Ecom_users_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Проект e-commerce: вариант 1 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Начало решения задач находится чуть дальше после нескольких пустых подряд идущих ячеек."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# импортируем необходимые библиотеки\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import requests\n",
    "from urllib.parse import urlencode\n",
    "from datetime import timedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# заранее установим удобные для нас параметры графиков: шрифт, стиль, размер\n",
    "\n",
    "sns.set(\n",
    "    font_scale =2,\n",
    "    style      ='whitegrid',\n",
    "    rc         ={'figure.figsize': (20, 7)}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# импортируем данные по ссылкам из Яндекс Диска\n",
    "\n",
    "base_url = 'https://cloud-api.yandex.net/v1/disk/public/resources/download?'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ссылка на таблицу с уникальными идентификаторами пользователей\n",
    "\n",
    "public_key = 'https://yadi.sk/d/FUi5uSd6BfG_ig'\n",
    "final_url = base_url + urlencode(dict(public_key=public_key))\n",
    "response = requests.get(final_url)\n",
    "download_url = response.json()['href']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "customers_data = pd.read_csv(download_url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>customer_id</th>\n",
       "      <th>customer_unique_id</th>\n",
       "      <th>customer_zip_code_prefix</th>\n",
       "      <th>customer_city</th>\n",
       "      <th>customer_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>06b8999e2fba1a1fbc88172c00ba8bc7</td>\n",
       "      <td>861eff4711a542e4b93843c6dd7febb0</td>\n",
       "      <td>14409</td>\n",
       "      <td>franca</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18955e83d337fd6b2def6b18a428ac77</td>\n",
       "      <td>290c77bc529b7ac935b93aa66c333dc3</td>\n",
       "      <td>9790</td>\n",
       "      <td>sao bernardo do campo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4e7b3e00288586ebd08712fdd0374a03</td>\n",
       "      <td>060e732b5b29e8181a18229c7b0b2b5e</td>\n",
       "      <td>1151</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b2b6027bc5c5109e529d4dc6358b12c3</td>\n",
       "      <td>259dac757896d24d7702b9acbbff3f3c</td>\n",
       "      <td>8775</td>\n",
       "      <td>mogi das cruzes</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4f2d8ab171c80ec8364f7c12e35b23ad</td>\n",
       "      <td>345ecd01c38d18a9036ed96c73b8d066</td>\n",
       "      <td>13056</td>\n",
       "      <td>campinas</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        customer_id                customer_unique_id  \\\n",
       "0  06b8999e2fba1a1fbc88172c00ba8bc7  861eff4711a542e4b93843c6dd7febb0   \n",
       "1  18955e83d337fd6b2def6b18a428ac77  290c77bc529b7ac935b93aa66c333dc3   \n",
       "2  4e7b3e00288586ebd08712fdd0374a03  060e732b5b29e8181a18229c7b0b2b5e   \n",
       "3  b2b6027bc5c5109e529d4dc6358b12c3  259dac757896d24d7702b9acbbff3f3c   \n",
       "4  4f2d8ab171c80ec8364f7c12e35b23ad  345ecd01c38d18a9036ed96c73b8d066   \n",
       "\n",
       "   customer_zip_code_prefix          customer_city customer_state  \n",
       "0                     14409                 franca             SP  \n",
       "1                      9790  sao bernardo do campo             SP  \n",
       "2                      1151              sao paulo             SP  \n",
       "3                      8775        mogi das cruzes             SP  \n",
       "4                     13056               campinas             SP  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# таблица с уникальными идентификаторами пользователей\n",
    "\n",
    "customers_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(99441, 5)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# посмотрим на размерность\n",
    "\n",
    "customers_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id                 object\n",
       "customer_unique_id          object\n",
       "customer_zip_code_prefix     int64\n",
       "customer_city               object\n",
       "customer_state              object\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# посмотрим на типы данных в таблице\n",
    "\n",
    "customers_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ссылка на таблицу заказов\n",
    "\n",
    "public_key_2 = 'https://yadi.sk/d/t9Li4JOfgxuUrg'\n",
    "final_url_2 = base_url + urlencode(dict(public_key=public_key_2))\n",
    "response_2 = requests.get(final_url_2)\n",
    "download_url_2 = response_2.json()['href']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "orders_data = pd.read_csv(download_url_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>order_status</th>\n",
       "      <th>order_purchase_timestamp</th>\n",
       "      <th>order_approved_at</th>\n",
       "      <th>order_delivered_carrier_date</th>\n",
       "      <th>order_delivered_customer_date</th>\n",
       "      <th>order_estimated_delivery_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>e481f51cbdc54678b7cc49136f2d6af7</td>\n",
       "      <td>9ef432eb6251297304e76186b10a928d</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-10-02 10:56:33</td>\n",
       "      <td>2017-10-02 11:07:15</td>\n",
       "      <td>2017-10-04 19:55:00</td>\n",
       "      <td>2017-10-10 21:25:13</td>\n",
       "      <td>2017-10-18 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53cdb2fc8bc7dce0b6741e2150273451</td>\n",
       "      <td>b0830fb4747a6c6d20dea0b8c802d7ef</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-07-24 20:41:37</td>\n",
       "      <td>2018-07-26 03:24:27</td>\n",
       "      <td>2018-07-26 14:31:00</td>\n",
       "      <td>2018-08-07 15:27:45</td>\n",
       "      <td>2018-08-13 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47770eb9100c2d0c44946d9cf07ec65d</td>\n",
       "      <td>41ce2a54c0b03bf3443c3d931a367089</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-08-08 08:38:49</td>\n",
       "      <td>2018-08-08 08:55:23</td>\n",
       "      <td>2018-08-08 13:50:00</td>\n",
       "      <td>2018-08-17 18:06:29</td>\n",
       "      <td>2018-09-04 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>949d5b44dbf5de918fe9c16f97b45f8a</td>\n",
       "      <td>f88197465ea7920adcdbec7375364d82</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-11-18 19:28:06</td>\n",
       "      <td>2017-11-18 19:45:59</td>\n",
       "      <td>2017-11-22 13:39:59</td>\n",
       "      <td>2017-12-02 00:28:42</td>\n",
       "      <td>2017-12-15 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ad21c59c0840e6cb83a9ceb5573f8159</td>\n",
       "      <td>8ab97904e6daea8866dbdbc4fb7aad2c</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-02-13 21:18:39</td>\n",
       "      <td>2018-02-13 22:20:29</td>\n",
       "      <td>2018-02-14 19:46:34</td>\n",
       "      <td>2018-02-16 18:17:02</td>\n",
       "      <td>2018-02-26 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           order_id                       customer_id  \\\n",
       "0  e481f51cbdc54678b7cc49136f2d6af7  9ef432eb6251297304e76186b10a928d   \n",
       "1  53cdb2fc8bc7dce0b6741e2150273451  b0830fb4747a6c6d20dea0b8c802d7ef   \n",
       "2  47770eb9100c2d0c44946d9cf07ec65d  41ce2a54c0b03bf3443c3d931a367089   \n",
       "3  949d5b44dbf5de918fe9c16f97b45f8a  f88197465ea7920adcdbec7375364d82   \n",
       "4  ad21c59c0840e6cb83a9ceb5573f8159  8ab97904e6daea8866dbdbc4fb7aad2c   \n",
       "\n",
       "  order_status order_purchase_timestamp    order_approved_at  \\\n",
       "0    delivered      2017-10-02 10:56:33  2017-10-02 11:07:15   \n",
       "1    delivered      2018-07-24 20:41:37  2018-07-26 03:24:27   \n",
       "2    delivered      2018-08-08 08:38:49  2018-08-08 08:55:23   \n",
       "3    delivered      2017-11-18 19:28:06  2017-11-18 19:45:59   \n",
       "4    delivered      2018-02-13 21:18:39  2018-02-13 22:20:29   \n",
       "\n",
       "  order_delivered_carrier_date order_delivered_customer_date  \\\n",
       "0          2017-10-04 19:55:00           2017-10-10 21:25:13   \n",
       "1          2018-07-26 14:31:00           2018-08-07 15:27:45   \n",
       "2          2018-08-08 13:50:00           2018-08-17 18:06:29   \n",
       "3          2017-11-22 13:39:59           2017-12-02 00:28:42   \n",
       "4          2018-02-14 19:46:34           2018-02-16 18:17:02   \n",
       "\n",
       "  order_estimated_delivery_date  \n",
       "0           2017-10-18 00:00:00  \n",
       "1           2018-08-13 00:00:00  \n",
       "2           2018-09-04 00:00:00  \n",
       "3           2017-12-15 00:00:00  \n",
       "4           2018-02-26 00:00:00  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# таблица заказов\n",
    "\n",
    "orders_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(99441, 8)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# размерность таблицы\n",
    "\n",
    "orders_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Переведём колонки со временем в нормальный формат datetime\n",
    "\n",
    "orders_data[['order_purchase_timestamp', 'order_approved_at', 'order_delivered_carrier_date', 'order_delivered_customer_date', \\\n",
    "             'order_estimated_delivery_date']] = \\\n",
    "orders_data[['order_purchase_timestamp', 'order_approved_at', 'order_delivered_carrier_date', 'order_delivered_customer_date', \\\n",
    "             'order_estimated_delivery_date']].apply(pd.to_datetime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_id                                 object\n",
       "customer_id                              object\n",
       "order_status                             object\n",
       "order_purchase_timestamp         datetime64[ns]\n",
       "order_approved_at                datetime64[ns]\n",
       "order_delivered_carrier_date     datetime64[ns]\n",
       "order_delivered_customer_date    datetime64[ns]\n",
       "order_estimated_delivery_date    datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Проверяем. Всё ОК\n",
    "\n",
    "orders_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ссылка на товарные позиции, входящие в заказы\n",
    "\n",
    "public_key_3 = 'https://yadi.sk/d/Gbt-yAcQrOe3Pw'\n",
    "final_url_3 = base_url + urlencode(dict(public_key=public_key_3))\n",
    "response_3 = requests.get(final_url_3)\n",
    "download_url_3 = response_3.json()['href']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "order_items = pd.read_csv(download_url_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>order_item_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>shipping_limit_date</th>\n",
       "      <th>price</th>\n",
       "      <th>freight_value</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00010242fe8c5a6d1ba2dd792cb16214</td>\n",
       "      <td>1</td>\n",
       "      <td>4244733e06e7ecb4970a6e2683c13e61</td>\n",
       "      <td>48436dade18ac8b2bce089ec2a041202</td>\n",
       "      <td>2017-09-19 09:45:35</td>\n",
       "      <td>58.90</td>\n",
       "      <td>13.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00018f77f2f0320c557190d7a144bdd3</td>\n",
       "      <td>1</td>\n",
       "      <td>e5f2d52b802189ee658865ca93d83a8f</td>\n",
       "      <td>dd7ddc04e1b6c2c614352b383efe2d36</td>\n",
       "      <td>2017-05-03 11:05:13</td>\n",
       "      <td>239.90</td>\n",
       "      <td>19.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000229ec398224ef6ca0657da4fc703e</td>\n",
       "      <td>1</td>\n",
       "      <td>c777355d18b72b67abbeef9df44fd0fd</td>\n",
       "      <td>5b51032eddd242adc84c38acab88f23d</td>\n",
       "      <td>2018-01-18 14:48:30</td>\n",
       "      <td>199.00</td>\n",
       "      <td>17.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00024acbcdf0a6daa1e931b038114c75</td>\n",
       "      <td>1</td>\n",
       "      <td>7634da152a4610f1595efa32f14722fc</td>\n",
       "      <td>9d7a1d34a5052409006425275ba1c2b4</td>\n",
       "      <td>2018-08-15 10:10:18</td>\n",
       "      <td>12.99</td>\n",
       "      <td>12.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00042b26cf59d7ce69dfabb4e55b4fd9</td>\n",
       "      <td>1</td>\n",
       "      <td>ac6c3623068f30de03045865e4e10089</td>\n",
       "      <td>df560393f3a51e74553ab94004ba5c87</td>\n",
       "      <td>2017-02-13 13:57:51</td>\n",
       "      <td>199.90</td>\n",
       "      <td>18.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           order_id  order_item_id  \\\n",
       "0  00010242fe8c5a6d1ba2dd792cb16214              1   \n",
       "1  00018f77f2f0320c557190d7a144bdd3              1   \n",
       "2  000229ec398224ef6ca0657da4fc703e              1   \n",
       "3  00024acbcdf0a6daa1e931b038114c75              1   \n",
       "4  00042b26cf59d7ce69dfabb4e55b4fd9              1   \n",
       "\n",
       "                         product_id                         seller_id  \\\n",
       "0  4244733e06e7ecb4970a6e2683c13e61  48436dade18ac8b2bce089ec2a041202   \n",
       "1  e5f2d52b802189ee658865ca93d83a8f  dd7ddc04e1b6c2c614352b383efe2d36   \n",
       "2  c777355d18b72b67abbeef9df44fd0fd  5b51032eddd242adc84c38acab88f23d   \n",
       "3  7634da152a4610f1595efa32f14722fc  9d7a1d34a5052409006425275ba1c2b4   \n",
       "4  ac6c3623068f30de03045865e4e10089  df560393f3a51e74553ab94004ba5c87   \n",
       "\n",
       "   shipping_limit_date   price  freight_value  \n",
       "0  2017-09-19 09:45:35   58.90          13.29  \n",
       "1  2017-05-03 11:05:13  239.90          19.93  \n",
       "2  2018-01-18 14:48:30  199.00          17.87  \n",
       "3  2018-08-15 10:10:18   12.99          12.79  \n",
       "4  2017-02-13 13:57:51  199.90          18.14  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# таблица с товарными позициями, входящими в заказы\n",
    "\n",
    "order_items.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# переведём колонку в формат datetime\n",
    "\n",
    "order_items['shipping_limit_date'] = pd.to_datetime(order_items['shipping_limit_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(112650, 7)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# смотрим на размерность этой таблицы\n",
    "\n",
    "order_items.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_id                       object\n",
       "order_item_id                   int64\n",
       "product_id                     object\n",
       "seller_id                      object\n",
       "shipping_limit_date    datetime64[ns]\n",
       "price                         float64\n",
       "freight_value                 float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# смотрим на типы данных\n",
    "\n",
    "order_items.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# объединим таблицу с уникальными айди пользователей с инфой по заказам, оставив все строки с заказами, прикрепив к ним\n",
    "# уникальные айди пользователей (customer_unique_id) и инфу по пользователям\n",
    "\n",
    "customers_orders = orders_data.merge(customers_data, how='left', on='customer_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(99441, 12)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# количество заказов осталось прежним\n",
    "\n",
    "customers_orders.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>order_status</th>\n",
       "      <th>order_purchase_timestamp</th>\n",
       "      <th>order_approved_at</th>\n",
       "      <th>order_delivered_carrier_date</th>\n",
       "      <th>order_delivered_customer_date</th>\n",
       "      <th>order_estimated_delivery_date</th>\n",
       "      <th>customer_unique_id</th>\n",
       "      <th>customer_zip_code_prefix</th>\n",
       "      <th>customer_city</th>\n",
       "      <th>customer_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>e481f51cbdc54678b7cc49136f2d6af7</td>\n",
       "      <td>9ef432eb6251297304e76186b10a928d</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-10-02 10:56:33</td>\n",
       "      <td>2017-10-02 11:07:15</td>\n",
       "      <td>2017-10-04 19:55:00</td>\n",
       "      <td>2017-10-10 21:25:13</td>\n",
       "      <td>2017-10-18</td>\n",
       "      <td>7c396fd4830fd04220f754e42b4e5bff</td>\n",
       "      <td>3149</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53cdb2fc8bc7dce0b6741e2150273451</td>\n",
       "      <td>b0830fb4747a6c6d20dea0b8c802d7ef</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-07-24 20:41:37</td>\n",
       "      <td>2018-07-26 03:24:27</td>\n",
       "      <td>2018-07-26 14:31:00</td>\n",
       "      <td>2018-08-07 15:27:45</td>\n",
       "      <td>2018-08-13</td>\n",
       "      <td>af07308b275d755c9edb36a90c618231</td>\n",
       "      <td>47813</td>\n",
       "      <td>barreiras</td>\n",
       "      <td>BA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47770eb9100c2d0c44946d9cf07ec65d</td>\n",
       "      <td>41ce2a54c0b03bf3443c3d931a367089</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-08-08 08:38:49</td>\n",
       "      <td>2018-08-08 08:55:23</td>\n",
       "      <td>2018-08-08 13:50:00</td>\n",
       "      <td>2018-08-17 18:06:29</td>\n",
       "      <td>2018-09-04</td>\n",
       "      <td>3a653a41f6f9fc3d2a113cf8398680e8</td>\n",
       "      <td>75265</td>\n",
       "      <td>vianopolis</td>\n",
       "      <td>GO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>949d5b44dbf5de918fe9c16f97b45f8a</td>\n",
       "      <td>f88197465ea7920adcdbec7375364d82</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-11-18 19:28:06</td>\n",
       "      <td>2017-11-18 19:45:59</td>\n",
       "      <td>2017-11-22 13:39:59</td>\n",
       "      <td>2017-12-02 00:28:42</td>\n",
       "      <td>2017-12-15</td>\n",
       "      <td>7c142cf63193a1473d2e66489a9ae977</td>\n",
       "      <td>59296</td>\n",
       "      <td>sao goncalo do amarante</td>\n",
       "      <td>RN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ad21c59c0840e6cb83a9ceb5573f8159</td>\n",
       "      <td>8ab97904e6daea8866dbdbc4fb7aad2c</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-02-13 21:18:39</td>\n",
       "      <td>2018-02-13 22:20:29</td>\n",
       "      <td>2018-02-14 19:46:34</td>\n",
       "      <td>2018-02-16 18:17:02</td>\n",
       "      <td>2018-02-26</td>\n",
       "      <td>72632f0f9dd73dfee390c9b22eb56dd6</td>\n",
       "      <td>9195</td>\n",
       "      <td>santo andre</td>\n",
       "      <td>SP</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           order_id                       customer_id  \\\n",
       "0  e481f51cbdc54678b7cc49136f2d6af7  9ef432eb6251297304e76186b10a928d   \n",
       "1  53cdb2fc8bc7dce0b6741e2150273451  b0830fb4747a6c6d20dea0b8c802d7ef   \n",
       "2  47770eb9100c2d0c44946d9cf07ec65d  41ce2a54c0b03bf3443c3d931a367089   \n",
       "3  949d5b44dbf5de918fe9c16f97b45f8a  f88197465ea7920adcdbec7375364d82   \n",
       "4  ad21c59c0840e6cb83a9ceb5573f8159  8ab97904e6daea8866dbdbc4fb7aad2c   \n",
       "\n",
       "  order_status order_purchase_timestamp   order_approved_at  \\\n",
       "0    delivered      2017-10-02 10:56:33 2017-10-02 11:07:15   \n",
       "1    delivered      2018-07-24 20:41:37 2018-07-26 03:24:27   \n",
       "2    delivered      2018-08-08 08:38:49 2018-08-08 08:55:23   \n",
       "3    delivered      2017-11-18 19:28:06 2017-11-18 19:45:59   \n",
       "4    delivered      2018-02-13 21:18:39 2018-02-13 22:20:29   \n",
       "\n",
       "  order_delivered_carrier_date order_delivered_customer_date  \\\n",
       "0          2017-10-04 19:55:00           2017-10-10 21:25:13   \n",
       "1          2018-07-26 14:31:00           2018-08-07 15:27:45   \n",
