e-commerce_project_aeremina.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Проект e-commerce: вариант 1 \n",
    "# Продакт-менеджер Василий попросил вас проанализировать совершенные покупки и ответить на следующие вопросы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Импортируем библиотеки\n",
    "import pandas as pd\n",
    "import requests\n",
    "from urllib.parse import urlencode\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "from datetime import timedelta\n",
    "from operator import attrgetter\n",
    "\n",
    "# Для визуализации графика в юпитерхабе\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Загружаем датасеты по ссылке на яндекс диске\n",
    "base_url = 'https://cloud-api.yandex.net/v1/disk/public/resources/download?'\n",
    "\n",
    "# Ссылки на наши датасеты на яндекс диске\n",
    "public_key1 = 'https://yadi.sk/d/QXrVDUoQvFhIcg' \n",
    "public_key2 = 'https://yadi.sk/d/0zWmh1bYP6REbw'\n",
    "public_key3 = 'https://yadi.sk/d/xCsQ0FiC-Ue4Lg'\n",
    "\n",
    "# Получаем загрузочные ссылки\n",
    "final_url1 = base_url + urlencode(dict(public_key = public_key1))\n",
    "final_url2 = base_url + urlencode(dict(public_key = public_key2))\n",
    "final_url3 = base_url + urlencode(dict(public_key = public_key3))\n",
    "\n",
    "response1 = requests.get(final_url1)\n",
    "response2 = requests.get(final_url2)\n",
    "response3 = requests.get(final_url3)\n",
    "\n",
    "download_url1 = response1.json()['href']\n",
    "download_url2 = response2.json()['href']\n",
    "download_url3 = response3.json()['href']\n",
    "\n",
    "# Загружаем файлы в df \n",
    "download_response1 = requests.get(download_url1) \n",
    "download_response2 = requests.get(download_url2) \n",
    "download_response3 = requests.get(download_url3) \n",
    "\n",
    "customers   = pd.read_csv(download_url1, sep = ',') \n",
    "orders      = pd.read_csv(download_url2, sep = ',') \n",
    "order_items = pd.read_csv(download_url3, sep = ',') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Взглянем на наши df и проверим их на пропуски:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <td>14409</td>\n",
       "      <td>franca</td>\n",
       "      <td>SP</td>\n",
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       "    <tr>\n",
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       "      <td>18955e83d337fd6b2def6b18a428ac77</td>\n",
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       "      <td>sao bernardo do campo</td>\n",
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       "    <tr>\n",
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       "      <td>1151</td>\n",
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       "      <td>345ecd01c38d18a9036ed96c73b8d066</td>\n",
       "      <td>13056</td>\n",
       "      <td>campinas</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                        customer_id                customer_unique_id  \\\n",
       "0  06b8999e2fba1a1fbc88172c00ba8bc7  861eff4711a542e4b93843c6dd7febb0   \n",
       "1  18955e83d337fd6b2def6b18a428ac77  290c77bc529b7ac935b93aa66c333dc3   \n",
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       "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Таблица с уникальными идентификаторами пользователей:\n",
    "# customer_id — позаказный идентификатор пользователя\n",
    "# customer_unique_id —  уникальный идентификатор пользователя (аналог номера паспорта)\n",
    "# customer_zip_code_prefix — почтовый индекс пользователя\n",
    "# customer_city —  город доставки пользователя\n",
    "# customer_state —  штат доставки пользователя\n",
    "\n",
    "customers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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 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": [
    "customers.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Пропущенных значений нет"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <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",
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       "      <td>2018-08-17 18:06:29</td>\n",
       "      <td>2018-09-04 00:00:00</td>\n",
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      "text/plain": [
       "                           order_id                       customer_id  \\\n",
       "0  e481f51cbdc54678b7cc49136f2d6af7  9ef432eb6251297304e76186b10a928d   \n",
       "1  53cdb2fc8bc7dce0b6741e2150273451  b0830fb4747a6c6d20dea0b8c802d7ef   \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  \n",
       "0           2017-10-18 00:00:00  \n",
       "1           2018-08-13 00:00:00  \n",
       "2           2018-09-04 00:00:00  \n",
       "3           2017-12-15 00:00:00  \n",
       "4           2018-02-26 00:00:00  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Таблица заказов:\n",
    "# order_id —  уникальный идентификатор заказа (номер чека)\n",
    "# customer_id —  позаказный идентификатор пользователя\n",
    "# order_status —  статус заказа\n",
    "# order_purchase_timestamp —  время создания заказа\n",
    "# order_approved_at —  время подтверждения оплаты заказа\n",
    "# order_delivered_carrier_date —  время передачи заказа в логистическую службу\n",
    "# order_delivered_customer_date —  время доставки заказа\n",
    "# order_estimated_delivery_date —  обещанная дата доставки\n",
    "\n",
    "orders.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "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  object\n",
      " 4   order_approved_at              99281 non-null  object\n",
      " 5   order_delivered_carrier_date   97658 non-null  object\n",
      " 6   order_delivered_customer_date  96476 non-null  object\n",
      " 7   order_estimated_delivery_date  99441 non-null  object\n",
      "dtypes: object(8)\n",
      "memory usage: 6.1+ MB\n"
     ]
    }
   ],
   "source": [
    "orders.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Есть пропуски в колонках: \n",
    "- order_approved_at (т.е. заказ не был оплачен)\n",
    "- order_delivered_carrier_date (заказ не был передан в логистическую службу) \n",
    "- order_delivered_customer_date (заказ не был доставлен)\n",
    "\n",
    "Это означает, что не все заказы успешно завершены"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "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": [
    "# Исправим некорректный для дат тип данных\n",
    "orders[['order_purchase_timestamp', 'order_approved_at', 'order_delivered_carrier_date', 'order_delivered_customer_date', \n",
    "        'order_estimated_delivery_date']] = orders[['order_purchase_timestamp', 'order_approved_at', \n",
    "                                                    'order_delivered_carrier_date', 'order_delivered_customer_date', \n",
    "                                                    'order_estimated_delivery_date']].apply(pd.to_datetime )\n",
    "orders.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>order_item_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>shipping_limit_date</th>\n",
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       "      <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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Товарные позиции, входящие в заказы:\n",
    "# order_id —  уникальный идентификатор заказа (номер чека)\n",
    "# order_item_id —  идентификатор товара внутри одного заказа\n",
    "# product_id —  ид товара (аналог штрихкода)\n",
    "# seller_id — ид производителя товара\n",
    "# shipping_limit_date —  максимальная дата доставки продавцом для передачи заказа партнеру по логистике\n",
