Sales_analysis_cohort_RFM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2492859b",
   "metadata": {},
   "source": [
    "Проект e-commerce: вариант 1 Продакт-менеджер Василий попросил вас проанализировать совершенные покупки и ответить на следующие вопросы:\n",
    "\n",
    "1. Сколько у нас пользователей, которые совершили покупку только один раз? (7 баллов)\n",
    "\n",
    "2. Сколько заказов в месяц в среднем не доставляется по разным причинам (вывести детализацию по причинам)? (5 баллов)\n",
    "\n",
    "3. По каждому товару определить, в какой день недели товар чаще всего покупается. (5 баллов)\n",
    "\n",
    "4. Сколько у каждого из пользователей в среднем покупок в неделю (по месяцам)? Не стоит забывать, что внутри месяца может быть не целое количество недель. Например, в ноябре 2021 года 4,28 недели. И внутри метрики это нужно учесть. (8 баллов)\n",
    "\n",
    "5. Используя pandas, проведи когортный анализ пользователей. В период с января по декабрь выяви когорту с самым высоким retention на 3й месяц. Описание подхода можно найти тут. (10 баллов)\n",
    "\n",
    "6. Часто для качественного анализа аудитории использую подходы, основанные на сегментации. Используя python, построй RFM-сегментацию пользователей, чтобы качественно оценить свою аудиторию. В кластеризации можешь выбрать следующие метрики: R - время от последней покупки пользователя до текущей даты, F - суммарное количество покупок у пользователя за всё время, M - сумма покупок за всё время. Подробно опиши, как ты создавал кластеры. Для каждого RFM-сегмента построй границы метрик recency, frequency и monetary для интерпретации этих кластеров. Пример такого описания: RFM-сегмент 132 (recency=1, frequency=3, monetary=2) имеет границы метрик recency от 130 до 500 дней, frequency от 2 до 5 заказов в неделю, monetary от 1780 до 3560 рублей в неделю. Описание подхода можно найти тут. (35 баллов)\n",
    "\n",
    "Для решения задачи проведи предварительное исследование данных и сформулируй, что должно считаться покупкой. Обосновать свой выбор ты можешь с помощью фактов оплат, статусов заказов и других имеющихся данных.\n",
    "\n",
    "ФАЙЛ: olist_customers_datase.csv — таблица с уникальными идентификаторами пользователей\n",
    "\n",
    "customer_id — позаказный идентификатор пользователя\n",
    "\n",
    "customer_unique_id — уникальный идентификатор пользователя (аналог номера паспорта)\n",
    "\n",
    "customer_zip_code_prefix — почтовый индекс пользователя\n",
    "\n",
    "customer_city — город доставки пользователя\n",
    "\n",
    "customer_state — штат доставки пользователя\n",
    "\n",
    "ФАЙЛ: olist_orders_dataset.csv — таблица заказов \n",
    "    \n",
    "order_id — уникальный идентификатор заказа (номер чека)\n",
    "\n",
    "customer_id — позаказный идентификатор пользователя\n",
    "\n",
    "order_status — статус заказа\n",
    "\n",
    "order_purchase_timestamp — время создания заказа\n",
    "\n",
    "order_approved_at — время подтверждения оплаты заказа\n",
    "\n",
    "order_delivered_carrier_date — время передачи заказа в логистическую службу\n",
    "\n",
    "order_delivered_customer_date — время доставки заказа\n",
    "\n",
    "order_estimated_delivery_date — обещанная дата доставки\n",
    "\n",
    "ФАЙЛ: olist_order_items_dataset.csv — товарные позиции, входящие в заказы\n",
    "\n",
    "order_id — уникальный идентификатор заказа (номер чека)\n",
    "\n",
    "order_item_id — идентификатор товара внутри одного заказа\n",
    "\n",
    "product_id — ид товара (аналог штрихкода)\n",
    "\n",
    "seller_id — ид производителя товара\n",
    "\n",
    "shipping_limit_date — максимальная дата доставки продавцом для передачи заказа партнеру по логистике\n",
    "\n",
    "price — цена за единицу товара\n",
    "\n",
    "freight_value — вес товара\n",
    "\n",
    "— Пример структуры данных можно визуализировать по order_id == 00143d0f86d6fbd9f9b38ab440ac16f5\n",
    "\n",
    "Уникальные статусы заказов в таблице olist_orders_dataset:\n",
    "\n",
    "created — создан\n",
    "\n",
    "approved — подтверждён\n",
    "\n",
    "invoiced — выставлен счёт\n",
    "\n",
    "processing — в процессе сборки заказа\n",
    "\n",
    "shipped — отгружен со склада\n",
    "\n",
    "delivered — доставлен пользователю\n",
    "\n",
    "unavailable — недоступен\n",
    "\n",
    "canceled — отменён"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b1ba22bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# required libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import requests\n",
    "from urllib.parse import urlencode\n",
    "import datetime\n",
    "from datetime import datetime\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "0892f231",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(rc={'figure.figsize':(16,6)}, style=\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4b7fb9bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import таблица с уникальными идентификаторами пользователей from Yandex.cloud\n",
    "base_url = 'https://cloud-api.yandex.net/v1/disk/public/resources/download?'\n",
    "public_key = 'https://disk.yandex.ru/d/FUi5uSd6BfG_ig'  # Сюда вписываете вашу ссылку\n",
    "# Получаем загрузочную ссылку\n",
    "final_url = base_url + urlencode(dict(public_key=public_key))\n",
    "response = requests.get(final_url)\n",
    "download_url = response.json()['href']\n",
    "df_users=pd.read_csv(download_url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8f3f8459",
   "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_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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_users.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e716a4eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id                 99441\n",
       "customer_unique_id          96096\n",
       "customer_zip_code_prefix    14994\n",
       "customer_city                4119\n",
       "customer_state                 27\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_users.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a179533f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99441 entries, 0 to 99440\n",
      "Data columns (total 5 columns):\n",
      " #   Column                    Non-Null Count  Dtype \n",
      "---  ------                    --------------  ----- \n",
      " 0   customer_id               99441 non-null  object\n",
      " 1   customer_unique_id        99441 non-null  object\n",
      " 2   customer_zip_code_prefix  99441 non-null  int64 \n",
      " 3   customer_city             99441 non-null  object\n",
      " 4   customer_state            99441 non-null  object\n",
      "dtypes: int64(1), object(4)\n",
      "memory usage: 3.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df_users.info() # нет нулевых значений!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2ff4ac65",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import таблица заказов from Yandex.cloud\n",
    "base_url = 'https://cloud-api.yandex.net/v1/disk/public/resources/download?'\n",
    "public_key = 'https://disk.yandex.ru/d/t9Li4JOfgxuUrg'  # Сюда вписываете вашу ссылку\n",
    "# Получаем загрузочную ссылку\n",
    "final_url = base_url + urlencode(dict(public_key=public_key))\n",
    "response = requests.get(final_url)\n",
    "download_url = response.json()['href']\n",
    "df_orders=pd.read_csv(download_url, parse_dates=['order_purchase_timestamp',\n",
    "       'order_approved_at', 'order_delivered_carrier_date',\n",
    "       'order_delivered_customer_date', 'order_estimated_delivery_date'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "32945c38",
   "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_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</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",
       "    </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",
       "    </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",
       "    </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",
       "    </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  \n",
       "1                    2018-08-13  \n",
       "2                    2018-09-04  \n",
       "3                    2017-12-15  \n",
       "4                    2018-02-26  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orders.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "ae20d61f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99441"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orders.order_id.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "15bc6e2b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99441 entries, 0 to 99440\n",
      "Data columns (total 8 columns):\n",
      " #   Column                         Non-Null Count  Dtype         \n",
      "---  ------                         --------------  -----         \n",
      " 0   order_id                       99441 non-null  object        \n",
      " 1   customer_id                    99441 non-null  object        \n",
      " 2   order_status                   99441 non-null  object        \n",
      " 3   order_purchase_timestamp       99441 non-null  datetime64[ns]\n",
      " 4   order_approved_at              99281 non-null  datetime64[ns]\n",
      " 5   order_delivered_carrier_date   97658 non-null  datetime64[ns]\n",
      " 6   order_delivered_customer_date  96476 non-null  datetime64[ns]\n",
      " 7   order_estimated_delivery_date  99441 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](5), object(3)\n",
      "memory usage: 6.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df_orders.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6ea2a095",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import товарные позиции, входящие в заказы from Yandex.cloud\n",
    "base_url = 'https://cloud-api.yandex.net/v1/disk/public/resources/download?'\n",
    "public_key = 'https://disk.yandex.ru/d/Gbt-yAcQrOe3Pw'  # Сюда вписываете вашу ссылку\n",
    "# Получаем загрузочную ссылку\n",
    "final_url = base_url + urlencode(dict(public_key=public_key))\n",
    "response = requests.get(final_url)\n",
    "download_url = response.json()['href']\n",
    "df_products=pd.read_csv(download_url, parse_dates=['shipping_limit_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "956640c8",
   "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_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",
       "    </tr>\n",
       "  </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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_products.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e62128c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 112650 entries, 0 to 112649\n",
      "Data columns (total 7 columns):\n",
      " #   Column               Non-Null Count   Dtype         \n",
      "---  ------               --------------   -----         \n",
      " 0   order_id             112650 non-null  object        \n",
      " 1   order_item_id        112650 non-null  int64         \n",
      " 2   product_id           112650 non-null  object        \n",
      " 3   seller_id            112650 non-null  object        \n",
      " 4   shipping_limit_date  112650 non-null  datetime64[ns]\n",
      " 5   price                112650 non-null  float64       \n",
      " 6   freight_value        112650 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(2), int64(1), object(3)\n",
      "memory usage: 6.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df_products.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1744c886",
   "metadata": {},
   "source": [
    "1. Сколько у нас пользователей, которые совершили покупку только один раз? (7 баллов)\n",
    "Для решения задачи проведи предварительное исследование данных и сформулируй, что должно считаться покупкой. Обосновать свой выбор ты можешь с помощью фактов оплат, статусов заказов и других имеющихся данных.\n",
    "\n",
    "Данные не идеальные, поскольку в них есть строчки, в которых статус delivered, \n",
    "а дата доставки (order_delivered_customer_date) отсутствует и также, наоборот, есть дата доставки, но статус не delivered. Кроме того, при статусе delivered\n",
    "могут отсутствовать дата order_approved_at и т.д.\n",
    "Эти строки удаляем, а расчёт числа пользователей с количеством покупок делаем без них. \n",
    "\n",
    "Итого: \n",
    "Покупками считаем только те строки, где есть как статус delivered и также заполнены другие критичные даты:\n",
    "    \n",
    "order_approved_at\n",
    "\n",
    "order_delivered_carrier_date\n",
    "\n",
    "order_delivered_customer_date\n",
    "\n",
    "Кроме того, для решения объединяем данные по заказам с данными по клиентам, чтобы использовать уникальные id клиентов для расчётов.\n",
    "\n",
    "Далее после необходимой фильтрации группируем по уникальным id клиентов и считаем количество заказов для каждого клиента, переименовываем колонку с количеством заказов, фильтруем по количеству заказов равному 1 и делаем окончательный расчёт количества клиентов, которые сделали только один заказ.\n",
    "\n",
    "Answer: 90536.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf9f5e47",
   "metadata": {},
   "source": [
    "Оставляем строки по которым есть все необходимые даты и статус delivered. По строкам, где нет какой-то информации необходимо дополнительное изучение отделу, ответственному за заполнение информации."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1da6325a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_unique_id    90536\n",
       "n_orders              90536\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orders.merge(df_users, on='customer_id')\\\n",
    "         .query('order_status==\"delivered\"')\\\n",
    "         .loc[df_orders.order_delivered_customer_date.notnull()]\\\n",
    "         .loc[df_orders.order_approved_at.notnull()]\\\n",
    "         .loc[df_orders.order_delivered_carrier_date.notnull()]\\\n",
    "         .groupby(['customer_unique_id'],as_index=False)\\\n",
    "         .agg({'order_id':'count'})\\\n",
    "         .rename(columns={'order_id':'n_orders'})\\\n",
    "         .query('n_orders==1')\\\n",
    "         .count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee57a7fe",
   "metadata": {},
   "source": [