       "2          2018-08-08 13:50:00           2018-08-17 18:06:29   \n",
       "3          2017-11-22 13:39:59           2017-12-02 00:28:42   \n",
       "4          2018-02-14 19:46:34           2018-02-16 18:17:02   \n",
       "\n",
       "  order_estimated_delivery_date                customer_unique_id  \\\n",
       "0                    2017-10-18  7c396fd4830fd04220f754e42b4e5bff   \n",
       "1                    2018-08-13  af07308b275d755c9edb36a90c618231   \n",
       "2                    2018-09-04  3a653a41f6f9fc3d2a113cf8398680e8   \n",
       "3                    2017-12-15  7c142cf63193a1473d2e66489a9ae977   \n",
       "4                    2018-02-26  72632f0f9dd73dfee390c9b22eb56dd6   \n",
       "\n",
       "   customer_zip_code_prefix            customer_city customer_state  \n",
       "0                      3149                sao paulo             SP  \n",
       "1                     47813                barreiras             BA  \n",
       "2                     75265               vianopolis             GO  \n",
       "3                     59296  sao goncalo do amarante             RN  \n",
       "4                      9195              santo andre             SP  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# посмотрим, что получилось в результате объединения таблиц\n",
    "\n",
    "customers_orders.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# На всякий случай проверим, не встречаются ли в таблице дубликаты\n",
    "\n",
    "customers_orders.shape == customers_orders.drop_duplicates().shape\n",
    "\n",
    "# Всё в порядке (99441, 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Решение"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Что считается покупкой? Будем считать, что после создания заказа (статус created) покупатель пока не оплатил заказ, а только собрал в корзину товары и оформляет заказ, вводя свои данные, в т.ч. банковские.\n",
    "Статус invoiced присваевается заказу, только когда человек нажал кнопку \"оформить заказ\" и ему в какой-либо форме предлагается оплатить заказ, с этого момента товары резервируются в магазине, скажем, на 20 минут (их никто не может купить), и в течение этого времени человек должен оплатить заказ.\n",
    "После оплаты товара заказу присваевается статус \"approved\". И вот этот момент я бы считал уже именно одной покупкой.\n",
    "Я не стал рассматривать как одну покупку один приобретённый товар, т.к. в моём понимании сделать покупку в магазине 2 раза - это прийти туда дважды, а не купить два товара за раз.\n",
    "Таким образом, отбираем заказы, у которых один из следующих статусов: \"delivered\", \"shipped\", \"processing\" и \"approved\", поскольку все они по смыслу идут после именно оплаты заказа.\n",
    "В расчёт, конечно, не идут заказы со статусом \"unavailable\" (какие-то недоступные из-за технической ошибки, скажем) или \"canceled\" (отменённые)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "91816"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Сколько у нас пользователей, которые совершили покупку только один раз?\n",
    "\n",
    "# Группируем данные по уникальному айди пользователя (чуть ранее я объединил таблицы по пользователям и заказам)\n",
    "# и считаем количество заказов на каждого.\n",
    "# Оставляем только те, у которых количество заказов равно 1.\n",
    "\n",
    "customers_orders \\\n",
    "    .query(\"order_status == 'delivered' \\\n",
    "         or order_status == 'shipped' \\\n",
    "         or order_status == 'processing' \\\n",
    "         or order_status == 'approved'\") \\\n",
    "    .groupby('customer_unique_id') \\\n",
    "    .agg({'order_id': 'count'}) \\\n",
    "    .query('order_id == 1') \\\n",
    "    .shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. Сколько заказов в месяц в среднем не доставляется по разным причинам (вывести детализацию по причинам)?\n",
    "\n",
    "# Оставим только интересующие нас по статусу заказы и те, у которых нет даты доставки\n",
    "\n",
    "undelivered_orders = orders_data \\\n",
    "    .query(\"(order_status == 'canceled' \\\n",
    "          or order_status == 'unavailable') \\\n",
    "         and order_delivered_customer_date.isna()\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1228"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Посмотрим, сколько таких заказов\n",
    "\n",
    "undelivered_orders.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_id                            0\n",
       "customer_id                         0\n",
       "order_status                        0\n",
       "order_purchase_timestamp            0\n",
       "order_approved_at                 141\n",
       "order_delivered_carrier_date     1159\n",
       "order_delivered_customer_date    1228\n",
       "order_estimated_delivery_date       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Проверим количество пропущенных значений\n",
    "\n",
    "undelivered_orders.isna().sum()\n",
    "\n",
    "# Работать буду по колонке order_purchase_timestamp - времени создания заказа, более того, в нём нет пропущенных значений"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# установим колонку order_purchase_timestamp в качестве индекса\n",
    "\n",
    "undelivered_orders = undelivered_orders.set_index('order_purchase_timestamp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2017-11-16 15:09:28</th>\n",
       "      <td>8e24261a7e58791d10cb1bf9da94df5c</td>\n",
       "      <td>64a254d30eed42cd0e6c36dddb88adf0</td>\n",
       "      <td>unavailable</td>\n",
       "      <td>2017-11-16 15:26:57</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2017-12-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-04 14:29:27</th>\n",
       "      <td>1b9ecfe83cdc259250e1a8aca174f0ad</td>\n",
       "      <td>6d6b50b66d79f80827b6d96751528d30</td>\n",
       "      <td>canceled</td>\n",
       "      <td>2018-08-07 04:10:26</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2018-08-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-31 11:31:37</th>\n",
       "      <td>c272bcd21c287498b4883c7512019702</td>\n",
       "      <td>9582c5bbecc65eb568e2c1d839b5cba1</td>\n",
       "      <td>unavailable</td>\n",
       "      <td>2018-01-31 14:23:50</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2018-02-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26 21:34:08</th>\n",
       "      <td>714fb133a6730ab81fa1d3c1b2007291</td>\n",
       "      <td>e3fe72696c4713d64d3c10afe71e75ed</td>\n",
       "      <td>canceled</td>\n",
       "      <td>2018-01-26 21:58:39</td>\n",
       "      <td>2018-01-29 22:33:25</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2018-02-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-14 17:38:02</th>\n",
       "      <td>37553832a3a89c9b2db59701c357ca67</td>\n",
       "      <td>7607cd563696c27ede287e515812d528</td>\n",
       "      <td>unavailable</td>\n",
       "      <td>2017-08-17 00:15:18</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2017-09-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  order_id  \\\n",
       "order_purchase_timestamp                                     \n",
       "2017-11-16 15:09:28       8e24261a7e58791d10cb1bf9da94df5c   \n",
       "2018-08-04 14:29:27       1b9ecfe83cdc259250e1a8aca174f0ad   \n",
       "2018-01-31 11:31:37       c272bcd21c287498b4883c7512019702   \n",
       "2018-01-26 21:34:08       714fb133a6730ab81fa1d3c1b2007291   \n",
       "2017-08-14 17:38:02       37553832a3a89c9b2db59701c357ca67   \n",
       "\n",
       "                                               customer_id order_status  \\\n",
       "order_purchase_timestamp                                                  \n",
       "2017-11-16 15:09:28       64a254d30eed42cd0e6c36dddb88adf0  unavailable   \n",
       "2018-08-04 14:29:27       6d6b50b66d79f80827b6d96751528d30     canceled   \n",
       "2018-01-31 11:31:37       9582c5bbecc65eb568e2c1d839b5cba1  unavailable   \n",
       "2018-01-26 21:34:08       e3fe72696c4713d64d3c10afe71e75ed     canceled   \n",
       "2017-08-14 17:38:02       7607cd563696c27ede287e515812d528  unavailable   \n",
       "\n",
       "                           order_approved_at order_delivered_carrier_date  \\\n",
       "order_purchase_timestamp                                                    \n",
       "2017-11-16 15:09:28      2017-11-16 15:26:57                          NaT   \n",
       "2018-08-04 14:29:27      2018-08-07 04:10:26                          NaT   \n",
       "2018-01-31 11:31:37      2018-01-31 14:23:50                          NaT   \n",
       "2018-01-26 21:34:08      2018-01-26 21:58:39          2018-01-29 22:33:25   \n",
       "2017-08-14 17:38:02      2017-08-17 00:15:18                          NaT   \n",
       "\n",
       "                         order_delivered_customer_date  \\\n",
       "order_purchase_timestamp                                 \n",
       "2017-11-16 15:09:28                                NaT   \n",
       "2018-08-04 14:29:27                                NaT   \n",
       "2018-01-31 11:31:37                                NaT   \n",
       "2018-01-26 21:34:08                                NaT   \n",
       "2017-08-14 17:38:02                                NaT   \n",
       "\n",
       "                         order_estimated_delivery_date  \n",
       "order_purchase_timestamp                                \n",
       "2017-11-16 15:09:28                         2017-12-05  \n",
       "2018-08-04 14:29:27                         2018-08-14  \n",
       "2018-01-31 11:31:37                         2018-02-16  \n",
       "2018-01-26 21:34:08                         2018-02-22  \n",
       "2017-08-14 17:38:02                         2017-09-05  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# проверяем. Всё ОК\n",
    "\n",
    "undelivered_orders.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# сгруппируем недоставленные заказы по статусу (unavailable или canceled) и месяцу\n",
    "# посчитаем количество заказов на каждый месяц\n",
    "\n",
    "undev_orders_by_date = undelivered_orders \\\n",
    "    .groupby('order_status') \\\n",
    "    .resample(rule='M') \\\n",
    "    .agg({'order_id': 'count'}) \\\n",
    "    .reset_index() \\\n",
    "    .sort_values('order_status') \\\n",
    "    .sort_values('order_purchase_timestamp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_status</th>\n",
       "      <th>order_purchase_timestamp</th>\n",
       "      <th>order_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>canceled</td>\n",
       "      <td>2016-09-30</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>unavailable</td>\n",
       "      <td>2016-10-31</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>canceled</td>\n",
       "      <td>2016-10-31</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>canceled</td>\n",
       "      <td>2016-11-30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>unavailable</td>\n",
       "      <td>2016-11-30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_status order_purchase_timestamp  order_id\n",
       "0      canceled               2016-09-30         2\n",
       "26  unavailable               2016-10-31         7\n",
       "1      canceled               2016-10-31        19\n",
       "2      canceled               2016-11-30         0\n",
       "27  unavailable               2016-11-30         0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Проверяем. Всё ОК\n",
    "\n",
    "undev_orders_by_date.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Посчитаем среднее количество выбранных заказов в месяц по их статусу\n",
    "\n",
    "mean_undev_orders_by_month = undev_orders_by_date \\\n",
    "    .groupby('order_status') \\\n",
    "    .agg({'order_id': 'mean'}) \\\n",
    "    .rename(columns={'order_id': 'mean_number_undev_orders'}) \\\n",
    "    .reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>order_status</th>\n",
       "      <th>mean_number_undev_orders</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>canceled</td>\n",
       "      <td>23.807692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>unavailable</td>\n",
       "      <td>26.478261</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  order_status  mean_number_undev_orders\n",
       "0     canceled                 23.807692\n",
       "1  unavailable                 26.478261"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ответ на вопрос\n",
    "\n",
    "mean_undev_orders_by_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# для наглядности распределение по месяцам - ради интереса\n",
    "\n",
    "ax = sns.barplot(x='order_purchase_timestamp', y='order_id', hue='order_status', data=undev_orders_by_date)\n",
    "x_dates = pd.Series(undev_orders_by_date['order_purchase_timestamp'].unique()).dt.strftime('%Y - %m')\n",
    "ax.set_title('Недоставленные заказы')\n",
    "ax.set_xticklabels(labels=x_dates, rotation=90)\n",
    "ax.set(xlabel = '\\nГод и месяц', ylabel='Количество заказов\\n')\n",
    "ax.legend(title='Статус заказа')\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>order_status</th>\n",
       "      <th>order_purchase_timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>00010242fe8c5a6d1ba2dd792cb16214</td>\n",
       "      <td>4244733e06e7ecb4970a6e2683c13e61</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-09-13 08:59:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>e5f2d52b802189ee658865ca93d83a8f</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-04-26 10:53:06</td>\n",
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       "      <td>c777355d18b72b67abbeef9df44fd0fd</td>\n",
       "      <td>delivered</td>\n",
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       "      <th>3</th>\n",
       "      <td>00024acbcdf0a6daa1e931b038114c75</td>\n",
       "      <td>7634da152a4610f1595efa32f14722fc</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-08-08 10:00:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00042b26cf59d7ce69dfabb4e55b4fd9</td>\n",
       "      <td>ac6c3623068f30de03045865e4e10089</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-02-04 13:57:51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           order_id                        product_id  \\\n",
       "0  00010242fe8c5a6d1ba2dd792cb16214  4244733e06e7ecb4970a6e2683c13e61   \n",
       "1  00018f77f2f0320c557190d7a144bdd3  e5f2d52b802189ee658865ca93d83a8f   \n",
       "2  000229ec398224ef6ca0657da4fc703e  c777355d18b72b67abbeef9df44fd0fd   \n",
       "3  00024acbcdf0a6daa1e931b038114c75  7634da152a4610f1595efa32f14722fc   \n",
       "4  00042b26cf59d7ce69dfabb4e55b4fd9  ac6c3623068f30de03045865e4e10089   \n",
       "\n",
       "  order_status order_purchase_timestamp  \n",
       "0    delivered      2017-09-13 08:59:02  \n",
       "1    delivered      2017-04-26 10:53:06  \n",
       "2    delivered      2018-01-14 14:33:31  \n",
       "3    delivered      2018-08-08 10:00:35  \n",
       "4    delivered      2017-02-04 13:57:51  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3. По каждому товару определить, в какой день недели товар чаще всего покупается.\n",
    "\n",
    "# Соединим следующие таблицы: \"таблица заказов\" и \"товарные позиции, входящие в заказы\"\n",
    "\n",
    "products_orders = order_items[['order_id', 'product_id', ]].merge(orders_data[['order_id', 'order_status', 'order_purchase_timestamp']], \\\n",
    "                                                                 how='left', \\\n",
    "                                                                 on='order_id')\n",
    "products_orders.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# посмотрим размерность таблицы (ничего не потерялось, судя по строкам)\n",
    "\n",
    "products_orders.shape[0] == order_items.shape[0]\n",
    "\n",
    "# (112650, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_id                            object\n",
       "product_id                          object\n",
       "order_status                        object\n",
       "order_purchase_timestamp    datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# типы данных\n",
    "\n",
    "products_orders.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# создадим новую колонку с днём недели для каждого товара\n",
    "\n",
    "products_orders['weekday'] = products_orders['order_purchase_timestamp'].dt.day_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>product_id</th>\n",
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       "      <td>Wednesday</td>\n",
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       "      <td>Sunday</td>\n",
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       "      <td>delivered</td>\n",
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       "      <td>Wednesday</td>\n",
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       "      <th>4</th>\n",
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       "      <td>ac6c3623068f30de03045865e4e10089</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-02-04 13:57:51</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           order_id                        product_id  \\\n",
       "0  00010242fe8c5a6d1ba2dd792cb16214  4244733e06e7ecb4970a6e2683c13e61   \n",
       "1  00018f77f2f0320c557190d7a144bdd3  e5f2d52b802189ee658865ca93d83a8f   \n",
       "2  000229ec398224ef6ca0657da4fc703e  c777355d18b72b67abbeef9df44fd0fd   \n",
       "3  00024acbcdf0a6daa1e931b038114c75  7634da152a4610f1595efa32f14722fc   \n",
       "4  00042b26cf59d7ce69dfabb4e55b4fd9  ac6c3623068f30de03045865e4e10089   \n",
       "\n",
       "  order_status order_purchase_timestamp    weekday  \n",
       "0    delivered      2017-09-13 08:59:02  Wednesday  \n",
       "1    delivered      2017-04-26 10:53:06  Wednesday  \n",
       "2    delivered      2018-01-14 14:33:31     Sunday  \n",
       "3    delivered      2018-08-08 10:00:35  Wednesday  \n",
       "4    delivered      2017-02-04 13:57:51   Saturday  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# проверяем таблицу\n",
    "\n",
    "products_orders.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>3dd2a17168ec895c781a9191c1e95ad7</td>\n",
       "      <td>Wednesday</td>\n",
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      ],
      "text/plain": [
       "                         product_id    weekday  number_of_purchases\n",
       "0  422879e10f46682990de24d770e7f83d  Wednesday                   93\n",
       "1  99a4788cb24856965c36a24e339b6058     Monday                   92\n",
       "2  aca2eb7d00ea1a7b8ebd4e68314663af   Thursday                   89\n",
       "3  53b36df67ebb7c41585e8d54d6772e08    Tuesday                   76\n",
       "4  368c6c730842d78016ad823897a372db     Friday                   67\n",
       "5  389d119b48cf3043d311335e499d9c6b   Thursday                   67\n",
       "6  53759a2ecddad2bb87a079a1f1519f73  Wednesday                   66\n",
       "7  d1c427060a0f73f6b889a5c7c61f2ac4     Monday                   64\n",
       "8  3dd2a17168ec895c781a9191c1e95ad7  Wednesday                   54\n",
       "9  a62e25e09e05e6faf31d90c6ec1aa3d1   Thursday                   49"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Как и в первом задании, оставляем только заказы с нужными статусами, unavailable и canceled не считаем.\n",
    "# С помощью группировки считаем кол-во покупок каждого товара по дням недели.\n",
    "# И отбираем максимальные значения в колонке number_of_purchases.\n",
    "\n",
    "products_by_date = products_orders \\\n",
    "    .query(\"order_status == 'delivered' \\\n",
    "         or order_status == 'shipped' \\\n",
    "         or order_status == 'processing' \\\n",