    "# price —  цена за единицу товара\n",
    "# freight_value —  вес товара\n",
    "\n",
    "order_items.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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  object \n",
      " 5   price                112650 non-null  float64\n",
      " 6   freight_value        112650 non-null  float64\n",
      "dtypes: float64(2), int64(1), object(4)\n",
      "memory usage: 6.0+ MB\n"
     ]
    }
   ],
   "source": [
    "order_items.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Пропущенных значений нет"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "# Исправим некорректный для дат тип данных\n",
    "order_items[['shipping_limit_date']] = order_items[['shipping_limit_date']].apply(pd.to_datetime )\n",
    "order_items.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Для решения задачи проведи предварительное исследование данных и сформулируй, что должно считаться покупкой. \n",
    "Обосновать свой выбор ты можешь с помощью фактов оплат, статусов заказов и других имеющихся данных.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['delivered', 'invoiced', 'shipped', 'processing', 'unavailable',\n",
       "       'canceled', 'approved'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Можно предположить, что заказы, имеющие значение order_approved_at —  время подтверждения оплаты заказа, считаются покупкой\n",
    "# Проверим статусы таких заказов\n",
    "orders.query('order_approved_at != \"NaN\"').order_status.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видим, что заказы со всеми имеющимися статусами, кроме \"created\", имеют дату оплаты\n",
    "Делаем вывод, что такое предположение не совсем корректно, разве можно считать покупкой заказ со статусом \"unavailable\"?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['canceled', 'delivered', 'created'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Проверим, заказы с какими статусами не имеют значения в колонке order_approved_at\n",
    "orders.query('order_approved_at == \"NaN\"').order_status.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Со статусами \"закрыт\" и \"создан\" понятно, их за покупку точно не считаем,\n",
    "но как доставленный заказ (\"delivered\") может не содержать дату оплаты?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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",
       "      <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>e04abd8149ef81b95221e88f6ed9ab6a</td>\n",
       "      <td>2127dc6603ac33544953ef05ec155771</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-02-18 14:40:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2017-02-23 12:04:47</td>\n",
       "      <td>2017-03-01 13:25:33</td>\n",
       "      <td>2017-03-17</td>\n",
       "      <td>1</td>\n",
       "      <td>0e20a07ca1714df21f9b07ca3bf7c682</td>\n",
       "      <td>4e7c18b98d84e05cbae3ff0ff03846c2</td>\n",
       "      <td>2017-02-22 13:40:00</td>\n",
       "      <td>309.90</td>\n",
       "      <td>39.11</td>\n",
       "      <td>8a9a08c7ca8900a200d83cf838a07e0b</td>\n",
       "      <td>6708</td>\n",
       "      <td>cotia</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8a9adc69528e1001fc68dd0aaebbb54a</td>\n",
       "      <td>4c1ccc74e00993733742a3c786dc3c1f</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-02-18 12:45:31</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2017-02-23 09:01:52</td>\n",
       "      <td>2017-03-02 10:05:06</td>\n",
       "      <td>2017-03-21</td>\n",
       "      <td>1</td>\n",
       "      <td>2c2b6a28924791234bd386bddb17512e</td>\n",
       "      <td>a4b6b9b992b46e9ef863637af96e04bc</td>\n",
       "      <td>2017-02-22 11:45:31</td>\n",
       "      <td>379.00</td>\n",
       "      <td>17.86</td>\n",
       "      <td>91efb7fcabc17925099dced52435837f</td>\n",
       "      <td>93548</td>\n",
       "      <td>novo hamburgo</td>\n",
       "      <td>RS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7013bcfc1c97fe719a7b5e05e61c12db</td>\n",
       "      <td>2941af76d38100e0f8740a374f1a5dc3</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-02-18 13:29:47</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2017-02-22 16:25:25</td>\n",
       "      <td>2017-03-01 08:07:38</td>\n",
       "      <td>2017-03-17</td>\n",
       "      <td>1</td>\n",
       "      <td>583916a5dae918f5e89baec139141c54</td>\n",
       "      <td>3481aa57cd91f9f9d3fa1fa12d9a3bf7</td>\n",
       "      <td>2017-02-22 12:29:47</td>\n",
       "      <td>49.99</td>\n",
       "      <td>15.53</td>\n",
       "      <td>e1f01a1bd6485e58ad3c769a5427d8a8</td>\n",
       "      <td>8230</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5cf925b116421afa85ee25e99b4c34fb</td>\n",
       "      <td>29c35fc91fc13fb5073c8f30505d860d</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-02-18 16:48:35</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2017-02-22 11:23:10</td>\n",
       "      <td>2017-03-09 07:28:47</td>\n",
       "      <td>2017-03-31</td>\n",
       "      <td>1</td>\n",
       "      <td>c6dd917a0be2a704582055949915ab32</td>\n",
       "      <td>7a67c85e85bb2ce8582c35f2203ad736</td>\n",
       "      <td>2017-02-22 15:48:35</td>\n",
       "      <td>79.99</td>\n",
       "      <td>26.82</td>\n",
       "      <td>7e1a5ca61b572d76b64b6688b9f96473</td>\n",
       "      <td>62700</td>\n",
       "      <td>caninde</td>\n",
       "      <td>CE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12a95a3c06dbaec84bcfb0e2da5d228a</td>\n",
       "      <td>1e101e0daffaddce8159d25a8e53f2b2</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-02-17 13:05:55</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2017-02-22 11:23:11</td>\n",
       "      <td>2017-03-02 11:09:19</td>\n",
       "      <td>2017-03-20</td>\n",
       "      <td>1</td>\n",
       "      <td>c6dd917a0be2a704582055949915ab32</td>\n",
       "      <td>7a67c85e85bb2ce8582c35f2203ad736</td>\n",
       "      <td>2017-02-21 12:05:55</td>\n",
       "      <td>79.99</td>\n",
       "      <td>15.77</td>\n",
       "      <td>c8822fce1d0bfa7ddf0da24fff947172</td>\n",
       "      <td>27945</td>\n",
       "      <td>macae</td>\n",
       "      <td>RJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>c1d4211b3dae76144deccd6c74144a88</td>\n",
       "      <td>684cb238dc5b5d6366244e0e0776b450</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-01-19 12:48:08</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2017-01-25 14:56:50</td>\n",
       "      <td>2017-01-30 18:16:01</td>\n",
       "      <td>2017-03-01</td>\n",
       "      <td>1</td>\n",
       "      <td>5ab02ca028398131a5ae91401eb49788</td>\n",
       "      <td>80e6699fe29150b372a0c8a1ebf7dcc8</td>\n",
       "      <td>2017-01-23 12:48:08</td>\n",
       "      <td>39.99</td>\n",
       "      <td>14.52</td>\n",
       "      <td>6ff8b0d7b35d5c945633b8d60165691b</td>\n",
       "      <td>11030</td>\n",
       "      <td>santos</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>d69e5d356402adc8cf17e08b5033acfb</td>\n",
       "      <td>68d081753ad4fe22fc4d410a9eb1ca01</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-02-19 01:28:47</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2017-02-23 03:11:48</td>\n",
       "      <td>2017-03-02 03:41:58</td>\n",
       "      <td>2017-03-27</td>\n",
       "      <td>1</td>\n",
       "      <td>cae2e38942c8489d9d7a87a3f525c06b</td>\n",
       "      <td>cca3071e3e9bb7d12640c9fbe2301306</td>\n",