    "2. Сколько заказов в месяц в среднем не доставляется по разным причинам (вывести детализацию по причинам)? (5 баллов)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fd2c731",
   "metadata": {},
   "source": [
    "Предварительные рассуждения и постановка задачи:\n",
    "Для начала определим, что значит \"...в месяц в среднем не доставляется\", поскольку,например, доставка может быть осуществлена в следующий месяц после месяца создания заказа. Как в этом случае считать была ли доставка или нет в конкретном месяце?\n",
    "Например, возможно, доставка была задержана по каким-то причинам, что не хорошо и это должно быть отражено в требуемом показателе. \n",
    "\n",
    "Кроме того, поскольку у нас данные за ограниченный период времени, то, возможно, что некоторые заказы созданы в конце периода, по которому есть информация, а факт их доставки не отражён в существующих данных.\n",
    "\n",
    "Также могут быть перекосы с датами заказов, когда большая часть заказов была размещена в конце месяца и тогда доставка по таким заказам осуществились только в следующем месяце, что повлияет на средние показатели в месяц. \n",
    "\n",
    "Поскольку у нас нет обратной связи с менеджером, чтобы уточнить постановку задачи, в частности, что делать с заказами, которые были размещена в конце месяца, но из-за логистических операций были доставлены в следующих месяцах, то УПРОЩАЕМ постановку задачи, предполагая, что ВСЕ ЗАКАЗЫ ДОЛЖНЫ БЫТЬ ДОСТАВЛЕНЫ В ТОТ ЖЕ МЕСЯЦ, КОГДА БЫЛ СДЕЛАН ЗАКАЗ!!! \n",
    "\n",
    "Тогда из дальнейшего расчёта удаляем все доставленные (статус - delivered) заказы с месяцем доставки  совпадающим с месяцем создания заказа. В этом случае причинами непоставки будут все остальные статусы и статус delivered для поставок в следующих месяцах после месяца создания заказа, который далее обозначим как delivered_after. \n",
    "\n",
    "Также, как в ответе на вопрос 1 удалим из расчётов все заказы со статусом delivered, у которых не выставлены даты по критическим событиям, поскольку отсутствует ключевая подтверждающая информация по якобы доставленным заказам:\n",
    "\n",
    "order_approved_at\n",
    "\n",
    "order_delivered_carrier_date\n",
    "\n",
    "order_delivered_customer_date\n",
    "\n",
    "В идеальном варианте решения необходимо согласовать KPI с руководством по каждой операции/статусу. Например, подробный анализ причин непоставок против SLAs (Service Level Agreements) по каждой операции, что включает целевое время на транспортировку в разные города на разные расстояния, время обработки заказа-сбора заказа в зависимости от объёма заказа и т.д. В этом случае, задача станет более определённой и потребует изменения подхода к анализу."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2495ee8d",
   "metadata": {},
   "source": [
    "Оставляем для дальнейшего анализа все заказы, которые по статусу НЕ delivered и заказы со статусом delivered с месяцем доставки несовпадающим с месяцем доставки."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ae8e3fbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_orders_not_delivered=df_orders.query('(order_status!=\"delivered\") | \\\n",
    "                                        ((order_status==\"delivered\") & \\\n",
    "                                         (order_purchase_timestamp.dt.month!=order_delivered_customer_date.dt.month))')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1c87f99",
   "metadata": {},
   "source": [
    "Убираем заказы со статусом delivered, но с NaN/NaT по обязательным и не заполненным полям (см. комментарий выше):\n",
    "\n",
    "order_delivered_customer_date\n",
    "\n",
    "order_approved_at\n",
    "\n",
    "order_delivered_carrier_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "07c98f4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "remove_list=df_orders_not_delivered.query('order_status==\"delivered\"')\\\n",
    "         .loc[df_orders.order_delivered_customer_date.isnull()].index # список с индексами строк, которые надо удалить"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5786c3cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([3002, 20618, 43834, 79263, 82868, 92643, 97647, 98038], dtype='int64')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "remove_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f03dbb7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_orders_not_delivered.drop(remove_list,inplace=True, errors='ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f9e650c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "remove_list=df_orders_not_delivered.query('order_status==\"delivered\"')\\\n",
    "         .loc[df_orders.order_approved_at.isnull()].index # список с индексами строк, которые надо удалить"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cb351d7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([ 5323, 16567, 19031, 22663, 23156, 38290, 39334, 48401, 61743,\n",
       "            63052, 67697, 72407, 84999],\n",
       "           dtype='int64')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "remove_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f5536025",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_orders_not_delivered.drop(remove_list,inplace=True, errors='ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7b4a6be4",
   "metadata": {},
   "outputs": [],
   "source": [
    "remove_list=df_orders_not_delivered.query('order_status==\"delivered\"')\\\n",
    "         .loc[df_orders.order_delivered_carrier_date.isnull()].index # список с индексами строк, которые надо удалить"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "50f5bcec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([73222], dtype='int64')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "remove_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "995e7403",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_orders_not_delivered.drop(remove_list,inplace=True, errors='ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58cefa14",
   "metadata": {},
   "source": [
    "Необходимо ещё сделать одну \"чистку\": удалить строки, в которых есть ненулевая дата доставки, но сам статус \"delivered\" отсутствует."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9fd700f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "remove_list=df_orders_not_delivered\\\n",
    "         .query('order_status!=\"delivered\"')\\\n",
    "         .loc[df_orders.order_delivered_customer_date.notnull()].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8475ce39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([2921, 8791, 58266, 59332, 92636, 94399], dtype='int64')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "remove_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3dc501f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_orders_not_delivered.drop(remove_list,inplace=True, errors='ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e18edf62",
   "metadata": {},
   "source": [
    "                 \"ЧИСТКА\" И ВЫБОР НЕОБХОДИМЫХ СТРОК ЗАКОНЧЕН!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e60b110b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 39370 entries, 1 to 99438\n",
      "Data columns (total 8 columns):\n",
      " #   Column                         Non-Null Count  Dtype         \n",
      "---  ------                         --------------  -----         \n",
      " 0   order_id                       39370 non-null  object        \n",
      " 1   customer_id                    39370 non-null  object        \n",
      " 2   order_status                   39370 non-null  object        \n",
      " 3   order_purchase_timestamp       39370 non-null  datetime64[ns]\n",
      " 4   order_approved_at              39224 non-null  datetime64[ns]\n",
      " 5   order_delivered_carrier_date   37589 non-null  datetime64[ns]\n",
      " 6   order_delivered_customer_date  36413 non-null  datetime64[ns]\n",
      " 7   order_estimated_delivery_date  39370 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](5), object(3)\n",
      "memory usage: 2.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df_orders_not_delivered.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2bb02f6",
   "metadata": {},
   "source": [
    "Заменим ещё название статуса delivered на delivered_after, что подразумевает, что доставка произошла не в месяц заказа по пока неизвестным причинам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e9ef5730",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\grebe\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:5516: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self[name] = value\n"
     ]
    }
   ],
   "source": [
    "df_orders_not_delivered.order_status=df_orders_not_delivered.order_status.str.replace('delivered','delivered_after')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "94b4a237",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1        delivered_after\n",
       "3        delivered_after\n",
       "6               invoiced\n",
       "8        delivered_after\n",
       "9        delivered_after\n",
       "              ...       \n",
       "99431    delivered_after\n",
       "99432    delivered_after\n",
       "99434    delivered_after\n",
       "99435    delivered_after\n",
       "99438    delivered_after\n",
       "Name: order_status, Length: 39370, dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orders_not_delivered.order_status # проверка ... всё работает"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b4de1228",
   "metadata": {},
   "outputs": [
    {
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       "      <th>order_id</th>\n",
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       "      <td>2018-08-07 15:27:45</td>\n",
       "      <td>2018-08-13</td>\n",
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       "      <td>NaT</td>\n",
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       "      <td>2017-02-02 14:08:10</td>\n",
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       "      <td>31ad1d1b63eb9962463f764d4e6e0c9d</td>\n",
       "      <td>delivered_after</td>\n",
       "      <td>2017-07-29 11:55:02</td>\n",
       "      <td>2017-07-29 12:05:32</td>\n",
       "      <td>2017-08-10 19:45:24</td>\n",
       "      <td>2017-08-16 17:14:30</td>\n",
       "      <td>2017-08-23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           order_id                       customer_id  \\\n",
       "1  53cdb2fc8bc7dce0b6741e2150273451  b0830fb4747a6c6d20dea0b8c802d7ef   \n",
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       "9  e69bfb5eb88e0ed6a785585b27e16dbf  31ad1d1b63eb9962463f764d4e6e0c9d   \n",
       "\n",
       "      order_status order_purchase_timestamp   order_approved_at  \\\n",
       "1  delivered_after      2018-07-24 20:41:37 2018-07-26 03:24:27   \n",
       "3  delivered_after      2017-11-18 19:28:06 2017-11-18 19:45:59   \n",
       "6         invoiced      2017-04-11 12:22:08 2017-04-13 13:25:17   \n",
       "8  delivered_after      2017-01-23 18:29:09 2017-01-25 02:50:47   \n",
       "9  delivered_after      2017-07-29 11:55:02 2017-07-29 12:05:32   \n",
       "\n",
       "  order_delivered_carrier_date order_delivered_customer_date  \\\n",
       "1          2018-07-26 14:31:00           2018-08-07 15:27:45   \n",
       "3          2017-11-22 13:39:59           2017-12-02 00:28:42   \n",
       "6                          NaT                           NaT   \n",
       "8          2017-01-26 14:16:31           2017-02-02 14:08:10   \n",
       "9          2017-08-10 19:45:24           2017-08-16 17:14:30   \n",
       "\n",
       "  order_estimated_delivery_date  \n",
       "1                    2018-08-13  \n",
       "3                    2017-12-15  \n",
       "6                    2017-05-09  \n",
       "8                    2017-03-06  \n",
       "9                    2017-08-23  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orders_not_delivered.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75363304",
   "metadata": {},
   "source": [
    "Проверим на то, что у нас в колонке order_id только уникальные значения, чтобы не делать группировки. Всё ОК."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d4bd61cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "Empty DataFrame\n",
       "Columns: [order_id, customer_id, order_status, order_purchase_timestamp, order_approved_at, order_delivered_carrier_date, order_delivered_customer_date, order_estimated_delivery_date]\n",
       "Index: []"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orders_not_delivered[df_orders_not_delivered.order_id.duplicated()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "805bbe9d",
   "metadata": {},
   "source": [
    "Создаём столбец нужного формата, где есть номер месяца и год, и убираем ненужные для анализа данные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "7635bcb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_orders_not_delivered['month']=df_orders_not_delivered.order_purchase_timestamp.dt.month\n",
    "df_orders_not_delivered['year']=df_orders_not_delivered.order_purchase_timestamp.dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "3460c088",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_orders_not_delivered=df_orders_not_delivered.astype({'month':'str','year':'str'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "0b02c15a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1         7/2018\n",
       "3        11/2017\n",
       "6         4/2017\n",
       "8         1/2017\n",
       "9         7/2017\n",
       "          ...   \n",
       "99431    10/2017\n",
       "99432    12/2017\n",
       "99434     1/2017\n",
       "99435     2/2017\n",
       "99438     8/2017\n",
       "Name: M/Y, Length: 39370, dtype: object"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orders_not_delivered['M/Y']=df_orders_not_delivered.month+'/'+df_orders_not_delivered.year\n",
    "df_orders_not_delivered['M/Y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "6f93bfdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_orders_not_delivered.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "840eb7de",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2017-04-11 12:22:08</td>\n",
       "      <td>2017-04-13 13:25:17</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
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       "      <td>2017-07-29 12:05:32</td>\n",