    "         or order_status == 'approved'\") \\\n",
    "    .groupby(['product_id', 'weekday']) \\\n",
    "    .agg({'product_id': 'count'}) \\\n",
    "    .rename(columns={'product_id': 'number_of_purchases'}) \\\n",
    "    .sort_values('number_of_purchases', ascending=False) \\\n",
    "    .groupby('product_id') \\\n",
    "    .head(1) \\\n",
    "    .reset_index()\n",
    "\n",
    "\n",
    "# Проверяем, что получилось. Посмотрим на топ-10 по кол-ву покупок\n",
    "\n",
    "products_by_date.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4. Сколько у каждого из пользователей в среднем покупок в неделю (по месяцам)?\n",
    "# Не стоит забывать, что внутри месяца может быть не целое количество недель.\n",
    "# Например, в ноябре 2021 года 4,28 недели. И внутри метрики это нужно учесть.\n",
    "\n",
    "# Несмотря на схожесть формулировки данного вопроса с первым (имею в виду понятие покупки), есть некоторое различие.\n",
    "# В первом вопрос под покупкой подразумевался, по моему мнению, именно один заказ (order_id), здесь же одна покупка -\n",
    "# это одна позиция (product_id). Я бы это обосновал тонкостями великого и могучего русского языка."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>delivered</td>\n",
       "      <td>2018-02-13 22:20:29</td>\n",
       "      <td>72632f0f9dd73dfee390c9b22eb56dd6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           order_id order_status   order_approved_at  \\\n",
       "0  e481f51cbdc54678b7cc49136f2d6af7    delivered 2017-10-02 11:07:15   \n",
       "1  53cdb2fc8bc7dce0b6741e2150273451    delivered 2018-07-26 03:24:27   \n",
       "2  47770eb9100c2d0c44946d9cf07ec65d    delivered 2018-08-08 08:55:23   \n",
       "3  949d5b44dbf5de918fe9c16f97b45f8a    delivered 2017-11-18 19:45:59   \n",
       "4  ad21c59c0840e6cb83a9ceb5573f8159    delivered 2018-02-13 22:20:29   \n",
       "\n",
       "                 customer_unique_id  \n",
       "0  7c396fd4830fd04220f754e42b4e5bff  \n",
       "1  af07308b275d755c9edb36a90c618231  \n",
       "2  3a653a41f6f9fc3d2a113cf8398680e8  \n",
       "3  7c142cf63193a1473d2e66489a9ae977  \n",
       "4  72632f0f9dd73dfee390c9b22eb56dd6  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# удалим ненужные колонки\n",
    "\n",
    "temp_df = customers_orders.drop(columns=['customer_zip_code_prefix', 'customer_city', 'customer_state', 'order_estimated_delivery_date',\n",
    "                               'order_delivered_carrier_date', 'order_delivered_customer_date', 'order_purchase_timestamp', \n",
    "                               'customer_id'])\n",
    "\n",
    "temp_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# соединим информацию о товарах с информацией о клиентах и времени заказов\n",
    "\n",
    "customers_products = \\\n",
    "order_items[['order_id', 'order_item_id', 'product_id', ]].merge(temp_df, how='left', on='order_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>order_item_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>order_status</th>\n",
       "      <th>order_approved_at</th>\n",
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       "      <td>1</td>\n",
       "      <td>4244733e06e7ecb4970a6e2683c13e61</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-09-13 09:45:35</td>\n",
       "      <td>871766c5855e863f6eccc05f988b23cb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>1</td>\n",
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       "      <td>delivered</td>\n",
       "      <td>2017-04-26 11:05:13</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000229ec398224ef6ca0657da4fc703e</td>\n",
       "      <td>1</td>\n",
       "      <td>c777355d18b72b67abbeef9df44fd0fd</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-01-14 14:48:30</td>\n",
       "      <td>3818d81c6709e39d06b2738a8d3a2474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>1</td>\n",
       "      <td>7634da152a4610f1595efa32f14722fc</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-08-08 10:10:18</td>\n",
       "      <td>af861d436cfc08b2c2ddefd0ba074622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00042b26cf59d7ce69dfabb4e55b4fd9</td>\n",
       "      <td>1</td>\n",
       "      <td>ac6c3623068f30de03045865e4e10089</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-02-04 14:10:13</td>\n",
       "      <td>64b576fb70d441e8f1b2d7d446e483c5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           order_id  order_item_id  \\\n",
       "0  00010242fe8c5a6d1ba2dd792cb16214              1   \n",
       "1  00018f77f2f0320c557190d7a144bdd3              1   \n",
       "2  000229ec398224ef6ca0657da4fc703e              1   \n",
       "3  00024acbcdf0a6daa1e931b038114c75              1   \n",
       "4  00042b26cf59d7ce69dfabb4e55b4fd9              1   \n",
       "\n",
       "                         product_id order_status   order_approved_at  \\\n",
       "0  4244733e06e7ecb4970a6e2683c13e61    delivered 2017-09-13 09:45:35   \n",
       "1  e5f2d52b802189ee658865ca93d83a8f    delivered 2017-04-26 11:05:13   \n",
       "2  c777355d18b72b67abbeef9df44fd0fd    delivered 2018-01-14 14:48:30   \n",
       "3  7634da152a4610f1595efa32f14722fc    delivered 2018-08-08 10:10:18   \n",
       "4  ac6c3623068f30de03045865e4e10089    delivered 2017-02-04 14:10:13   \n",
       "\n",
       "                 customer_unique_id  \n",
       "0  871766c5855e863f6eccc05f988b23cb  \n",
       "1  eb28e67c4c0b83846050ddfb8a35d051  \n",
       "2  3818d81c6709e39d06b2738a8d3a2474  \n",
       "3  af861d436cfc08b2c2ddefd0ba074622  \n",
       "4  64b576fb70d441e8f1b2d7d446e483c5  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_products.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_id               0\n",
       "order_item_id          0\n",
       "product_id             0\n",
       "order_status           0\n",
       "order_approved_at     15\n",
       "customer_unique_id     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# есть ли пропущенные значения?\n",
    "\n",
    "customers_products.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# удалим пропущенные значения и оставим только заказы с нужным статусом\n",
    "\n",
    "customers_products = customers_products.dropna() \\\n",
    "                                                .query(\"order_status == 'delivered' \\\n",
    "                                                     or order_status == 'shipped' \\\n",
    "                                                     or order_status == 'processing' \\\n",
    "                                                     or order_status == 'approved'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# выделим из даты год и месяц для удобства группировки\n",
    "\n",
    "customers_products['order_approved_at'] = \\\n",
    "                    pd.to_datetime(customers_products['order_approved_at'], format='%Y-%m').dt.strftime('%Y-%m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# группируем наши покупки по пользователям и месяцам\n",
    "\n",
    "products_by_month_customer = customers_products \\\n",
    "    .groupby(['customer_unique_id', 'order_approved_at'], as_index=False) \\\n",
    "    .agg({'product_id': 'count'}) \\\n",
    "    .rename(columns={'product_id': 'number_of_purchased_products'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>customer_unique_id</th>\n",
       "      <th>order_approved_at</th>\n",
       "      <th>number_of_purchased_products</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0000366f3b9a7992bf8c76cfdf3221e2</td>\n",
       "      <td>2018-05</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0000f46a3911fa3c0805444483337064</td>\n",
       "      <td>2017-03</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>2017-10</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>2017-11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 customer_unique_id order_approved_at  \\\n",
       "0  0000366f3b9a7992bf8c76cfdf3221e2           2018-05   \n",
       "1  0000b849f77a49e4a4ce2b2a4ca5be3f           2018-05   \n",
       "2  0000f46a3911fa3c0805444483337064           2017-03   \n",
       "3  0000f6ccb0745a6a4b88665a16c9f078           2017-10   \n",
       "4  0004aac84e0df4da2b147fca70cf8255           2017-11   \n",
       "\n",
       "   number_of_purchased_products  \n",
       "0                             1  \n",
       "1                             1  \n",
       "2                             1  \n",
       "3                             1  \n",
       "4                             1  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products_by_month_customer.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# вычислим количество товаров в неделю, поделив количество товаров в месяц (number_of_purchased_products) на кол-во недель в\n",
    "# месяце (кол-во дней / 7)\n",
    "\n",
    "products_by_month_customer['order_approved_at'] = pd.to_datetime(products_by_month_customer['order_approved_at'])\n",
    "\n",
    "products_by_month_customer['products_per_week'] = (products_by_month_customer['number_of_purchased_products'] / \\\n",
    "                                                  (products_by_month_customer['order_approved_at'].dt.days_in_month / 7)) \\\n",
    "                                                  .round(2)\n",
    "\n",
    "products_by_month_customer['order_approved_at'] = products_by_month_customer['order_approved_at'].dt.strftime('%Y-%m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "                 customer_unique_id order_approved_at  \\\n",
       "0  0000366f3b9a7992bf8c76cfdf3221e2           2018-05   \n",
       "1  0000b849f77a49e4a4ce2b2a4ca5be3f           2018-05   \n",
       "2  0000f46a3911fa3c0805444483337064           2017-03   \n",
       "3  0000f6ccb0745a6a4b88665a16c9f078           2017-10   \n",
       "4  0004aac84e0df4da2b147fca70cf8255           2017-11   \n",
       "\n",
       "   number_of_purchased_products  products_per_week  \n",
       "0                             1               0.23  \n",
       "1                             1               0.23  \n",
       "2                             1               0.23  \n",
       "3                             1               0.23  \n",
       "4                             1               0.23  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products_by_month_customer.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2017-06</td>\n",
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       "    <tr>\n",
       "      <th>53194</th>\n",
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       "      <td>2017-07</td>\n",
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       "      <td>2017-09</td>\n",
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       "    <tr>\n",
       "      <th>53197</th>\n",
       "      <td>8d50f5eadf50201ccdcedfb9e2ac8455</td>\n",
       "      <td>2017-10</td>\n",
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       "    <tr>\n",
       "      <th>53198</th>\n",
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       "      <td>2017-11</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>2018-05</td>\n",
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       "      <td>2018-07</td>\n",
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       "    <tr>\n",
       "      <th>53201</th>\n",
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       "      <td>2018-08</td>\n",
       "      <td>3</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     customer_unique_id order_approved_at  \\\n",
       "53192  8d50f5eadf50201ccdcedfb9e2ac8455           2017-05   \n",
       "53193  8d50f5eadf50201ccdcedfb9e2ac8455           2017-06   \n",
       "53194  8d50f5eadf50201ccdcedfb9e2ac8455           2017-07   \n",
       "53195  8d50f5eadf50201ccdcedfb9e2ac8455           2017-08   \n",
       "53196  8d50f5eadf50201ccdcedfb9e2ac8455           2017-09   \n",
       "53197  8d50f5eadf50201ccdcedfb9e2ac8455           2017-10   \n",
       "53198  8d50f5eadf50201ccdcedfb9e2ac8455           2017-11   \n",
       "53199  8d50f5eadf50201ccdcedfb9e2ac8455           2018-05   \n",
       "53200  8d50f5eadf50201ccdcedfb9e2ac8455           2018-07   \n",
       "53201  8d50f5eadf50201ccdcedfb9e2ac8455           2018-08   \n",
       "\n",
       "       number_of_purchased_products  products_per_week  \n",
       "53192                             1               0.23  \n",
       "53193                             1               0.23  \n",
       "53194                             2               0.45  \n",
       "53195                             1               0.23  \n",
       "53196                             1               0.23  \n",
       "53197                             2               0.45  \n",
       "53198                             1               0.23  \n",
       "53199                             2               0.45  \n",
       "53200                             2               0.45  \n",
       "53201                             3               0.68  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# посмотрим для примера на пользователя, который покупал товары в 10 разных месяцах\n",
    "\n",
    "products_by_month_customer \\\n",
    "    .query(\"customer_unique_id == '8d50f5eadf50201ccdcedfb9e2ac8455'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5. Используя pandas, проведи когортный анализ пользователей.\n",
    "# В период с января по декабрь выяви когорту с самым высоким retention на 3й месяц.\n",
    "\n",
    "# Предлагаю в качестве основы взять уже созданную таблицу customers_orders, где уже есть данные по уникальным айди и\n",
    "# времени, когда они делали покупки."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# отфильтруем по статусу заказа, удалим строки с отсутствующими значениями (отсутствующие, возможно, по тех. причинам).\n",
    "# Оставим только строки с айди и датой совершения покупки.\n",
    "\n",
    "customers_dates = customers_orders \\\n",
    "                            .query(\"order_status == 'delivered' \\\n",
    "                                 or order_status == 'shipped' \\\n",
    "                                 or order_status == 'processing' \\\n",
    "                                 or order_status == 'approved'\") \\\n",
    "                            .dropna()[['customer_unique_id', 'order_approved_at']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# переведём дату в удобный вид \"год-месяц\"\n",
    "\n",
    "customers_dates['order_approved_at'] = customers_dates['order_approved_at'].dt.strftime('%Y-%m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_unique_id</th>\n",
       "      <th>order_approved_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7c396fd4830fd04220f754e42b4e5bff</td>\n",
       "      <td>2017-10</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>af07308b275d755c9edb36a90c618231</td>\n",
       "      <td>2018-07</td>\n",
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       "      <th>2</th>\n",
       "      <td>3a653a41f6f9fc3d2a113cf8398680e8</td>\n",
       "      <td>2018-08</td>\n",
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       "      <th>3</th>\n",
       "      <td>7c142cf63193a1473d2e66489a9ae977</td>\n",
       "      <td>2017-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72632f0f9dd73dfee390c9b22eb56dd6</td>\n",
       "      <td>2018-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 customer_unique_id order_approved_at\n",
       "0  7c396fd4830fd04220f754e42b4e5bff           2017-10\n",
       "1  af07308b275d755c9edb36a90c618231           2018-07\n",
       "2  3a653a41f6f9fc3d2a113cf8398680e8           2018-08\n",
       "3  7c142cf63193a1473d2e66489a9ae977           2017-11\n",
       "4  72632f0f9dd73dfee390c9b22eb56dd6           2018-02"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_dates.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Поскольку мы хотим анализировать показатель retention, то в качестве признака формирования когорты возьмём\n",
    "# первую покупку пользователя\n",
    "\n",
    "# Размер когорты - месяц\n",
    "\n",
    "# Отчётный период - \"период с января по декабрь\". Поскольку у нас есть данные за весь 2017 год и за часть 2016 и 2018 гг.,\n",
    "# то исследуем retention и в них, чтобы сравнить те же месяца в другие года и сравним показатели.\n",
    "# Вдруг, у когорты января 2018 retention больше, чем у когорты января 2017."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Найдём для каждого пользователя год и месяц его самой первой покупки\n",
    "\n",
    "first_buy = customers_dates \\\n",
    "                .groupby('customer_unique_id') \\\n",
    "                .agg({'order_approved_at': 'min'}) \\\n",
    "                .reset_index() \\\n",
    "                .rename(columns={'order_approved_at': 'first_buy'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>customer_unique_id</th>\n",
       "      <th>first_buy</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>2018-05</td>\n",
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       "      <th>1</th>\n",
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       "      <td>0000f46a3911fa3c0805444483337064</td>\n",
       "      <td>2017-03</td>\n",
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       "    <tr>\n",
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       "      <td>2017-10</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>2017-11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 customer_unique_id first_buy\n",
       "0  0000366f3b9a7992bf8c76cfdf3221e2   2018-05\n",
       "1  0000b849f77a49e4a4ce2b2a4ca5be3f   2018-05\n",
       "2  0000f46a3911fa3c0805444483337064   2017-03\n",
       "3  0000f6ccb0745a6a4b88665a16c9f078   2017-10\n",
       "4  0004aac84e0df4da2b147fca70cf8255   2017-11"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_buy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Создадим новую таблицу, в которой будет информация о пользователях, датах их покупок и датах первых покупок\n",
    "# Дата первой покупки появляется без исключения во всех строках, даже если пользователь появлялся в таблице 2, 3 или 100 раз.\n",
    "\n",
    "df = customers_dates.merge(first_buy, how='left', on='customer_unique_id')\n",
    "\n",
    "# Переведём в удобный формат для удобства вычисления\n",
    "\n",
    "df['order_approved_at'] = pd.to_datetime(df['order_approved_at'])\n",
    "df['first_buy'] = pd.to_datetime(df['first_buy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Вычислим разницу для каждого пользователя между его покупкой и его самой первой покупкой (в месяцах)\n",
    "\n",
    "df['cohort_lifetime'] = \\\n",
    "12 * (df['order_approved_at'].dt.year - df['first_buy'].dt.year) + (df['order_approved_at'].dt.month - df['first_buy'].dt.month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 customer_unique_id order_approved_at  first_buy  \\\n",
       "0  7c396fd4830fd04220f754e42b4e5bff        2017-10-01 2017-09-01   \n",
       "1  af07308b275d755c9edb36a90c618231        2018-07-01 2018-07-01   \n",
       "2  3a653a41f6f9fc3d2a113cf8398680e8        2018-08-01 2018-08-01   \n",
       "3  7c142cf63193a1473d2e66489a9ae977        2017-11-01 2017-11-01   \n",
       "4  72632f0f9dd73dfee390c9b22eb56dd6        2018-02-01 2018-02-01   \n",
       "\n",
       "   cohort_lifetime  \n",
       "0                1  \n",
       "1                0  \n",
       "2                0  \n",
       "3                0  \n",
       "4                0  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Возвращаемся обратно к краткой записи \"год - месяц\"\n",
    "\n",
    "df['order_approved_at'] = df['order_approved_at'].dt.strftime('%Y-%m')\n",