       "      <td>2017-02-26 01:28:47</td>\n",
       "      <td>149.80</td>\n",
       "      <td>13.63</td>\n",
       "      <td>2e0a2166aa23da2472c6a60c4af6f7a6</td>\n",
       "      <td>3573</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>d77031d6a3c8a52f019764e68f211c69</td>\n",
       "      <td>0bf35cac6cc7327065da879e2d90fae8</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-02-18 11:04:19</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2017-02-23 07:23:36</td>\n",
       "      <td>2017-03-02 16:15:23</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>1</td>\n",
       "      <td>02a79d79e818ad0be36cfc843a6af7ad</td>\n",
       "      <td>cb3dd9ce66268c7a3ca7241ac70ab58c</td>\n",
       "      <td>2017-02-25 10:04:19</td>\n",
       "      <td>28.99</td>\n",
       "      <td>10.96</td>\n",
       "      <td>c4c0011e639bdbcf26059ddc38bd3c18</td>\n",
       "      <td>13225</td>\n",
       "      <td>varzea paulista</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7002a78c79c519ac54022d4f8a65e6e8</td>\n",
       "      <td>d5de688c321096d15508faae67a27051</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-01-19 22:26:59</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2017-01-27 11:08:05</td>\n",
       "      <td>2017-02-06 14:22:19</td>\n",
       "      <td>2017-03-16</td>\n",
       "      <td>1</td>\n",
       "      <td>c3b271f47e73d0c9ccf1b43b7606c705</td>\n",
       "      <td>cca3071e3e9bb7d12640c9fbe2301306</td>\n",
       "      <td>2017-01-29 22:26:59</td>\n",
       "      <td>45.90</td>\n",
       "      <td>14.52</td>\n",
       "      <td>d49f3dae6bad25d05160fc17aca5942d</td>\n",
       "      <td>36400</td>\n",
       "      <td>conselheiro lafaiete</td>\n",
       "      <td>MG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2eecb0d85f281280f79fa00f9cec1a95</td>\n",
       "      <td>a3d3c38e58b9d2dfb9207cab690b6310</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-02-17 17:21:55</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2017-02-22 11:42:51</td>\n",
       "      <td>2017-03-03 12:16:03</td>\n",
       "      <td>2017-03-20</td>\n",
       "      <td>1</td>\n",
       "      <td>4fd676d9c4723d475026e40aeae56957</td>\n",
       "      <td>5fd924b4836098a5be0ecf81ba054ce0</td>\n",
       "      <td>2017-02-21 16:21:55</td>\n",
       "      <td>135.00</td>\n",
       "      <td>19.23</td>\n",
       "      <td>5a4fa4919cbf2b049e72be460a380e5b</td>\n",
       "      <td>35620</td>\n",
       "      <td>abaete</td>\n",
       "      <td>MG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>51eb2eebd5d76a24625b31c33dd41449</td>\n",
       "      <td>07a2a7e0f63fd8cb757ed77d4245623c</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-02-18 15:52:27</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2017-02-23 03:09:14</td>\n",
       "      <td>2017-03-07 13:57:47</td>\n",
       "      <td>2017-03-29</td>\n",
       "      <td>1</td>\n",
       "      <td>7868a64aa111bbb4f41f8e1146c0becb</td>\n",
       "      <td>cca3071e3e9bb7d12640c9fbe2301306</td>\n",
       "      <td>2017-02-28 14:52:27</td>\n",
       "      <td>59.90</td>\n",
       "      <td>17.16</td>\n",
       "      <td>79af1bbf230a2630487975aa5d7d6220</td>\n",
       "      <td>37660</td>\n",
       "      <td>paraisopolis</td>\n",
       "      <td>MG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>88083e8f64d95b932164187484d90212</td>\n",
       "      <td>f67cd1a215aae2a1074638bbd35a223a</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-02-18 22:49:19</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2017-02-22 11:31:06</td>\n",
       "      <td>2017-03-02 12:06:06</td>\n",
       "      <td>2017-03-21</td>\n",
       "      <td>1</td>\n",
       "      <td>8c5876b1c7768217964f353bc7e64393</td>\n",
       "      <td>0db783cfcd3b73998abc6e10e59a102f</td>\n",
       "      <td>2017-02-22 21:49:19</td>\n",
       "      <td>49.00</td>\n",
       "      <td>14.52</td>\n",
       "      <td>bc1896dc77f49e6dec880445a9b443a3</td>\n",
       "      <td>21020</td>\n",
       "      <td>rio de janeiro</td>\n",
       "      <td>RJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>88083e8f64d95b932164187484d90212</td>\n",
       "      <td>f67cd1a215aae2a1074638bbd35a223a</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-02-18 22:49:19</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2017-02-22 11:31:06</td>\n",
       "      <td>2017-03-02 12:06:06</td>\n",
       "      <td>2017-03-21</td>\n",
       "      <td>2</td>\n",
       "      <td>8c5876b1c7768217964f353bc7e64393</td>\n",
       "      <td>0db783cfcd3b73998abc6e10e59a102f</td>\n",
       "      <td>2017-02-22 21:49:19</td>\n",
       "      <td>49.00</td>\n",
       "      <td>14.52</td>\n",
       "      <td>bc1896dc77f49e6dec880445a9b443a3</td>\n",
       "      <td>21020</td>\n",
       "      <td>rio de janeiro</td>\n",
       "      <td>RJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3c0b8706b065f9919d0505d3b3343881</td>\n",
       "      <td>d85919cb3c0529589c6fa617f5f43281</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-02-17 15:53:27</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2017-02-22 11:31:30</td>\n",
       "      <td>2017-03-03 11:47:47</td>\n",
       "      <td>2017-03-23</td>\n",
       "      <td>1</td>\n",
       "      <td>db8ed3d08891d16a2438a67ab3acb740</td>\n",
       "      <td>004c9cd9d87a3c30c522c48c4fc07416</td>\n",
       "      <td>2017-02-22 14:53:27</td>\n",
       "      <td>133.99</td>\n",
       "      <td>23.20</td>\n",
       "      <td>c094ac95fcd52f821809ec232a7a6956</td>\n",
       "      <td>95795</td>\n",
       "      <td>sao vendelino</td>\n",
       "      <td>RS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2babbb4b15e6d2dfe95e2de765c97bce</td>\n",
       "      <td>74bebaf46603f9340e3b50c6b086f992</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-02-18 17:15:03</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2017-02-22 11:23:11</td>\n",
       "      <td>2017-03-03 18:43:43</td>\n",
       "      <td>2017-03-31</td>\n",
       "      <td>1</td>\n",
       "      <td>c6dd917a0be2a704582055949915ab32</td>\n",
       "      <td>7a67c85e85bb2ce8582c35f2203ad736</td>\n",
       "      <td>2017-02-22 16:15:03</td>\n",
       "      <td>79.99</td>\n",
       "      <td>26.82</td>\n",
       "      <td>f79be7c08dd24b72d34634f1b89333a4</td>\n",
       "      <td>65110</td>\n",
       "      <td>sao jose de ribamar</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            order_id                       customer_id  \\\n",
       "0   e04abd8149ef81b95221e88f6ed9ab6a  2127dc6603ac33544953ef05ec155771   \n",
       "1   8a9adc69528e1001fc68dd0aaebbb54a  4c1ccc74e00993733742a3c786dc3c1f   \n",
       "2   7013bcfc1c97fe719a7b5e05e61c12db  2941af76d38100e0f8740a374f1a5dc3   \n",
       "3   5cf925b116421afa85ee25e99b4c34fb  29c35fc91fc13fb5073c8f30505d860d   \n",
       "4   12a95a3c06dbaec84bcfb0e2da5d228a  1e101e0daffaddce8159d25a8e53f2b2   \n",
       "5   c1d4211b3dae76144deccd6c74144a88  684cb238dc5b5d6366244e0e0776b450   \n",
       "6   d69e5d356402adc8cf17e08b5033acfb  68d081753ad4fe22fc4d410a9eb1ca01   \n",
       "7   d77031d6a3c8a52f019764e68f211c69  0bf35cac6cc7327065da879e2d90fae8   \n",
       "8   7002a78c79c519ac54022d4f8a65e6e8  d5de688c321096d15508faae67a27051   \n",
       "9   2eecb0d85f281280f79fa00f9cec1a95  a3d3c38e58b9d2dfb9207cab690b6310   \n",
       "10  51eb2eebd5d76a24625b31c33dd41449  07a2a7e0f63fd8cb757ed77d4245623c   \n",