       "      <td>2017-08-10 19:45:24</td>\n",
       "      <td>2017-08-16 17:14:30</td>\n",
       "      <td>2017-08-23</td>\n",
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      ],
      "text/plain": [
       "                           order_id                       customer_id  \\\n",
       "0  53cdb2fc8bc7dce0b6741e2150273451  b0830fb4747a6c6d20dea0b8c802d7ef   \n",
       "1  949d5b44dbf5de918fe9c16f97b45f8a  f88197465ea7920adcdbec7375364d82   \n",
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       "4  e69bfb5eb88e0ed6a785585b27e16dbf  31ad1d1b63eb9962463f764d4e6e0c9d   \n",
       "\n",
       "      order_status order_purchase_timestamp   order_approved_at  \\\n",
       "0  delivered_after      2018-07-24 20:41:37 2018-07-26 03:24:27   \n",
       "1  delivered_after      2017-11-18 19:28:06 2017-11-18 19:45:59   \n",
       "2         invoiced      2017-04-11 12:22:08 2017-04-13 13:25:17   \n",
       "3  delivered_after      2017-01-23 18:29:09 2017-01-25 02:50:47   \n",
       "4  delivered_after      2017-07-29 11:55:02 2017-07-29 12:05:32   \n",
       "\n",
       "  order_delivered_carrier_date order_delivered_customer_date  \\\n",
       "0          2018-07-26 14:31:00           2018-08-07 15:27:45   \n",
       "1          2017-11-22 13:39:59           2017-12-02 00:28:42   \n",
       "2                          NaT                           NaT   \n",
       "3          2017-01-26 14:16:31           2017-02-02 14:08:10   \n",
       "4          2017-08-10 19:45:24           2017-08-16 17:14:30   \n",
       "\n",
       "  order_estimated_delivery_date month  year      M/Y  \n",
       "0                    2018-08-13     7  2018   7/2018  \n",
       "1                    2017-12-15    11  2017  11/2017  \n",
       "2                    2017-05-09     4  2017   4/2017  \n",
       "3                    2017-03-06     1  2017   1/2017  \n",
       "4                    2017-08-23     7  2017   7/2017  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orders_not_delivered.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "3c7f9438",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_analysis=df_orders_not_delivered[['order_id', 'order_status','M/Y']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "1f1f45fc",
   "metadata": {},
   "outputs": [
    {
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       "<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_id</th>\n",
       "      <th>order_status</th>\n",
       "      <th>M/Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>53cdb2fc8bc7dce0b6741e2150273451</td>\n",
       "      <td>delivered_after</td>\n",
       "      <td>7/2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>949d5b44dbf5de918fe9c16f97b45f8a</td>\n",
       "      <td>delivered_after</td>\n",
       "      <td>11/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>136cce7faa42fdb2cefd53fdc79a6098</td>\n",
       "      <td>invoiced</td>\n",
       "      <td>4/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>76c6e866289321a7c93b82b54852dc33</td>\n",
       "      <td>delivered_after</td>\n",
       "      <td>1/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>e69bfb5eb88e0ed6a785585b27e16dbf</td>\n",
       "      <td>delivered_after</td>\n",
       "      <td>7/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39365</th>\n",
       "      <td>b0f4af5c1b06e24fef510703bfe9f0a6</td>\n",
       "      <td>delivered_after</td>\n",
       "      <td>10/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39366</th>\n",
       "      <td>cfa78b997e329a5295b4ee6972c02979</td>\n",
       "      <td>delivered_after</td>\n",
       "      <td>12/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39367</th>\n",
       "      <td>aa04ef5214580b06b10e2a378300db44</td>\n",
       "      <td>delivered_after</td>\n",
       "      <td>1/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39368</th>\n",
       "      <td>880675dff2150932f1601e1c07eadeeb</td>\n",
       "      <td>delivered_after</td>\n",
       "      <td>2/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39369</th>\n",
       "      <td>83c1379a015df1e13d02aae0204711ab</td>\n",
       "      <td>delivered_after</td>\n",
       "      <td>8/2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>39370 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               order_id     order_status      M/Y\n",
       "0      53cdb2fc8bc7dce0b6741e2150273451  delivered_after   7/2018\n",
       "1      949d5b44dbf5de918fe9c16f97b45f8a  delivered_after  11/2017\n",
       "2      136cce7faa42fdb2cefd53fdc79a6098         invoiced   4/2017\n",
       "3      76c6e866289321a7c93b82b54852dc33  delivered_after   1/2017\n",
       "4      e69bfb5eb88e0ed6a785585b27e16dbf  delivered_after   7/2017\n",
       "...                                 ...              ...      ...\n",
       "39365  b0f4af5c1b06e24fef510703bfe9f0a6  delivered_after  10/2017\n",
       "39366  cfa78b997e329a5295b4ee6972c02979  delivered_after  12/2017\n",
       "39367  aa04ef5214580b06b10e2a378300db44  delivered_after   1/2017\n",
       "39368  880675dff2150932f1601e1c07eadeeb  delivered_after   2/2017\n",
       "39369  83c1379a015df1e13d02aae0204711ab  delivered_after   8/2017\n",
       "\n",
       "[39370 rows x 3 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26e17e4f",
   "metadata": {},
   "source": [
    "Для общего понимания ситуации нарисуем графики по месяцам и разным статусам. Ниже исключим доставленные заказы, которые доставлены не в месяц заказа. В целом, видно, что больше всего заказов находятся в пути. Также отметим, что часто заказ не был выполнен по причине отсутствие нужной продукции на складе (статус - unavailable). \n",
    "NB: Для дальнейших расчётов не принципиально, что 'M/Y' не является временным типом данных, поскольку смотрим в конечном итоге на среднее количество неотгруженных заказов в месяц, а по 'M/Y' уже данные отсортированы по месяцам."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "7228751c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='M/Y', ylabel='count'>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='M/Y', data=df_analysis.query('order_status!=\"delivered_after\"'), hue='order_status')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "805eca6d",
   "metadata": {},
   "source": [
    "Нарисуем график по месяцам со всеми статусами. Видно, что число заказов доставленных в месяц, не совпадающем с месяцем заказа, значительно больше чем недоставленных в месяц заказа по другим причинам."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "67e4c706",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='M/Y', ylabel='count'>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='M/Y', data=df_analysis, hue='order_status')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06724fbc",
   "metadata": {},
   "source": [
    "Финальный расчёт по причинам не доставки заказов в среднем в месяц:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "d804c317",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_analysis_final = df_analysis.groupby(['M/Y','order_status'],as_index=False)\\\n",
    "                               .count()\\\n",
    "                               .pivot(index='M/Y', columns='order_status', values='order_id')\\\n",
    "                               .fillna(0).mean().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "8c4c15db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_status\n",
       "approved              0.08\n",
       "canceled             25.79\n",
       "created               0.21\n",
       "delivered_after    1517.21\n",
       "invoiced             13.08\n",
       "processing           12.54\n",
       "shipped              46.12\n",
       "unavailable          25.38\n",
       "dtype: float64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_analysis_final"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bc92cdb",
   "metadata": {},
   "source": [
    "Выводы:\n",
    "1. В среднем за месяц, основное количество недоставленных в тот же месяц заказов находится в пути (delivered_after=1517.21 and shipped=46.12).\n",
    "2. В среднем за месяц, примерно, по 25-26 заказов или отменены (canceled) или не отгружены по причине отсутствия заказанной продукции (unavailable).\n",
    "3. Остальные заказы находятся в статусах подготовки к отгрузке."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d63a051",
   "metadata": {},
   "source": [
    "И для дополнительно наглядности построим еще бар чарт по статусам в среднем в месяц без статуса delivered_after:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "6cfcbe3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "t=pd.DataFrame(df_analysis_final).reset_index().rename(columns={0:'avg_month'}).query('order_status!=\"delivered_after\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "24bda790",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 7 artists>"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(t.order_status, t.avg_month)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a6e720f",
   "metadata": {},
   "source": [
    "3. По каждому товару определить, в какой день недели товар чаще всего покупается. (5 баллов)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e40f0bee",
   "metadata": {},
   "source": [
    "Предположение:\n",
    "\n",
    "Будем считать, что в этом вопросе интересуемся в какой день недели товар чаще всего ЗАКАЗЫВАЕТСЯ, поскольку для поставщика, главное, обеспечить наличие товара на день заказа с последующим резервированием товара для отгрузки. Тогда анализ по дням недели будем проводить по order_purchase_timestamp."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "d1dab7c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_3=df_orders.merge(df_products, on='order_id') # объединяем с данными по продуктам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "d996e132",
   "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_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>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",
       "    </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>1</td>\n",
       "      <td>87285b34884572647811a353c7ac498a</td>\n",
       "      <td>3504c0cb71d7fa48d967e0e4c94d59d9</td>\n",
       "      <td>2017-10-06 11:07:15</td>\n",
       "      <td>29.99</td>\n",
       "      <td>8.72</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>1</td>\n",
       "      <td>595fac2a385ac33a80bd5114aec74eb8</td>\n",
       "      <td>289cdb325fb7e7f891c38608bf9e0962</td>\n",
       "      <td>2018-07-30 03:24:27</td>\n",
       "      <td>118.70</td>\n",
       "      <td>22.76</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>1</td>\n",
       "      <td>aa4383b373c6aca5d8797843e5594415</td>\n",
       "      <td>4869f7a5dfa277a7dca6462dcf3b52b2</td>\n",
       "      <td>2018-08-13 08:55:23</td>\n",
       "      <td>159.90</td>\n",
       "      <td>19.22</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>1</td>\n",
       "      <td>d0b61bfb1de832b15ba9d266ca96e5b0</td>\n",
       "      <td>66922902710d126a0e7d26b0e3805106</td>\n",
       "      <td>2017-11-23 19:45:59</td>\n",
       "      <td>45.00</td>\n",
       "      <td>27.20</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>1</td>\n",
       "      <td>65266b2da20d04dbe00c5c2d3bb7859e</td>\n",
       "      <td>2c9e548be18521d1c43cde1c582c6de8</td>\n",
       "      <td>2018-02-19 20:31:37</td>\n",
       "      <td>19.90</td>\n",
       "      <td>8.72</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  order_item_id  \\\n",
       "0                    2017-10-18              1   \n",
       "1                    2018-08-13              1   \n",
       "2                    2018-09-04              1   \n",
       "3                    2017-12-15              1   \n",
       "4                    2018-02-26              1   \n",
       "\n",
       "                         product_id                         seller_id  \\\n",
       "0  87285b34884572647811a353c7ac498a  3504c0cb71d7fa48d967e0e4c94d59d9   \n",
       "1  595fac2a385ac33a80bd5114aec74eb8  289cdb325fb7e7f891c38608bf9e0962   \n",
       "2  aa4383b373c6aca5d8797843e5594415  4869f7a5dfa277a7dca6462dcf3b52b2   \n",
       "3  d0b61bfb1de832b15ba9d266ca96e5b0  66922902710d126a0e7d26b0e3805106   \n",
       "4  65266b2da20d04dbe00c5c2d3bb7859e  2c9e548be18521d1c43cde1c582c6de8   \n",
       "\n",
       "  shipping_limit_date   price  freight_value  \n",
       "0 2017-10-06 11:07:15   29.99           8.72  \n",
       "1 2018-07-30 03:24:27  118.70          22.76  \n",
       "2 2018-08-13 08:55:23  159.90          19.22  \n",
       "3 2017-11-23 19:45:59   45.00          27.20  \n",
       "4 2018-02-19 20:31:37   19.90           8.72  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "4bf6d0ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_3['day_n']=df_3.order_purchase_timestamp.dt.weekday #добавим новый столбец с номером дня недели (0 - Monday)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "42835e10",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_3_analysis = df_3.groupby(['product_id', 'day_n'], as_index=False)\\\n",
    "                    .count()\\\n",
    "                    .rename(columns={'order_id':'N_orders'})\\\n",
    "                    [['product_id','day_n','N_orders']].pivot(index='product_id', columns='day_n', values='N_orders').fillna(0)\n",
    "# сгруппировали по коду продукта и дням недели и посчитали сколько заказов в день. Далее оставили только нужные столбцы \n",
    "# в формате пивот и занулили NaNs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "763bc778",
   "metadata": {},
   "outputs": [
    {
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       "day_n                               0    1    2    3    4    5    6\n",
       "product_id                                                         \n",
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     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_3_analysis.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "a5a47659",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Транспонируем датафрейм, при этом в столбцах коды продукта, по строкам дни недели\n",