    "df['first_buy'] = df['first_buy'].dt.strftime('%Y-%m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 customer_unique_id order_approved_at first_buy  \\\n",
       "0  7c396fd4830fd04220f754e42b4e5bff           2017-10   2017-09   \n",
       "1  af07308b275d755c9edb36a90c618231           2018-07   2018-07   \n",
       "2  3a653a41f6f9fc3d2a113cf8398680e8           2018-08   2018-08   \n",
       "3  7c142cf63193a1473d2e66489a9ae977           2017-11   2017-11   \n",
       "4  72632f0f9dd73dfee390c9b22eb56dd6           2018-02   2018-02   \n",
       "\n",
       "   cohort_lifetime  \n",
       "0                1  \n",
       "1                0  \n",
       "2                0  \n",
       "3                0  \n",
       "4                0  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Используя pivot_table, мы получаем таблицу, в которой есть информация о наших когортах: их названия (first_buy)\n",
    "# и количество уникальных пользователей (customer_unique_id) в определённый промежуток времени (cohort_lifetime)\n",
    "\n",
    "cohorts = df.pivot_table(index=['first_buy', 'cohort_lifetime'],\n",
    "                         aggfunc={'customer_unique_id': 'nunique'}) \\\n",
    "            .reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>2016-09</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-10</td>\n",
       "      <td>0</td>\n",
       "      <td>262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-10</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-10</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-10</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-10</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-10</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016-10</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016-10</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2016-10</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  first_buy  cohort_lifetime  customer_unique_id\n",
       "0   2016-09                0                   1\n",
       "1   2016-10                0                 262\n",
       "2   2016-10                6                   1\n",
       "3   2016-10                9                   1\n",
       "4   2016-10               11                   1\n",
       "5   2016-10               13                   1\n",
       "6   2016-10               15                   1\n",
       "7   2016-10               17                   1\n",
       "8   2016-10               19                   2\n",
       "9   2016-10               20                   2"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohorts.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Теперь нужно посчитать количество пользователей по когортам (то есть по дате их первой покупки)\n",
    "\n",
    "initial_users_count = cohorts.query(\"cohort_lifetime == 0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Оставим две колонки\n",
    "\n",
    "initial_users_count = initial_users_count[['first_buy', 'customer_unique_id']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Переименуем для удобства\n",
    "# first_buy - название когорты\n",
    "# cohort_users - количество пользователей в этой когорте\n",
    "\n",
    "initial_users_count = initial_users_count.rename(columns={'customer_unique_id': 'cohort_users'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
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       "      <td>683</td>\n",
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       "      <th>30</th>\n",
       "      <td>2017-02</td>\n",
       "      <td>1612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2017-03</td>\n",
       "      <td>2511</td>\n",
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       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2017-04</td>\n",
       "      <td>2232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>2017-05</td>\n",
       "      <td>3452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>2017-06</td>\n",
       "      <td>3044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>2017-07</td>\n",
       "      <td>3709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>2017-08</td>\n",
       "      <td>4081</td>\n",
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       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>2017-09</td>\n",
       "      <td>4024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>2017-10</td>\n",
       "      <td>4294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>2017-11</td>\n",
       "      <td>6926</td>\n",
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       "    <tr>\n",
       "      <th>174</th>\n",
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       "      <th>191</th>\n",
       "      <td>2018-02</td>\n",
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       "      <th>198</th>\n",
       "      <td>2018-03</td>\n",
       "      <td>6851</td>\n",
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       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>2018-04</td>\n",
       "      <td>6428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>2018-05</td>\n",
       "      <td>6692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>2018-06</td>\n",
       "      <td>5872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>2018-07</td>\n",
       "      <td>5843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    first_buy  cohort_users\n",
       "0     2016-09             1\n",
       "1     2016-10           262\n",
       "10    2016-12             1\n",
       "12    2017-01           683\n",
       "30    2017-02          1612\n",
       "48    2017-03          2511\n",
       "66    2017-04          2232\n",
       "83    2017-05          3452\n",
       "99    2017-06          3044\n",
       "114   2017-07          3709\n",
       "128   2017-08          4081\n",
       "141   2017-09          4024\n",
       "153   2017-10          4294\n",
       "164   2017-11          6926\n",
       "174   2017-12          5490\n",
       "183   2018-01          6770\n",
       "191   2018-02          6268\n",
       "198   2018-03          6851\n",
       "204   2018-04          6428\n",
       "209   2018-05          6692\n",
       "213   2018-06          5872\n",
       "216   2018-07          5843\n",
       "218   2018-08          6290"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "initial_users_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Добавим информацию о размере когорты в нашу таблицу cohorts\n",
    "\n",
    "cohorts = cohorts.merge(initial_users_count, on='first_buy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Вычислим retention, поделив количество оставшихся юзеров в определённый месяц на количество всех юзеров,\n",
    "# совершивших первую покупку в определённый месяц\n",
    "\n",
    "cohorts['retention'] = cohorts['customer_unique_id'] / cohorts['cohort_users']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>4</th>\n",
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      "text/plain": [
       "  first_buy  cohort_lifetime  customer_unique_id  cohort_users  retention\n",
       "0   2016-09                0                   1             1   1.000000\n",
       "1   2016-10                0                 262           262   1.000000\n",
       "2   2016-10                6                   1           262   0.003817\n",
       "3   2016-10                9                   1           262   0.003817\n",
       "4   2016-10               11                   1           262   0.003817"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohorts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# \"Развернём\" таблицу: индексами станут названия когорт, колонками - месяцы, значения - retention rate\n",
    "\n",
    "retention_pivot = cohorts.pivot_table(index   ='first_buy',\n",
    "                                      columns ='cohort_lifetime',\n",
    "                                      values  ='retention')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.003817</td>\n",
       "      <td>0.007634</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.001464</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>2017-03</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.004381</td>\n",
       "      <td>0.003584</td>\n",
       "      <td>0.003584</td>\n",
       "      <td>0.003584</td>\n",
       "      <td>0.001593</td>\n",
       "      <td>0.001593</td>\n",
       "      <td>0.003186</td>\n",
       "      <td>0.002788</td>\n",
       "      <td>0.001195</td>\n",
       "      <td>0.003584</td>\n",
       "      <td>0.001195</td>\n",
       "      <td>0.001991</td>\n",
       "      <td>0.001195</td>\n",
       "      <td>0.001593</td>\n",
       "      <td>0.002389</td>\n",
       "      <td>0.000796</td>\n",
       "      <td>0.001195</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.006272</td>\n",
       "      <td>0.002240</td>\n",
       "      <td>0.001792</td>\n",
       "      <td>0.002240</td>\n",
       "      <td>0.002688</td>\n",
       "      <td>0.003584</td>\n",
       "      <td>0.002688</td>\n",
       "      <td>0.003584</td>\n",
       "      <td>0.001792</td>\n",
       "      <td>0.002240</td>\n",
       "      <td>0.000896</td>\n",
       "      <td>0.000448</td>\n",
       "      <td>0.000448</td>\n",
       "      <td>0.000896</td>\n",
       "      <td>0.000896</td>\n",
       "      <td>0.001344</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005214</td>\n",
       "      <td>0.004635</td>\n",
       "      <td>0.002897</td>\n",
       "      <td>0.002897</td>\n",
       "      <td>0.003187</td>\n",
       "      <td>0.004056</td>\n",
       "      <td>0.001448</td>\n",
       "      <td>0.002607</td>\n",
       "      <td>0.002607</td>\n",
       "      <td>0.002897</td>\n",
       "      <td>0.003476</td>\n",
       "      <td>0.001738</td>\n",
       "      <td>0.000579</td>\n",
       "      <td>0.001738</td>\n",
       "      <td>0.002028</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.004928</td>\n",
       "      <td>0.003942</td>\n",
       "      <td>0.004271</td>\n",
       "      <td>0.002957</td>\n",
       "      <td>0.003614</td>\n",
       "      <td>0.003614</td>\n",
       "      <td>0.002300</td>\n",
       "      <td>0.001314</td>\n",
       "      <td>0.001971</td>\n",
       "      <td>0.002957</td>\n",
       "      <td>0.003942</td>\n",
       "      <td>0.001643</td>\n",
       "      <td>0.001643</td>\n",
       "      <td>0.002300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005392</td>\n",
       "      <td>0.003235</td>\n",
       "      <td>0.001887</td>\n",
       "      <td>0.003505</td>\n",
       "      <td>0.001887</td>\n",
       "      <td>0.003235</td>\n",
       "      <td>0.001078</td>\n",
       "      <td>0.001887</td>\n",
       "      <td>0.002427</td>\n",
       "      <td>0.002157</td>\n",
       "      <td>0.002966</td>\n",
       "      <td>0.001348</td>\n",
       "      <td>0.002427</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.006861</td>\n",
       "      <td>0.003676</td>\n",
       "      <td>0.002695</td>\n",
       "      <td>0.003676</td>\n",
       "      <td>0.005146</td>\n",
       "      <td>0.002695</td>\n",
       "      <td>0.002695</td>\n",
       "      <td>0.001225</td>\n",
       "      <td>0.001715</td>\n",
       "      <td>0.002450</td>\n",
       "      <td>0.001715</td>\n",
       "      <td>0.001470</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.006710</td>\n",
       "      <td>0.004970</td>\n",
       "      <td>0.003231</td>\n",
       "      <td>0.004225</td>\n",
       "      <td>0.002734</td>\n",
       "      <td>0.002237</td>\n",
       "      <td>0.002237</td>\n",
       "      <td>0.002982</td>\n",
       "      <td>0.001740</td>\n",
       "      <td>0.002485</td>\n",
       "      <td>0.000746</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.006986</td>\n",
       "      <td>0.003027</td>\n",
       "      <td>0.000932</td>\n",
       "      <td>0.002329</td>\n",
       "      <td>0.001863</td>\n",
       "      <td>0.002562</td>\n",
       "      <td>0.003726</td>\n",
       "      <td>0.002795</td>\n",
       "      <td>0.001863</td>\n",
       "      <td>0.002096</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005631</td>\n",
       "      <td>0.003610</td>\n",
       "      <td>0.001588</td>\n",
       "      <td>0.001733</td>\n",
       "      <td>0.001588</td>\n",
       "      <td>0.001299</td>\n",
       "      <td>0.001877</td>\n",
       "      <td>0.001155</td>\n",
       "      <td>0.000722</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.002186</td>\n",
       "      <td>0.002732</td>\n",
       "      <td>0.003097</td>\n",
       "      <td>0.002368</td>\n",
       "      <td>0.002004</td>\n",
       "      <td>0.001639</td>\n",
       "      <td>0.000182</td>\n",
       "      <td>0.001821</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003545</td>\n",
       "      <td>0.003840</td>\n",
       "      <td>0.002954</td>\n",
       "      <td>0.003250</td>\n",
       "      <td>0.001625</td>\n",
       "      <td>0.001773</td>\n",
       "      <td>0.002216</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003191</td>\n",
       "      <td>0.003989</td>\n",
       "      <td>0.002872</td>\n",
       "      <td>0.002553</td>\n",
       "      <td>0.001755</td>\n",
       "      <td>0.002234</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003941</td>\n",
       "      <td>0.003211</td>\n",
       "      <td>0.002919</td>\n",
       "      <td>0.001314</td>\n",
       "      <td>0.001314</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.006067</td>\n",
       "      <td>0.002800</td>\n",
       "      <td>0.002489</td>\n",
       "      <td>0.001400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005529</td>\n",
       "      <td>0.002540</td>\n",
       "      <td>0.001943</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003917</td>\n",
       "      <td>0.002725</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005134</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cohort_lifetime   0         1         2         3         4         5   \\\n",
       "first_buy                                                                \n",
       "2016-09          1.0       NaN       NaN       NaN       NaN       NaN   \n",
       "2016-10          1.0       NaN       NaN       NaN       NaN       NaN   \n",
       "2016-12          1.0  1.000000       NaN       NaN       NaN       NaN   \n",
       "2017-01          1.0  0.004392  0.002928  0.001464  0.004392  0.001464   \n",
       "2017-02          1.0  0.001861  0.002481  0.001861  0.004342  0.001241   \n",
       "2017-03          1.0  0.004381  0.003584  0.003584  0.003584  0.001593   \n",
       "2017-04          1.0  0.006272  0.002240  0.001792  0.002240  0.002688   \n",
       "2017-05          1.0  0.005214  0.004635  0.002897  0.002897  0.003187   \n",
       "2017-06          1.0  0.004928  0.003942  0.004271  0.002957  0.003614   \n",
       "2017-07          1.0  0.005392  0.003235  0.001887  0.003505  0.001887   \n",
       "2017-08          1.0  0.006861  0.003676  0.002695  0.003676  0.005146   \n",
       "2017-09          1.0  0.006710  0.004970  0.003231  0.004225  0.002734   \n",
       "2017-10          1.0  0.006986  0.003027  0.000932  0.002329  0.001863   \n",
       "2017-11          1.0  0.005631  0.003610  0.001588  0.001733  0.001588   \n",
       "2017-12          1.0  0.002186  0.002732  0.003097  0.002368  0.002004   \n",
       "2018-01          1.0  0.003545  0.003840  0.002954  0.003250  0.001625   \n",
       "2018-02          1.0  0.003191  0.003989  0.002872  0.002553  0.001755   \n",
       "2018-03          1.0  0.003941  0.003211  0.002919  0.001314  0.001314   \n",
       "2018-04          1.0  0.006067  0.002800  0.002489  0.001400       NaN   \n",
       "2018-05          1.0  0.005529  0.002540  0.001943       NaN       NaN   \n",
       "2018-06          1.0  0.003917  0.002725       NaN       NaN       NaN   \n",
       "2018-07          1.0  0.005134       NaN       NaN       NaN       NaN   \n",
       "2018-08          1.0       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "cohort_lifetime        6         7         8         9         10        11  \\\n",
       "first_buy                                                                     \n",
       "2016-09               NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2016-10          0.003817       NaN       NaN  0.003817       NaN  0.003817   \n",
       "2016-12               NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2017-01          0.004392  0.001464  0.001464       NaN  0.002928  0.002928   \n",
       "2017-02          0.002481  0.001861  0.000620  0.001861  0.001241  0.003102   \n",
       "2017-03          0.001593  0.003186  0.002788  0.001195  0.003584  0.001195   \n",
       "2017-04          0.003584  0.002688  0.003584  0.001792  0.002240  0.000896   \n",
       "2017-05          0.004056  0.001448  0.002607  0.002607  0.002897  0.003476   \n",
       "2017-06          0.003614  0.002300  0.001314  0.001971  0.002957  0.003942   \n",
       "2017-07          0.003235  0.001078  0.001887  0.002427  0.002157  0.002966   \n",
       "2017-08          0.002695  0.002695  0.001225  0.001715  0.002450  0.001715   \n",
       "2017-09          0.002237  0.002237  0.002982  0.001740  0.002485  0.000746   \n",
       "2017-10          0.002562  0.003726  0.002795  0.001863  0.002096       NaN   \n",
       "2017-11          0.001299  0.001877  0.001155  0.000722       NaN       NaN   \n",
       "2017-12          0.001639  0.000182  0.001821       NaN       NaN       NaN   \n",
       "2018-01          0.001773  0.002216       NaN       NaN       NaN       NaN   \n",
       "2018-02          0.002234       NaN       NaN       NaN       NaN       NaN   \n",
       "2018-03               NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2018-04               NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2018-05               NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2018-06               NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2018-07               NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2018-08               NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "cohort_lifetime        12        13        14        15        16        17  \\\n",
       "first_buy                                                                     \n",
       "2016-09               NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2016-10               NaN  0.003817       NaN  0.003817       NaN  0.003817   \n",
       "2016-12               NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2017-01          0.007321  0.004392  0.001464  0.001464  0.002928  0.004392   \n",
       "2017-02          0.001241  0.001861  0.001241  0.000620  0.000620  0.001861   \n",
       "2017-03          0.001991  0.001195  0.001593  0.002389  0.000796  0.001195   \n",
       "2017-04          0.000448  0.000448  0.000896  0.000896  0.001344       NaN   \n",
       "2017-05          0.001738  0.000579  0.001738  0.002028       NaN       NaN   \n",
       "2017-06          0.001643  0.001643  0.002300       NaN       NaN       NaN   \n",
       "2017-07          0.001348  0.002427       NaN       NaN       NaN       NaN   \n",
       "2017-08          0.001470       NaN       NaN       NaN       NaN       NaN   \n",
