAwEYk4AAAAAAAmIhEHAABl4siRI+rfv79q1Kih4OBgXXfdddq5c6ej3DAMjRs3TnXq1FFwcLDi4uL0ww8/+DBiAADMQSIOAAC87q+//lK7du0UEBCgTz75RN9++61eeOEFVa9e3VFnxowZmj17thYsWKBt27apSpUqio+PV3Z2tg8jBwCg7Hn9d8QBAACmT5+uevXqafHixY55MTExjr8Nw9CsWbM0ZswY9e7dW5K0bNkyRUREaOXKlerbt6/pMQMAYBZGxAEAgNd99NFHatmype666y7Vrl1b119/vV577TVH+cGDB5WWlqa4uDjHvNDQULVp00apqam+CBkAANMwIg4AALzu559/1vz58zV8+HCNHj1aO3bs0OOPP67AwEAlJiYqLS1NkhQREeG0XEREhKOsoJycHOXk5DimMzMzJUk2m002m+2C4rUvf6HtXErsfWX1M0qsg3PYzzxHn3mOPnPNWqno/1X2/2Pe6DN32yARBwAAXpefn6+WLVtqypQpkqTrr79e+/bt04IFC5SYmFiqNqdOnaoJEyYUmr9+/XpVrlz5guK1S0lJ8Uo7l5JJLfOLLFuzZo2JkVQc7Geeo888R585m9G65Dre6LMzZ864VY9EHAAAeF2dOnXUuHFjp3mNGjXSBx98IEmKjIyUJB0/flx16tRx1Dl+/LiaN2/uss1Ro0Zp+PDhjunMzEzVq1dPXbp0UUhIyAXFa7PZlJKSos6dOysgIOCC2rpU2Pts7E4/5eRbXNbZlxRvclTlG/uZ5+gzz9FnrjVJWldkmdXP0KSW+V7pM/vVWiUhEQcAAF7Xrl07HThwwGne999/r/r160s69+C2yMhIbdiwwZF4Z2Zmatu2bXrkkUdctmm1WmW1WgvNDwgI8NqXTW+2danIybcoJ891Ik5fusZ+5jn6zHP0mbOi/k+dzxt95u7yJOIAAMDrnnzySd14442aMmWK7r77bm3fvl0LFy7UwoULJUkWi0XDhg3T5MmT1bBhQ8XExGjs2LGKiopSnz59fBs8AABljEQcF4UGI1c7TR+a1sNHkQAAJKlVq1ZasWKFRo0apYkTJyomJkazZs1Sv379HHVGjBihrKwsDR48WOnp6brpppu0du1aBQUF+TByAADKHok4AAAoEz179lTPnj2LLLdYLJo4caImTpxoYlQAAPgevyMOAAAAAICJSMQBAAAAADARiTgAAAAAACYiEQcAAAAAwEQk4gAAAAAAmIhEHAAAAAAAE5GIAwAAAABgIhJxAAAAAABMRCIOAAAAAICJSMQBAAAAADARiTgAAAAAACYiEQcAAAAAwEQk4gAAAAAAmIhEHAAAAAAAE5GIAwAAAABgIhJxAAAAAABMRCIOAAAAAICJ/H0dAAAAAADAu5okrVNOnqXQ/EPTevggGhTEiDgAAAAAACYiEQcAAAAAwERcmg6gXGkwcrXTNJdPAQAA4GLDiDgAAAAAACYiEQcAAAAAwEQk4gAAAAAAmIhEHAAAAAAAE5GIAwAAAABgIhJxAAAAAABMRCIOAAAAAICJSMQBAAAAADARiTgAAAAAACYiEQcAAAAAwEQk4gAAAAAAmIhEHAAAAAAAE5GIAwAAAABgIhJxAAAAAABMRCIOAAAAAICJ/H0dAOCOBiNXO00fmtbDR5EAAAAAwIVhRBwAAAAAABORiAMAAAAAYCIScQAAAAAATEQiDgAAAACAiUjEAQAAAAAwEYk4AAAAAAAmIhEHAAAAAMBEJOIAAAAAAJiIRBwAAAAAABOVSSJ+5MgR9e/fXzVq1FBwcLCuu+467dy501FuGIbGjRunOnXqKDg4WHFxcfrhhx+c2jh58qT69eunkJAQhYWFaeDAgTp9+nRZhAsAAAAAgGm8noj/9ddfateunQICAvTJJ5/o22+/1QsvvKDq1as76syYMUOzZ8/WggULtG3bNlWpUkXx8fHKzs521OnXr5+++eYbpaSkaNWqVdq6dasGDx7s7XABAAAAADCVv7cbnD59uurVq6fFixc75sXExDj+NgxDs2bN0pgxY9S7d29J0rJlyxQREaGVK1eqb9++2r9/v9auXasdO3aoZcuWkqQ5c+aoe/fuev755xUVFeXtsAEAAAAAMIXXE/GPPvpI8fHxuuuuu7RlyxbVrVtXjz76qAYNGiRJOnjwoNLS0hQXF+dYJjQ0VG3atFFqaqr69u2r1NRUhYWFOZJwSYqLi5Ofn5+2bdum22+/vdB6c3JylJOT45jOzMyUJNlsNtlsNpex2ucXVV7eVfT4Jfe3wVrJcLmcu+Vl1Za9zOpnXHBbJdX3Zlv2cnvcVj/DtD4ri/6vqJ8B4vctX8RfUfsKAAB4l8UwDKPkau4LCgqSJA0fPlx33XWXduzYoSeeeEILFixQYmKivvjiC7Vr105Hjx5VnTp1HMvdfffdslgsevfddzVlyhQtXbpUBw4ccGq7du3amjBhgh555JFC601KStKECRMKzU9OTlblypW9uYkAAJTKmTNndN999ykjI0MhISG+DqfCy8zMVGhoqFf602azac2aNerevbsCAgK8FOHFzd5nI7ZXUk6exWWdQ9N6mBxV+cZ+5jn6zHMlfTYv1c9lg5GriyyzVjI0o3WeV/Yzd49NXh8Rz8/PV8uWLTVlyhRJ0vXXX699+/Y5EvGyMmrUKA0fPtwxnZmZqXr16qlLly5FdoDNZlNKSoo6d+5cIT/YFT1+yf1taJK0zml6X1K8R+Vl1ZY9/rE7/ZSTb7mgtkqq78227OVWP0OTWuZr7E4/7RrX9YLacje2suj/ivoZIH7f8kX89qu1AADApc3riXidOnXUuHFjp3mNGjXSBx98IEmKjIyUJB0/ftxpRPz48eNq3ry5o86JEyec2jh79qxOnjzpWL4gq9Uqq9VaaH5AQECJX7DcqVOeVfT4pZK3oeDZvIJ1Syovq7Ycy+RblJNnueC2iqvvzbYKlufkF469tG2VFFtZ9H9F/wwQv2+ZGX9F7icAAOA9Xn9qert27QpdUv7999+rfv36ks49uC0yMlIbNmxwlGdmZmrbtm2KjY2VJMXGxio9PV27du1y1Nm4caPy8/PVpk0bb4cMAAAAAIBpvD4i/uSTT+rGG2/UlClTdPfdd2v79u1auHChFi5cKEmyWCwaNmyYJk+erIYNGyomJkZjx45VVFSU+vTpI+ncCHrXrl01aNAgLViwQDabTUOHDlXfvn15YjoAAAAAoELzeiLeqlUrrVixQqNGjdLEiRMVExOjWbNmqV+/fo46I0aMUFZWlgYPHqz09HTddNNNWrt2reNBb5L09ttva+jQoerUqZP8/PyUkJCg2bNneztcAAAAAABM5fVEXJJ69uypnj17FllusVg0ceJETZw4scg64eHhSk5OLovwAAAAAADwGa/fIw4AAAAAAIpGIg4AAAAAgIlIxAEAAAAAMBGJOAAAAAAAJiIRBwAAAADARCTiAAAAAACYiEQcAAAAAAATkYgDAAAAAGAiEnEAFV6TpHVqMHK1r8MAAAAA3EIiDgAAAACAiUjEAQAAAAAwEYk4AAAAAAAmIhEHAAAAAMBEJOIAAAAAAJiIRBwAAAAAABORiAMAAAAAYCIScQAAAAAATEQiDgAAAACAiUjEAQAAAAAwEYk4AAAAAAAmIhEHAAAAAMBEJOIAAAAAAJiIRBwAAAAAABORiAMAAAAAYCIScQAAAAAATEQiDgAAyty0adNksVg0bNgwx7zs7GwNGTJENWrUUNWqVZWQkKDjx4/7LkgAAExCIg4AAMrUjh079Oqrr6pp06ZO85988kl9/PHHWr58ubZs2aKjR4/qjjvu8FGUAACYh0QcAACUmdOnT6tfv3567bXXVL16dcf8jIwMvfHGG3rxxRd16623qkWLFlq8eLG++OILffnllz6MGACAsufv6wAAAMDFa8iQIerRo4fi4uI0efJkx/xdu3bJZrMpLi7OMe+aa65RdHS0UlNT1bZt20Jt5eTkKCcnxzGdmZkpSbLZbLLZbBcUp335C23nUmLvK6ufUWIdnMN+5jn6zHMlfTYv1b60Vir6f5W9r7zRN+62QSIOAADKxDvvvKPdu3drx44dhcrS0tIUGBiosLAwp/kRERFKS0tz2d7UqVM1YcKEQvPXr1+vypUreyXmlJQUr7RzKZnUMr/IsjVr1pgYScXBfuY5+sxzRX02L9XP5YzWJdfxxn525swZt+qRiAMAAK/79ddf9cQTTyglJUVBQUFeaXPUqFEaPny4YzozM1P16tVTly5dFBISckFt22w2paSkqHPnzgoICLjQUC8J9j4bu9NPOfkWl3X2JcWbHFX5xn7mOfrMcyV9Ni/Vz2WTpHVFlln9DE1qme+V/cx+tVZJSMQBXLQajFztNH1oWg8fRQJcenbt2qUTJ07ohhtucMzLy8vT1q1b9corr2jdunXKzc1Venq606j48ePHFRkZ6bJNq9Uqq9VaaH5AQIDXvqB7s61LRU6+RTl5rhNx+tI19jPP0WeeK+qzean2Y1H/p87njf3M3eVJxAEAgNd16tRJe/fudZr3wAMP6JprrtEzzzyjevXqKSAgQBs2bFBCQoIk6cCBAzp8+LBiY2N9ETIAAKYhEQcAAF5XrVo1NWnSxGlelSpVVKNGDcf8gQMHavjw4QoPD1dISIgee+wxxcbGunxQGwAAFxMScQAA4BMvvfSS/Pz8lJCQoJycHMXHx2vevHm+DgsAgDJHIg4AAEyxefNmp+mgoCDNnTtXc+fO9U1AAAD4iJ+vAwAAAAAA4FJCIg4AAAAAgIlIxAEAAAAAMBGJOAAAAAAAJiIRBwAAAADARDw1HT7TYORqWSsZmtHa15EAAAAAgHkYEQcAAAAAwEQk4gAAAAAAmIhEHAAAAAAAE5GIAwAAAABgIhJxAAAAAABMRCIOAAAAAICJSMQBAAAAADARiTgAAAAAACYiEQcAAAAAwEQk4gAAAAAAmIhEHAAAAAAAE5GIAwAAAABgIhJxAAAAAABMRCIOAAAAAICJSMQBAAAAADARiTgAAAAAACYiEQcAAAAAwEQk4gAAAAAAmIhEHAAAAAAAE/n7OgAAAAAAKE6TpHXKybO4LDs0rYfJ0QAXjhFxAAAAAABMVOaJ+LRp02SxWDRs2DDHvOzsbA0ZMkQ1atRQ1apVlZCQoOPHjzstd/jwYfXo0UOVK1dW7dq19fTTT+vs2bNlHS4AAAAAAGWqTBPxHTt26NVXX1XTpk2d5j/55JP6+OOPtXz5cm3ZskVHjx7VHXfc4SjPy8tTjx49lJubqy+++EJLly7VkiVLNG7cuLIMFwAAAACAMldmifjp06fVr18/vfbaa6pevbpjfkZGht544w29+OKLuvXWW9WiRQstXrxYX3zxhb788ktJ0vr16/Xtt9/qrbfeUvPmzdWtWzdNmjRJc+fOVW5ublmFDAAAAABAmSuzh7UNGTJEPXr0UFxcnCZPnuyYv2vXLtlsNsXFxTnmXXPNNYqOjlZqaqratm2r1NRUXXfddYqIiHDUiY+P1yOPPKJvvvlG119/faH15eTkKCcnxzGdmZkpSbLZbLLZbC5jtM8vqry8q+jxWysZsvoZkkreBmslw2m6YP2SysuqLXtZUdvhSVsl1fdmW/Zye9xWP8O0PivP/W+2iv4ZJv7SrxMAAFzaLIZhGCVX88w777yj5557Tjt27FBQUJA6dOig5s2ba9asWUpOTtYDDzzglDRLUuvWrdWxY0dNnz5dgwcP1i+//KJ169Y5ys+cOaMqVapozZo16tatW6F1JiUlacKECYXmJycnq3Llyt7eRAAAPHbmzBndd999ysjIUEhIiK/DqfAyMzMVGhrqlf602Wxas2aNunfvroCAAC9FeHGz99mI7ZV4mrWb2M88x37muZL67FLtrwYjVxdZZq1kaEbrPK98Nt09Nnl9RPzXX3/VE088oZSUFAUFBXm7+SKNGjVKw4cPd0xnZmaqXr166tKlS5EdYLPZlJKSos6dO1fIf4YVPf4mSetk9TM0qWV+idvQJGmd0/S+pHiPysuqLft7MHann3LyLRfUVkn1vdmWvdze/2N3+mnXuK4X1Ja7sVXU/i8LFf0zTPyes1+tBQAALm1eT8R37dqlEydO6IYbbnDMy8vL09atW/XKK69o3bp1ys3NVXp6usLCwhx1jh8/rsjISElSZGSktm/f7tSu/anq9joFWa1WWa3WQvMDAgJK/ILlTp3yrKLGf/4ZupK2oeDZvIJ1Syovq7Ycy+RblJNnueC2iqvvzbYKlufkF469tG2VFFtF7f+yVFE/w3bE79m6AAAAvP6wtk6dOmnv3r3as2eP49WyZUv169fP8XdAQIA2bNjgWObAgQM6fPiwYmNjJUmxsbHau3evTpw44aiTkpKikJAQNW7c2NshAwAAAABgGq+PiFerVk1NmjRxmlelShXVqFHDMX/gwIEaPny4wsPDFRISoscee0yxsbFq27atJKlLly5q3Lix/vGPf2jGjBlKS0vTmDFjNGTIEJej3gAAAAAAVBRl9tT04rz00kvy8/NTQkKCcnJyFB8fr3nz5jnKK1WqpFWrVumRRx5RbGysqlSposTERE2cONEX4QIAAAAA4DWmJOKbN292mg4KCtLcuXM1d+7cIpepX7++1qxZU8aRAQAAAABgLq/fIw4AAAAAAIpGIg4AAAAAgIlIxAEAAAAAMBGJOAAAAAAAJiIRBwAAAADARCTiAAAAAACYiEQcAAAAAAATkYgDAAAAAGAiEnEAAAAAAExEIg4AAAAAgIlIxAEAAAAAMBGJOAAAAAAAJiIRBwAAAADARCTiAAAAAACYiEQcAAAAAAATkYgDAAAAAGAiEnEAAAAAAExEIg4AAAAAgIlIxAEAAAAAMJG/rwPAxavByNVO04em9fBRJAAAAABQfjAiDgAAAACAiUjEAQAAAAAwEYk4AAAAAAAmIhEHAAAAAMBEJOIAAAAAAJiIRBwAAAAAABORiAMAAAAAYCIScQAAAAAATEQiDgAAAACAiUjEAQAAAAAwEYk4AAAAAAAmIhEHAAAAAMBEJOIAAAAAAJiIRBwAAAAAABP5+zoAAKgoGoxc7fj70LQePowEAAAAFRkj4gAAAAAAmIhEHAAAAAAAE5GIAwAAAABgIhJxAAAAAABMRCIOAAAAAICJeGo6AJSB85+wLvGUdQAAAPwPI+IAAAAAAJiIRBwAAAAAABORiAMAAK+bOnWqWrVqpWrVqql27drq06ePDhw44FQnOztbQ4YMUY0aNVS1alUlJCTo+PHjPooYAADzkIgDAACv27Jli4YMGaIvv/xSKSkpstls6tKli7Kyshx1nnzySX388cdavny5tmzZoqNHj+qOO+7wYdQAAJiDh7UBAACvW7t2rdP0kiVLVLt2be3atUu33HKLMjIy9MYbbyg5OVm33nqrJGnx4sVq1KiRvvzyS7Vt29YXYQMAYAoScQAAUOYyMjIkSeHh4ZKkXbt2yWazKS4uzlHnmmuuUXR0tFJTU10m4jk5OcrJyXFMZ2ZmSpJsNptsNtsFxWdf/kLbuZTY+8rqZ5RYB+ewn3mO/cxzJfXZpdpf1kpF70P2vvJG37jbBok4AAAoU/n5+Ro2bJjatWunJk2aSJLS0tIUGBiosLAwp7oRERFKS0tz2c7UqVM1YcKEQvPXr1+vypUreyXWlJQUr7RzKZnUMr/IsjVr1pgYScXBfuY59jPPFdVnl2p/zWhdch1vfDbPnDnjVj0ScQAAUKaGDBmiffv26bPPPrugdkaNGqXhw4c7pjMzM1WvXj116dJFISEhF9S2zWZTSkqKOnfurICAgAtq61Jh77OxO/2Uk29xWWdfUrzJUZVv7GeeYz/zXEl9dqn2V5OkdUWWWf0MTWqZ75XPpv1qrZKQiAMAgDIzdOhQrVq1Slu3btVll13mmB8ZGanc3Fylp6c7jYofP35ckZGRLtuyWq2yWq2F5gcEBHgtqfFmW5eKnHyLcvJcJ0j0pWvsZ55jP/NcUX12qfZXUfvP+bzx2XR3eZ6aDgAAvM4wDA0dOlQrVqzQxo0bFRMT41TeokULBQQEaMOGDY55Bw4c0OHDhxUbG2t2uAAAmIoRcQAA4HVDhgxRcnKy/vOf/6hatWqO+75DQ0MVHBys0NBQDRw4UMOHD1d4eLhCQkL02GOPKTY2liemAwAueiTiAADA6+bPny9J6tChg9P8xYsXa8CAAZKkl156SX5+fkpISFBOTo7i4+M1b948kyMFAMB8JOIAAMDrDKPon4mxCwoK0ty5czV37lwTIgIAoPwgEYfbGoxc7TR9aFoPH0UCAAAAABUXD2sDAAAAAMBEJOIAAAAAAJiIRBwAAAAAABORiAMAAAAAYCIScQAAAAAATEQiDgAAAACAiUjEAQAAAAAwkdcT8alTp6pVq1aqVq2aateurT59+ujAgQNOdbKzszVkyBDVqFFDVatWVUJCgo4fP+5U5/Dhw+rRo4cqV66s2rVr6+mnn9bZs2e9HS4AAAAAAKbyeiK+ZcsWDRkyRF9++aVSUlJks9nUpUsXZWVlOeo8+eST+vjjj7V8+XJt2bJFR48e1R133OEoz8vLU48ePZSbm6svvvhCS5cu1ZIlSzRu3DhvhwsAAAAAgKn8vd3g2rVrnaaXLFmi2rVra9euXbrllluUkZGhN954Q8nJybr11lslSYsXL1ajRo305Zdfqm3btlq/fr2+/fZbffrpp4qIiFDz5s01adIkPfPMM0pKSlJgYKC3wwYAAAAAwBReT8QLysjIkCSFh4dLknbt2iWbzaa4uDhHnWuuuUbR0dFKTU1V27ZtlZqaquuuu04RERGOOvHx8XrkkUf0zTff6Prrry+0npycHOXk5DimMzMzJUk2m002m81lbPb5RZWXd2bHb61kuFx/aetbKxmy+hlea8vd2LzZlr2sqO3wZp+VZf9b/QzT+qyi9n/B8gtt6/x5/A/yDV/EX1H7CgAAeJfFMAyj5Gqlk5+fr9tuu03p6en67LPPJEnJycl64IEHnJJmSWrdurU6duyo6dOna/Dgwfrll1+0bt06R/mZM2dUpUoVrVmzRt26dSu0rqSkJE2YMKHQ/OTkZFWuXNnLWwYAgOfOnDmj++67TxkZGQoJCfF1OBVeZmamQkNDvdKfNptNa9asUffu3RUQEOClCC9u9j4bsb2ScvIsLuscmtbD5KjKN/Yzz7Gfea6kPrtU+6vByNVFllkrGZrROs8rn013j01lOiI+ZMgQ7du3z5GEl6VRo0Zp+PDhjunMzEzVq1dPXbp0KbIDbDabUlJS1Llz5wr5z9Ds+JskrXOa3pcUf0H1myStk9XP0KSW+SVugzttuRubN9uyvwdjd/opJ99yQW2VVL8s+3/sTj/tGtf1gtpyN7aK2v8Fy83s//KK/6Ges1+tBQAALm1llogPHTpUq1at0tatW3XZZZc55kdGRio3N1fp6ekKCwtzzD9+/LgiIyMddbZv3+7Unv2p6vY6BVmtVlmt1kLzAwICSvyC5U6d8sys+AueUStpnSXVP7+8pG3wpK2SYvNmW45l8i3KybNccFvF1S/L/s/JLxx7adsqKbaK2v8Fy83s//KO/6GerQsAAMDrT003DENDhw7VihUrtHHjRsXExDiVt2jRQgEBAdqwYYNj3oEDB3T48GHFxsZKkmJjY7V3716dOHHCUSclJUUhISFq3Lixt0MGAAAAAMA0Xh8RHzJkiJKTk/Wf//xH1apVU1pamiQpNDRUwcHBCg0N1cCBAzV8+HCFh4crJCREjz32mGJjY9W2bVtJUpcuXdS4cWP94x//0IwZM5SWlqYxY8ZoyJAhLke9AQAAAACoKLyeiM+fP1+S1KFDB6f5ixcv1oABAyRJL730kvz8/JSQkKCcnBzFx8dr3rx5jrqVKlXSqlWr9Mgjjyg2NlZVqlRRYmKiJk6c6O1wAQAAAAAwldcTcXcewh4UFKS5c+dq7ty5RdapX7++1qxZ483Q4IaCTxO8VJ+qCAAAAABlxev3iAMAAAAAgKKRiAMAAAAAYCIScQAAAAAATEQiDgAAAACAiUjEAQAAAAAwEYk4AAAAAAAm8vrPlwEAvIufFQQAALi4MCIOAAAAAICJSMQBAAAAADARiTgAAAAAACYiEQcAAAAAwEQk4gAAAAAAmIhEHAAAAAAAE5GIAwAAAABgIn5H/CLH7w8DAAAAQPnCiDgAAAAAACZiRBwAKjiufAEAAKhYGBEHAAAAAMBEJOIAAAAAAJiIRBwAAAAAABORiAMAAAAAYCIScQAAAAAATEQiDgAAAACAiUjEAQAAAAAwEYk4AAAAAAAmIhEHAAAAAMBEJOIAAAAAAJiIRBwAAAAAABORiAMAAAAAYCIScQAAAAAATEQiDgAAAACAifx9HQAAwFwNRq52/H1oWg8fRgIAAHBpIhF30/lfXKXy9eW1SdI65eRZJJWvuAAAAAAAhXFpOgAAAAAAJiIRBwAAAADARCTiAAAAAACYiEQcAAAAAAATkYgDAAAAAGAiEnEAAAAAAExEIg4AAAAAgIlIxAEAAAAAMJG/rwMAAJQfDUaudpo+NK2HjyIBAAC4eDEiDgAAAACAiUjEAQAAAAAwEYk4AAAAAAAmIhEHAAAAAMBEJOIAAAAAAJiIp6aXQzy1GAAAAAAuXoyIAwAAAABgIkbEAQAXpEnSOuXkWSRxBQ8AAIA7GBEHAAAAAMBEJOIAAAAAAJiIRBwAAAAAABORiAMAAAAAYCIScQAAAAAATMRT032A3wkHAAAAgEsXI+IAAAAAAJiIRBwAAAAAABORiAMAAAAAYCIScQAAAAAATEQiDgAAAACAiUjEAQAAAAAwET9fBgAoE/xUIwAAgGvlekR87ty5atCggYKCgtSmTRtt377d1yEBAAAv43gPALjUlNtE/N1339Xw4cM1fvx47d69W82aNVN8fLxOnDjh69AAAGWgwcjVTi9cGjjeAwAuReU2EX/xxRc1aNAgPfDAA2rcuLEWLFigypUra9GiRb4ODQAAeAnHewDApahc3iOem5urXbt2adSoUY55fn5+iouLU2pqqstlcnJylJOT45jOyMiQJJ08eVI2m83lMjabTWfOnNGff/6pgICAYmPyP5vlNP3nn3+6tS2lacvdddnj97f5KS/fckFtuVPfm23Zy/3zDZ05k1/ie+CtPvN2WwXfg4ra//42P9P6rKL2f8Hyitr/Bet7s/8L1vd2/7eZusHx97ZRnYpt6/y6ruq3mbpBVj9DY67PV/NnP9TWZ+JKvS5PnDp1SpJkGIbX2qzIPD3el+ZY7y5PvhPgHFf/Awq6kO9LFyP2M8+xn3mupD67VPur4PcMpzI3cxJ3uH2sN8qhI0eOGJKML774wmn+008/bbRu3drlMuPHjzck8eLFixcvXuX+9euvv5pxOC33PD3ec6znxYsXL14V5VXSsb5cjoiXxqhRozR8+HDHdH5+vk6ePKkaNWrIYnF99iwzM1P16tXTr7/+qpCQELNC9ZqKHr9U8beB+H2L+H2L+D1nGIZOnTqlqKgoU9Z3sSnNsd5dFX1/9gX6zHP0mefoM8/RZ57zZp+5e6wvl4l4zZo1ValSJR0/ftxp/vHjxxUZGelyGavVKqvV6jQvLCzMrfWFhIRU6J20oscvVfxtIH7fIn7fIn7PhIaGmrau8s7T4/2FHOvdVdH3Z1+gzzxHn3mOPvMcfeY5b/WZO8f6cvmwtsDAQLVo0UIbNvzvHr38/Hxt2LBBsbGxPowMAAB4C8d7AMClqlyOiEvS8OHDlZiYqJYtW6p169aaNWuWsrKy9MADD/g6NAAA4CUc7wEAl6Jym4jfc889+v333zVu3DilpaWpefPmWrt2rSIiIry2DqvVqvHjxxe6zK2iqOjxSxV/G4jft4jft4gf3mDG8d4d7A+eo888R595jj7zHH3mOV/0mcUw+A0VAAAAAADMUi7vEQcAAAAA4GJFIg4AAAAAgIlIxAEAAAAAMBGJOAAAAAAAJrqkE/G5c+eqQYMGCgoKUps2bbR9+3Zfh+SWpKQkWSwWp9c111zj67CKtHXrVvXq1UtRUVGyWCxauXKlU7lhGBo3bpzq1Kmj4OBgxcXF6YcffvBNsC6UFP+AAQMKvR9du3b1TbAuTJ06Va1atVK1atVUu3Zt9enTRwcOHHCqk52drSFDhqhGjRqqWrWqEhISdPz4cR9F7Myd+Dt06FDoPXj44Yd9FLGz+fPnq2nTpgoJCVFISIhiY2P1ySefOMrLc99LJcdfnvvelWnTpslisWjYsGGOeeX9PYA5Kup3Al8o6biIwtw5lsFZSccfFM/V8Q7OfJ1TXbKJ+Lvvvqvhw4dr/Pjx2r17t5o1a6b4+HidOHHC16G55dprr9WxY8ccr88++8zXIRUpKytLzZo109y5c12Wz5gxQ7Nnz9aCBQu0bds2ValSRfHx8crOzjY5UtdKil+Sunbt6vR+/Pvf/zYxwuJt2bJFQ4YM0ZdffqmUlBTZbDZ16dJFWVlZjjpPPvmkPv74Yy1fvlxbtmzR0aNHdccdd/gw6v9xJ35JGjRokNN7MGPGDB9F7Oyyyy7TtGnTtGvXLu3cuVO33nqrevfurW+++UZS+e57qeT4pfLb9wXt2LFDr776qpo2beo0v7y/Byh7Ff07gdncOS7CmbvHMvyPO8cfuFbU8Q6F+TSnMi5RrVu3NoYMGeKYzsvLM6KiooypU6f6MCr3jB8/3mjWrJmvwygVScaKFSsc0/n5+UZkZKQxc+ZMx7z09HTDarUa//73v30QYfEKxm8YhpGYmGj07t3bJ/GUxokTJwxJxpYtWwzDONffAQEBxvLlyx119u/fb0gyUlNTfRVmkQrGbxiG0b59e+OJJ57wXVAeql69uvH6669XuL63s8dvGBWn70+dOmU0bNjQSElJcYq5or4H8K6K/J3A11wdF1EyV8cylOz84w9cK+p4h8J8nVNdkiPiubm52rVrl+Li4hzz/Pz8FBcXp9TUVB9G5r4ffvhBUVFRuvzyy9WvXz8dPnzY1yGVysGDB5WWlub0XoSGhqpNmzYV5r2QpM2bN6t27dq6+uqr9cgjj+jPP//0dUhFysjIkCSFh4dLknbt2iWbzeb0HlxzzTWKjo4ul+9Bwfjt3n77bdWsWVNNmjTRqFGjdObMGV+EV6y8vDy98847ysrKUmxsbIXr+4Lx21WEvh8yZIh69Ojh1NdSxdv/4X0Xw3cCVDxFHcvgWlHHHxRW1PEOrvkyp/I3bU3lyB9//KG8vDxFREQ4zY+IiNB3333no6jc16ZNGy1ZskRXX321jh07pgkTJujmm2/Wvn37VK1aNV+H55G0tDRJcvle2MvKu65du+qOO+5QTEyMfvrpJ40ePVrdunVTamqqKlWq5OvwnOTn52vYsGFq166dmjRpIuncexAYGKiwsDCnuuXxPXAVvyTdd999ql+/vqKiovT111/rmWee0YEDB/Thhx/6MNr/2bt3r2JjY5Wdna2qVatqxYoVaty4sfbs2VMh+r6o+KXy3/eS9M4772j37t3asWNHobKKtP+jbFT07wSoeIo6lqGw4o4/KKy44x0K83VOdUkm4hVdt27dHH83bdpUbdq0Uf369fXee+9p4MCBPozs0tS3b1/H39ddd52aNm2qK664Qps3b1anTp18GFlhQ4YM0b59+8r1MwWKU1T8gwcPdvx93XXXqU6dOurUqZN++uknXXHFFWaHWcjVV1+tPXv2KCMjQ++//74SExO1ZcsWX4fltqLib9y4cbnv+19//VVPPPGEUlJSFBQU5OtwAKDCH4vNVNzxB8443nnO1znVJXlpes2aNVWpUqVCT8U9fvy4IiMjfRRV6YWFhemqq67Sjz/+6OtQPGbv74vlvZCkyy+/XDVr1ix378fQoUO1atUqbdq0SZdddpljfmRkpHJzc5Wenu5Uv7y9B0XF70qbNm0kqdy8B4GBgbryyivVokULTZ06Vc2aNdPLL79cYfq+qPhdKW99v2vXLp04cUI33HCD/P395e/vry1btmj27Nny9/dXREREhXgPUHYutu8EKN88OZbBs+PPpa6k411eXp6vQyz3zM6pLslEPDAwUC1atNCGDRsc8/Lz87Vhw4YKed/J6dOn9dNPP6lOnTq+DsVjMTExioyMdHovMjMztW3btgr5XkjSb7/9pj///LPcvB+GYWjo0KFasWKFNm7cqJiYGKfyFi1aKCAgwOk9OHDggA4fPlwu3oOS4ndlz549klRu3oOC8vPzlZOTU+77vij2+F0pb33fqVMn7d27V3v27HG8WrZsqX79+jn+rojvAbznYvtOgPKpNMcyFFbc8edSV9LxrrzdLlkemZ1TXbKXpg8fPlyJiYlq2bKlWrdurVmzZikrK0sPPPCAr0Mr0b/+9S/16tVL9evX19GjRzV+/HhVqlRJ9957r69Dc+n06dNOZ5YOHjyoPXv2KDw8XNHR0Ro2bJgmT56shg0bKiYmRmPHjlVUVJT69Onju6DPU1z84eHhmjBhghISEhQZGamffvpJI0aM0JVXXqn4+HgfRv0/Q4YMUXJysv7zn/+oWrVqjvteQ0NDFRwcrNDQUA0cOFDDhw9XeHi4QkJC9Nhjjyk2NlZt27b1cfQlx//TTz8pOTlZ3bt3V40aNfT111/rySef1C233FIufrZj1KhR6tatm6Kjo3Xq1CklJydr8+bNWrduXbnve6n4+Mt730tStWrVCt2DWaVKFdWoUcMxv7y/Byh7Ffk7gS+UdFxHYSUdy1BYcccfFObO8Q7OfJ5T+ex57eXAnDlzjOjoaCMwMNBo3bq18eWXX/o6JLfcc889Rp06dYzAwECjbt26xj333GP8+OOPvg6rSJs2bTIkFXolJiYahnHuJ8zGjh1rREREGFar1ejUqZNx4MAB3wZ9nuLiP3PmjNGlSxejVq1aRkBAgFG/fn1j0KBBRlpamq/DdnAVuyRj8eLFjjp///238eijjxrVq1c3KleubNx+++3GsWPHfBf0eUqK//Dhw8Ytt9xihIeHG1ar1bjyyiuNp59+2sjIyPBt4P/fgw8+aNSvX98IDAw0atWqZXTq1MlYv369o7w8971hFB9/ee/7ohT8OZfy/h7AHBX1O4EvlHRcR2HuHIvhrKTjJ0rGz5cVz9c5lcUwDKPMs30AAAAAACDpEr1HHAAAAAAAXyERBwAAAADARCTiAAAAAACYiEQcAAAAAAATkYgDAAAAAGAiEnEAAAAAAExEIg4AAAAAgIlIxAEAAAAAMBGJOAAAAAAAJiIRBwAAAADARCTigA916NBBFotFFotFwcHBat68udauXesoHzBggPr06aMJEyaoVq1aCgkJ0cMPP6zc3FxHnfz8fE2dOlUxMTEKDg5Ws2bN9P777zvKN2/eLIvFoqZNmzqt+z//+Y8sFos6dOjgmHf69GkNGDBAERERjrgsFov27Nnj9jYNGDBAFotFL774otP822+/XRaLRUuWLJEkHTp0qFDbY8eOlcVi0axZsxzzLBaLVq5cWajfhg0b5nZMAAD4Csd6jvWAKyTigI8NGjRIx44d0759+9SkSRMlJiY6lW/YsEH79+/X5s2b9e9//1sffvihJkyY4CifOnWqli1bpgULFuibb77Rk08+qf79+2vLli1O7Zw8eVJffvmlY/rVV19V3bp1nepMmTJF69ev13vvvadjx45p+/btpdqmunXr6rXXXnNMHz16VJ9//rkqV65c5DK//fabZs2apeDg4FKtEwCA8opj/Tkc64H/IREHfKxy5cqKjIxU/fr1Vbt2bYWGhjqVBwYGatGiRbr22mvVo0cPTZw4UbNnz1Z+fr5ycnI0ZcoULVq0SPHx8br88ss1YMAA9e/fX6+++qpTOw8++KDjgHn48GHt2rVLt912m1OdPXv2qGfPnmrfvr0iIyNVq1atUm1Ty5YtFRgYqP/+97+SpEWLFqlv374KCAgocplnn31W99xzj2rXrl2qdQIAUF5xrD+HYz3wPyTigI/NmzdPVatWVXBwsN58800tXbrUqbxZs2ZOZ5djY2N1+vRp/frrr/rxxx915swZde7cWVWrVnW8li1bpp9++smpncTERK1cuVKZmZl6/fXX1b9/fwUGBjrViYmJ0ebNm3XkyJEL3q5BgwZp4cKFys/P1xtvvKFBgwYVWXf37t1asWKFJk2a5LL83nvvddo++0EfAICKgGM9x3qgIH9fBwBc6vr166dnn31W2dnZWrp0qe666y59++23CgkJKXHZ06dPS5JWr15d6NIzq9XqNF2jRg3Fx8dr2bJlWrRokT799FMtWLDAqc64ceP0/fff67LLLlOVKlVkGEapt6t///4aP3683nnnHUVGRuq6664rsu5TTz2lf/3rX6pTp47L8pdeeklxcXGO6X79+pU6LgAAzMaxnmM9UBCJOOBjoaGhuvLKKyVJ48eP1/PPP6/t27c7DkZfffWV/v77b8f9VF9++aWqVq2qevXqKTw8XFarVYcPH1b79u1LXNc///lP9erVS82bN9c111xTqDwiIkJPPPGEdu/erTVr1ig7O9vpAS+eCAsL02233aaHH37Y6YEsBX300Uf6/vvvtXr16iLrREZGOvpIEveWAQAqFI71HOuBgkjEAR87c+aM0tLSlJOTo6VLl8rf39/pQJSbm6uBAwdqzJgxOnTokMaPH6+hQ4fKz89P1apV07/+9S89+eSTys/P10033aSMjAx9/vnnCgkJKfQwmPbt22vChAmKjY11GcvPP/+sxMRELVu2TG3atNGhQ4ecyo8cOaJOnTpp2bJlat26dYnbNnLkSF199dW65557iqwzY8YMzZkzp9iHu5Rk1KhROnLkiJYtW1bqNgAAKCsc6znWAwWRiAM+9tprr+m1115TYGCgGjZsqLffflsNGjRwlHfq1EkNGzbULbfcopycHN17771KSkpylE+aNEm1atXS1KlT9fPPPyssLEw33HCDRo8e7XJ9Tz75pMv5f//9txISEvToo4+qR48eLuvYbDYdOHBAZ86ccWvbrr76ao0cObLYOldeeWWhLxGeOnbsmA4fPnxBbQAAUFY41nOsBwqyGBdyYwiAMjVgwAClp6cX+m1NAABwceBYD1yaeGo6AAAAAAAmIhEHAAAAAMBEXJoOAAAAAICJGBEHAAAAAMBEJOIAAAAAAJiIRBwAAAAAABORiAMAAAAAYCIScQAAAAAATEQiDgAAAACAiUjEAQAAAAAwEYk4AAAAAAAmIhEHAAAAAMBE/w9NZpyLqh4hfQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# создадим фигуру для двух графиков\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 6))\n",
    "fig.suptitle('Длительность одной поездки', fontsize=16)\n",
    "# гистограмма для всего диапазона значений\n",