       "11  88083e8f64d95b932164187484d90212  f67cd1a215aae2a1074638bbd35a223a   \n",
       "12  88083e8f64d95b932164187484d90212  f67cd1a215aae2a1074638bbd35a223a   \n",
       "13  3c0b8706b065f9919d0505d3b3343881  d85919cb3c0529589c6fa617f5f43281   \n",
       "14  2babbb4b15e6d2dfe95e2de765c97bce  74bebaf46603f9340e3b50c6b086f992   \n",
       "\n",
       "   order_status order_purchase_timestamp order_approved_at  \\\n",
       "0     delivered      2017-02-18 14:40:00               NaT   \n",
       "1     delivered      2017-02-18 12:45:31               NaT   \n",
       "2     delivered      2017-02-18 13:29:47               NaT   \n",
       "3     delivered      2017-02-18 16:48:35               NaT   \n",
       "4     delivered      2017-02-17 13:05:55               NaT   \n",
       "5     delivered      2017-01-19 12:48:08               NaT   \n",
       "6     delivered      2017-02-19 01:28:47               NaT   \n",
       "7     delivered      2017-02-18 11:04:19               NaT   \n",
       "8     delivered      2017-01-19 22:26:59               NaT   \n",
       "9     delivered      2017-02-17 17:21:55               NaT   \n",
       "10    delivered      2017-02-18 15:52:27               NaT   \n",
       "11    delivered      2017-02-18 22:49:19               NaT   \n",
       "12    delivered      2017-02-18 22:49:19               NaT   \n",
       "13    delivered      2017-02-17 15:53:27               NaT   \n",
       "14    delivered      2017-02-18 17:15:03               NaT   \n",
       "\n",
       "   order_delivered_carrier_date order_delivered_customer_date  \\\n",
       "0           2017-02-23 12:04:47           2017-03-01 13:25:33   \n",
       "1           2017-02-23 09:01:52           2017-03-02 10:05:06   \n",
       "2           2017-02-22 16:25:25           2017-03-01 08:07:38   \n",
       "3           2017-02-22 11:23:10           2017-03-09 07:28:47   \n",
       "4           2017-02-22 11:23:11           2017-03-02 11:09:19   \n",
       "5           2017-01-25 14:56:50           2017-01-30 18:16:01   \n",
       "6           2017-02-23 03:11:48           2017-03-02 03:41:58   \n",
       "7           2017-02-23 07:23:36           2017-03-02 16:15:23   \n",
       "8           2017-01-27 11:08:05           2017-02-06 14:22:19   \n",
       "9           2017-02-22 11:42:51           2017-03-03 12:16:03   \n",
       "10          2017-02-23 03:09:14           2017-03-07 13:57:47   \n",
       "11          2017-02-22 11:31:06           2017-03-02 12:06:06   \n",
       "12          2017-02-22 11:31:06           2017-03-02 12:06:06   \n",
       "13          2017-02-22 11:31:30           2017-03-03 11:47:47   \n",
       "14          2017-02-22 11:23:11           2017-03-03 18:43:43   \n",
       "\n",
       "   order_estimated_delivery_date  order_item_id  \\\n",
       "0                     2017-03-17              1   \n",
       "1                     2017-03-21              1   \n",
       "2                     2017-03-17              1   \n",
       "3                     2017-03-31              1   \n",
       "4                     2017-03-20              1   \n",
       "5                     2017-03-01              1   \n",
       "6                     2017-03-27              1   \n",
       "7                     2017-03-22              1   \n",
       "8                     2017-03-16              1   \n",
       "9                     2017-03-20              1   \n",
       "10                    2017-03-29              1   \n",
       "11                    2017-03-21              1   \n",
       "12                    2017-03-21              2   \n",
       "13                    2017-03-23              1   \n",
       "14                    2017-03-31              1   \n",
       "\n",
       "                          product_id                         seller_id  \\\n",
       "0   0e20a07ca1714df21f9b07ca3bf7c682  4e7c18b98d84e05cbae3ff0ff03846c2   \n",
       "1   2c2b6a28924791234bd386bddb17512e  a4b6b9b992b46e9ef863637af96e04bc   \n",
       "2   583916a5dae918f5e89baec139141c54  3481aa57cd91f9f9d3fa1fa12d9a3bf7   \n",
       "3   c6dd917a0be2a704582055949915ab32  7a67c85e85bb2ce8582c35f2203ad736   \n",
       "4   c6dd917a0be2a704582055949915ab32  7a67c85e85bb2ce8582c35f2203ad736   \n",
       "5   5ab02ca028398131a5ae91401eb49788  80e6699fe29150b372a0c8a1ebf7dcc8   \n",
       "6   cae2e38942c8489d9d7a87a3f525c06b  cca3071e3e9bb7d12640c9fbe2301306   \n",
       "7   02a79d79e818ad0be36cfc843a6af7ad  cb3dd9ce66268c7a3ca7241ac70ab58c   \n",
       "8   c3b271f47e73d0c9ccf1b43b7606c705  cca3071e3e9bb7d12640c9fbe2301306   \n",
       "9   4fd676d9c4723d475026e40aeae56957  5fd924b4836098a5be0ecf81ba054ce0   \n",
       "10  7868a64aa111bbb4f41f8e1146c0becb  cca3071e3e9bb7d12640c9fbe2301306   \n",
       "11  8c5876b1c7768217964f353bc7e64393  0db783cfcd3b73998abc6e10e59a102f   \n",
       "12  8c5876b1c7768217964f353bc7e64393  0db783cfcd3b73998abc6e10e59a102f   \n",
       "13  db8ed3d08891d16a2438a67ab3acb740  004c9cd9d87a3c30c522c48c4fc07416   \n",
       "14  c6dd917a0be2a704582055949915ab32  7a67c85e85bb2ce8582c35f2203ad736   \n",
       "\n",
       "   shipping_limit_date   price  freight_value  \\\n",
       "0  2017-02-22 13:40:00  309.90          39.11   \n",
       "1  2017-02-22 11:45:31  379.00          17.86   \n",
       "2  2017-02-22 12:29:47   49.99          15.53   \n",
       "3  2017-02-22 15:48:35   79.99          26.82   \n",
       "4  2017-02-21 12:05:55   79.99          15.77   \n",
       "5  2017-01-23 12:48:08   39.99          14.52   \n",
       "6  2017-02-26 01:28:47  149.80          13.63   \n",
       "7  2017-02-25 10:04:19   28.99          10.96   \n",
       "8  2017-01-29 22:26:59   45.90          14.52   \n",
       "9  2017-02-21 16:21:55  135.00          19.23   \n",
       "10 2017-02-28 14:52:27   59.90          17.16   \n",
       "11 2017-02-22 21:49:19   49.00          14.52   \n",
       "12 2017-02-22 21:49:19   49.00          14.52   \n",
       "13 2017-02-22 14:53:27  133.99          23.20   \n",
       "14 2017-02-22 16:15:03   79.99          26.82   \n",
       "\n",
       "                  customer_unique_id  customer_zip_code_prefix  \\\n",
       "0   8a9a08c7ca8900a200d83cf838a07e0b                      6708   \n",
       "1   91efb7fcabc17925099dced52435837f                     93548   \n",
       "2   e1f01a1bd6485e58ad3c769a5427d8a8                      8230   \n",
       "3   7e1a5ca61b572d76b64b6688b9f96473                     62700   \n",
       "4   c8822fce1d0bfa7ddf0da24fff947172                     27945   \n",
       "5   6ff8b0d7b35d5c945633b8d60165691b                     11030   \n",
       "6   2e0a2166aa23da2472c6a60c4af6f7a6                      3573   \n",
       "7   c4c0011e639bdbcf26059ddc38bd3c18                     13225   \n",
       "8   d49f3dae6bad25d05160fc17aca5942d                     36400   \n",
       "9   5a4fa4919cbf2b049e72be460a380e5b                     35620   \n",
       "10  79af1bbf230a2630487975aa5d7d6220                     37660   \n",
       "11  bc1896dc77f49e6dec880445a9b443a3                     21020   \n",
       "12  bc1896dc77f49e6dec880445a9b443a3                     21020   \n",
       "13  c094ac95fcd52f821809ec232a7a6956                     95795   \n",
       "14  f79be7c08dd24b72d34634f1b89333a4                     65110   \n",
       "\n",
       "           customer_city customer_state  \n",
       "0                  cotia             SP  \n",
       "1          novo hamburgo             RS  \n",
       "2              sao paulo             SP  \n",