    "# Далее по каждому столбцу- коду продукта находим номер строки-номер дня недели в которой максимальное значение и записываем их\n",
    "# в отдельные списки, в которых каждому коду продукта соответствует одно значение дня недели и максимального значения \n",
    "# кол-ва заказов, поскольку согласно алгоритму поиск и запись максимального значения и соответствующего дня идёт по кодам\n",
    "a=[]\n",
    "b=[]\n",
    "c=[]\n",
    "for i in df_3_analysis.T.columns:\n",
    "    a.append(i)\n",
    "    b.append(df_3_analysis.T[i].idxmax())\n",
    "    c.append(df_3_analysis.T[i].max())    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "93f0a22a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# создаём датафрейм (таблицу) из соответствующих значений\n",
    "final = pd.DataFrame(list(zip(a,b,c)), columns =['product_id', 'day_n', 'max_orders']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "d75f0a00",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                             product_id  day_n  max_orders\n",
       "8613   422879e10f46682990de24d770e7f83d      2          93\n",
       "19742  99a4788cb24856965c36a24e339b6058      0          92\n",
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       "7079   368c6c730842d78016ad823897a372db      4          67\n",
       "...                                 ...    ...         ...\n",
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       "\n",
       "[32951 rows x 3 columns]"
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     },
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     "metadata": {},
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    }
   ],
   "source": [
    "# ОТВЕТ: таблица с каждым кодом и соответствующим днем недели с максимальным количеством заказов по сравнению с другими днями\n",
    "final.sort_values('max_orders', ascending=False).astype({'max_orders':'int'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c05dbe46",
   "metadata": {},
   "source": [
    "4. Сколько у каждого из пользователей в среднем покупок в неделю (по месяцам)? Не стоит забывать, что внутри месяца может быть не целое количество недель. Например, в ноябре 2021 года 4,28 недели. И внутри метрики это нужно учесть. (8 баллов)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60cb7fe2",
   "metadata": {},
   "source": [
    "Шаги по решению задачи:\n",
    "1. Объединяем данные по заказам с данными по пользователям.\n",
    "2. Убираем неполные записи согласно выбранному определению покупки (см. решение задачи 1).\n",
    "3. Потребуется функция, которая высчитывает количество полных и неполных недель в соответствующем месяце. Для этой функции необходима библиотека calendar. Импортируем в начале кода. Разработана и протестирована отдельно.\n",
    "4. По дате создания заказа создаём новую колонку, где приводим все даты к периоду - месяцу создания заказа для дальнейшей необходимой группировки.\n",
    "5. Группируем по уникальным кодам пользователей и периоду для расчёта количества заказов, переименовавываем колонку количества заказов.\n",
    "6. Создаём новую колонку Nw с использованием функции расчёта общего количества недель (полных и неполных).\n",
    "7. Делим количество заказов, сделанное в период-месяц, на соответствующее количество недель и переименовываем колонку n_order на n_orders_week.\n",
    "\n",
    "    Поскольку фраза \"...в среднем покупок в неделю (по месяцам)\" в условии задачи может допускать и другую трактовку (не только \"среднее количество покупок в неделю в разбивке по месяцам\"), то рассчитаем также и \"среднее количество покупок в неделю осреднённое по всем месяцам за весь период\".\n",
    "    \n",
    "7+. Переводим данные в пивот-таблицу и рассчитываем среднее по месяцам от средних покупок за неделю по каждому периоду-месяцу."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "b0c7b51d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import calendar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "dee5d114",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 1, 2\n",
    "df_orders_clean=df_orders.merge(df_users, on='customer_id')\\\n",
    "         .query('order_status==\"delivered\"')\\\n",
    "         .loc[df_orders.order_delivered_customer_date.notnull()]\\\n",
    "         .loc[df_orders.order_approved_at.notnull()]\\\n",
    "         .loc[df_orders.order_delivered_carrier_date.notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "ea57ede1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 3\n",
    "def W_q(date):\n",
    "    Year = date.year\n",
    "    Month= date.month\n",
    "    i,j,k = 0,0,0\n",
    "    mc = calendar.monthcalendar(Year,Month)\n",
    "    while i < len(mc):\n",
    "        while j < 7:\n",
    "            if mc[i][j]!=0:\n",
    "                k+=1/7\n",
    "            j+=1\n",
    "        j=0\n",
    "        i+=1\n",
    "    return round(k,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "9dc0770b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 4\n",
    "df_orders_clean['period']=df_orders_clean.order_purchase_timestamp.dt.to_period('M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "970bb9ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 5\n",
    "df_orders_analysis = df_orders_clean\\\n",
    "                     .groupby(['customer_unique_id','period'],as_index=False)\\\n",
    "                     .agg({'order_id':'count'})\\\n",
    "                     .rename(columns={'order_id':'n_orders'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "4f568b69",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_unique_id</th>\n",
       "      <th>period</th>\n",
       "      <th>n_orders</th>\n",
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       "      <td>0000f46a3911fa3c0805444483337064</td>\n",
       "      <td>2017-03</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0000f6ccb0745a6a4b88665a16c9f078</td>\n",
       "      <td>2017-10</td>\n",
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       "      <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   period  n_orders\n",
       "0  0000366f3b9a7992bf8c76cfdf3221e2  2018-05         1\n",
       "1  0000b849f77a49e4a4ce2b2a4ca5be3f  2018-05         1\n",
       "2  0000f46a3911fa3c0805444483337064  2017-03         1\n",
       "3  0000f6ccb0745a6a4b88665a16c9f078  2017-10         1\n",
       "4  0004aac84e0df4da2b147fca70cf8255  2017-11         1"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orders_analysis.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "id": "0a57f290",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 6\n",
    "df_orders_analysis['Nw']=df_orders_analysis.period.apply(W_q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "469aae94",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 7\n",
    "df_orders_analysis.n_orders=df_orders_analysis.n_orders/df_orders_analysis.Nw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "id": "408e4d91",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>0</th>\n",
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       "      <td>4.43</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0000f46a3911fa3c0805444483337064</td>\n",
       "      <td>2017-03</td>\n",
       "      <td>0.225734</td>\n",
       "      <td>4.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0000f6ccb0745a6a4b88665a16c9f078</td>\n",
       "      <td>2017-10</td>\n",
       "      <td>0.225734</td>\n",
       "      <td>4.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0004aac84e0df4da2b147fca70cf8255</td>\n",
       "      <td>2017-11</td>\n",
       "      <td>0.233100</td>\n",
       "      <td>4.29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 customer_unique_id   period  n_orders    Nw\n",
       "0  0000366f3b9a7992bf8c76cfdf3221e2  2018-05  0.225734  4.43\n",
       "1  0000b849f77a49e4a4ce2b2a4ca5be3f  2018-05  0.225734  4.43\n",
       "2  0000f46a3911fa3c0805444483337064  2017-03  0.225734  4.43\n",
       "3  0000f6ccb0745a6a4b88665a16c9f078  2017-10  0.225734  4.43\n",
       "4  0004aac84e0df4da2b147fca70cf8255  2017-11  0.233100  4.29"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orders_analysis.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "id": "1cb7109c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 7\n",
    "df_orders_analysis.rename(columns={'n_orders':'n_orders_week'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "id": "9b1a2ef0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_unique_id</th>\n",
       "      <th>period</th>\n",
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       "      <td>4.43</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>2018-05</td>\n",
       "      <td>0.225734</td>\n",
       "      <td>4.43</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0000f46a3911fa3c0805444483337064</td>\n",
       "      <td>2017-03</td>\n",
       "      <td>0.225734</td>\n",
       "      <td>4.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0000f6ccb0745a6a4b88665a16c9f078</td>\n",
       "      <td>2017-10</td>\n",
       "      <td>0.225734</td>\n",
       "      <td>4.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0004aac84e0df4da2b147fca70cf8255</td>\n",
       "      <td>2017-11</td>\n",
       "      <td>0.233100</td>\n",
       "      <td>4.29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 customer_unique_id   period  n_orders_week    Nw\n",
       "0  0000366f3b9a7992bf8c76cfdf3221e2  2018-05       0.225734  4.43\n",
       "1  0000b849f77a49e4a4ce2b2a4ca5be3f  2018-05       0.225734  4.43\n",
       "2  0000f46a3911fa3c0805444483337064  2017-03       0.225734  4.43\n",
       "3  0000f6ccb0745a6a4b88665a16c9f078  2017-10       0.225734  4.43\n",
       "4  0004aac84e0df4da2b147fca70cf8255  2017-11       0.233100  4.29"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orders_analysis.head() \n",
    "# Это ответ на поставленный вопрос в трактовке \"среднее количество покупок в неделю в разбивке по периодам - месяц\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "e18bddee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "               customer_unique_id              \n",
       "n_orders_week  0000366f3b9a7992bf8c76cfdf3221e2    0.009815\n",
       "               0000b849f77a49e4a4ce2b2a4ca5be3f    0.009815\n",
       "               0000f46a3911fa3c0805444483337064    0.009815\n",
       "               0000f6ccb0745a6a4b88665a16c9f078    0.009815\n",
       "               0004aac84e0df4da2b147fca70cf8255    0.010135\n",
       "                                                     ...   \n",
       "               fffcf5a5ff07b0908bd4e2dbc735a684    0.010135\n",
       "               fffea47cd6d3cc0a88bd621562a9d061    0.009815\n",
       "               ffff371b4d645b6ecea244b27531430a    0.010870\n",
       "               ffff5962728ec6157033ef9805bacc48    0.009815\n",
       "               ffffd2657e2aad2907e67c3e9daecbeb    0.009815\n",
       "Length: 93336, dtype: float64"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 7+\n",
    "pd.pivot(df_orders_analysis,\n",
    "               index=['period'],\n",
    "               columns='customer_unique_id',\n",
    "               values=['n_orders_week'],\n",
    "               ).fillna(0).mean() # второй столбец показывает среднее количество покупок в неделю осреднённое по месяцам"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c1f1c9f",
   "metadata": {},
   "source": [
    "5. Используя pandas, проведи когортный анализ пользователей. В период с января по декабрь выяви когорту с самым высоким retention на 3й месяц. Описание подхода можно найти тут. (10 баллов)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f13db09",
   "metadata": {},
   "source": [
    "    ПОСТАНОВКА ЗАДАЧИ:\n",
    "1. Исходя из формулировки задачи с \"...retention на 3й месяц\" (а не, например, за предыдущие 3 месяца) в формуле учитываем  только пользователей, которые сделали повторную покупку точно на 3-м месяце, считая, что месяц покупки у них был нулевым.\n",
    "    Возможен, альтернативный подход, который предполагает, что перестаём учитывать в расчётах пользователей в том случае, если пользователь не делал покупки в течение 3 месяцев (первом после нулевого, во втором и в третьем) после покупки в нулевом месяце. Такой подход, пожалуй, будет более информативным для бизнеса, поскольку пользователь может делать покупки более случайным образом, а не, регулярно, каждые 3 месяца. \n",
    "2. Используем формулу: Retention = Количество пользователей из числа пользователей (купивших в течение нулевого месяца), которые сделали покупку на 3-м месяце / число пользователей, купивших в течение нулевого месяца.\n",
    "3. Когортой будем считать тех, кто делал покупки в определённый месяц."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8785a5f5",
   "metadata": {},
   "source": [
    "Описание шагов решения:\n",
    "\n",
    "1. Выделяем только \"закрытые\" покупки согласно выбранному критерию с наличием всех критических данных.\n",
    "2. Вставляем колонку с месяцем покупки и сортируем по месяцу покупки.\n",
    "3. Аггрегируем по месяцу покупки и номеру клиента, подсчитывая количество покупок по каждому клиенту и месяцу, оставляя \n",
    "    датафрейм с только месяцем покупки, уникальным номером клиента и количеством покупок.\n",
    "4. Создаём новый датафрейм с уникальным номеров клиента и месяцем, когда им была сделана первая покупка.\n",
    "5. Соединяем датафрейм из п.3 с датафреймом из п.4 по уникальному номеру клиента.\n",
    "6. Создаём новую колонку в датафрейме из п.5 как разницу месяца покупки и месяца первой покупки.\n",
    "7. Итого получаем датафрейм со следующими колонками: номер клиента, месяц покупки, \n",
    "    первый месяц покупки, разница между месяцем покупки и первым месяцем покупки (cohort_period).\n",
    "8. Создаём пивот, где индексами являются месяц покупки и cohort_period, а значениями количество клиентов, \n",
    "   купивших в этот месяц.\n",
    "9. Создаём отдельный датафрейм по когортам с кол-м клиентов, разбитым по месяцам покупки.\n",
    "10. Подтягиваем разбивку по когортам в датафрейм по месяцу первой покупки, рассчитываем retention rate и\n",
    "    преобразуем в пивот-таблицу и строим heatmap.\n",
    "\n",
    "ОТВЕТ: Самый максимальный retention на 3-й месяц в 2017 году был у когорты пользователей, сделавших покупку в июне 2017 года."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "5387111f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 1.\n",