    "axes[0].hist(df_rides['duration'],bins=100)\n",
    "axes[0].set_title(' весь диапазон значений.')\n",
    "axes[0].set_xlabel('время. мин.')\n",
    "axes[0].grid(True)\n",
    "\n",
    "# диаграмма для диапазона (0,5)\n",
    "axes[1].hist(df_rides['duration'],bins=50,range = (0,5))\n",
    "axes[1].set_title('диапазон от 0 до 5 мин.')\n",
    "axes[1].set_xlabel('время. мин.')\n",
    "axes[1].set_ylim(0,100)\n",
    "axes[1].grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ab406ee",
   "metadata": {},
   "source": [
    "На гистограммах заметен выброс при значении 1 мин. Выручка сервиса зависит от времени ииспользования самоката. Исследуем поздки\n",
    "длительностью в одну минуту, проанализируем расстояния таких поездок. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ddb90aeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>95.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5113.168421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>658.312505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4030.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4611.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5053.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5488.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7211.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          distance\n",
       "count    95.000000\n",
       "mean   5113.168421\n",
       "std     658.312505\n",
       "min    4030.000000\n",
       "25%    4611.500000\n",
       "50%    5053.000000\n",
       "75%    5488.500000\n",
       "max    7211.000000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rides.query('duration ==1 ')[['distance']].describe()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "506244a3",
   "metadata": {},
   "source": [
    " Исследуемые  поездки находятся в диапазоне от 4030м до 7211м, что позволяет сделать вывод о недостоверности указанного времени в 1 минуту на преодоление таких расстояний. Доля таких поездок составляет около 1%\n",
    "Классифицируем их как  аномалии и удалим такие записи.\n",
    "\n",
    "Рекомендуем техническим службам более детально изучить и устранить прчины таких аномалий. Даже небольшая доля таких случаев снижает  выручку."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d532ad9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_rides = df_rides.query('duration !=1')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "508af97a",
   "metadata": {},
   "source": [
    "Проверим  короткие поездки на предмет аномальных расстояний."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f9012f2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1310</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          distance\n",
       "duration          \n",
       "3              466\n",
       "4              753\n",
       "5             1310"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rides.query('duration<=5').pivot_table(index='duration',values='distance', aggfunc='max')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b48ad39",
   "metadata": {},
   "source": [
    "В оставшихся коротких поездках аномалий невыявлено."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81b45ac2",
   "metadata": {},
   "source": [
    "После произведенных измений выведем статистические данные, отражающие основные данные о поездках  и подведем итоги."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "65766a18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>distance</th>\n",
       "      <th>duration</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17973.000000</td>\n",
       "      <td>17973.000000</td>\n",
       "      <td>17973.000000</td>\n",
       "      <td>17973.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>841.551995</td>\n",
       "      <td>3059.862905</td>\n",
       "      <td>18.394981</td>\n",
       "      <td>6.486786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>435.144929</td>\n",
       "      <td>1108.815513</td>\n",
       "      <td>5.979419</td>\n",
       "      <td>3.436024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>484.000000</td>\n",
       "      <td>2539.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>887.000000</td>\n",
       "      <td>3129.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1213.000000</td>\n",
       "      <td>3763.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1534.000000</td>\n",
       "      <td>7066.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            user_id      distance      duration         month\n",
       "count  17973.000000  17973.000000  17973.000000  17973.000000\n",
       "mean     841.551995   3059.862905     18.394981      6.486786\n",
       "std      435.144929   1108.815513      5.979419      3.436024\n",
       "min        1.000000      1.000000      3.000000      1.000000\n",
       "25%      484.000000   2539.000000     14.000000      4.000000\n",
       "50%      887.000000   3129.000000     18.000000      6.000000\n",
       "75%     1213.000000   3763.000000     22.000000      9.000000\n",
       "max     1534.000000   7066.000000     41.000000     12.000000"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rides.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85b5294d",
   "metadata": {},
   "source": [
    "Общее количество пользователей сотавляет 1534 человека, из них 699 с подпиской 835 без.\n",
    "Количество пользователей без подписки превышает общее число пользователей, за исключением Москвы и Омска, где оно одинаково.\n",
    "\n",
    "Среднее значение возраста составляют около 25 лет. Максимальный возраст клиентов составляет 43 года, в то время как минимальный возраст — 12 лет.\n",
    "\n",
    "Среднее расстояние поездки составляет 3059м, при этом максимальное расстояние достигает 7066м., а минимальное 1м.\n",
    "\n",
    "Среднее время, затраченное на одну поездку составляет около 18мин.м,при этом максимальное время составляет 41мин., а минимальное 3 мин.\n",
    "\n",
    "В данных были аномальные значения поездок длительность 1 мин на расстояние в диапазоне от 4030м до 7211м.\n",
    "Рекомендуем техническим службам более детально изучить устранить причины появления таких аномалий."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ef51331",
   "metadata": {},
   "source": [
    "#### Шаг 4. Объединение данных"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57cd6c29",
   "metadata": {},
   "source": [
    "Объединим  датафремы `df_rides`,`df_users`,`df_subscriptions` и запишем в новую таблицу:  \n",
    "**`data_total `**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "025ee920",
   "metadata": {},
   "outputs": [],
   "source": [
    "# присоединеним df_users к df_rides по общему столбцу user_id и запишем в data_total.\n",
    "data_total = df_rides.merge(right=df_users, how='left', on='user_id')\n",
    "\n",
    "# к data_total присоединим df_subscriptions по общему столбцу subscription_type.\n",
    "data_total = data_total.merge(right=df_subscriptions, on='subscription_type',how='left' )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d66b391d",
   "metadata": {},
   "source": [
    "Оценим корректность объединения таблиц."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a2de24d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер df_rides:(17973, 5), размер df_users:(1534, 5), размер df_subscriptions:(2, 4)\n",
      "размер итогового датафрема data_total:(17973, 12)\n"
     ]
    }
   ],
   "source": [
    "print(f'Размер df_rides:{df_rides.shape}, размер df_users:{df_users.shape}, размер df_subscriptions:{df_subscriptions.shape}')\n",
    "print(f'размер итогового датафрема data_total:{data_total.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "babf9c52",
   "metadata": {},
   "source": [
    "Данные присоединяли к `data_rides`.\n",
    "Kоличество строк в `data_total` и в `data_rides` равное.\n",
    "Количество столбцов `data_total` равно 12, что соответствует количеству  столбцов в объединяемых датафреймах за минусом двух\n",
    "общих.\n",
    "\n",
    "Проверим пропуски и тип данных в `data_total` и выведем несколько строк итоговой таблицы.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b029fcca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 17973 entries, 0 to 17972\n",
      "Data columns (total 12 columns):\n",
      " #   Column             Non-Null Count  Dtype         \n",
      "---  ------             --------------  -----         \n",
      " 0   user_id            17973 non-null  int64         \n",
      " 1   distance           17973 non-null  int32         \n",
      " 2   duration           17973 non-null  int32         \n",
      " 3   date               17973 non-null  datetime64[ns]\n",