       "2017-09               NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2017-10               NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2017-11               NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2017-12               NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2018-01               NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2018-02               NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2018-03               NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2018-04               NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2018-05               NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2018-06               NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2018-07               NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2018-08               NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "cohort_lifetime        19        20  \n",
       "first_buy                            \n",
       "2016-09               NaN       NaN  \n",
       "2016-10          0.007634  0.007634  \n",
       "2016-12               NaN       NaN  \n",
       "2017-01          0.001464       NaN  \n",
       "2017-02               NaN       NaN  \n",
       "2017-03               NaN       NaN  \n",
       "2017-04               NaN       NaN  \n",
       "2017-05               NaN       NaN  \n",
       "2017-06               NaN       NaN  \n",
       "2017-07               NaN       NaN  \n",
       "2017-08               NaN       NaN  \n",
       "2017-09               NaN       NaN  \n",
       "2017-10               NaN       NaN  \n",
       "2017-11               NaN       NaN  \n",
       "2017-12               NaN       NaN  \n",
       "2018-01               NaN       NaN  \n",
       "2018-02               NaN       NaN  \n",
       "2018-03               NaN       NaN  \n",
       "2018-04               NaN       NaN  \n",
       "2018-05               NaN       NaN  \n",
       "2018-06               NaN       NaN  \n",
       "2018-07               NaN       NaN  \n",
       "2018-08               NaN       NaN  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retention_pivot\n",
    "\n",
    "# Значения NaN означают, что в данный промежуток времени не было возможности рассчитать retention, т.к. количество\n",
    "# юзеров, что-то купивших в этот месяц из этой когорты, равнялось 0.\n",
    "# Какой-то более-менее нормальный retention можно вычислить только начиная с января 2017 года и до августа 2018, т.к.\n",
    "# там уже и пользователей намного больше (200 в октябре 2016 года и 600 и более, начиная с января 2017).\n",
    "\n",
    "# Далее - более презентативная версия данной таблицы."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
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       "            color:  #000000;\n",
       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row0_col14,#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row11_col8,#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row13_col4,#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row14_col1{\n",
       "            background-color:  #29798e;\n",
       "            color:  #000000;\n",
       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row0_col15,#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row10_col7{\n",
       "            background-color:  #228b8d;\n",
       "            color:  #000000;\n",
       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row1_col2,#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row10_col5{\n",
       "            background-color:  #482071;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row1_col3{\n",
       "            background-color:  #375a8c;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row1_col4{\n",
       "            background-color:  #f4e61e;\n",
       "            color:  #000000;\n",
       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row1_col6,#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row8_col5{\n",
       "            background-color:  #2c738e;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row1_col7{\n",
       "            background-color:  #238a8d;\n",
       "            color:  #000000;\n",
       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row1_col9,#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row9_col9{\n",
       "            background-color:  #23a983;\n",
       "            color:  #000000;\n",
       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row1_col11,#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row2_col4,#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row10_col1{\n",
       "            background-color:  #56c667;\n",
       "            color:  #000000;\n",
       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row1_col12{\n",
       "            background-color:  #482979;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row1_col13,#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row4_col7,#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row14_col2{\n",
       "            background-color:  #2e6e8e;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row1_col14{\n",
       "            background-color:  #3c508b;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row1_col17{\n",
       "            background-color:  #404688;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row1_col18,#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row2_col18,#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row3_col18,#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row4_col18,#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row5_col18,#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row6_col18,#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row7_col18,#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row8_col18,#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row9_col18,#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row10_col18,#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row11_col18,#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row12_col18,#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row13_col18,#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row14_col18,#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row15_col18,#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row16_col18,#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row17_col18,#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row18_col18,#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row19_col18{\n",
       "            background-color:  #440154;\n",
       "            color:  #f1f1f1;\n",
       "            background-color:  white;\n",
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       "            background-color:  #218e8d;\n",
       "            color:  #000000;\n",
       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row2_col3,#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row4_col15,#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row8_col8{\n",
       "            background-color:  #77d153;\n",
       "            color:  #000000;\n",
       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row2_col5{\n",
       "            background-color:  #482173;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row2_col6{\n",
       "            background-color:  #482374;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row2_col7{\n",
       "            background-color:  #98d83e;\n",
       "            color:  #000000;\n",
       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row2_col8,#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row5_col10,#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row9_col8{\n",
       "            background-color:  #54c568;\n",
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       "            background-color:  #46307e;\n",
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       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row2_col12{\n",
       "            background-color:  #3e4a89;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row2_col13,#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row4_col12{\n",
       "            background-color:  #424086;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #21908d;\n",
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       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row2_col16{\n",
       "            background-color:  #481c6e;\n",
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       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row3_col1{\n",
       "            background-color:  #a2da37;\n",
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       "            background-color:  #3a538b;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #34608d;\n",
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       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row3_col5{\n",
       "            background-color:  #2d718e;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row3_col6{\n",
       "            background-color:  #58c765;\n",
       "            color:  #000000;\n",
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       "            background-color:  #48c16e;\n",
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       "            background-color:  #1fa188;\n",
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       "            background-color:  #277f8e;\n",
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       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row3_col11{\n",
       "            background-color:  #471365;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #453581;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row3_col16{\n",
       "            background-color:  #33638d;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row4_col1{\n",
       "            background-color:  #31b57b;\n",
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       "            background-color:  #addc30;\n",
       "            color:  #000000;\n",
       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row4_col3,#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row12_col2{\n",
       "            background-color:  #21a585;\n",
       "            color:  #000000;\n",
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       "            background-color:  #20938c;\n",
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       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row4_col6{\n",
       "            background-color:  #b8de29;\n",
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       "            background-color:  #37b878;\n",
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       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row4_col10{\n",
       "            background-color:  #46c06f;\n",
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       "            background-color:  #9dd93b;\n",
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       "            background-color:  #470d60;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #22a884;\n",
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       "            background-color:  #27ad81;\n",
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       "            background-color:  #1f988b;\n",
       "            color:  #000000;\n",
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       "            background-color:  #24aa83;\n",
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       "            background-color:  #5cc863;\n",
       "            color:  #000000;\n",
       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row5_col7,#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row14_col3{\n",
       "            background-color:  #22a785;\n",
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       "            background-color:  #3d4d8a;\n",
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       "            background-color:  #34b679;\n",
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       "            background-color:  #443b84;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #3fbc73;\n",
       "            color:  #000000;\n",
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       "            background-color:  #2d708e;\n",
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       "            background-color:  #365c8d;\n",
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       "            background-color:  #4ac16d;\n",
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       "            background-color:  #443983;\n",
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       "            background-color:  #28ae80;\n",
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       "            background-color:  #c0df25;\n",
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       "            background-color:  #2a768e;\n",
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       "            background-color:  #40bd72;\n",
       "            color:  #000000;\n",
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       "            background-color:  #472e7c;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #21918c;\n",
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       "            background-color:  #efe51c;\n",
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       "            background-color:  #1f968b;\n",
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       "            background-color:  #1f978b;\n",
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       "            background-color:  #67cc5c;\n",
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       "            background-color:  #25848e;\n",
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       "            background-color:  #404588;\n",
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       "            background-color:  #1f948c;\n",
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       "            background-color:  #463480;\n",
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       "            background-color:  #dde318;\n",
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       "            background-color:  #20a386;\n",
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       "            background-color:  #1f9a8a;\n",
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       "            background-color:  #31678e;\n",
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       "            background-color:  #297a8e;\n",
       "            color:  #000000;\n",
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       "            background-color:  #414287;\n",
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       "            background-color:  #472f7d;\n",
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       "            background-color:  #433e85;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #48186a;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #2eb37c;\n",
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       "            background-color:  #306a8e;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #482878;\n",
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       "            background-color:  #482475;\n",
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       "            background-color:  #1fa187;\n",
       "            color:  #000000;\n",
       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row13_col1{\n",
       "            background-color:  #3a548c;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row13_col2,#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row18_col1{\n",
       "            background-color:  #2cb17e;\n",
       "            color:  #000000;\n",
       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row14_col5{\n",
       "            background-color:  #46075a;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row15_col1{\n",
       "            background-color:  #89d548;\n",
       "            color:  #000000;\n",
       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row15_col3{\n",
       "            background-color:  #23888e;\n",
       "            color:  #000000;\n",
       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row15_col4{\n",
       "            background-color:  #460b5e;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row16_col1{\n",
       "            background-color:  #4cc26c;\n",
       "            color:  #000000;\n",
       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row17_col1{\n",
       "            background-color:  #2a788e;\n",
       "            color:  #000000;\n",
       "        }#T_996b001a_b8d5_11ec_bdbb_96000040dfb6row17_col2{\n",
       "            background-color:  #433d84;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6\" ><caption>Retention Rate per Cohort</caption><thead>    <tr>        <th class=\"index_name level0\" >cohort_lifetime</th>        <th class=\"col_heading level0 col0\" >0</th>        <th class=\"col_heading level0 col1\" >1</th>        <th class=\"col_heading level0 col2\" >2</th>        <th class=\"col_heading level0 col3\" >3</th>        <th class=\"col_heading level0 col4\" >4</th>        <th class=\"col_heading level0 col5\" >5</th>        <th class=\"col_heading level0 col6\" >6</th>        <th class=\"col_heading level0 col7\" >7</th>        <th class=\"col_heading level0 col8\" >8</th>        <th class=\"col_heading level0 col9\" >9</th>        <th class=\"col_heading level0 col10\" >10</th>        <th class=\"col_heading level0 col11\" >11</th>        <th class=\"col_heading level0 col12\" >12</th>        <th class=\"col_heading level0 col13\" >13</th>        <th class=\"col_heading level0 col14\" >14</th>        <th class=\"col_heading level0 col15\" >15</th>        <th class=\"col_heading level0 col16\" >16</th>        <th class=\"col_heading level0 col17\" >17</th>        <th class=\"col_heading level0 col18\" >19</th>    </tr>    <tr>        <th class=\"index_name level0\" >first_buy</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6level0_row0\" class=\"row_heading level0 row0\" >2017-01</th>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row0_col0\" class=\"data row0 col0\" >100.00%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row0_col1\" class=\"data row0 col1\" >0.44%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row0_col2\" class=\"data row0 col2\" >0.29%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row0_col3\" class=\"data row0 col3\" >0.15%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row0_col4\" class=\"data row0 col4\" >0.44%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row0_col5\" class=\"data row0 col5\" >0.15%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row0_col6\" class=\"data row0 col6\" >0.44%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row0_col7\" class=\"data row0 col7\" >0.15%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row0_col8\" class=\"data row0 col8\" >0.15%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row0_col9\" class=\"data row0 col9\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row0_col10\" class=\"data row0 col10\" >0.29%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row0_col11\" class=\"data row0 col11\" >0.29%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row0_col12\" class=\"data row0 col12\" >0.73%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row0_col13\" class=\"data row0 col13\" >0.44%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row0_col14\" class=\"data row0 col14\" >0.15%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row0_col15\" class=\"data row0 col15\" >0.15%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row0_col16\" class=\"data row0 col16\" >0.29%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row0_col17\" class=\"data row0 col17\" >0.44%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row0_col18\" class=\"data