    "df_orders_clean=df_orders.merge(df_users, on='customer_id')\\\n",
    "         .query('order_status==\"delivered\"')\\\n",
    "         .loc[df_orders.order_delivered_customer_date.notnull()]\\\n",
    "         .loc[df_orders.order_approved_at.notnull()]\\\n",
    "         .loc[df_orders.order_delivered_carrier_date.notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "id": "1acff77c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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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",
       "      <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",
       "    </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                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": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orders_clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "9a9c6815",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 2.\n",
    "df_orders_clean['purchase_month']=df_orders_clean.order_purchase_timestamp.dt.strftime('%Y-%m')\n",
    "df_orders_clean.sort_values('purchase_month', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "5e3c559c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 3. Аггрегируем по месяцу покупки и номеру клиента, подсчитывая количество покупок по каждому клиенту и месяцу, оставляя \n",
    "# датафрейм с только месяцем покупки, уникальным номером клиента и количеством покупок.\n",
    "df_analysis=df_orders_clean.groupby(['purchase_month', 'customer_unique_id'], as_index=False).agg({'order_id':'count'})\\\n",
    "                           .rename(columns={'order_id':'N_orders'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "id": "61356fce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 4. Создаём новый датафрейм с уникальным номером клиента и месяцем, когда клиентом была сделана первая покупка.\n",
    "# Подготовка к созданию когортов.\n",
    "df_first_purchase = df_analysis.groupby('customer_unique_id', as_index=False)\\\n",
    "                               .purchase_month.min()\\\n",
    "                               .sort_values('purchase_month')\\\n",
    "                               .reset_index(drop=True)\\\n",
    "                               .rename(columns={'purchase_month':'first_purchase'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "09b2c653",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_unique_id</th>\n",
       "      <th>first_purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>830d5b7aaa3b6f1e9ad63703bec97d23</td>\n",
       "      <td>2016-09</td>\n",
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       "      <th>1</th>\n",
       "      <td>bb5f2d03e1e945a1e5c1ab23c3de4c64</td>\n",
       "      <td>2016-10</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4e23e1826902ec9f208e8cc61329b494</td>\n",
       "      <td>2016-10</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>2016-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27ede105a6042b71a261bf57f75fdcf8</td>\n",
       "      <td>2016-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 customer_unique_id first_purchase\n",
       "0  830d5b7aaa3b6f1e9ad63703bec97d23        2016-09\n",
       "1  bb5f2d03e1e945a1e5c1ab23c3de4c64        2016-10\n",
       "2  4e23e1826902ec9f208e8cc61329b494        2016-10\n",
       "3  e6a1d274cbba12da4cd29eb462b34be5        2016-10\n",
       "4  27ede105a6042b71a261bf57f75fdcf8        2016-10"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_first_purchase.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "c4282244",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5. Соединяем датафрейм из п.3 с датафреймом из п.4 по уникальному номеру клиента и убираем \"лишнюю\" колонку\n",
    "test = df_analysis.merge(df_first_purchase, on='customer_unique_id', how='left')\n",
    "test.drop('N_orders', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "141a43cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>purchase_month</th>\n",
       "      <th>customer_unique_id</th>\n",
       "      <th>first_purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-09</td>\n",
       "      <td>830d5b7aaa3b6f1e9ad63703bec97d23</td>\n",
       "      <td>2016-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-10</td>\n",
       "      <td>0032c76b20340da25249092a268ce66c</td>\n",
       "      <td>2016-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-10</td>\n",
       "      <td>01f156677184504063bd19739f924af1</td>\n",
       "      <td>2016-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-10</td>\n",
       "      <td>0636d30c77f0f9cfad81f1c9b58c791f</td>\n",
       "      <td>2016-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-10</td>\n",
       "      <td>06bdfbbe1857c3c925ec81abfb1c9666</td>\n",
       "      <td>2016-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  purchase_month                customer_unique_id first_purchase\n",
       "0        2016-09  830d5b7aaa3b6f1e9ad63703bec97d23        2016-09\n",
       "1        2016-10  0032c76b20340da25249092a268ce66c        2016-10\n",
       "2        2016-10  01f156677184504063bd19739f924af1        2016-10\n",
       "3        2016-10  0636d30c77f0f9cfad81f1c9b58c791f        2016-10\n",
       "4        2016-10  06bdfbbe1857c3c925ec81abfb1c9666        2016-10"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "4a5978e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 6. Создаём новую колонку в датафрейме из п.5 как разницу месяца покупки и месяца первой покупки. \n",
    "# Это обозначит периоды, связанные с расчётами retention. Дj этого также изменим формат колонок\n",
    "# с месяцами, поскольку сейчас они не являются временными.\n",
    "test=test.astype({'purchase_month':'datetime64', 'first_purchase':'datetime64'})\n",
    "test['cohort_period']=((test.purchase_month.dt.year-test.first_purchase.dt.year)*12\\\n",
    "                       +test.purchase_month.dt.month-test.first_purchase.dt.month).sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "10546ea3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>purchase_month</th>\n",
       "      <th>customer_unique_id</th>\n",
       "      <th>first_purchase</th>\n",
       "      <th>cohort_period</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>2016-09-01</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-10-01</td>\n",
       "      <td>01f156677184504063bd19739f924af1</td>\n",
       "      <td>2016-10-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-10-01</td>\n",
       "      <td>0636d30c77f0f9cfad81f1c9b58c791f</td>\n",
       "      <td>2016-10-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-10-01</td>\n",
       "      <td>06bdfbbe1857c3c925ec81abfb1c9666</td>\n",
       "      <td>2016-10-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  purchase_month                customer_unique_id first_purchase  \\\n",
       "0     2016-09-01  830d5b7aaa3b6f1e9ad63703bec97d23     2016-09-01   \n",
       "1     2016-10-01  0032c76b20340da25249092a268ce66c     2016-10-01   \n",
       "2     2016-10-01  01f156677184504063bd19739f924af1     2016-10-01   \n",
       "3     2016-10-01  0636d30c77f0f9cfad81f1c9b58c791f     2016-10-01   \n",
       "4     2016-10-01  06bdfbbe1857c3c925ec81abfb1c9666     2016-10-01   \n",
       "\n",
       "   cohort_period  \n",
       "0              0  \n",
       "1              0  \n",
       "2              0  \n",
       "3              0  \n",
       "4              0  "
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 7. Итого получаем датафрейм со следующими колонками: номер клиента, месяц покупки, \n",
    "#    первый месяц покупки, разница между месяцем покупки и первым месяцем покупки (cohort_period).\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "84c7ea97",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 8. Создаём пивот, где индексами являются месяц покупки и cohort_period, а значениями количество клиентов, \n",
    "#    купивших в этот месяц\n",
    "cohorts = test.pivot_table(index=['first_purchase', 'cohort_period'], \n",
    "                           aggfunc={'customer_unique_id':'nunique'})\\\n",
    "                            .reset_index().rename({'customer_unique_id':'N_customers'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "24e616ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-10-01</td>\n",
       "      <td>0</td>\n",
       "      <td>262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-10-01</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-10-01</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-10-01</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>2018-06-01</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>2018-06-01</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>2018-07-01</td>\n",
       "      <td>0</td>\n",
       "      <td>5946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>2018-07-01</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>2018-08-01</td>\n",
       "      <td>0</td>\n",
       "      <td>6144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>219 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    first_purchase  cohort_period  customer_unique_id\n",
       "0       2016-09-01              0                   1\n",
       "1       2016-10-01              0                 262\n",
       "2       2016-10-01              6                   1\n",
       "3       2016-10-01              9                   1\n",
       "4       2016-10-01             11                   1\n",
       "..             ...            ...                 ...\n",
       "214     2018-06-01              1                  25\n",
       "215     2018-06-01              2                  16\n",
       "216     2018-07-01              0                5946\n",
       "217     2018-07-01              1                  31\n",
       "218     2018-08-01              0                6144\n",
       "\n",
       "[219 rows x 3 columns]"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohorts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "ff833ef7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 9. Создаём отдельный датафрейм по когортам с кол-м клиентов, разбитым по месяцам покупки\n",
    "initial_customers_count = cohorts[cohorts['cohort_period'] == 0]\n",
    "initial_customers_count = initial_customers_count[['first_purchase','customer_unique_id']]\\\n",
    "                        .rename(columns={'customer_unique_id':'cohort_users'}) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "bd2fc5e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 10. Подтягиваем разбивку по когортам в датафрейм по месяцу первой покупки, рассчитываем retention rate и\n",
    "# преобразуем в пивот-таблицу\n",
    "cohorts = cohorts.merge(initial_customers_count,on='first_purchase')\n",
    "cohorts['retention'] = cohorts['customer_unique_id']/cohorts['cohort_users'] \n",
    "retention_pivot = cohorts.pivot_table(index='first_purchase',\n",
    "                                      columns='cohort_period',\n",
    "                                      values='retention',\n",
    "                                      aggfunc='sum') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "64bd8eeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>cohort_period</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2017-01-01</th>\n",
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       "      <td>0.001399</td>\n",
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       "      <td>0.001399</td>\n",
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       "      <td>0.001399</td>\n",
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       "      <th>2017-02-01</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.001238</td>\n",
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       "      <td>0.001856</td>\n",
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       "    <tr>\n",
       "      <th>2017-03-01</th>\n",
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       "      <td>0.003196</td>\n",
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       "      <th>2017-04-01</th>\n",
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       "      <td>0.003103</td>\n",
       "      <td>0.001773</td>\n",
       "      <td>0.002660</td>\n",
       "      <td>0.000887</td>\n",
       "      <td>0.000443</td>\n",
       "      <td>0.000443</td>\n",
       "      <td>0.000887</td>\n",
       "      <td>0.000887</td>\n",
       "      <td>0.001330</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2017-05-01</th>\n",
       "      <td>1.0</td>\n",
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       "      <th>2017-06-01</th>\n",
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       "      <td>0.001646</td>\n",
       "      <td>0.001646</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0.001866</td>\n",
       "      <td>0.002665</td>\n",
       "      <td>0.002132</td>\n",
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       "      <td>0.002399</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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       "    <tr>\n",
       "      <th>2017-08-01</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.002711</td>\n",
       "      <td>0.003451</td>\n",
       "      <td>0.005176</td>\n",
       "      <td>0.002958</td>\n",
       "      <td>0.002711</td>\n",
       "      <td>0.001479</td>\n",
       "      <td>0.001479</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2017-09-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.006995</td>\n",
       "      <td>0.005496</td>\n",
       "      <td>0.002748</td>\n",
       "      <td>0.004497</td>\n",
       "      <td>0.002248</td>\n",
       "      <td>0.002248</td>\n",
       "      <td>0.002498</td>\n",
       "      <td>0.002748</td>\n",
       "      <td>0.001749</td>\n",
       "      <td>0.002498</td>\n",
       "      <td>0.000749</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",
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       "    <tr>\n",
       "      <th>2017-10-01</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2017-11-01</th>\n",