      " 4   month              17973 non-null  int64         \n",
      " 5   name               17973 non-null  object        \n",
      " 6   age                17973 non-null  int64         \n",
      " 7   city               17973 non-null  object        \n",
      " 8   subscription_type  17973 non-null  object        \n",
      " 9   minute_price       17973 non-null  int64         \n",
      " 10  start_ride_price   17973 non-null  int64         \n",
      " 11  subscription_fee   17973 non-null  int64         \n",
      "dtypes: datetime64[ns](1), int32(2), int64(6), object(3)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "data_total.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f1300eb2",
   "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>distance</th>\n",
       "      <th>duration</th>\n",
       "      <th>date</th>\n",
       "      <th>month</th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>city</th>\n",
       "      <th>subscription_type</th>\n",
       "      <th>minute_price</th>\n",
       "      <th>start_ride_price</th>\n",
       "      <th>subscription_fee</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4410</td>\n",
       "      <td>26</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>Кира</td>\n",
       "      <td>22</td>\n",
       "      <td>Тюмень</td>\n",
       "      <td>ultra</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2618</td>\n",
       "      <td>16</td>\n",
       "      <td>2021-01-18</td>\n",
       "      <td>1</td>\n",
       "      <td>Кира</td>\n",
       "      <td>22</td>\n",
       "      <td>Тюмень</td>\n",
       "      <td>ultra</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>754</td>\n",
       "      <td>7</td>\n",
       "      <td>2021-04-20</td>\n",
       "      <td>4</td>\n",
       "      <td>Кира</td>\n",
       "      <td>22</td>\n",
       "      <td>Тюмень</td>\n",
       "      <td>ultra</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2695</td>\n",
       "      <td>19</td>\n",
       "      <td>2021-08-11</td>\n",
       "      <td>8</td>\n",
       "      <td>Кира</td>\n",
       "      <td>22</td>\n",
       "      <td>Тюмень</td>\n",
       "      <td>ultra</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>4029</td>\n",
       "      <td>27</td>\n",
       "      <td>2021-08-28</td>\n",
       "      <td>8</td>\n",
       "      <td>Кира</td>\n",
       "      <td>22</td>\n",
       "      <td>Тюмень</td>\n",
       "      <td>ultra</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  distance  duration       date  month  name  age    city  \\\n",
       "0        1      4410        26 2021-01-01      1  Кира   22  Тюмень   \n",
       "1        1      2618        16 2021-01-18      1  Кира   22  Тюмень   \n",
       "2        1       754         7 2021-04-20      4  Кира   22  Тюмень   \n",
       "3        1      2695        19 2021-08-11      8  Кира   22  Тюмень   \n",
       "4        1      4029        27 2021-08-28      8  Кира   22  Тюмень   \n",
       "\n",
       "  subscription_type  minute_price  start_ride_price  subscription_fee  \n",
       "0             ultra             6                 0               199  \n",
       "1             ultra             6                 0               199  \n",
       "2             ultra             6                 0               199  \n",
       "3             ultra             6                 0               199  \n",
       "4             ultra             6                 0               199  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_total.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "64c94756",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_total_ultra = data_total.query('subscription_type == \"ultra\"')\n",
    "data_total_free = data_total.query('subscription_type == \"free\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6bf288b5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>distance</th>\n",
       "      <th>duration</th>\n",
       "      <th>date</th>\n",
       "      <th>month</th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>city</th>\n",
       "      <th>subscription_type</th>\n",
       "      <th>minute_price</th>\n",
       "      <th>start_ride_price</th>\n",
       "      <th>subscription_fee</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4086</th>\n",
       "      <td>440</td>\n",
       "      <td>2760</td>\n",
       "      <td>18</td>\n",
       "      <td>2021-05-14</td>\n",
       "      <td>5</td>\n",
       "      <td>Амалия</td>\n",
       "      <td>20</td>\n",
       "      <td>Тюмень</td>\n",
       "      <td>ultra</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>199</td>\n",
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       "      <th>256</th>\n",
       "      <td>27</td>\n",
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       "      <td>2021-07-12</td>\n",
       "      <td>7</td>\n",
       "      <td>Эдуард</td>\n",
       "      <td>20</td>\n",
       "      <td>Краснодар</td>\n",
       "      <td>ultra</td>\n",
       "      <td>6</td>\n",
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       "      <th>2967</th>\n",
       "      <td>319</td>\n",
       "      <td>2760</td>\n",
       "      <td>13</td>\n",
       "      <td>2021-02-17</td>\n",
       "      <td>2</td>\n",
       "      <td>Юрий</td>\n",
       "      <td>26</td>\n",
       "      <td>Омск</td>\n",
       "      <td>ultra</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4532</th>\n",
       "      <td>488</td>\n",
       "      <td>2966</td>\n",
       "      <td>18</td>\n",
       "      <td>2021-11-24</td>\n",
       "      <td>11</td>\n",
       "      <td>Владимир</td>\n",
       "      <td>31</td>\n",
       "      <td>Пятигорск</td>\n",
       "      <td>ultra</td>\n",
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       "      <td>535</td>\n",
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       "      <td>16</td>\n",
       "      <td>2021-07-10</td>\n",
       "      <td>7</td>\n",
       "      <td>Юрий</td>\n",
       "      <td>25</td>\n",
       "      <td>Ростов-на-Дону</td>\n",
       "      <td>ultra</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  distance  duration       date  month      name  age  \\\n",
       "4086      440      2760        18 2021-05-14      5    Амалия   20   \n",
       "256        27      3087        16 2021-07-12      7    Эдуард   20   \n",
       "2967      319      2760        13 2021-02-17      2      Юрий   26   \n",
       "4532      488      2966        18 2021-11-24     11  Владимир   31   \n",
       "4971      535      3482        16 2021-07-10      7      Юрий   25   \n",
       "\n",
       "                city subscription_type  minute_price  start_ride_price  \\\n",
       "4086          Тюмень             ultra             6                 0   \n",
       "256        Краснодар             ultra             6                 0   \n",
       "2967            Омск             ultra             6                 0   \n",
       "4532       Пятигорск             ultra             6                 0   \n",
       "4971  Ростов-на-Дону             ultra             6                 0   \n",
       "\n",
       "      subscription_fee  \n",
       "4086               199  \n",
       "256                199  \n",
       "2967               199  \n",
       "4532               199  \n",
       "4971               199  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_total_ultra.sample(5) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d8bf03bb",
   "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>user_id</th>\n",
       "      <th>distance</th>\n",
       "      <th>duration</th>\n",
       "      <th>date</th>\n",
       "      <th>month</th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>city</th>\n",
       "      <th>subscription_type</th>\n",
       "      <th>minute_price</th>\n",
       "      <th>start_ride_price</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11360</th>\n",
       "      <td>1058</td>\n",
       "      <td>191</td>\n",
       "      <td>5</td>\n",
       "      <td>2021-09-27</td>\n",
       "      <td>9</td>\n",
       "      <td>Артемий</td>\n",
       "      <td>25</td>\n",
       "      <td>Омск</td>\n",
       "      <td>free</td>\n",
       "      <td>8</td>\n",
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       "      <td>0</td>\n",
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       "      <th>7021</th>\n",
       "      <td>736</td>\n",
       "      <td>2951</td>\n",
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       "      <td>2021-05-21</td>\n",
       "      <td>5</td>\n",