       "3                caninde             CE  \n",
       "4                  macae             RJ  \n",
       "5                 santos             SP  \n",
       "6              sao paulo             SP  \n",
       "7        varzea paulista             SP  \n",
       "8   conselheiro lafaiete             MG  \n",
       "9                 abaete             MG  \n",
       "10          paraisopolis             MG  \n",
       "11        rio de janeiro             RJ  \n",
       "12        rio de janeiro             RJ  \n",
       "13         sao vendelino             RS  \n",
       "14   sao jose de ribamar             MA  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Посмотрим поподробнее на такие заказы, объединив их с df order_items, а затем с customers\n",
    "orders_delivered_not_approved = orders.query('order_approved_at == \"NaN\" and order_status == \"delivered\"')\n",
    "df = orders_delivered_not_approved.merge(order_items, on = 'order_id', how = 'left')\n",
    "df_full = df.merge(customers, on = 'customer_id', how = 'left')\n",
    "df_full"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "По итогу видим, что таких заказов всего 14, и между собой они не имеют какой-то особой взаимосвязи, у всех товаров есть цена\n",
    "Вероятно, оплата задерживается из-за длительной транзакции или какой-то подобной проблемы\n",
    "\n",
    "Тогда обращать внимание на order_approved_at опрометчиво, выберем наши покупки только исходя из статуса заказа\n",
    "\n",
    "**Примем решение, что заказ считается покупкой, когда он уже либо собирается (т.е. он точно не недоступен),\n",
    "либо уже отбыл к своему заказчику, т.е. order_status будет \"delivered\", \"shipped\" и \"processing\"**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Сколько у нас пользователей, которые совершили покупку только один раз? (считаем покупка = заказ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Объединим датафреймы с пользователями и заказами. Число строк в df одинаковое и нет пропусков в customer_id, \n",
    "# поэтому объединяем how = 'inner'\n",
    "customers_orders = customers.merge(orders, on = 'customer_id', how = 'inner') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "91814"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Считаем заказы с отобранными нами ранее статусами ('processing', 'shipped', 'delivered') \n",
    "# с группировкой по уникальным пользователям ('customer_unique_id') и выбираем тех, у кого 1 заказ (count = 1).\n",
    "customers_orders.query('order_status == [\"processing\", \"shipped\", \"delivered\"]') \\\n",
    "    .groupby('customer_unique_id', as_index = False) \\\n",
    "    .agg({'order_id' : 'count'}) \\\n",
    "    .query('order_id == 1') \\\n",
    "    .count() \\\n",
    "    .customer_unique_id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ответ: 91814 пользователей совершили покупку только один раз"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "93099"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Давайте еще посчитаем пользователей с одной покупкой без уточнения статуса заказа, т.е. просто с одним заказом\n",
    "customers_orders.groupby('customer_unique_id', as_index = False) \\\n",
    "    .agg({'order_id' : 'count'}) \\\n",
    "    .query('order_id == 1') \\\n",
    "    .count() \\\n",
    "    .customer_unique_id "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Получается, не так много строк вы вырезали нашим условием по статусу заказа, количество увеличилось всего на 1285 человек,\n",
    "Можно сделать вывод, что большинство пользователей все-таки успешно доходят до покупки, конверсия в покупателя большая."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Сколько заказов в месяц в среднем не доставляется по разным причинам (вывести детализацию по причинам)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['delivered', 'invoiced', 'shipped', 'processing', 'unavailable',\n",
       "       'canceled', 'created', 'approved'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Посмотрим, какие у нас есть статусы заказов\n",
    "orders.order_status.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# created —  создан\n",
    "# approved —  подтверждён\n",
    "# invoiced —  выставлен счёт\n",
    "# processing —  в процессе сборки заказа\n",
    "# shipped —  отгружен со склада\n",
    "# delivered —  доставлен пользователю\n",
    "# unavailable —  недоступен\n",
    "# canceled —  отменён"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Проверим, есть ли у нас дубли заказов с одним order_id, но с разными статусами\n",
    "orders.groupby('order_id')\\\n",
    "    .agg({'order_status':'count'}) \\\n",
    "    .order_status.unique() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Таких заказов нет, все order_id имеют единственный статус"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2965"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Что будем считать \"не доставленным\" заказом?\n",
    "# У нас есть стоблец order_delivered_customer_date с датой доставки, в котором есть пропуски. Посчитаем их\n",
    "orders.query('order_delivered_customer_date == \"NaN\"').order_id.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2963"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# И также посмотрим, сколько заказов останется, если мы исключим статус \"delivered\"\n",
    "orders.query('order_status != \"delivered\"').order_id.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>order_status</th>\n",
       "      <th>order_purchase_timestamp</th>\n",
       "      <th>order_approved_at</th>\n",
       "      <th>order_delivered_carrier_date</th>\n",
       "      <th>order_delivered_customer_date</th>\n",
       "      <th>order_estimated_delivery_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3002</th>\n",
       "      <td>2d1e2d5bf4dc7227b3bfebb81328c15f</td>\n",
       "      <td>ec05a6d8558c6455f0cbbd8a420ad34f</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-11-28 17:44:07</td>\n",
       "      <td>2017-11-28 17:56:40</td>\n",
       "      <td>2017-11-30 18:12:23</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2017-12-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20618</th>\n",
       "      <td>f5dd62b788049ad9fc0526e3ad11a097</td>\n",
       "      <td>5e89028e024b381dc84a13a3570decb4</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-06-20 06:58:43</td>\n",
       "      <td>2018-06-20 07:19:05</td>\n",
       "      <td>2018-06-25 08:05:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2018-07-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43834</th>\n",
       "      <td>2ebdfc4f15f23b91474edf87475f108e</td>\n",
       "      <td>29f0540231702fda0cfdee0a310f11aa</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-07-01 17:05:11</td>\n",
       "      <td>2018-07-01 17:15:12</td>\n",
       "      <td>2018-07-03 13:57:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2018-07-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79263</th>\n",
       "      <td>e69f75a717d64fc5ecdfae42b2e8e086</td>\n",
       "      <td>cfda40ca8dd0a5d486a9635b611b398a</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-07-01 22:05:55</td>\n",
       "      <td>2018-07-01 22:15:14</td>\n",
       "      <td>2018-07-03 13:57:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2018-07-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82868</th>\n",
       "      <td>0d3268bad9b086af767785e3f0fc0133</td>\n",
       "      <td>4f1d63d35fb7c8999853b2699f5c7649</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-07-01 21:14:02</td>\n",