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       "      <td>0.003683</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2017-12-01</th>\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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       "      <th>2018-01-01</th>\n",
       "      <td>1.0</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",
       "      <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>2018-02-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003499</td>\n",
       "      <td>0.003976</td>\n",
       "      <td>0.003022</td>\n",
       "      <td>0.002545</td>\n",
       "      <td>0.002226</td>\n",
       "      <td>0.002067</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>2018-03-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003986</td>\n",
       "      <td>0.002952</td>\n",
       "      <td>0.002952</td>\n",
       "      <td>0.001181</td>\n",
       "      <td>0.001181</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>2018-04-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005925</td>\n",
       "      <td>0.003039</td>\n",
       "      <td>0.002431</td>\n",
       "      <td>0.001367</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>2018-05-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005226</td>\n",
       "      <td>0.002613</td>\n",
       "      <td>0.001844</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>2018-06-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.004255</td>\n",
       "      <td>0.002723</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>2018-07-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005214</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>2018-08-01</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_period    0         1         2         3         4         5   \\\n",
       "first_purchase                                                          \n",
       "2016-09-01      1.0       NaN       NaN       NaN       NaN       NaN   \n",
       "2016-10-01      1.0       NaN       NaN       NaN       NaN       NaN   \n",
       "2016-12-01      1.0  1.000000       NaN       NaN       NaN       NaN   \n",
       "2017-01-01      1.0  0.002797  0.002797  0.001399  0.004196  0.001399   \n",
       "2017-02-01      1.0  0.001856  0.003094  0.001238  0.004332  0.001238   \n",
       "2017-03-01      1.0  0.004395  0.003596  0.003995  0.003596  0.001598   \n",
       "2017-04-01      1.0  0.006206  0.002216  0.001773  0.002660  0.002660   \n",
       "2017-05-01      1.0  0.004638  0.004638  0.002899  0.002899  0.003188   \n",
       "2017-06-01      1.0  0.004939  0.003951  0.004281  0.002963  0.003951   \n",
       "2017-07-01      1.0  0.005330  0.003465  0.002399  0.002932  0.002132   \n",
       "2017-08-01      1.0  0.006902  0.003451  0.002711  0.003451  0.005176   \n",
       "2017-09-01      1.0  0.006995  0.005496  0.002748  0.004497  0.002248   \n",
       "2017-10-01      1.0  0.007161  0.002541  0.000924  0.002310  0.002079   \n",
       "2017-11-01      1.0  0.005667  0.003683  0.001700  0.001700  0.001842   \n",
       "2017-12-01      1.0  0.002061  0.002810  0.003372  0.002623  0.002061   \n",
       "2018-01-01      1.0  0.003362  0.003654  0.002923  0.002923  0.001608   \n",
       "2018-02-01      1.0  0.003499  0.003976  0.003022  0.002545  0.002226   \n",
       "2018-03-01      1.0  0.003986  0.002952  0.002952  0.001181  0.001181   \n",
       "2018-04-01      1.0  0.005925  0.003039  0.002431  0.001367       NaN   \n",
       "2018-05-01      1.0  0.005226  0.002613  0.001844       NaN       NaN   \n",
       "2018-06-01      1.0  0.004255  0.002723       NaN       NaN       NaN   \n",
       "2018-07-01      1.0  0.005214       NaN       NaN       NaN       NaN   \n",
       "2018-08-01      1.0       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "cohort_period         6         7         8         9         10        11  \\\n",
       "first_purchase                                                               \n",
       "2016-09-01           NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2016-10-01      0.003817       NaN       NaN  0.003817       NaN  0.003817   \n",
       "2016-12-01           NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2017-01-01      0.004196  0.001399  0.001399       NaN  0.004196  0.001399   \n",
       "2017-02-01      0.002475  0.001856  0.000619  0.001856  0.001238  0.003094   \n",
       "2017-03-01      0.001598  0.003196  0.003196  0.000799  0.003596  0.001199   \n",
       "2017-04-01      0.003546  0.003103  0.003103  0.001773  0.002660  0.000887   \n",
       "2017-05-01      0.004058  0.001449  0.002609  0.002609  0.002609  0.003478   \n",
       "2017-06-01      0.003622  0.002305  0.001317  0.001976  0.002963  0.003622   \n",
       "2017-07-01      0.003198  0.001066  0.001866  0.002665  0.002132  0.002932   \n",
       "2017-08-01      0.002958  0.002711  0.001479  0.001479  0.002465  0.001972   \n",
       "2017-09-01      0.002248  0.002498  0.002748  0.001749  0.002498  0.000749   \n",
       "2017-10-01      0.002079  0.003696  0.002772  0.001848  0.002079       NaN   \n",
       "2017-11-01      0.001133  0.001842  0.001275  0.000567       NaN       NaN   \n",
       "2017-12-01      0.001686  0.000187  0.001873       NaN       NaN       NaN   \n",
       "2018-01-01      0.001754  0.002338       NaN       NaN       NaN       NaN   \n",
       "2018-02-01      0.002067       NaN       NaN       NaN       NaN       NaN   \n",
       "2018-03-01           NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2018-04-01           NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2018-05-01           NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2018-06-01           NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2018-07-01           NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2018-08-01           NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "cohort_period         12        13        14        15        16        17  \\\n",
       "first_purchase                                                               \n",
       "2016-09-01           NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2016-10-01           NaN  0.003817       NaN  0.003817       NaN  0.003817   \n",
       "2016-12-01           NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2017-01-01      0.006993  0.004196  0.001399  0.001399  0.002797  0.004196   \n",
       "2017-02-01      0.001238  0.001856  0.001238  0.000619  0.000619  0.001856   \n",
       "2017-03-01      0.001998  0.001199  0.001598  0.002397  0.000799  0.001199   \n",
       "2017-04-01      0.000443  0.000443  0.000887  0.000887  0.001330       NaN   \n",
       "2017-05-01      0.002319  0.000290  0.001739  0.002029       NaN       NaN   \n",
       "2017-06-01      0.001646  0.001646  0.002305       NaN       NaN       NaN   \n",
       "2017-07-01      0.001333  0.002399       NaN       NaN       NaN       NaN   \n",
       "2017-08-01      0.001232       NaN       NaN       NaN       NaN       NaN   \n",
       "2017-09-01           NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2017-10-01           NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2017-11-01           NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2017-12-01           NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2018-01-01           NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2018-02-01           NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2018-03-01           NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2018-04-01           NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2018-05-01           NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2018-06-01           NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2018-07-01           NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2018-08-01           NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "cohort_period         19        20  \n",
       "first_purchase                      \n",
       "2016-09-01           NaN       NaN  \n",
       "2016-10-01      0.007634  0.007634  \n",
       "2016-12-01           NaN       NaN  \n",
       "2017-01-01      0.001399       NaN  \n",
       "2017-02-01           NaN       NaN  \n",
       "2017-03-01           NaN       NaN  \n",
       "2017-04-01           NaN       NaN  \n",
       "2017-05-01           NaN       NaN  \n",
       "2017-06-01           NaN       NaN  \n",
       "2017-07-01           NaN       NaN  \n",
       "2017-08-01           NaN       NaN  \n",
       "2017-09-01           NaN       NaN  \n",
       "2017-10-01           NaN       NaN  \n",
       "2017-11-01           NaN       NaN  \n",
       "2017-12-01           NaN       NaN  \n",
       "2018-01-01           NaN       NaN  \n",
       "2018-02-01           NaN       NaN  \n",
       "2018-03-01           NaN       NaN  \n",
       "2018-04-01           NaN       NaN  \n",
       "2018-05-01           NaN       NaN  \n",
       "2018-06-01           NaN       NaN  \n",
       "2018-07-01           NaN       NaN  \n",
       "2018-08-01           NaN       NaN  "
      ]
     },
     "execution_count": 323,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retention_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "id": "50290713",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1008 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Рисуем heatmap\n",
    "plt.figure(figsize=(20, 14))\n",
    "plt.title('Retention Rate per Cohorts')\n",
    "sns.heatmap(retention_pivot.iloc[3:15, 0:], annot=True, cmap='crest',\n",
    "            fmt='.2%', linewidths=1, linecolor='white'); "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "368c3226",
   "metadata": {},
   "source": [
    "6. Часто для качественного анализа аудитории использую подходы, основанные на сегментации. Используя python, построй RFM-сегментацию пользователей, чтобы качественно оценить свою аудиторию. В кластеризации можешь выбрать следующие метрики:\n",
    "\n",
    "R - время от последней покупки пользователя до текущей даты, \n",
    "F - суммарное количество покупок у пользователя за всё время, \n",
    "M - сумма покупок за всё время. \n",
    "\n",
    "Подробно опиши, как ты создавал кластеры. Для каждого RFM-сегмента построй границы метрик recency, frequency и monetary для интерпретации этих кластеров. Пример такого описания: RFM-сегмент 132 (recency=1, frequency=3, monetary=2) имеет границы метрик recency от 130 до 500 дней, frequency от 2 до 5 заказов в неделю, monetary от 1780 до 3560 рублей в неделю. Описание подхода можно найти тут. (35 баллов)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96fc8b10",
   "metadata": {},
   "source": [
    "Шаги по решению задачи:\n",
    "    \n",
    "    1. Выберем \"завершённые\" покупки согласно выбранному критерию с наличием все критической информации в данных, заодно, объединив 2 датасета.\n",
    "    \n",
    "    2. Также создадим новый датафрейм, объединив с датафреймом по продукции в заказах.\n",
    "    \n",
    "    3. Оставим только необходимые колонки для дальнейших расчётов. Это колонки: order_id, order_purchase_timestamp, customer_unique_id, product_id, price.\n",
    "    \n",
    "    4. Поскольку данные приведены за 2016-2018 годы, то расчёт времени от последней покупки пользователей до текущей даты в 2022 году даст \"утяжелённые\" числа и увеличит время расчётов. Предположим, что текущая дата это последняя дата покупки. При необходимости это допущение можно легко заменить на текущую дату и на RFM анализ наше допущение не влияет.\n",
    "    \n",
    "    5. Создаём таблицу RFM согласно выбранным метрикам и переименовываем столбцы.\n",
    "    \n",
    "    6. Рассчитываем квантили для последующей разбивки по сегментам.\n",
    "    \n",
    "    7. Создаём структуру сегментов (кластеров) с нумераций по каждой метрике.\n",
    "    \n",
    "    8. Проводим анализ по разным запросам."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "id": "b7459692",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 1.\n",
    "df_orders_clean=df_orders.merge(df_users, on='customer_id')\\\n",
    "         .query('order_status==\"delivered\"')\\\n",
    "         .loc[df_orders.order_delivered_customer_date.notnull()]\\\n",
    "         .loc[df_orders.order_approved_at.notnull()]\\\n",
    "         .loc[df_orders.order_delivered_carrier_date.notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "id": "a01fe5fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 2.\n",
    "df_orders_full=df_orders_clean.merge(df_products, how='left', on='order_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "id": "0e3235f4",
   "metadata": {},
   "outputs": [
    {
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       "                           order_id                       customer_id  \\\n",
       "0  e481f51cbdc54678b7cc49136f2d6af7  9ef432eb6251297304e76186b10a928d   \n",
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       "\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",
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       "\n",
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       "0                      3149                sao paulo             SP   \n",
       "1                     47813                barreiras             BA   \n",
       "2                     75265               vianopolis             GO   \n",
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       "4                      9195              santo andre             SP   \n",
       "\n",
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       "0              1  87285b34884572647811a353c7ac498a   \n",
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       "4              1  65266b2da20d04dbe00c5c2d3bb7859e   \n",
       "\n",
       "                          seller_id shipping_limit_date   price  freight_value  \n",
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       "4  2c9e548be18521d1c43cde1c582c6de8 2018-02-19 20:31:37   19.90           8.72  "
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orders_full.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "0f7cab53",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 110173 entries, 0 to 110172\n",
      "Data columns (total 18 columns):\n",