       "      <td>Ильдар</td>\n",
       "      <td>26</td>\n",
       "      <td>Пятигорск</td>\n",
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       "      <td>8</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
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       "      <td>836</td>\n",
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       "      <td>2021-07-23</td>\n",
       "      <td>7</td>\n",
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       "      <td>21</td>\n",
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       "      <td>free</td>\n",
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       "      <td>0</td>\n",
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       "      <td>946</td>\n",
       "      <td>4057</td>\n",
       "      <td>15</td>\n",
       "      <td>2021-07-29</td>\n",
       "      <td>7</td>\n",
       "      <td>Олег</td>\n",
       "      <td>29</td>\n",
       "      <td>Екатеринбург</td>\n",
       "      <td>free</td>\n",
       "      <td>8</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>16208</th>\n",
       "      <td>1409</td>\n",
       "      <td>2128</td>\n",
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       "      <td>2021-12-16</td>\n",
       "      <td>12</td>\n",
       "      <td>Юрий</td>\n",
       "      <td>29</td>\n",
       "      <td>Екатеринбург</td>\n",
       "      <td>free</td>\n",
       "      <td>8</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "       user_id  distance  duration       date  month      name  age  \\\n",
       "11360     1058       191         5 2021-09-27      9   Артемий   25   \n",
       "7021       736      2951        15 2021-05-21      5    Ильдар   26   \n",
       "8320       836      4170        22 2021-07-23      7  Всеволод   21   \n",
       "9792       946      4057        15 2021-07-29      7      Олег   29   \n",
       "16208     1409      2128        11 2021-12-16     12      Юрий   29   \n",
       "\n",
       "               city subscription_type  minute_price  start_ride_price  \\\n",
       "11360          Омск              free             8                50   \n",
       "7021      Пятигорск              free             8                50   \n",
       "8320         Тюмень              free             8                50   \n",
       "9792   Екатеринбург              free             8                50   \n",
       "16208  Екатеринбург              free             8                50   \n",
       "\n",
       "       subscription_fee  \n",
       "11360                 0  \n",
       "7021                  0  \n",
       "8320                  0  \n",
       "9792                  0  \n",
       "16208                 0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_total_free.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "644b7d78",
   "metadata": {},
   "source": [
    "Проверим размерности полученных датофреймов  с исходным"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "983acc53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Совокупный размер созданных датафремов:17973 \n",
      "Размер исходного датафрейма:17973\n"
     ]
    }
   ],
   "source": [
    "# размеры определим методом shape[0], выведем сумму полученных датафреймов и размер исходного.\n",
    "print(f'Совокупный размер созданных датафремов:{data_total_free.shape[0]+data_total_ultra.shape[0]} \\n\\\n",
    "Размер исходного датафрейма:{data_total.shape[0]}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edd58468",
   "metadata": {},
   "source": [
    "Визуализируем информацию о расстоянии и времени поездок для пользователей обеих категорий"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2b46fb17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# создадим фигуру для двух графиков\n",
    "fig, axes = plt.subplots(figsize=(12, 6))\n",
    "fig.suptitle('Распределение времени одной поездки для клиентов с подпиской и без.'\n",
    "             , fontsize=16)\n",
    "# гистограмма для всего диапазона значений\n",
    "axes.hist(data_total_ultra['duration'],bins=50,label='есть подписка', alpha=0.7)\n",
    "axes.set_xlabel('время. мин.',fontsize=12)\n",
    "axes.grid(True)\n",
    "axes.set_ylabel('частота',fontsize=12)\n",
    "\n",
    "# диаграмма для диапазона (0,5)\n",
    "axes.hist(data_total_free['duration'],bins=50, label='без подписки',alpha=0.5)\n",
    "axes.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4f7e257d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# создадим фигуру для двух графиков\n",
    "fig, axes = plt.subplots(figsize=(12, 6))\n",
    "fig.suptitle('Распределение расстояния одной поездки для клиентов с подпиской и без.'\n",
    "             , fontsize=16)\n",
    "# гистограмма для всего диапазона значений\n",
    "axes.hist(data_total_ultra['distance'],bins=100,alpha=0.5,label='есть подписка')\n",
    "axes.set_xlabel('расстояние, м.',fontsize=12)\n",
    "axes.grid(True)\n",
    "axes.set_ylabel('частота',fontsize=12)\n",
    "# диаграмма для диапазона (0,5)\n",
    "\n",
    "axes.hist(data_total_free['distance'],bins=100,alpha=0.3,label='без подписки')\n",
    "axes.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "660afb66",
   "metadata": {},
   "source": [
    "Составим таблицу с основными статтистическим  характеристиками:\n",
    "1. Клиенты с подпиской - столбцы `distance_ultra`,`duration-ultra`, соответственно расстояние  и время.\n",
    "2. Клиенты без подписки - столбцы `distance_ultra`,`duration-ultra`, соответственно расстояние  и время."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6b3d3126",
   "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>distance_ultra</th>\n",
       "      <th>duration_ultra</th>\n",
       "      <th>distance_free</th>\n",
       "      <th>duration_free</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6500.000000</td>\n",
       "      <td>6500.000000</td>\n",
       "      <td>11473.000000</td>\n",
       "      <td>11473.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3115.443385</td>\n",
       "      <td>19.037692</td>\n",
       "      <td>3028.373921</td>\n",
       "      <td>18.030855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>836.898137</td>\n",
       "      <td>5.577439</td>\n",
       "      <td>1235.541284</td>\n",
       "      <td>6.166175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>244.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2785.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>2361.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3149.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>3102.000000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3561.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>3884.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5700.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>7066.000000</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       distance_ultra  duration_ultra  distance_free  duration_free\n",
       "count     6500.000000     6500.000000   11473.000000   11473.000000\n",
       "mean      3115.443385       19.037692    3028.373921      18.030855\n",
       "std        836.898137        5.577439    1235.541284       6.166175\n",
       "min        244.000000        4.000000       1.000000       3.000000\n",
       "25%       2785.000000       15.000000    2361.000000      14.000000\n",
       "50%       3149.000000       19.000000    3102.000000      18.000000\n",
       "75%       3561.000000       22.000000    3884.000000      22.000000\n",
       "max       5700.000000       41.000000    7066.000000      35.000000"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# создадим датафрейм data_stat, объединив  в него таблицы  со стат. данными, полученными\n",
    "# методом .describe().\n",
    "data_stat = pd.concat(\n",
    "      [data_total_ultra[['distance','duration']].describe(),\n",
    "      data_total_free[['distance','duration']].describe()],\n",
    "      axis=1 )\n",
    "# переименуем столбцы.\n",
    "data_stat.columns =['distance_ultra','duration_ultra','distance_free','duration_free']  \n",
    "data_stat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d57da4e",
   "metadata": {},
   "source": [
    "Сравнительный анализ между клиентами с подпиской и без показывает, что средние и медианные значения для расстояния и времени поездки в обеих категориях не имеют значительных различий..К тому же среднее значение близкое к медианному,\n",
    " указывает на отсутствие существенных выбросов и нормальное распределение данных.\n",
    " \n",
    "Ллиенты без подписки имеют более высокие максимальные показатели дистанции по сравнению с теми, у кого есть подписка, при этом\n",