row0 col18\" >0.15%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6level0_row1\" class=\"row_heading level0 row1\" >2017-02</th>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row1_col0\" class=\"data row1 col0\" >100.00%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row1_col1\" class=\"data row1 col1\" >0.19%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row1_col2\" class=\"data row1 col2\" >0.25%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row1_col3\" class=\"data row1 col3\" >0.19%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row1_col4\" class=\"data row1 col4\" >0.43%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row1_col5\" class=\"data row1 col5\" >0.12%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row1_col6\" class=\"data row1 col6\" >0.25%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row1_col7\" class=\"data row1 col7\" >0.19%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row1_col8\" class=\"data row1 col8\" >0.06%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row1_col9\" class=\"data row1 col9\" >0.19%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row1_col10\" class=\"data row1 col10\" >0.12%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row1_col11\" class=\"data row1 col11\" >0.31%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row1_col12\" class=\"data row1 col12\" >0.12%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row1_col13\" class=\"data row1 col13\" >0.19%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row1_col14\" class=\"data row1 col14\" >0.12%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row1_col15\" class=\"data row1 col15\" >0.06%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row1_col16\" class=\"data row1 col16\" >0.06%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row1_col17\" class=\"data row1 col17\" >0.19%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row1_col18\" class=\"data row1 col18\" ></td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6level0_row2\" class=\"row_heading level0 row2\" >2017-03</th>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row2_col0\" class=\"data row2 col0\" >100.00%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row2_col1\" class=\"data row2 col1\" >0.44%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row2_col2\" class=\"data row2 col2\" >0.36%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row2_col3\" class=\"data row2 col3\" >0.36%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row2_col4\" class=\"data row2 col4\" >0.36%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row2_col5\" class=\"data row2 col5\" >0.16%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row2_col6\" class=\"data row2 col6\" >0.16%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row2_col7\" class=\"data row2 col7\" >0.32%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row2_col8\" class=\"data row2 col8\" >0.28%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row2_col9\" class=\"data row2 col9\" >0.12%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row2_col10\" class=\"data row2 col10\" >0.36%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row2_col11\" class=\"data row2 col11\" >0.12%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row2_col12\" class=\"data row2 col12\" >0.20%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row2_col13\" class=\"data row2 col13\" >0.12%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row2_col14\" class=\"data row2 col14\" >0.16%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row2_col15\" class=\"data row2 col15\" >0.24%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row2_col16\" class=\"data row2 col16\" >0.08%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row2_col17\" class=\"data row2 col17\" >0.12%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row2_col18\" class=\"data row2 col18\" ></td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6level0_row3\" class=\"row_heading level0 row3\" >2017-04</th>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row3_col0\" class=\"data row3 col0\" >100.00%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row3_col1\" class=\"data row3 col1\" >0.63%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row3_col2\" class=\"data row3 col2\" >0.22%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row3_col3\" class=\"data row3 col3\" >0.18%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row3_col4\" class=\"data row3 col4\" >0.22%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row3_col5\" class=\"data row3 col5\" >0.27%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row3_col6\" class=\"data row3 col6\" >0.36%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row3_col7\" class=\"data row3 col7\" >0.27%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row3_col8\" class=\"data row3 col8\" >0.36%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row3_col9\" class=\"data row3 col9\" >0.18%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row3_col10\" class=\"data row3 col10\" >0.22%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row3_col11\" class=\"data row3 col11\" >0.09%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row3_col12\" class=\"data row3 col12\" >0.04%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row3_col13\" class=\"data row3 col13\" >0.04%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row3_col14\" class=\"data row3 col14\" >0.09%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row3_col15\" class=\"data row3 col15\" >0.09%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row3_col16\" class=\"data row3 col16\" >0.13%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row3_col17\" class=\"data row3 col17\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row3_col18\" class=\"data row3 col18\" ></td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6level0_row4\" class=\"row_heading level0 row4\" >2017-05</th>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row4_col0\" class=\"data row4 col0\" >100.00%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row4_col1\" class=\"data row4 col1\" >0.52%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row4_col2\" class=\"data row4 col2\" >0.46%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row4_col3\" class=\"data row4 col3\" >0.29%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row4_col4\" class=\"data row4 col4\" >0.29%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row4_col5\" class=\"data row4 col5\" >0.32%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row4_col6\" class=\"data row4 col6\" >0.41%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row4_col7\" class=\"data row4 col7\" >0.14%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row4_col8\" class=\"data row4 col8\" >0.26%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row4_col9\" class=\"data row4 col9\" >0.26%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row4_col10\" class=\"data row4 col10\" >0.29%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row4_col11\" class=\"data row4 col11\" >0.35%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row4_col12\" class=\"data row4 col12\" >0.17%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row4_col13\" class=\"data row4 col13\" >0.06%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row4_col14\" class=\"data row4 col14\" >0.17%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row4_col15\" class=\"data row4 col15\" >0.20%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row4_col16\" class=\"data row4 col16\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row4_col17\" class=\"data row4 col17\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row4_col18\" class=\"data row4 col18\" ></td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6level0_row5\" class=\"row_heading level0 row5\" >2017-06</th>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row5_col0\" class=\"data row5 col0\" >100.00%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row5_col1\" class=\"data row5 col1\" >0.49%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row5_col2\" class=\"data row5 col2\" >0.39%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row5_col3\" class=\"data row5 col3\" >0.43%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row5_col4\" class=\"data row5 col4\" >0.30%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row5_col5\" class=\"data row5 col5\" >0.36%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row5_col6\" class=\"data row5 col6\" >0.36%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row5_col7\" class=\"data row5 col7\" >0.23%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row5_col8\" class=\"data row5 col8\" >0.13%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row5_col9\" class=\"data row5 col9\" >0.20%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row5_col10\" class=\"data row5 col10\" >0.30%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row5_col11\" class=\"data row5 col11\" >0.39%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row5_col12\" class=\"data row5 col12\" >0.16%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row5_col13\" class=\"data row5 col13\" >0.16%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row5_col14\" class=\"data row5 col14\" >0.23%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row5_col15\" class=\"data row5 col15\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row5_col16\" class=\"data row5 col16\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row5_col17\" class=\"data row5 col17\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row5_col18\" class=\"data row5 col18\" ></td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6level0_row6\" class=\"row_heading level0 row6\" >2017-07</th>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row6_col0\" class=\"data row6 col0\" >100.00%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row6_col1\" class=\"data row6 col1\" >0.54%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row6_col2\" class=\"data row6 col2\" >0.32%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row6_col3\" class=\"data row6 col3\" >0.19%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row6_col4\" class=\"data row6 col4\" >0.35%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row6_col5\" class=\"data row6 col5\" >0.19%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row6_col6\" class=\"data row6 col6\" >0.32%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row6_col7\" class=\"data row6 col7\" >0.11%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row6_col8\" class=\"data row6 col8\" >0.19%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row6_col9\" class=\"data row6 col9\" >0.24%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row6_col10\" class=\"data row6 col10\" >0.22%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row6_col11\" class=\"data row6 col11\" >0.30%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row6_col12\" class=\"data row6 col12\" >0.13%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row6_col13\" class=\"data row6 col13\" >0.24%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row6_col14\" class=\"data row6 col14\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row6_col15\" class=\"data row6 col15\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row6_col16\" class=\"data row6 col16\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row6_col17\" class=\"data row6 col17\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row6_col18\" class=\"data row6 col18\" ></td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6level0_row7\" class=\"row_heading level0 row7\" >2017-08</th>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row7_col0\" class=\"data row7 col0\" >100.00%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row7_col1\" class=\"data row7 col1\" >0.69%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row7_col2\" class=\"data row7 col2\" >0.37%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row7_col3\" class=\"data row7 col3\" >0.27%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row7_col4\" class=\"data row7 col4\" >0.37%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row7_col5\" class=\"data row7 col5\" >0.51%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row7_col6\" class=\"data row7 col6\" >0.27%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row7_col7\" class=\"data row7 col7\" >0.27%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row7_col8\" class=\"data row7 col8\" >0.12%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row7_col9\" class=\"data row7 col9\" >0.17%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row7_col10\" class=\"data row7 col10\" >0.25%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row7_col11\" class=\"data row7 col11\" >0.17%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row7_col12\" class=\"data row7 col12\" >0.15%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row7_col13\" class=\"data row7 col13\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row7_col14\" class=\"data row7 col14\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row7_col15\" class=\"data row7 col15\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row7_col16\" class=\"data row7 col16\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row7_col17\" class=\"data row7 col17\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row7_col18\" class=\"data row7 col18\" ></td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6level0_row8\" class=\"row_heading level0 row8\" >2017-09</th>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row8_col0\" class=\"data row8 col0\" >100.00%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row8_col1\" class=\"data row8 col1\" >0.67%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row8_col2\" class=\"data row8 col2\" >0.50%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row8_col3\" class=\"data row8 col3\" >0.32%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row8_col4\" class=\"data row8 col4\" >0.42%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row8_col5\" class=\"data row8 col5\" >0.27%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row8_col6\" class=\"data row8 col6\" >0.22%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row8_col7\" class=\"data row8 col7\" >0.22%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row8_col8\" class=\"data row8 col8\" >0.30%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row8_col9\" class=\"data row8 col9\" >0.17%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row8_col10\" class=\"data row8 col10\" >0.25%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row8_col11\" class=\"data row8 col11\" >0.07%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row8_col12\" class=\"data row8 col12\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row8_col13\" class=\"data row8 col13\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row8_col14\" class=\"data row8 col14\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row8_col15\" class=\"data row8 col15\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row8_col16\" class=\"data row8 col16\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row8_col17\" class=\"data row8 col17\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row8_col18\" class=\"data row8 col18\" ></td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6level0_row9\" class=\"row_heading level0 row9\" >2017-10</th>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row9_col0\" class=\"data row9 col0\" >100.00%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row9_col1\" class=\"data row9 col1\" >0.70%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row9_col2\" class=\"data row9 col2\" >0.30%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row9_col3\" class=\"data row9 col3\" >0.09%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row9_col4\" class=\"data row9 col4\" >0.23%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row9_col5\" class=\"data row9 col5\" >0.19%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row9_col6\" class=\"data row9 col6\" >0.26%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row9_col7\" class=\"data row9 col7\" >0.37%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row9_col8\" class=\"data row9 col8\" >0.28%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row9_col9\" class=\"data row9 col9\" >0.19%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row9_col10\" class=\"data row9 col10\" >0.21%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row9_col11\" class=\"data row9 col11\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row9_col12\" class=\"data row9 col12\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row9_col13\" class=\"data row9 col13\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row9_col14\" class=\"data row9 col14\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row9_col15\" class=\"data row9 col15\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row9_col16\" class=\"data row9 col16\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row9_col17\" class=\"data row9 col17\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row9_col18\" class=\"data row9 col18\" ></td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6level0_row10\" class=\"row_heading level0 row10\" >2017-11</th>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row10_col0\" class=\"data row10 col0\" >100.00%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row10_col1\" class=\"data row10 col1\" >0.56%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row10_col2\" class=\"data row10 col2\" >0.36%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row10_col3\" class=\"data row10 col3\" >0.16%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row10_col4\" class=\"data row10 col4\" >0.17%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row10_col5\" class=\"data row10 col5\" >0.16%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row10_col6\" class=\"data row10 col6\" >0.13%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row10_col7\" class=\"data row10 col7\" >0.19%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row10_col8\" class=\"data row10 col8\" >0.12%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row10_col9\" class=\"data row10 col9\" >0.07%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row10_col10\" class=\"data row10 col10\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row10_col11\" class=\"data row10 col11\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row10_col12\" class=\"data row10 col12\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row10_col13\" class=\"data row10 col13\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row10_col14\" class=\"data row10 col14\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row10_col15\" class=\"data row10 col15\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row10_col16\" class=\"data row10 col16\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row10_col17\" class=\"data row10 col17\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row10_col18\" class=\"data row10 col18\" ></td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6level0_row11\" class=\"row_heading level0 row11\" >2017-12</th>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row11_col0\" class=\"data row11 col0\" >100.00%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row11_col1\" class=\"data row11 col1\" >0.22%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row11_col2\" class=\"data row11 col2\" >0.27%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row11_col3\" class=\"data row11 col3\" >0.31%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row11_col4\" class=\"data row11 