      " #   Column                         Non-Null Count   Dtype         \n",
      "---  ------                         --------------   -----         \n",
      " 0   order_id                       110173 non-null  object        \n",
      " 1   customer_id                    110173 non-null  object        \n",
      " 2   order_status                   110173 non-null  object        \n",
      " 3   order_purchase_timestamp       110173 non-null  datetime64[ns]\n",
      " 4   order_approved_at              110173 non-null  datetime64[ns]\n",
      " 5   order_delivered_carrier_date   110173 non-null  datetime64[ns]\n",
      " 6   order_delivered_customer_date  110173 non-null  datetime64[ns]\n",
      " 7   order_estimated_delivery_date  110173 non-null  datetime64[ns]\n",
      " 8   customer_unique_id             110173 non-null  object        \n",
      " 9   customer_zip_code_prefix       110173 non-null  int64         \n",
      " 10  customer_city                  110173 non-null  object        \n",
      " 11  customer_state                 110173 non-null  object        \n",
      " 12  order_item_id                  110173 non-null  int64         \n",
      " 13  product_id                     110173 non-null  object        \n",
      " 14  seller_id                      110173 non-null  object        \n",
      " 15  shipping_limit_date            110173 non-null  datetime64[ns]\n",
      " 16  price                          110173 non-null  float64       \n",
      " 17  freight_value                  110173 non-null  float64       \n",
      "dtypes: datetime64[ns](6), float64(2), int64(2), object(8)\n",
      "memory usage: 16.0+ MB\n"
     ]
    }
   ],
   "source": [
    "# check for NaNs etc.\n",
    "df_orders_full.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "id": "6b57cbe8",
   "metadata": {},
   "outputs": [
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       "                           order_id order_purchase_timestamp  \\\n",
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       "1  af07308b275d755c9edb36a90c618231  595fac2a385ac33a80bd5114aec74eb8  118.70  \n",
       "2  3a653a41f6f9fc3d2a113cf8398680e8  aa4383b373c6aca5d8797843e5594415  159.90  \n",
       "3  7c142cf63193a1473d2e66489a9ae977  d0b61bfb1de832b15ba9d266ca96e5b0   45.00  \n",
       "4  72632f0f9dd73dfee390c9b22eb56dd6  65266b2da20d04dbe00c5c2d3bb7859e   19.90  "
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 3.\n",
    "temp=['order_id', 'order_purchase_timestamp', 'customer_unique_id', 'product_id', 'price']\n",
    "RFM_data=df_orders_full[temp]\n",
    "RFM_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "id": "4b49b89c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2018-08-29 15:00:37')"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 4.\n",
    "NOW = RFM_data.order_purchase_timestamp.max()\n",
    "NOW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "id": "7419ad80",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 5.\n",
    "RFM_table = RFM_data.groupby('customer_unique_id').agg({'order_purchase_timestamp': lambda x: (NOW - x.max()).days,\n",
    "                                                        'order_id': lambda x: len(x.unique()),\n",
    "                                                        'price': lambda x: x.sum()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "id": "a3a4ce0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary_value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_unique_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0000366f3b9a7992bf8c76cfdf3221e2</th>\n",
       "      <td>111</td>\n",
       "      <td>1</td>\n",
       "      <td>129.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0000b849f77a49e4a4ce2b2a4ca5be3f</th>\n",
       "      <td>114</td>\n",
       "      <td>1</td>\n",
       "      <td>18.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0000f46a3911fa3c0805444483337064</th>\n",
       "      <td>536</td>\n",
       "      <td>1</td>\n",
       "      <td>69.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0000f6ccb0745a6a4b88665a16c9f078</th>\n",
       "      <td>320</td>\n",
       "      <td>1</td>\n",
       "      <td>25.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0004aac84e0df4da2b147fca70cf8255</th>\n",
       "      <td>287</td>\n",
       "      <td>1</td>\n",
       "      <td>180.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  recency  frequency  monetary_value\n",
       "customer_unique_id                                                  \n",
       "0000366f3b9a7992bf8c76cfdf3221e2      111          1          129.90\n",
       "0000b849f77a49e4a4ce2b2a4ca5be3f      114          1           18.90\n",
       "0000f46a3911fa3c0805444483337064      536          1           69.00\n",
       "0000f6ccb0745a6a4b88665a16c9f078      320          1           25.99\n",
       "0004aac84e0df4da2b147fca70cf8255      287          1          180.00"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFM_table['order_purchase_timestamp'] = RFM_table['order_purchase_timestamp'].astype(int)\n",
    "\n",
    "RFM_table.rename(columns={'order_purchase_timestamp': 'recency', \n",
    "                         'order_id': 'frequency',\n",
    "                         'price': 'monetary_value'}, inplace=True)\n",
    "RFM_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "id": "7e537f55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>113.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>47.6500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>218.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>89.7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <td>345.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>154.6925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      recency  frequency  monetary_value\n",
       "0.25    113.0        1.0         47.6500\n",
       "0.50    218.0        1.0         89.7000\n",
       "0.75    345.0        1.0        154.6925"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 6.\n",
    "quantiles = RFM_table.quantile(q=[0.25,0.5,0.75])\n",
    "quantiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "id": "64abb815",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 7.\n",
    "# Преобразуем в словарь для более удобного использования.\n",
    "quantiles = quantiles.to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "id": "d7f97c57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'recency': {0.25: 113.0, 0.5: 218.0, 0.75: 345.0},\n",
       " 'frequency': {0.25: 1.0, 0.5: 1.0, 0.75: 1.0},\n",
       " 'monetary_value': {0.25: 47.65, 0.5: 89.7, 0.75: 154.6925}}"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quantiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "id": "bb0ffa27",
   "metadata": {},
   "outputs": [],
   "source": [
    "##  Создаём функции для RFM сегментация\n",
    "RFM_Segment = RFM_table.copy()\n",
    "# Аргументы (x = value, p = recency, monetary_value, frequency, k = quartiles dict)\n",
    "def R_Class(x,p,d):\n",
    "    if x <= d[p][0.25]:\n",
    "        return 4\n",
    "    elif x <= d[p][0.50]:\n",
    "        return 3\n",
    "    elif x <= d[p][0.75]: \n",
    "        return 2\n",
    "    else:\n",
    "        return 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "id": "934f868a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Arguments (x = value, p = recency, monetary_value, frequency, k = quartiles dict)\n",
    "def FM_Class(x,p,d):\n",
    "    if x <= d[p][0.25]:\n",
    "        return 1\n",
    "    elif x <= d[p][0.50]:\n",
    "        return 2\n",
    "    elif x <= d[p][0.75]: \n",
    "        return 3\n",
    "    else:\n",
    "        return 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "id": "3312820a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Сегментируем\n",
    "RFM_Segment['R_Quartile'] = RFM_Segment['recency'].apply(R_Class, args=('recency',quantiles,))\n",
    "RFM_Segment['F_Quartile'] = RFM_Segment['frequency'].apply(FM_Class, args=('frequency',quantiles,))\n",
    "RFM_Segment['M_Quartile'] = RFM_Segment['monetary_value'].apply(FM_Class, args=('monetary_value',quantiles,))\n",
    "RFM_Segment['RFMClass'] = RFM_Segment.R_Quartile.map(str) \\\n",
    "                            + RFM_Segment.F_Quartile.map(str) \\\n",
    "                            + RFM_Segment.M_Quartile.map(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "id": "c6e3212a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>frequency</th>\n",
       "      <th>monetary_value</th>\n",
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       "    <tr>\n",
       "      <th>customer_unique_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>0000366f3b9a7992bf8c76cfdf3221e2</th>\n",
       "      <td>111</td>\n",
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       "      <td>4</td>\n",
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       "      <td>1</td>\n",
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       "      <td>112</td>\n",
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       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0004aac84e0df4da2b147fca70cf8255</th>\n",
       "      <td>287</td>\n",
       "      <td>1</td>\n",
       "      <td>180.00</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": [
       "                                  recency  frequency  monetary_value  \\\n",
       "customer_unique_id                                                     \n",
       "0000366f3b9a7992bf8c76cfdf3221e2      111          1          129.90   \n",
       "0000b849f77a49e4a4ce2b2a4ca5be3f      114          1           18.90   \n",
       "0000f46a3911fa3c0805444483337064      536          1           69.00   \n",
       "0000f6ccb0745a6a4b88665a16c9f078      320          1           25.99   \n",
       "0004aac84e0df4da2b147fca70cf8255      287          1          180.00   \n",
       "\n",
       "                                  R_Quartile  F_Quartile  M_Quartile RFMClass  \n",
       "customer_unique_id                                                             \n",
       "0000366f3b9a7992bf8c76cfdf3221e2           4           1           3      413  \n",
       "0000b849f77a49e4a4ce2b2a4ca5be3f           3           1           1      311  \n",
       "0000f46a3911fa3c0805444483337064           1           1           2      112  \n",
       "0000f6ccb0745a6a4b88665a16c9f078           2           1           1      211  \n",
       "0004aac84e0df4da2b147fca70cf8255           2           1           4      214  "
      ]
     },
     "execution_count": 365,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFM_Segment.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb0192f5",
   "metadata": {},
   "source": [
    "#  STEP 8. ПРОВОДИМ АНАЛИЗ ПО РАЗНЫМ ЗАПРОСАМ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6f891fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "Список лучших клиентов (RFMClass = 444):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "id": "729c90f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary_value</th>\n",
       "      <th>R_Quartile</th>\n",
       "      <th>F_Quartile</th>\n",
       "      <th>M_Quartile</th>\n",
       "      <th>RFMClass</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_unique_id</th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>c8460e4251689ba205045f3ea17884a1</th>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>4080.00</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
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       "      <th>7b0eaf68a16e4808e5388c67345033c9</th>\n",
       "      <td>102</td>\n",
       "      <td>2</td>\n",
       "      <td>2238.42</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>906a8a4ec9f3d4c3e64fa6d1c4fe6009</th>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>1835.00</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0341bbd5c969923a0f801b9e2d10a7b8</th>\n",
       "      <td>91</td>\n",
       "      <td>2</td>\n",
       "      <td>1828.44</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4facc2e6fbc2bffab2fea92d2b4aa7e4</th>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>1686.90</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334d0235ca6f2f09dd55804a26efe952</th>\n",
       "      <td>84</td>\n",
       "      <td>2</td>\n",
       "      <td>157.80</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7cf08f01ab6a6a1365e1613989a65158</th>\n",
       "      <td>112</td>\n",
       "      <td>2</td>\n",
       "      <td>156.90</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221d59abddcd719f3ec72c50078b9e31</th>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>156.00</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91352e06066eef42b60b24049ccde4fd</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>155.90</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>22</td>\n",
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       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>495 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  recency  frequency  monetary_value  \\\n",
       "customer_unique_id                                                     \n",
       "c8460e4251689ba205045f3ea17884a1       21          4         4080.00   \n",
       "7b0eaf68a16e4808e5388c67345033c9      102          2         2238.42   \n",
       "906a8a4ec9f3d4c3e64fa6d1c4fe6009       40          2         1835.00   \n",
       "0341bbd5c969923a0f801b9e2d10a7b8       91          2         1828.44   \n",
       "4facc2e6fbc2bffab2fea92d2b4aa7e4       15          4         1686.90   \n",
       "...                                   ...        ...             ...   \n",
       "334d0235ca6f2f09dd55804a26efe952       84          2          157.80   \n",
       "7cf08f01ab6a6a1365e1613989a65158      112          2          156.90   \n",
       "221d59abddcd719f3ec72c50078b9e31       35          2          156.00   \n",
       "91352e06066eef42b60b24049ccde4fd        6          2          155.90   \n",
       "dd20866f2a6d0f83a61cf8887a09528d       22          2          155.80   \n",
       "\n",
       "                                  R_Quartile  F_Quartile  M_Quartile RFMClass  \n",
       "customer_unique_id                                                             \n",