    "время одной поездки остается примерно одинаковым для обеих категорий.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "402d620f",
   "metadata": {},
   "source": [
    "#### Шаг 5. Подсчёт выручки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "67bdad8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 17973 entries, 0 to 17972\n",
      "Data columns (total 12 columns):\n",
      " #   Column             Non-Null Count  Dtype         \n",
      "---  ------             --------------  -----         \n",
      " 0   user_id            17973 non-null  int64         \n",
      " 1   distance           17973 non-null  int32         \n",
      " 2   duration           17973 non-null  int32         \n",
      " 3   date               17973 non-null  datetime64[ns]\n",
      " 4   month              17973 non-null  int64         \n",
      " 5   name               17973 non-null  object        \n",
      " 6   age                17973 non-null  int64         \n",
      " 7   city               17973 non-null  object        \n",
      " 8   subscription_type  17973 non-null  object        \n",
      " 9   minute_price       17973 non-null  int64         \n",
      " 10  start_ride_price   17973 non-null  int64         \n",
      " 11  subscription_fee   17973 non-null  int64         \n",
      "dtypes: datetime64[ns](1), int32(2), int64(6), object(3)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "data_total.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09afa3a4",
   "metadata": {},
   "source": [
    "Создадим датафрем `data_total_agg`с агрегированными c данными по каждому клиенту помесячно:\n",
    "1. Суммарное расстояние.\n",
    "2. Количество поездок\n",
    "3. Суммарное время.\n",
    "\n",
    "Также добавим в таблицу `data_total_agg` столбец  с помесячной выручкой от каждого пользователя.\n",
    "\n",
    "За основу возьмем данные датафрема `data_total`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f23169e",
   "metadata": {},
   "source": [
    "Создадим data_total_agg, объект типа DataFrameGroupBy, сгруппированный по столбцам `user_id`, `month`датафрема `data_total`.\n",
    "Сформируем агрегированный датофрейм `data_total_agg`со столбцами:\n",
    "1. `distance` - суммарное расстояние.(метод .sum )\n",
    "2. `name`- количество поездок. (метод .count)\n",
    "3.  `duration`-cуммарное время поездок (метод .sum)\n",
    "4. `start_ride_price`- стоимость поездки без подписки (метод .max)\n",
    "5. `subscription_fee`- стоимость подписки на месяц (метод.max)\n",
    "6.  `minute_price ` - стоимость минуты(метод .max)\n",
    "\n",
    "Добавим столбец `revenue`, для этого перемножим значения соответствующих ячеек в столбцах `minute_price` и `duration`, затем прибавим значения соответствующих ячеек столбцов `start_ride_price` и `subscription_fee`.\n",
    "Результатом будет помесячная выручка от каждого клиента.\n",
    "\n",
    "Переименуем столбец `name` в `trips`.\n",
    "\n",
    "                                                                                               "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3cae2852",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>month</th>\n",
       "      <th>distance</th>\n",
       "      <th>trips</th>\n",
       "      <th>duration</th>\n",
       "      <th>minute_price</th>\n",
       "      <th>start_ride_price</th>\n",
       "      <th>subscription_fee</th>\n",
       "      <th>revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7028</td>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>199</td>\n",
       "      <td>451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>754</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>199</td>\n",
       "      <td>241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>6724</td>\n",
       "      <td>2</td>\n",
       "      <td>46</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>199</td>\n",
       "      <td>475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>5810</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>199</td>\n",
       "      <td>391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>7004</td>\n",
       "      <td>3</td>\n",
       "      <td>56</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>199</td>\n",
       "      <td>535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11295</th>\n",
       "      <td>1534</td>\n",
       "      <td>6</td>\n",
       "      <td>3409</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>8</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11296</th>\n",
       "      <td>1534</td>\n",
       "      <td>8</td>\n",
       "      <td>7623</td>\n",
       "      <td>2</td>\n",
       "      <td>48</td>\n",
       "      <td>8</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11297</th>\n",
       "      <td>1534</td>\n",
       "      <td>9</td>\n",
       "      <td>4928</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11298</th>\n",
       "      <td>1534</td>\n",
       "      <td>11</td>\n",
       "      <td>13349</td>\n",
       "      <td>4</td>\n",
       "      <td>78</td>\n",
       "      <td>8</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11299</th>\n",
       "      <td>1534</td>\n",
       "      <td>12</td>\n",
       "      <td>2372</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11300 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id  month  distance  trips  duration  minute_price  \\\n",
       "0            1      1      7028      2        42             6   \n",
       "1            1      4       754      1         7             6   \n",
       "2            1      8      6724      2        46             6   \n",
       "3            1     10      5810      2        32             6   \n",
       "4            1     11      7004      3        56             6   \n",
       "...        ...    ...       ...    ...       ...           ...   \n",
       "11295     1534      6      3409      2        26             8   \n",
       "11296     1534      8      7623      2        48             8   \n",
       "11297     1534      9      4928      1        23             8   \n",
       "11298     1534     11     13349      4        78             8   \n",
       "11299     1534     12      2372      1        16             8   \n",
       "\n",
       "       start_ride_price  subscription_fee  revenue  \n",
       "0                     0               199      451  \n",
       "1                     0               199      241  \n",
       "2                     0               199      475  \n",
       "3                     0               199      391  \n",
       "4                     0               199      535  \n",
       "...                 ...               ...      ...  \n",
       "11295                50                 0      308  \n",
       "11296                50                 0      484  \n",
       "11297                50                 0      234  \n",
       "11298                50                 0      824  \n",
       "11299                50                 0      178  \n",
       "\n",
       "[11300 rows x 9 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cгруппируем датафрейм 'data-total' по столбцам `user_id`, `month`.\n",
    "data_total_agg = data_total.groupby(by=['user_id','month'])\n",
    "# сформируем датафрейм 'data_total_agg'\n",
    "data_total_agg= data_total_agg.agg({'distance':'sum','name':'count','duration':'sum','minute_price':'max',\n",
    "                                      'start_ride_price':'max','subscription_fee':'max'}).reset_index()\n",
    "# добавим столбец 'revenue'\n",
    "data_total_agg['revenue'] =data_total_agg\\\n",
    ".apply(lambda x: x['minute_price']*x['duration']+x['start_ride_price']*x['name']+x['subscription_fee'],axis=1)\n",
    "\n",
    "# переименуем столбец 'name' в 'trips' \n",
    "data_total_agg.rename(columns={'name':'trips'}, inplace=True)\n",
    "\n",
    "data_total_agg "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "239308dd",
   "metadata": {},
   "source": [
    "#### Шаг 6. Проверка гипотез"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5c23efc",
   "metadata": {},
   "source": [
    "##### Тратят ли пользователи с подпиской больше времени на поездки."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "002ccc6d",
   "metadata": {},
   "source": [
    "Сформулируем нулевую гипотезу **HO: время, затраченное на поездки пользователями с подпиской и без одинковое**.\n",
    "Альтернативная гипотеза **Н1 время, затраченное на поездки  пользователей с подпиской в целом больше времени затраченного на поездки пользователей без подписки**.\n",
    "\n",