       "      <td>2018-07-01 21:29:54</td>\n",
       "      <td>2018-07-03 09:28:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2018-07-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92643</th>\n",
       "      <td>2d858f451373b04fb5c984a1cc2defaf</td>\n",
       "      <td>e08caf668d499a6d643dafd7c5cc498a</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-05-25 23:22:43</td>\n",
       "      <td>2017-05-25 23:30:16</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2017-06-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97647</th>\n",
       "      <td>ab7c89dc1bf4a1ead9d6ec1ec8968a84</td>\n",
       "      <td>dd1b84a7286eb4524d52af4256c0ba24</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-06-08 12:09:39</td>\n",
       "      <td>2018-06-08 12:36:39</td>\n",
       "      <td>2018-06-12 14:10:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2018-06-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98038</th>\n",
       "      <td>20edc82cf5400ce95e1afacc25798b31</td>\n",
       "      <td>28c37425f1127d887d7337f284080a0f</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-06-27 16:09:12</td>\n",
       "      <td>2018-06-27 16:29:30</td>\n",
       "      <td>2018-07-03 19:26:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2018-07-19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               order_id                       customer_id  \\\n",
       "3002   2d1e2d5bf4dc7227b3bfebb81328c15f  ec05a6d8558c6455f0cbbd8a420ad34f   \n",
       "20618  f5dd62b788049ad9fc0526e3ad11a097  5e89028e024b381dc84a13a3570decb4   \n",
       "43834  2ebdfc4f15f23b91474edf87475f108e  29f0540231702fda0cfdee0a310f11aa   \n",
       "79263  e69f75a717d64fc5ecdfae42b2e8e086  cfda40ca8dd0a5d486a9635b611b398a   \n",
       "82868  0d3268bad9b086af767785e3f0fc0133  4f1d63d35fb7c8999853b2699f5c7649   \n",
       "92643  2d858f451373b04fb5c984a1cc2defaf  e08caf668d499a6d643dafd7c5cc498a   \n",
       "97647  ab7c89dc1bf4a1ead9d6ec1ec8968a84  dd1b84a7286eb4524d52af4256c0ba24   \n",
       "98038  20edc82cf5400ce95e1afacc25798b31  28c37425f1127d887d7337f284080a0f   \n",
       "\n",
       "      order_status order_purchase_timestamp   order_approved_at  \\\n",
       "3002     delivered      2017-11-28 17:44:07 2017-11-28 17:56:40   \n",
       "20618    delivered      2018-06-20 06:58:43 2018-06-20 07:19:05   \n",
       "43834    delivered      2018-07-01 17:05:11 2018-07-01 17:15:12   \n",
       "79263    delivered      2018-07-01 22:05:55 2018-07-01 22:15:14   \n",
       "82868    delivered      2018-07-01 21:14:02 2018-07-01 21:29:54   \n",
       "92643    delivered      2017-05-25 23:22:43 2017-05-25 23:30:16   \n",
       "97647    delivered      2018-06-08 12:09:39 2018-06-08 12:36:39   \n",
       "98038    delivered      2018-06-27 16:09:12 2018-06-27 16:29:30   \n",
       "\n",
       "      order_delivered_carrier_date order_delivered_customer_date  \\\n",
       "3002           2017-11-30 18:12:23                           NaT   \n",
       "20618          2018-06-25 08:05:00                           NaT   \n",
       "43834          2018-07-03 13:57:00                           NaT   \n",
       "79263          2018-07-03 13:57:00                           NaT   \n",
       "82868          2018-07-03 09:28:00                           NaT   \n",
       "92643                          NaT                           NaT   \n",
       "97647          2018-06-12 14:10:00                           NaT   \n",
       "98038          2018-07-03 19:26:00                           NaT   \n",
       "\n",
       "      order_estimated_delivery_date  \n",
       "3002                     2017-12-18  \n",
       "20618                    2018-07-16  \n",
       "43834                    2018-07-30  \n",
       "79263                    2018-07-30  \n",
       "82868                    2018-07-24  \n",
       "92643                    2017-06-23  \n",
       "97647                    2018-06-26  \n",
       "98038                    2018-07-19  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Есть незначительные различия в полученных числах, посмотрим, что это за доставленные заказы без даты доставки\n",
    "orders.query('order_delivered_customer_date == \"NaN\" and order_status == \"delivered\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2957"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Возможно, так получилось из-за какого-то сервисного сбоя, заказ еще не дошел, но статус уже сменился\n",
    "# Попробуем объединить два условия, возьмём все заказы без даты доставки со статусом, отличным от \"delivered\"\n",
    "orders.query('order_delivered_customer_date == \"NaN\" and order_status != \"delivered\"').order_id.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Далее в этом задании будем работать с данным датафреймом\n",
    "not_delivered_orders = orders.query('order_delivered_customer_date == \"NaN\" and order_status != \"delivered\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_status</th>\n",
       "      <th>mean_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>shipped</td>\n",
       "      <td>48.130435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>unavailable</td>\n",
       "      <td>29.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>canceled</td>\n",
       "      <td>23.807692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>processing</td>\n",
       "      <td>15.842105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>invoiced</td>\n",
       "      <td>14.952381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>created</td>\n",
       "      <td>1.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>approved</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  order_status  mean_count\n",
       "5      shipped   48.130435\n",
       "6  unavailable   29.000000\n",
       "1     canceled   23.807692\n",
       "4   processing   15.842105\n",
       "3     invoiced   14.952381\n",
       "2      created    1.666667\n",
       "0     approved    1.000000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Посчитаем среднее (mean) число не доставленных заказов (not_delivered_orders) в месяц c разбивкой по статусу заказа\n",
    "\n",
    "# Ключом для группировки будем считать обещанную дату доставки (order_estimated_delivery_date), точнее ее месяц \n",
    "# - это сделаем с помощью pd.Grouper и указания ежемесячной частоты (freq = 'M'), также сгруппируем по статусу заказа\n",
    "# Посчитаем число заказов в каждой группе, снова сгруппируем только по статусу заказа, \n",
    "# и посчитаем уже среднее по месяцам число заказов, отсортировав полученные данные по убыванию\n",
    "mean_not_delivered_orders = not_delivered_orders \\\n",
    "    .groupby([pd.Grouper(freq = 'M', key = 'order_estimated_delivery_date'),'order_status'], as_index = False) \\\n",
    "    .agg({'order_id' : 'count'}) \\\n",
    "    .groupby('order_status', as_index = False)\\\n",
    "    .agg({'order_id' : 'mean'}) \\\n",
    "    .rename(columns = {'order_id' : 'mean_count'}) \\\n",
    "    .sort_values('mean_count', ascending = False)\n",
    "\n",
    "# Таблица - среднее количество заказов в месяц, которые не доставляются по разным причинам\n",
    "mean_not_delivered_orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Average count of not delivered orders due to various reasons\\n')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Для большей наглядности выведем mean_not_delivered_orders с причинами не доставленных товаров в виде круговой диаграммы\n",
    "fig, ax = plt.subplots(figsize = (8, 8))\n",