       "c8460e4251689ba205045f3ea17884a1           4           4           4      444  \n",
       "7b0eaf68a16e4808e5388c67345033c9           4           4           4      444  \n",
       "906a8a4ec9f3d4c3e64fa6d1c4fe6009           4           4           4      444  \n",
       "0341bbd5c969923a0f801b9e2d10a7b8           4           4           4      444  \n",
       "4facc2e6fbc2bffab2fea92d2b4aa7e4           4           4           4      444  \n",
       "...                                      ...         ...         ...      ...  \n",
       "334d0235ca6f2f09dd55804a26efe952           4           4           4      444  \n",
       "7cf08f01ab6a6a1365e1613989a65158           4           4           4      444  \n",
       "221d59abddcd719f3ec72c50078b9e31           4           4           4      444  \n",
       "91352e06066eef42b60b24049ccde4fd           4           4           4      444  \n",
       "dd20866f2a6d0f83a61cf8887a09528d           4           4           4      444  \n",
       "\n",
       "[495 rows x 7 columns]"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFM_Segment.query('RFMClass==\"444\"').sort_values('monetary_value', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e703f8e6",
   "metadata": {},
   "source": [
    "Список лояльных клиенты с высоким frequency    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "id": "bf428df8",
   "metadata": {},
   "outputs": [
    {
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       "      <th>d77aa95864ae5b42160937615628723a</th>\n",
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       "      <td>2400.00</td>\n",
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       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>144</td>\n",
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       "      <th>7b0eaf68a16e4808e5388c67345033c9</th>\n",
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       "      <td>4</td>\n",
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       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
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       "      <th>728e4a7d0db4845270091ded0923c71f</th>\n",
       "      <td>601</td>\n",
       "      <td>3</td>\n",
       "      <td>20.70</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
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       "<p>2800 rows × 7 columns</p>\n",
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       "                                  recency  frequency  monetary_value  \\\n",
       "customer_unique_id                                                     \n",
       "da122df9eeddfedc1dc1f5349a1a690c      514          2         7388.00   \n",
       "c8460e4251689ba205045f3ea17884a1       21          4         4080.00   \n",
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       "d77aa95864ae5b42160937615628723a      364          2         2400.00   \n",
       "7b0eaf68a16e4808e5388c67345033c9      102          2         2238.42   \n",
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       "dd7b0e9b51e5377bd9c1fd3e286c7b9d      184          2           11.98   \n",
       "\n",
       "                                  R_Quartile  F_Quartile  M_Quartile RFMClass  \n",
       "customer_unique_id                                                             \n",
       "da122df9eeddfedc1dc1f5349a1a690c           1           4           4      144  \n",
       "c8460e4251689ba205045f3ea17884a1           4           4           4      444  \n",
       "59d66d72939bc9497e19d89c61a96d5f           1           4           4      144  \n",
       "d77aa95864ae5b42160937615628723a           1           4           4      144  \n",
       "7b0eaf68a16e4808e5388c67345033c9           4           4           4      444  \n",
       "...                                      ...         ...         ...      ...  \n",
       "4fe67915d44791a5deab2957c2641048           1           4           1      141  \n",
       "728e4a7d0db4845270091ded0923c71f           1           4           1      141  \n",
       "031e19fc630c4121f1238716f41675c3           4           4           1      441  \n",
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       "dd7b0e9b51e5377bd9c1fd3e286c7b9d           3           4           1      341  \n",
       "\n",
       "[2800 rows x 7 columns]"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFM_Segment.query('F_Quartile >= 4').sort_values('monetary_value', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a42dae0f",
   "metadata": {},
   "source": [
    "Какие клиенты давно делали заказы и, возможно, перестанут делать заказы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "id": "8a91196f",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "                                  recency  frequency  monetary_value  \\\n",
       "customer_unique_id                                                     \n",
       "da122df9eeddfedc1dc1f5349a1a690c      514          2         7388.00   \n",
       "dc4802a71eae9be1dd28f5d788ceb526      562          1         6735.00   \n",
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       "6f5b9d1cdccc4d28f0483a612edecacf      360          1            3.85   \n",
       "bd06ce0e06ad77a7f681f1a4960a3cc6      349          1            2.29   \n",
       "\n",
       "                                  R_Quartile  F_Quartile  M_Quartile RFMClass  \n",
       "customer_unique_id                                                             \n",
       "da122df9eeddfedc1dc1f5349a1a690c           1           4           4      144  \n",
       "dc4802a71eae9be1dd28f5d788ceb526           1           1           4      114  \n",
       "ff4159b92c40ebe40454e3e6a7c35ed6           1           1           4      114  \n",
       "eebb5dda148d3893cdaf5b5ca3040ccb           1           1           4      114  \n",
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       "...                                      ...         ...         ...      ...  \n",
       "4079d39cb3fdea47201540107c1673dc           1           1           1      111  \n",
       "2bca5fe01d46ab5cc2bc5aaade88d850           1           1           1      111  \n",
       "5a1b7cd1a94f3483b6415f8609db83dc           1           1           1      111  \n",
       "6f5b9d1cdccc4d28f0483a612edecacf           1           1           1      111  \n",
       "bd06ce0e06ad77a7f681f1a4960a3cc6           1           1           1      111  \n",
       "\n",
       "[23297 rows x 7 columns]"
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFM_Segment.query('R_Quartile<=1').sort_values('monetary_value', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4303ee3b",
   "metadata": {},
   "source": [
    "Список с низкими показателями, которые, возможно, окончательно, потеряны"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "id": "51ee48db",
   "metadata": {},
   "outputs": [
    {
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       "      <th>frequency</th>\n",
       "      <th>monetary_value</th>\n",
       "      <th>R_Quartile</th>\n",
       "      <th>F_Quartile</th>\n",
       "      <th>M_Quartile</th>\n",
       "      <th>RFMClass</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_unique_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2f64e403852e6893ae37485d5fcacdaf</th>\n",
       "      <td>694</td>\n",
       "      <td>1</td>\n",
       "      <td>21.90</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14359ea0c7a105749c0a56478825b015</th>\n",
       "      <td>694</td>\n",
       "      <td>1</td>\n",
       "      <td>32.49</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61db744d2f835035a5625b59350c6b63</th>\n",
       "      <td>694</td>\n",
       "      <td>1</td>\n",
       "      <td>36.49</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f922896769e9517ea3c630f3c8de86d0</th>\n",
       "      <td>694</td>\n",
       "      <td>1</td>\n",
       "      <td>39.99</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3f4f614c632af7fc7508462a7cb55ac2</th>\n",
       "      <td>694</td>\n",
       "      <td>1</td>\n",
       "      <td>9.90</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>df28a378659710f7afb9ab93f642db3d</th>\n",
       "      <td>346</td>\n",
       "      <td>1</td>\n",
       "      <td>23.99</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d401a17e88e606a3ce275061995a2074</th>\n",
       "      <td>346</td>\n",
       "      <td>1</td>\n",
       "      <td>12.90</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127f8f62505196f9181003fb59ae9e9d</th>\n",
       "      <td>346</td>\n",
       "      <td>1</td>\n",
       "      <td>29.90</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9a628d6b0699f11c823f218be04bf1cb</th>\n",
       "      <td>346</td>\n",
       "      <td>1</td>\n",
       "      <td>19.90</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973629b00306fca6592d2726d375dba4</th>\n",
       "      <td>346</td>\n",
       "      <td>1</td>\n",
       "      <td>16.50</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5952 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  recency  frequency  monetary_value  \\\n",
       "customer_unique_id                                                     \n",
       "2f64e403852e6893ae37485d5fcacdaf      694          1           21.90   \n",
       "14359ea0c7a105749c0a56478825b015      694          1           32.49   \n",
       "61db744d2f835035a5625b59350c6b63      694          1           36.49   \n",
       "f922896769e9517ea3c630f3c8de86d0      694          1           39.99   \n",
       "3f4f614c632af7fc7508462a7cb55ac2      694          1            9.90   \n",
       "...                                   ...        ...             ...   \n",
       "df28a378659710f7afb9ab93f642db3d      346          1           23.99   \n",
       "d401a17e88e606a3ce275061995a2074      346          1           12.90   \n",
       "127f8f62505196f9181003fb59ae9e9d      346          1           29.90   \n",
       "9a628d6b0699f11c823f218be04bf1cb      346          1           19.90   \n",
       "973629b00306fca6592d2726d375dba4      346          1           16.50   \n",
       "\n",
       "                                  R_Quartile  F_Quartile  M_Quartile RFMClass  \n",
       "customer_unique_id                                                             \n",
       "2f64e403852e6893ae37485d5fcacdaf           1           1           1      111  \n",
       "14359ea0c7a105749c0a56478825b015           1           1           1      111  \n",
       "61db744d2f835035a5625b59350c6b63           1           1           1      111  \n",
       "f922896769e9517ea3c630f3c8de86d0           1           1           1      111  \n",
       "3f4f614c632af7fc7508462a7cb55ac2           1           1           1      111  \n",
       "...                                      ...         ...         ...      ...  \n",
       "df28a378659710f7afb9ab93f642db3d           1           1           1      111  \n",
       "d401a17e88e606a3ce275061995a2074           1           1           1      111  \n",
       "127f8f62505196f9181003fb59ae9e9d           1           1           1      111  \n",
       "9a628d6b0699f11c823f218be04bf1cb           1           1           1      111  \n",
       "973629b00306fca6592d2726d375dba4           1           1           1      111  \n",
       "\n",
       "[5952 rows x 7 columns]"
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFM_Segment.query('RFMClass==\"111\"').sort_values('recency',ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "299a3834",
   "metadata": {},
   "source": [
    "TOP 10 клиентов, которые принесли больше всего денег и сделали больше всех заказов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "id": "01239680",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</div>"
      ],
      "text/plain": [
       "                                  recency  frequency  monetary_value  \\\n",
       "customer_unique_id                                                     \n",
       "da122df9eeddfedc1dc1f5349a1a690c      514          2         7388.00   \n",
       "c8460e4251689ba205045f3ea17884a1       21          4         4080.00   \n",
       "59d66d72939bc9497e19d89c61a96d5f      383          2         3459.00   \n",
       "d77aa95864ae5b42160937615628723a      364          2         2400.00   \n",
       "7b0eaf68a16e4808e5388c67345033c9      102          2         2238.42   \n",
       "86df00dc5fd68f4dd5d5945ca19f3ed6      319          3         2200.00   \n",
       "6ddbc64bd04d40f7768ff088d94cbeb8      141          2         2108.00   \n",
       "73601b1eec55943e90ce8d61253d5c09      301          2         2009.98   \n",
       "1da09dd64e235e7c2f29a4faff33535c      230          3         1980.28   \n",
       "906a8a4ec9f3d4c3e64fa6d1c4fe6009       40          2         1835.00   \n",
       "\n",
       "                                  R_Quartile  F_Quartile  M_Quartile RFMClass  \n",
       "customer_unique_id                                                             \n",
       "da122df9eeddfedc1dc1f5349a1a690c           1           4           4      144  \n",
       "c8460e4251689ba205045f3ea17884a1           4           4           4      444  \n",
       "59d66d72939bc9497e19d89c61a96d5f           1           4           4      144  \n",
       "d77aa95864ae5b42160937615628723a           1           4           4      144  \n",
       "7b0eaf68a16e4808e5388c67345033c9           4           4           4      444  \n",
       "86df00dc5fd68f4dd5d5945ca19f3ed6           2           4           4      244  \n",
       "6ddbc64bd04d40f7768ff088d94cbeb8           3           4           4      344  \n",
       "73601b1eec55943e90ce8d61253d5c09           2           4           4      244  \n",
       "1da09dd64e235e7c2f29a4faff33535c           2           4           4      244  \n",
       "906a8a4ec9f3d4c3e64fa6d1c4fe6009           4           4           4      444  "
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFM_Segment.query('M_Quartile >= 4 & F_Quartile >=4').sort_values('monetary_value', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50fa1908",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "750f7e1d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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