    "Проверим Н0 на уровне статитстической значимости 5%.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "089dc310",
   "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>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>19.037692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.577439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>41.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          duration\n",
       "count  6500.000000\n",
       "mean     19.037692\n",
       "std       5.577439\n",
       "min       4.000000\n",
       "25%      15.000000\n",
       "50%      19.000000\n",
       "75%      22.000000\n",
       "max      41.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>11473.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>18.030855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.166175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           duration\n",
       "count  11473.000000\n",
       "mean      18.030855\n",
       "std        6.166175\n",
       "min        3.000000\n",
       "25%       14.000000\n",
       "50%       18.000000\n",
       "75%       22.000000\n",
       "max       35.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(data_total_ultra[['duration']].describe(),data_total_free[['duration']].describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25ffd89f",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "64570e22",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "значение p-value : 8.577910347796266e-28\n",
      "На уровне значимости 0.05, отвергается нулевая гипотеза H0\n"
     ]
    }
   ],
   "source": [
    "alpha= 0.05\n",
    "results = st.ttest_ind(data_total_ultra['duration'],data_total_free['duration']\n",
    "                       ,alternative='greater')\n",
    "print(f'значение p-value : {results.pvalue}')\n",
    "if results.pvalue < alpha:\n",
    "    print(f'На уровне значимости {alpha}, отвергается нулевая гипотеза H0')\n",
    "else:\n",
    "    print(f'Нулевая гипотеза H0:не отвергается на уровне значимости {alpha}') \n",
    "    \n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20bb77aa",
   "metadata": {},
   "source": [
    "Нулевая гипотеза Н0:время, затраченное на поездки пользователями с подпиской и без одинаковое, отвергнута,\n",
    "есть основания утверждать, что **время, затраченное на поездки  пользователей с подпиской в целом больше времени затраченного на поездки пользователей без подписки**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd2c6473",
   "metadata": {},
   "source": [
    "##### Соответствие расстояния одной поездки пользователей с подпиской максимальному."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d956b32b",
   "metadata": {},
   "source": [
    "С точки зрения износа самоката оптимальное расстояние поездки 3130 м.\n",
    "Сформулируем нулевую гипотезу **Н0:среднее расстояние одной поездки для пользователей с подпиской\n",
    " равно 3130м или меньше**.\n",
    "\n",
    "Альтернативная гипотеза **Н1: среднее расстояние одной поездки для пользователей с подпиской больше 3130м.**.\n",
    "\n",
    "Проверим Н0 на уровне статитстической значимости 5%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6d33ae24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "значение p-value : 0.9195649894697724\n",
      "Нулевая гипотеза H0:не отвергается на уровне значимости 0.05\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "distance = 3130\n",
    "# уровень статистической значимости\n",
    "alpha=0.05\n",
    "# датафрем data_total_ultra- данны  о пользователях с подпиской\n",
    "results =  st.ttest_1samp(data_total_ultra['distance'], distance, alternative='greater')\n",
    "print(f'значение p-value : {results.pvalue}')\n",
    "\n",
    "if results.pvalue < alpha:\n",
    "    print(f'На уровне значимости {alpha}, отвергается нулевая гипотеза H0')\n",
    "else:\n",
    "    print(f'Нулевая гипотеза H0:не отвергается на уровне значимости {alpha}') \n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30f7cd36",
   "metadata": {},
   "source": [
    "Результаты статистического теста говорят о том, что среднее расстояние поездки для пользователей с подпиской, вероятно, меньше или равно 3130 м., **это может считаться оптимальным расстоянием с точки зрения износа самоката, поскольку данное значение находится в пределах установленной нормы**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b583396d",
   "metadata": {},
   "source": [
    "#####  Сравнение помесячной выручки пользователей с подпиской и без."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddd84029",
   "metadata": {},
   "source": [
    "Сформулируем нулевую гипотезу: **H0: помесячная выручка у клиентов с подпиской и без не отличается**.\n",
    "\n",
    "Альтернативнач гипотеза: **Н1: помесячная выручка клиентов с подпиской выше чем у клиентов без подписки**.\n",
    "\n",
    "Проверим Н0 на уровне статитстической значимости 5%.\n",
    "\n",
    "Для проверки воспользуемся данными  таблицы `data_total_agg`, маркером пользователей без подписки\n",
    "является значение '0' в столбце `subscription_fee`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b7985a84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "значение p-value : 2.0314113674863288e-30\n",
      "На уровне значимости 0.05, отвергается нулевая гипотеза H0\n"
     ]
    }
   ],
   "source": [
    "# уровень статистической значимости\n",
    "alpha= 0.05\n",
    "# сформируем выборки клиентов с подпиской  и без.\n",
    "data_total_agg_ultra=data_total_agg.query('subscription_fee != 0')\n",
    "data_total_agg_free=data_total_agg.query('subscription_fee == 0')\n",
    "# проверка гипотезы на равенство среднего (правостороння H1)\n",
    "results = st.ttest_ind(data_total_agg_ultra['revenue'],data_total_agg_free['revenue'],\n",
    "                      alternative='greater' )\n",
    "print(f'значение p-value : {results.pvalue}')\n",
    "if results.pvalue < alpha:\n",
    "    print(f'На уровне значимости {alpha}, отвергается нулевая гипотеза H0')\n",
    "else:\n",
    "    print(f'Нулевая гипотеза H0:не отвергается на уровне значимости {alpha}') \n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fc0ec1d",
   "metadata": {},
   "source": [
    "Результаты статистического теста говорят о том, что **помесячная выручка клиентов с подпиской статистически значимо превышает выручку, клиентов без подписки**.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd25f29d",
   "metadata": {},
   "source": [
    "##### Оценка и выбор метода проверки гипотезы."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f20e2f3",
   "metadata": {},
   "source": [
    "В данном задании предлагается сравнить две зависимые выборки. Выборки\n",
    "в задании будут парные, для одних и тех же пользователей изменятся количество обращений до и после\n",
    "изменений. Для проверки гипотезы следует использовать метод **scipy.stats.ttest_rel()**, в качестве\n",
    "аргументов передать две парные выборки до изменения и после. Проверять будем в сторону уменьшения т.к. логика работы сервисной\n",
    "команды направлена на уменьшение количества обращений. При вызове этого метода параметру 'alternative'присвоим значение 'less', что позволяет проверить гипотезу о том, что среднее количество обращений во второй выборке меньше, чем в первой."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c79e1619",
   "metadata": {},
   "source": [
    "В результате проверки гипотез пришли кследующим выводам:\n",
    "1. Время, затраченное на поездки пользователей с подпиской в целом больше времени затраченного на поездки пользователей без подписки.\n",
    "2. Среднее расстояние поездки для пользователей с подпиской, вероятно, меньше или равно 3130 м., это может считаться оптимальным расстоянием с точки зрения износа самоката, поскольку данное значение находится в пределах установленной нормы.\n",
    "3. Результаты статистического теста говорят о том, что помесячная выручка клиентов с подпиской статистически значимо превышает выручку, клиентов без подписки.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61213c5e",
   "metadata": {},
   "source": [
    "#### Шаг 7. Распределения"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5931e405",
   "metadata": {},
   "source": [
    "#####  Расчет минимального количества промокодов для достижения целевого покрытия аудитории."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e36e590",
   "metadata": {},
   "source": [
    "Акция c раздачей промокодов на подписку моделируется биноминальным распределением c вероятностью упеха p=0.1. Число испытаний  n подберем таким, чтобы P(X>=100)>=95% ( вероятность продления 100 \n",
    "подписок 95%).Найдем такое значение n при котором выполняется условие:\n",