    "ax.pie(mean_not_delivered_orders.mean_count, labels = mean_not_delivered_orders.order_status, \n",
    "       autopct = '%1.1f%%', wedgeprops = {'lw':1, 'ls':'-','ec':\"k\"})\n",
    "plt.title('Average count of not delivered orders due to various reasons\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Согласно полученным результатам статусы заказов с наибольшим процентом отсутствия доставки \n",
    "- shipped (35.8% или 48 заказ в месяц в среднем)\n",
    "- unvailable (21.6% или 29 заказов) \n",
    "- canceled (17.7% или 24 заказа)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_status</th>\n",
       "      <th>mean_count</th>\n",
       "      <th>causer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>shipped</td>\n",
       "      <td>48.130435</td>\n",
       "      <td>service</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>unavailable</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>service</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>canceled</td>\n",
       "      <td>23.807692</td>\n",
       "      <td>customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>processing</td>\n",
       "      <td>15.842105</td>\n",
       "      <td>service</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>invoiced</td>\n",
       "      <td>14.952381</td>\n",
       "      <td>customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>created</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>approved</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>customer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  order_status  mean_count    causer\n",
       "5      shipped   48.130435   service\n",
       "6  unavailable   29.000000   service\n",
       "1     canceled   23.807692  customer\n",
       "4   processing   15.842105   service\n",
       "3     invoiced   14.952381  customer\n",
       "2      created    1.666667  customer\n",
       "0     approved    1.000000  customer"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Попробуем объединить статусы заказов по предполагаемой стороне причины не доставки:\n",
    "# Проблема со стороны сервиса - shipped, processing, unavailable\n",
    "# Проблема со стороны покупателя - created, approved, invoiced, canceled\n",
    "def causer(x):\n",
    "    if x == \"shipped\" or x == \"processing\" or x == \"unavailable\":\n",
    "        return 'service'\n",
    "    else:\n",
    "         return 'customer'\n",
    "\n",
    "mean_not_delivered_orders['causer'] = mean_not_delivered_orders.apply(lambda x: causer(x['order_status']), axis =  1)\n",
    "mean_not_delivered_orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>causer</th>\n",
       "      <th>mean_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>customer</td>\n",
       "      <td>41.42674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>service</td>\n",
       "      <td>92.97254</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     causer  mean_count\n",
       "0  customer    41.42674\n",
       "1   service    92.97254"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# И посчитаем сумму не доставленных заказов с разбивкой по \"виновникам\"\n",
    "causer_not_delivery = mean_not_delivered_orders.groupby('causer', as_index = False) \\\n",
    "    .agg({'mean_count' : 'sum'})\n",
    "causer_not_delivery"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Average count of not delivered orders due to causer\\n')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (6, 6))\n",
    "ax.pie(causer_not_delivery.mean_count, labels = causer_not_delivery.causer, \n",
    "       autopct = '%1.1f%%', wedgeprops = {'lw':1, 'ls':'-','ec':\"k\"})\n",
    "plt.title('Average count of not delivered orders due to causer\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В результате видим, что почти в 70% случаев заказ не доставлен по вине сервиса - это может быть сбой в системе,\n",
    "отсутствие товара, ошибка в логистике и т.д. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_status</th>\n",
       "      <th>order_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>delivered</td>\n",
       "      <td>96478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>shipped</td>\n",
       "      <td>1107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>canceled</td>\n",
       "      <td>625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>unavailable</td>\n",
       "      <td>609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>invoiced</td>\n",
       "      <td>314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>processing</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>created</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>approved</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  order_status  order_count\n",
       "3    delivered        96478\n",
       "6      shipped         1107\n",
       "1     canceled          625\n",
       "7  unavailable          609\n",
       "4     invoiced          314\n",
       "5   processing          301\n",
       "2      created            5\n",
       "0     approved            2"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cделаем еще распределение заказов по всем статусам в общем по начальному датафрейму\n",
    "orders_count = orders.groupby('order_status', as_index = False) \\\n",
    "    .agg({'order_id': 'count'}) \\\n",
    "    .rename(columns = {'order_id' : 'order_count'}) \\\n",
    "    .sort_values('order_count', ascending = False)\n",
    "orders_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. По каждому товару определить, в какой день недели товар чаще всего покупается."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Нам нужны данные по товарам и по их доставке, поэтому объединяем df orders и order_items\n",
    "# Пропуски не нужны, поэтому объединяем по совпадающим строкам\n",
    "\n",
    "orders_and_items = orders.merge(order_items, on = 'order_id') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Используем дату подтверждения оплаты заказа (order_approved_at), т.к. именно тогда происходит покупка\n",
    "# Модифицируем ее в день недели\n",
    "\n",
    "orders_and_items['order_approved_day'] = orders_and_items.order_approved_at.dt.day_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>product_id</th>\n",
       "      <th>top_approved_day</th>\n",
       "      <th>orders_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>40699</th>\n",
       "      <td>aca2eb7d00ea1a7b8ebd4e68314663af</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19963</th>\n",
       "      <td>53b36df67ebb7c41585e8d54d6772e08</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15791</th>\n",
       "      <td>422879e10f46682990de24d770e7f83d</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36222</th>\n",
       "      <td>99a4788cb24856965c36a24e339b6058</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12952</th>\n",
       "      <td>368c6c730842d78016ad823897a372db</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23767</th>\n",
       "      <td>63f916d8413bc439687d549e937d5b67</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23768</th>\n",
       "      <td>63f9b908e4626ece2e47cec24ff8dd9c</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23769</th>\n",
       "      <td>6400587d9225c4ea09f3d5ae53860c11</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23770</th>\n",
       "      <td>6401d2f82d6e6c52998ce626fe4eda24</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60573</th>\n",