col4\" >0.24%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row11_col5\" class=\"data row11 col5\" >0.20%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row11_col6\" class=\"data row11 col6\" >0.16%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row11_col7\" class=\"data row11 col7\" >0.02%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row11_col8\" class=\"data row11 col8\" >0.18%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row11_col9\" class=\"data row11 col9\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row11_col10\" class=\"data row11 col10\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row11_col11\" class=\"data row11 col11\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row11_col12\" class=\"data row11 col12\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row11_col13\" class=\"data row11 col13\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row11_col14\" class=\"data row11 col14\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row11_col15\" class=\"data row11 col15\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row11_col16\" class=\"data row11 col16\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row11_col17\" class=\"data row11 col17\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row11_col18\" class=\"data row11 col18\" ></td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6level0_row12\" class=\"row_heading level0 row12\" >2018-01</th>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row12_col0\" class=\"data row12 col0\" >100.00%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row12_col1\" class=\"data row12 col1\" >0.35%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row12_col2\" class=\"data row12 col2\" >0.38%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row12_col3\" class=\"data row12 col3\" >0.30%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row12_col4\" class=\"data row12 col4\" >0.32%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row12_col5\" class=\"data row12 col5\" >0.16%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row12_col6\" class=\"data row12 col6\" >0.18%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row12_col7\" class=\"data row12 col7\" >0.22%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row12_col8\" class=\"data row12 col8\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row12_col9\" class=\"data row12 col9\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row12_col10\" class=\"data row12 col10\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row12_col11\" class=\"data row12 col11\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row12_col12\" class=\"data row12 col12\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row12_col13\" class=\"data row12 col13\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row12_col14\" class=\"data row12 col14\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row12_col15\" class=\"data row12 col15\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row12_col16\" class=\"data row12 col16\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row12_col17\" class=\"data row12 col17\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row12_col18\" class=\"data row12 col18\" ></td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6level0_row13\" class=\"row_heading level0 row13\" >2018-02</th>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row13_col0\" class=\"data row13 col0\" >100.00%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row13_col1\" class=\"data row13 col1\" >0.32%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row13_col2\" class=\"data row13 col2\" >0.40%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row13_col3\" class=\"data row13 col3\" >0.29%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row13_col4\" class=\"data row13 col4\" >0.26%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row13_col5\" class=\"data row13 col5\" >0.18%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row13_col6\" class=\"data row13 col6\" >0.22%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row13_col7\" class=\"data row13 col7\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row13_col8\" class=\"data row13 col8\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row13_col9\" class=\"data row13 col9\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row13_col10\" class=\"data row13 col10\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row13_col11\" class=\"data row13 col11\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row13_col12\" class=\"data row13 col12\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row13_col13\" class=\"data row13 col13\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row13_col14\" class=\"data row13 col14\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row13_col15\" class=\"data row13 col15\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row13_col16\" class=\"data row13 col16\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row13_col17\" class=\"data row13 col17\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row13_col18\" class=\"data row13 col18\" ></td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6level0_row14\" class=\"row_heading level0 row14\" >2018-03</th>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row14_col0\" class=\"data row14 col0\" >100.00%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row14_col1\" class=\"data row14 col1\" >0.39%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row14_col2\" class=\"data row14 col2\" >0.32%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row14_col3\" class=\"data row14 col3\" >0.29%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row14_col4\" class=\"data row14 col4\" >0.13%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row14_col5\" class=\"data row14 col5\" >0.13%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row14_col6\" class=\"data row14 col6\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row14_col7\" class=\"data row14 col7\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row14_col8\" class=\"data row14 col8\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row14_col9\" class=\"data row14 col9\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row14_col10\" class=\"data row14 col10\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row14_col11\" class=\"data row14 col11\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row14_col12\" class=\"data row14 col12\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row14_col13\" class=\"data row14 col13\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row14_col14\" class=\"data row14 col14\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row14_col15\" class=\"data row14 col15\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row14_col16\" class=\"data row14 col16\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row14_col17\" class=\"data row14 col17\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row14_col18\" class=\"data row14 col18\" ></td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6level0_row15\" class=\"row_heading level0 row15\" >2018-04</th>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row15_col0\" class=\"data row15 col0\" >100.00%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row15_col1\" class=\"data row15 col1\" >0.61%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row15_col2\" class=\"data row15 col2\" >0.28%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row15_col3\" class=\"data row15 col3\" >0.25%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row15_col4\" class=\"data row15 col4\" >0.14%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row15_col5\" class=\"data row15 col5\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row15_col6\" class=\"data row15 col6\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row15_col7\" class=\"data row15 col7\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row15_col8\" class=\"data row15 col8\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row15_col9\" class=\"data row15 col9\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row15_col10\" class=\"data row15 col10\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row15_col11\" class=\"data row15 col11\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row15_col12\" class=\"data row15 col12\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row15_col13\" class=\"data row15 col13\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row15_col14\" class=\"data row15 col14\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row15_col15\" class=\"data row15 col15\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row15_col16\" class=\"data row15 col16\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row15_col17\" class=\"data row15 col17\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row15_col18\" class=\"data row15 col18\" ></td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6level0_row16\" class=\"row_heading level0 row16\" >2018-05</th>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row16_col0\" class=\"data row16 col0\" >100.00%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row16_col1\" class=\"data row16 col1\" >0.55%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row16_col2\" class=\"data row16 col2\" >0.25%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row16_col3\" class=\"data row16 col3\" >0.19%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row16_col4\" class=\"data row16 col4\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row16_col5\" class=\"data row16 col5\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row16_col6\" class=\"data row16 col6\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row16_col7\" class=\"data row16 col7\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row16_col8\" class=\"data row16 col8\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row16_col9\" class=\"data row16 col9\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row16_col10\" class=\"data row16 col10\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row16_col11\" class=\"data row16 col11\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row16_col12\" class=\"data row16 col12\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row16_col13\" class=\"data row16 col13\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row16_col14\" class=\"data row16 col14\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row16_col15\" class=\"data row16 col15\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row16_col16\" class=\"data row16 col16\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row16_col17\" class=\"data row16 col17\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row16_col18\" class=\"data row16 col18\" ></td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6level0_row17\" class=\"row_heading level0 row17\" >2018-06</th>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row17_col0\" class=\"data row17 col0\" >100.00%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row17_col1\" class=\"data row17 col1\" >0.39%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row17_col2\" class=\"data row17 col2\" >0.27%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row17_col3\" class=\"data row17 col3\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row17_col4\" class=\"data row17 col4\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row17_col5\" class=\"data row17 col5\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row17_col6\" class=\"data row17 col6\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row17_col7\" class=\"data row17 col7\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row17_col8\" class=\"data row17 col8\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row17_col9\" class=\"data row17 col9\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row17_col10\" class=\"data row17 col10\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row17_col11\" class=\"data row17 col11\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row17_col12\" class=\"data row17 col12\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row17_col13\" class=\"data row17 col13\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row17_col14\" class=\"data row17 col14\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row17_col15\" class=\"data row17 col15\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row17_col16\" class=\"data row17 col16\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row17_col17\" class=\"data row17 col17\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row17_col18\" class=\"data row17 col18\" ></td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6level0_row18\" class=\"row_heading level0 row18\" >2018-07</th>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row18_col0\" class=\"data row18 col0\" >100.00%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row18_col1\" class=\"data row18 col1\" >0.51%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row18_col2\" class=\"data row18 col2\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row18_col3\" class=\"data row18 col3\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row18_col4\" class=\"data row18 col4\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row18_col5\" class=\"data row18 col5\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row18_col6\" class=\"data row18 col6\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row18_col7\" class=\"data row18 col7\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row18_col8\" class=\"data row18 col8\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row18_col9\" class=\"data row18 col9\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row18_col10\" class=\"data row18 col10\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row18_col11\" class=\"data row18 col11\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row18_col12\" class=\"data row18 col12\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row18_col13\" class=\"data row18 col13\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row18_col14\" class=\"data row18 col14\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row18_col15\" class=\"data row18 col15\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row18_col16\" class=\"data row18 col16\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row18_col17\" class=\"data row18 col17\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row18_col18\" class=\"data row18 col18\" ></td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6level0_row19\" class=\"row_heading level0 row19\" >2018-08</th>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row19_col0\" class=\"data row19 col0\" >100.00%</td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row19_col1\" class=\"data row19 col1\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row19_col2\" class=\"data row19 col2\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row19_col3\" class=\"data row19 col3\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row19_col4\" class=\"data row19 col4\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row19_col5\" class=\"data row19 col5\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row19_col6\" class=\"data row19 col6\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row19_col7\" class=\"data row19 col7\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row19_col8\" class=\"data row19 col8\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row19_col9\" class=\"data row19 col9\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row19_col10\" class=\"data row19 col10\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row19_col11\" class=\"data row19 col11\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row19_col12\" class=\"data row19 col12\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row19_col13\" class=\"data row19 col13\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row19_col14\" class=\"data row19 col14\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row19_col15\" class=\"data row19 col15\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row19_col16\" class=\"data row19 col16\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row19_col17\" class=\"data row19 col17\" ></td>\n",
       "                        <td id=\"T_996b001a_b8d5_11ec_bdbb_96000040dfb6row19_col18\" class=\"data row19 col18\" ></td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f20e70bfa58>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ur_style = (retention_pivot.iloc[3:, 0:19]\n",
    "            .style\n",
    "            .set_caption('Retention Rate per Cohort')  # добавляем подпись\n",
    "            .background_gradient(cmap='viridis')  # раскрашиваем ячейки по столбцам\n",
    "            .highlight_null('white')  # делаем белый фон для значений NaN\n",
    "            .format(\"{:.2%}\", na_rep=\"\"))  # числа форматируем как проценты, NaN заменяем на пустоту\n",
    "ur_style\n",
    "\n",
    "# Самый высокий retention на 3-й месяц наблюдается у когорты с первой покупкой в июне 2017 года (2017-06)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 6. RFM-сегментация пользователей\n",
    "\n",
    "# Как и раньше, одна покупка - один заказ (order_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "                           order_id   price\n",
       "0  00010242fe8c5a6d1ba2dd792cb16214   58.90\n",
       "1  00018f77f2f0320c557190d7a144bdd3  239.90\n",
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      ]
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     "execution_count": 79,
     "metadata": {},
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    }
   ],
   "source": [
    "# посчитаем для каждого заказа его стоимость (сумму стоимости всех позиций)\n",
    "\n",
    "orders_prices = order_items \\\n",
    "    .groupby('order_id') \\\n",
    "    .agg({'price': 'sum'}) \\\n",
    "    .reset_index()\n",
    "\n",
    "orders_prices.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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      "text/plain": [
       "                 customer_unique_id                          order_id  \\\n",
       "0  7c396fd4830fd04220f754e42b4e5bff  e481f51cbdc54678b7cc49136f2d6af7   \n",
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       "4  72632f0f9dd73dfee390c9b22eb56dd6  ad21c59c0840e6cb83a9ceb5573f8159   \n",
       "\n",
       "  order_purchase_timestamp  \n",
       "0      2017-10-02 10:56:33  \n",
       "1      2018-07-24 20:41:37  \n",
       "2      2018-08-08 08:38:49  \n",
       "3      2017-11-18 19:28:06  \n",
       "4      2018-02-13 21:18:39  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# подготовим таблицу с нашими юзерами и их order_id для слияния с таблицей, которая получилась на предыдущем шаге\n",
    "\n",
    "customers_dates = customers_orders.query(\"order_status == 'delivered' \\\n",
    "                     or order_status == 'shipped' \\\n",
    "                     or order_status == 'processing' \\\n",
    "                     or order_status == 'approved'\")[['customer_unique_id', 'order_id', 'order_purchase_timestamp']]\n",
    "\n",
    "customers_dates.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "                 customer_unique_id                          order_id  \\\n",
       "0  7c396fd4830fd04220f754e42b4e5bff  e481f51cbdc54678b7cc49136f2d6af7   \n",
       "1  af07308b275d755c9edb36a90c618231  53cdb2fc8bc7dce0b6741e2150273451   \n",
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       "4  72632f0f9dd73dfee390c9b22eb56dd6  ad21c59c0840e6cb83a9ceb5573f8159   \n",
       "\n",
       "  order_purchase_timestamp   price  \n",
       "0      2017-10-02 10:56:33   29.99  \n",
       "1      2018-07-24 20:41:37  118.70  \n",
       "2      2018-08-08 08:38:49  159.90  \n",
       "3      2017-11-18 19:28:06   45.00  \n",