    "{F(99,n)<0.05,F(99,n-1)>=0.05)}. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d1c19eb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "количество промокодов 1161, P(X1=100)=95.02%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dmitr\\anaconda3\\envs\\final_practicum\\lib\\site-packages\\scipy\\stats\\_discrete_distns.py:69: RuntimeWarning: divide by zero encountered in _binom_pdf\n",
      "  return _boost._binom_pdf(x, n, p)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import binom\n",
    "# количество попыток.\n",
    "n=100\n",
    "# вероятность успеха\n",
    "p=0.1\n",
    "# количество успехов\n",
    "k=99 \n",
    "# цикл будет выполнятся до выполнения условия F(100)<5%\n",
    "while binom.cdf(k,n,p)>=0.05:\n",
    "    #print(binom.cdf(k,n,p),n)\n",
    "    n=n+1\n",
    "print(f'количество промокодов {n}, P(X1=100)={(1-binom.cdf(k,n,p)):.2%}')\n",
    "\n",
    "# distr_pmf, distr_cdf соответственно массивы для B(1172,0.1)  pmf и cdf соответственно\n",
    "distr_pmf = []\n",
    "distr_cdf = []\n",
    "for t in range(0,n+1):\n",
    "    distr_pmf.append (binom.pmf(t,n,p))\n",
    "    distr_cdf.append (binom.cdf(t,n,p))\n",
    "# создадим фигуру из двух строк для графиков pmf , cdf    \n",
    "fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(12, 12))\n",
    "# график pmf \n",
    "axes[0].bar(range(0,n+1),distr_pmf)\n",
    "axes[0].set_title('Биноминальное распределение P=0.1,n=1172.',fontsize = 14)\n",
    "axes[0].set_xlabel('\"количество \"успехов\"')\n",
    "axes[0].grid(True)\n",
    "axes[0].set_xlim(50, 175)\n",
    "\n",
    "# график сdf\n",
    "axes[1].plot(range(0,n+1),distr_cdf)\n",
    "axes[1].set_title('Функция распределения F(x;1172,0.1)',fontsize = 14)\n",
    "axes[1].set_xlabel('\"количество \"успехов\"')\n",
    "axes[1].grid(True)\n",
    "axes[1].set_xlim(50, 175)\n",
    "new_y_ticks = np.arange(0, 1, 0.05)  # Пример: от 0 до 1 с шагом 0.1\n",
    "axes[1].set_yticks(new_y_ticks)\n",
    "axes[1].scatter([99], [ 0.04954795210203071], color='red')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b68e8fc",
   "metadata": {},
   "source": [
    "Биноминальное распределене Х ~(1161,0.1) соответствует условию поставленной задачи. Для успешной реализации акции, в которой планируется привлечение сто новых подписчиков с вероятностью не выполнения плана менее 5%, необходимо раздать как минимум 1161 промокода."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e6659a2",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\" style=\"border-radius: 15px; box-shadow: 4px 4px 4px; border: 1px solid \">\n",
    "<h2> Комментарий ревьюера v_1: <a class=\"tocSkip\"> </h2>\n",
    "    \n",
    "⚠️ Только здесь k не 100, а 99 будет. Т.к. в задании указано, что как минимум 100 клиентов должны продлить подписку, а не \"более 100 клиентов\". Нужно найти такое минимальное значение, чтобы вероятность не выполнить план (меньше 100) была равна 0.05. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8b8441a",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\"  style=\"border-radius: 15px; box-shadow: 4px 4px 4px; border: 1px solid \">\n",
    "    \n",
    "<h2>Комментарий студента v_1: <a class=\"tocSkip\"> </h2>\n",
    "\n",
    "Исправил.\n",
    "\n",
    "При выполнении задания я долго колебался и выбрал неправильный вариант."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a26b75e",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\" style=\"border-radius: 15px; box-shadow: 4px 4px 4px; border: 1px solid \">\n",
    "<h2> Комментарий ревьюера v_2: <a class=\"tocSkip\"> </h2>\n",
    "    \n",
    "✅ 1161 - правильный результат"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "232c41d4",
   "metadata": {},
   "source": [
    "##### Оценка вероятности просмотра пользователями уведомления."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c9daea9",
   "metadata": {},
   "source": [
    "Вероятность просмотра пользователем уведомленя составляет 40%, p=0.4.\n",
    "Количество уведомлений 1.000.000 шт., n=1e+6.\n",
    "Найдем вероятность того, что уведомление откроют не болеее 399.500 человек.\n",
    "\n",
    "Данная задача моделируется биноминальным распределением, но исходя из характера вопроса, учитывая что вероятность 'успеха'приближается к 0.5 и количество опытов достаточно велико можно использовать апроксимацию биноминального распределения нормальным спарметрами:\n",
    "1. Метиматическое ожидание Е=n*p\n",
    "2. Диспрсия  SD^2=n*p(1-p)\n",
    "\n",
    "Значеник CDF(a) , a=399500 будет вероятностью того что уведомление прочитают не более 399500 человек.\n",
    "\n",
    "Также проверим условия апроксимации:интервал [E-3*SD,E+3*SD] лежит в диапазоне [0,n]\n",
    "( в коде применим более жесткое условие[E-4*SD,E+4*SD] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ff8d6d1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "искомая вероятность 0.15\n",
      "математическое ожидание400000, стандартное отклонение489.90,интервал [ 398040 ,401959 ]\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import norm\n",
    "from math import sqrt\n",
    "p = 0.4\n",
    "n=1e+6\n",
    "value =399500\n",
    "# расчитаем параметры для нормального распределения\n",
    "mu =int(n*p)\n",
    "sigma=sqrt(n*p*(1-p))\n",
    "# вычислим cdf в целевой точке.\n",
    "results = norm.cdf(value,mu,sigma)\n",
    "print(f'искомая вероятность {results:.2f}')\n",
    "\n",
    "# вычислим интервал распределения\n",
    "left = int(n * p - 4 * sigma)\n",
    "right = int(n * p + 4 * sigma)\n",
    "print(f'математическое ожидание{mu}, стандартное отклонение{sigma:.2f},интервал [ {left} ,{right} ]')\n",
    "\n",
    "# создадим массив значений в границах интервала [left, right] \n",
    "x = np.arange(left,right )\n",
    "# создадим дата фрейм  с массивом pdf значений и индексами соответствующим массиву x\n",
    "df_distr=pd.DataFrame(norm.pdf(x,mu,sigma))\n",
    "df_distr['tick']=x\n",
    "df_distr.set_index('tick', inplace=True)\n",
    "\n",
    "#построим график плотности вероятности \n",
    "ax=df_distr.plot(figsize=(12,8),grid=True)\n",
    "ax.scatter([399500], [ 0.000484], color='red')\n",
    "ax.set_title('Функция плотности вероятности X~ N(400000, 498.9)')\n",
    "ax.set_xlabel('Число пользователей')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b09a37fc",
   "metadata": {},
   "source": [
    "Данная задача моделируется  нормальным распределением с математическое ожиданием 400000, стандартным отклонением 489.90.\n",
    "Вероятность того, что уведомления откроют не более 399500 пользователей составляет 15%.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "374be6cd",
   "metadata": {},
   "source": [
    "#### Общий вывод\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73bfb540",
   "metadata": {},
   "source": [
    "В проекте исследованны данные сервиса аренды самокатов GoFast, проверены гипотезы, а также решены задачи с помощью\n",
    "распределений вероятности. На этапе предобработки  проверили данные на наличие дубликатов и пропусков.\n",
    "Обнаруженные дубликаты удалили, пропущенных значений не было. Данные в датафреймах привели к нужным форматам. \n",
    "В ходе исследования выяснили ,что для части  длительных поездок  в диапазоне от 4030м до 7211м. затрачивается\n",
    "аномально малое время- минута. Рекомендуем техническим службам более детально изучить и устранить причины аномалии.\n",
    "\n",
    "\n",
    "Объединили данные в один датафрейм, убедились в отсутсвии ошибок.\n",
    "\n",
    "Основные выводы на этапе исследования данных:\n",
    "1. GoFast имеет 1534 пользователя. Из них 699 с подпиской и 835 без. Количество пользователей без подписки превышает общее число пользователей, за исключением Москвы и Омска, где оно одинаково.\n",
    "2. Средний возраст клиентов — 25 лет. Максимальный — 43 года, минимальный — 12 лет.\n",
    "3. Среднее расстояние поездки — 3059 м, диапазон: 1м. - 7066 м.\n",
    "4. Среднее время поездки — 18 мин, диапазон: 3 мин - 41 мин.\n",
    "\n",
    "На этапе прверки гипотез пришли к следующим выводам:\n",
    "1. Время, затраченное на поездки пользователей превышает время затраченное на поездки пользователей без подписки.\n",
    "2. Среднее расстояние поездки для пользователей с подпиской, меньше или равно 3130 м., что является оптимальным с точки зрения\n",
    "    износа самоката.\n",
    "3. Помесячная выручка клиентов превышает выручку, клиентов без подписки.  \n",
    "\n",
    "Определили, что для успешной маркетинговой акции необходимо распределить не менее 1161 промокодов.  \n",
    "Рассчитали вероятность того, что заданное количество пользователей откроют уведомление - 15%.\n",
    "\n",
    "\n",
    "\n"
   ]
  }
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