       "4      2018-02-13 21:18:39   19.90  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = customers_dates.merge(orders_prices, how='left', on='order_id').dropna()\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "У нас заказы с 2016-09-04 21:15:19 по 2018-09-03 09:06:57.\n"
     ]
    }
   ],
   "source": [
    "print('У нас заказы с {} по {}.'.format(df['order_purchase_timestamp'].min(),\n",
    "                                       df['order_purchase_timestamp'].max()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2018-09-04 09:06:57')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Представим, что сегодня 4 сентября 2018 года (на 1 день позже самой последней покупки), чтобы удобно было вычислять\n",
    "# количество дней с момента последней покупки - Recency (смысл анализа от этого не изменится).\n",
    "\n",
    "today_date = df['order_purchase_timestamp'].max() + timedelta(days=1)\n",
    "\n",
    "today_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Один год обычно используется как значение периода в RFM - анилизе. Будем анализировать наших пользователей за последний год,\n",
    "# то есть начиная с 4 сентября 2017 года.\n",
    "\n",
    "period = 365"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                 customer_unique_id                          order_id  \\\n",
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       "\n",
       "  order_purchase_timestamp   price  days_since_order  \n",
       "0      2017-10-02 10:56:33   29.99               336  \n",
       "1      2018-07-24 20:41:37  118.70                41  \n",
       "2      2018-08-08 08:38:49  159.90                27  \n",
       "3      2017-11-18 19:28:06   45.00               289  \n",
       "4      2018-02-13 21:18:39   19.90               202  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# вычитаем из сегодняшней даты дату покупки\n",
    "\n",
    "df['days_since_order'] = (today_date - df['order_purchase_timestamp']).dt.days\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
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       "                 customer_unique_id  recency  frequency\n",
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     "metadata": {},
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   ],
   "source": [
    "# в новой таблице оставляем для каждого пользователя только его последнюю покупку (минимальная разница - days_since_order).\n",
    "# Вычисляем, сколько заказов сделал каждый пользователь за последний год.\n",
    "\n",
    "rfm = df \\\n",
    "    .groupby('customer_unique_id') \\\n",
    "    .agg({'days_since_order': 'min',\n",
    "          'order_purchase_timestamp': lambda x: len([date for date in x if date >= today_date - timedelta(days=period)]) }) \\\n",
    "    .reset_index()\n",
    "\n",
    "rfm = rfm.rename(columns={'days_since_order': 'recency', 'order_purchase_timestamp': 'frequency'})\n",
    "rfm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>customer_unique_id</th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0000366f3b9a7992bf8c76cfdf3221e2</td>\n",
       "      <td>116</td>\n",
       "      <td>1</td>\n",
       "      <td>129.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0000b849f77a49e4a4ce2b2a4ca5be3f</td>\n",
       "      <td>119</td>\n",
       "      <td>1</td>\n",
       "      <td>18.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0000f46a3911fa3c0805444483337064</td>\n",
       "      <td>542</td>\n",
       "      <td>0</td>\n",
       "      <td>69.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0000f6ccb0745a6a4b88665a16c9f078</td>\n",
       "      <td>326</td>\n",
       "      <td>1</td>\n",
       "      <td>25.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0004aac84e0df4da2b147fca70cf8255</td>\n",
       "      <td>293</td>\n",
       "      <td>1</td>\n",
       "      <td>180.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 customer_unique_id  recency  frequency  monetary\n",
       "0  0000366f3b9a7992bf8c76cfdf3221e2      116          1    129.90\n",
       "1  0000b849f77a49e4a4ce2b2a4ca5be3f      119          1     18.90\n",
       "2  0000f46a3911fa3c0805444483337064      542          0     69.00\n",
       "3  0000f6ccb0745a6a4b88665a16c9f078      326          1     25.99\n",
       "4  0004aac84e0df4da2b147fca70cf8255      293          1    180.00"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Вычисляем monetary\n",
    "\n",
    "rfm = rfm.merge(df.groupby('customer_unique_id').agg({'price': 'sum'}).reset_index(),\n",
    "          how='left',\n",
    "          on='customer_unique_id')\n",
    "\n",
    "rfm = rfm.rename(columns={'price': 'monetary'})\n",
    "rfm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>customer_unique_id</th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0000366f3b9a7992bf8c76cfdf3221e2</td>\n",
       "      <td>116</td>\n",
       "      <td>1</td>\n",
       "      <td>129.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0000b849f77a49e4a4ce2b2a4ca5be3f</td>\n",
       "      <td>119</td>\n",
       "      <td>1</td>\n",
       "      <td>18.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0000f6ccb0745a6a4b88665a16c9f078</td>\n",
       "      <td>326</td>\n",
       "      <td>1</td>\n",
       "      <td>25.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0004aac84e0df4da2b147fca70cf8255</td>\n",
       "      <td>293</td>\n",
       "      <td>1</td>\n",
       "      <td>180.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0004bd2a26a76fe21f786e4fbd80607f</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>154.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 customer_unique_id  recency  frequency  monetary\n",
       "0  0000366f3b9a7992bf8c76cfdf3221e2      116          1    129.90\n",
       "1  0000b849f77a49e4a4ce2b2a4ca5be3f      119          1     18.90\n",
       "3  0000f6ccb0745a6a4b88665a16c9f078      326          1     25.99\n",
       "4  0004aac84e0df4da2b147fca70cf8255      293          1    180.00\n",
       "5  0004bd2a26a76fe21f786e4fbd80607f      151          1    154.00"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# оставляем людей с recency менее 365 дней\n",
    "\n",
    "rfm = rfm.query(\"recency < 365\")\n",
    "rfm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f20e7bb4e48>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# распределение recency напоминает равномерное, поэтому не буду ничего выдумывать и поделю пользователей так:\n",
    "# ранг 5: <= 30 дней\n",
    "# ранг 4: 31 - 60 дней\n",
    "# ранг 3: 61 - 90 дней\n",
    "# ранг 2: 91 - 180 дней\n",
    "# ранг 1: 181 - 365 дней\n",
    "\n",
    "sns.histplot(rfm.recency)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "def r_score(x):\n",
    "    if x <= 30:\n",
    "        return 5\n",
    "    elif x <= 60:\n",
    "        return 4\n",
    "    elif x <= 90:\n",
    "        return 3\n",
    "    elif x <= 180:\n",
    "        return 2\n",
    "    else:\n",
    "        return 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfm['R'] = rfm['recency'].apply(r_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f20ea465470>"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# распределение frequency. Видно, что подавляющее большинство покупателей купили у нас что-то за последний год только один раз.\n",
    "# Будет только две градации F: 1 - один раз купили, 2 - два и больше раз.\n",
    "\n",
    "sns.histplot(rfm['frequency'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f_score(x):\n",
    "    if x == 1:\n",
    "        return 1\n",
    "    else:\n",
    "        return 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfm['F'] = rfm['frequency'].apply(f_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    72760.000000\n",
       "mean       141.924960\n",
       "std        212.109239\n",
       "min          0.850000\n",
       "25%         48.000000\n",
       "50%         89.900000\n",
       "75%        155.000000\n",
       "max      13440.000000\n",
       "Name: monetary, dtype: float64"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# информация о monetary\n",
    "\n",
    "rfm.monetary.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f20db439588>"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# почти все наши значения сосредоточены в левой части графика. Есть также немного выбросов\n",
    "\n",
    "sns.histplot(rfm.monetary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0.2: 39.9, 0.4: 69.9, 0.6: 110.0, 0.8: 179.9}"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# считаю целесообразным разделить наши наблюдения по квантилям 20, 40, 60 и 80\n",
    "\n",
    "quantiles = rfm['monetary'].quantile([0.2, 0.4, 0.6, 0.8]).to_dict()\n",
    "quantiles\n",
    "\n",
    "# У 80% пользователей покупок менее, чем на 179.9 денежных единиц"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Сегменты M:\n",
    "# 1 - monetary <= 39.9\n",
    "# 2 - 39.9 < monetary <= 69.9\n",
    "# 3 - 69.9 < monetary <= 110\n",
    "# 4 - 110 < monetary <= 179.9\n",
    "# 5 - 179 < monetary\n",
    "\n",
    "def m_score(x):\n",
    "    if x <= quantiles[0.2]:\n",
    "        return 1\n",
    "    elif x <= quantiles[0.4]:\n",
    "        return 2\n",
    "    elif x <= quantiles[0.6]:\n",
    "        return 3\n",
    "    elif x <= quantiles[0.8]:\n",
    "        return 4\n",
    "    else:\n",
    "        return 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfm['M'] = rfm['monetary'].apply(m_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "# создадим колонку RFM Score, которая включает в себя показатели R, F и M\n",
    "\n",
    "rfm['RFM_score'] = rfm['R'].map(str) + rfm['F'].map(str) + rfm['M'].map(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "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>customer_unique_id</th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "      <th>R</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>RFM_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0000366f3b9a7992bf8c76cfdf3221e2</td>\n",
       "      <td>116</td>\n",
       "      <td>1</td>\n",
       "      <td>129.90</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0000b849f77a49e4a4ce2b2a4ca5be3f</td>\n",
       "      <td>119</td>\n",
       "      <td>1</td>\n",
       "      <td>18.90</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0000f6ccb0745a6a4b88665a16c9f078</td>\n",
       "      <td>326</td>\n",
       "      <td>1</td>\n",
       "      <td>25.99</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0004aac84e0df4da2b147fca70cf8255</td>\n",
       "      <td>293</td>\n",
       "      <td>1</td>\n",
       "      <td>180.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0004bd2a26a76fe21f786e4fbd80607f</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>154.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 customer_unique_id  recency  frequency  monetary  R  F  M  \\\n",
       "0  0000366f3b9a7992bf8c76cfdf3221e2      116          1    129.90  2  1  4   \n",
       "1  0000b849f77a49e4a4ce2b2a4ca5be3f      119          1     18.90  2  1  1   \n",
       "3  0000f6ccb0745a6a4b88665a16c9f078      326          1     25.99  1  1  1   \n",
       "4  0004aac84e0df4da2b147fca70cf8255      293          1    180.00  1  1  5   \n",
       "5  0004bd2a26a76fe21f786e4fbd80607f      151          1    154.00  2  1  4   \n",
       "\n",
       "  RFM_score  \n",
       "0       214  \n",
       "1       211  \n",
       "3       111  \n",
       "4       115  \n",
       "5       214  "
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# получилось 50 различных RFM - сегментов (5 * 2 * 5).\n",
    "\n",
    "len(rfm.RFM_score.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Оставлю только 4 сегмента (из-за специфика покупки, т.к. большая часть покупала только 1 раз).\n",
    "\n",
    "# Золотые клиенты: покупали в этом месяце, неважно сколько раз.\n",
    "# Лояльные клиенты: покупали что-то два раза или больше, но менее двух месяцев назад, но не более полугода назад.\n",
    "# Типичные покупатели: покупали что-то один раз, но менее двух месяцев назад, но не более полугода назад.\n",
    "# Группа риска: покупали что-то более полугода назад."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "segt_map = {\n",
    "    r'5[1-2]': 'золотые клиенты',\n",
    "    r'[2-4]2': 'лояльные клиенты',\n",
    "    r'[2-4]1': 'типичные покупатели',\n",
    "    r'1[1-2]': 'группа риска'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfm['segment'] = rfm['R'].map(str) + rfm['F'].map(str)\n",
    "rfm['segment'] = rfm['segment'].replace(segt_map, regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "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>customer_unique_id</th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "      <th>R</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>RFM_score</th>\n",
       "      <th>segment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0000366f3b9a7992bf8c76cfdf3221e2</td>\n",
       "      <td>116</td>\n",
       "      <td>1</td>\n",
       "      <td>129.90</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>214</td>\n",
       "      <td>типичные покупатели</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0000b849f77a49e4a4ce2b2a4ca5be3f</td>\n",
       "      <td>119</td>\n",
       "      <td>1</td>\n",
       "      <td>18.90</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>211</td>\n",
       "      <td>типичные покупатели</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0000f6ccb0745a6a4b88665a16c9f078</td>\n",
       "      <td>326</td>\n",
       "      <td>1</td>\n",
       "      <td>25.99</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "      <td>группа риска</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0004aac84e0df4da2b147fca70cf8255</td>\n",
       "      <td>293</td>\n",
       "      <td>1</td>\n",
       "      <td>180.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>115</td>\n",
       "      <td>группа риска</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0004bd2a26a76fe21f786e4fbd80607f</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>154.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>214</td>\n",
       "      <td>типичные покупатели</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 customer_unique_id  recency  frequency  monetary  R  F  M  \\\n",
       "0  0000366f3b9a7992bf8c76cfdf3221e2      116          1    129.90  2  1  4   \n",
       "1  0000b849f77a49e4a4ce2b2a4ca5be3f      119          1     18.90  2  1  1   \n",
       "3  0000f6ccb0745a6a4b88665a16c9f078      326          1     25.99  1  1  1   \n",
       "4  0004aac84e0df4da2b147fca70cf8255      293          1    180.00  1  1  5   \n",
       "5  0004bd2a26a76fe21f786e4fbd80607f      151          1    154.00  2  1  4   \n",
       "\n",
       "  RFM_score              segment  \n",
       "0       214  типичные покупатели  \n",
       "1       211  типичные покупатели  \n",
       "3       111         группа риска  \n",
       "4       115         группа риска  \n",
       "5       214  типичные покупатели  "
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(x='segment', data=rfm)\n",
    "\n",
    "total = len(rfm['segment'])\n",
    "for p in ax.patches:\n",
    "        percentage = '{:.1f}%'.format(100 * p.get_height() / total)\n",
    "        x = p.get_x() + p.get_width() - 0.5\n",
    "        y = p.get_y() + p.get_height()\n",
    "        ax.annotate(percentage, (x, y))\n",
    "\n",
    "ax.set_title('Распределение пользователей по сегментам\\n')\n",
    "ax.set_xlabel(\"\\nСегмент\")\n",
    "ax.set_ylabel(\"Пользователей\\n\")\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(x='R', data=rfm, palette=\"Blues_d\")\n",
    "\n",
    "total = len(rfm['R'])\n",
    "for p in ax.patches:\n",
    "        percentage = '{:.1f}%'.format(100 * p.get_height() / total)\n",
    "        x = p.get_x() + p.get_width() - 0.5\n",
    "        y = p.get_y() + p.get_height()\n",
    "        ax.annotate(percentage, (x, y))\n",
    "        \n",
    "ax.set_title('Распределение recency\\n')\n",
    "ax.set_xlabel(\"\\nRecency\")\n",
    "ax.set_ylabel(\"Пользователей\\n\")\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(x='F', data=rfm, palette=\"magma\")\n",
    "\n",
    "total = len(rfm['F'])\n",
    "for p in ax.patches:\n",
    "        percentage = '{:.1f}%'.format(100 * p.get_height() / total)\n",
    "        x = p.get_x() + p.get_width() - 0.5\n",
    "        y = p.get_y() + p.get_height()\n",
    "        ax.annotate(percentage, (x, y))\n",
    "        \n",
    "ax.set_title('Распределение frequency\\n')\n",
    "ax.set_xlabel(\"\\nFrequency\")\n",
    "ax.set_ylabel(\"Пользователей\\n\")\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=5, ncols=2,\n",
    "                         sharex=False, sharey=True,\n",
    "                         figsize=(10, 10))\n",
    "\n",
    "fig.suptitle('Распределение M для каждого F и R',\n",
    "             fontsize=20,\n",
    "             y=1.05)\n",
    "\n",
    "r_range = range(1, 6)\n",
    "f_range = range(1, 3)\n",
    "for r in r_range:\n",
    "    for f in f_range:\n",
    "        y = rfm[(rfm['R'] == r) & (rfm['F'] == f)]['M'].value_counts().sort_index()\n",
    "        x = y.index\n",
    "        ax = axes[r - 1, f - 1]\n",
    "        bars = ax.bar(x, y, color='lavender')\n",
    "        if r == 5:\n",
    "            if f == 2:\n",
    "                ax.set_xlabel('{}\\n\\nF'.format(f), va='top')\n",
    "            else:\n",
    "                ax.set_xlabel('{}\\n'.format(f), va='top')\n",
    "        if f == 1:\n",
    "            if r == 3:\n",
    "                ax.set_ylabel('R\\n\\n{}'.format(r))\n",
    "            else:\n",
    "                ax.set_ylabel(r)\n",
    "        ax.set_frame_on(False)\n",
    "        ax.tick_params(left=False, labelleft=False, bottom=False)\n",
    "        ax.set_xticks(x)\n",
    "        ax.set_xticklabels(x, fontsize=12)\n",
    "\n",
    "        for bar in bars:\n",
    "            value = bar.get_height()\n",
    "            if value == y.max():\n",
    "                bar.set_color('purple')\n",
    "            ax.text(bar.get_x() + bar.get_width() / 2,\n",
    "                    value,\n",
    "                    int(value),\n",
    "                    ha='center',\n",
    "                    va='bottom',\n",
    "                    color='k',\n",
    "                    fontsize=15)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Выводы, исходя из финальной визуализации наших данных:\n",
    "\n",
    "# Почти половина (48.5%) пользователей находится в группе риска, то есть покупали у нас более полугода назад.\n",
    "# Чтобы это исправить, можно персонально для них устроить какую-либо акцию или сделать скидку на следующую покупку.\n",
    "# 75.5% пользователей сделали покупку в нашем магазине уже более 3 месяцев назад (R = 1-2).\n",
    "# 24.5% пользователей сделали покупку в нашем магазине за последние 3 месяца (R = 3-5).\n",
    "# Распределение frequency показывает, что 97.5% покупателей сделали лишь одну покупку у нас (F = 1).\n",
    "# Такое распределение говорит о том, что в нашем магазине продаются товары, приобритаемые лишь один раз за долгий промежуток\n",
    "# времени (как минимум раз в год).\n",
    "# Распределение monetary в сегментах с F = 1 напоминает равномерное, нет разницы в долях, кто сколько потратил (monetary).\n",
    "# А вот в сегментах с F = 2 распределение monetary больше \"сдвинуто\" вправо во всех случаях R.\n",
    "# Это говорит о том, что если человек делает покупку два и более раз в нашем магазине, то, скорее всего, он тратит очень\n",
    "# много денег (monetary > 180, то есть принадлежит сегменту M = 5)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}