       "      <td>fffe9eeff12fcbd74a2f2b007dde0c58</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32611 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             product_id top_approved_day  orders_count\n",
       "40699  aca2eb7d00ea1a7b8ebd4e68314663af          Tuesday           119\n",
       "19963  53b36df67ebb7c41585e8d54d6772e08          Tuesday           105\n",
       "15791  422879e10f46682990de24d770e7f83d          Tuesday            89\n",
       "36222  99a4788cb24856965c36a24e339b6058          Tuesday            82\n",
       "12952  368c6c730842d78016ad823897a372db         Thursday            80\n",
       "...                                 ...              ...           ...\n",
       "23767  63f916d8413bc439687d549e937d5b67         Saturday             1\n",
       "23768  63f9b908e4626ece2e47cec24ff8dd9c         Thursday             1\n",
       "23769  6400587d9225c4ea09f3d5ae53860c11          Tuesday             1\n",
       "23770  6401d2f82d6e6c52998ce626fe4eda24          Tuesday             1\n",
       "60573  fffe9eeff12fcbd74a2f2b007dde0c58        Wednesday             1\n",
       "\n",
       "[32611 rows x 3 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Узнаем, в какой день недели каждый товар чаще всего покупается:\n",
    "# Выберем заказы нужного статуса, сгруппируем их по product_id и дню недели,\n",
    "# посчитаем число заказов в каждой группе и отсортируем по убыванию числа заказов\n",
    "\n",
    "# Так как нам нужны дни, в которых заказ покупается чаще всего, то оставляем только первое упоминание каждого product_id\n",
    "# И переименуем колонки согласно их изменениям\n",
    "\n",
    "items_approved_day = orders_and_items.query('order_status == [\"processing\", \"shipped\", \"delivered\"]') \\\n",
    "    .groupby(['product_id','order_approved_day'], as_index = False) \\\n",
    "    .agg({'order_status' : 'count'}) \\\n",
    "    .sort_values('order_status', ascending = False) \\\n",
    "    .drop_duplicates(subset = ['product_id'], keep = 'first') \\\n",
    "    .rename(columns = {'order_approved_day' : 'top_approved_day', 'order_status' : 'orders_count'}) \n",
    "\n",
    "items_approved_day"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Кажется, покупатели больше всего готовы тратить деньги по вторникам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>week_day</th>\n",
       "      <th>approved_orders_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Monday</td>\n",
       "      <td>14534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>21599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>17735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>17545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Friday</td>\n",
       "      <td>16604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>13719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>9988</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    week_day  approved_orders_count\n",
       "0     Monday                  14534\n",
       "1    Tuesday                  21599\n",
       "2  Wednesday                  17735\n",
       "3   Thursday                  17545\n",
       "4     Friday                  16604\n",
       "5   Saturday                  13719\n",
       "6     Sunday                   9988"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Давайте еще посмотрим, как в целом распределялось количество заказов по дням недели\n",
    "# Не забудем сделать сортировку по дням недели\n",
    "\n",
    "week = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
    "\n",
    "orders_count_day = orders_and_items.query('order_status == [\"processing\", \"shipped\", \"delivered\"]') \\\n",
    "    .groupby('order_approved_day', as_index = False) \\\n",
    "    .agg({'order_status' : 'count'}) \\\n",
    "    .rename(columns = {'order_approved_day' : 'week_day', 'order_status' : 'approved_orders_count'}) \\\n",
    "    .set_index('week_day') \\\n",
    "    .reindex(week) \\\n",
    "    .reset_index()\n",
    "\n",
    "orders_count_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>week_day</th>\n",
       "      <th>purchase_orders_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Monday</td>\n",
       "      <td>18227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>18070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>17477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>16639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Friday</td>\n",
       "      <td>15936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>12072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>13318</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    week_day  purchase_orders_count\n",
       "0     Monday                  18227\n",
       "1    Tuesday                  18070\n",
       "2  Wednesday                  17477\n",
       "3   Thursday                  16639\n",
       "4     Friday                  15936\n",
       "5   Saturday                  12072\n",
       "6     Sunday                  13318"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Еще хочется посмотреть, сильно ли различаются распределения заказов между датой оплаты заказа и датой создания заказа\n",
    "\n",
    "orders_and_items['order_purchase_day'] = orders_and_items.order_purchase_timestamp.dt.day_name()\n",
    "\n",
    "orders_count_day_purchase = orders_and_items.query('order_status == [\"processing\", \"shipped\", \"delivered\"]') \\\n",
    "    .groupby('order_purchase_day', as_index = False) \\\n",
    "    .agg({'order_status' : 'count'}) \\\n",
    "    .rename(columns = {'order_purchase_day' : 'week_day', 'order_status' : 'purchase_orders_count'}) \\\n",
    "    .set_index('week_day') \\\n",
    "    .reindex(week) \\\n",
    "    .reset_index()\n",
    "\n",
    "orders_count_day_purchase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>week_day</th>\n",
       "      <th>approved_orders_count</th>\n",
       "      <th>purchase_orders_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Monday</td>\n",
       "      <td>14534</td>\n",
       "      <td>18227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>21599</td>\n",
       "      <td>18070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>17735</td>\n",
       "      <td>17477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>17545</td>\n",
       "      <td>16639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Friday</td>\n",
       "      <td>16604</td>\n",
       "      <td>15936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>13719</td>\n",
       "      <td>12072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>9988</td>\n",
       "      <td>13318</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    week_day  approved_orders_count  purchase_orders_count\n",
       "0     Monday                  14534                  18227\n",
       "1    Tuesday                  21599                  18070\n",
       "2  Wednesday                  17735                  17477\n",
       "3   Thursday                  17545                  16639\n",
       "4     Friday                  16604                  15936\n",
       "5   Saturday                  13719                  12072\n",
       "6     Sunday                   9988                  13318"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders_count_day = orders_count_day.merge(orders_count_day_purchase, on = 'week_day')\n",
    "orders_count_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Визуализируем полученные данные \n",
    "\n",
    "fig, ax = plt.subplots(figsize = (8, 6))\n",
    "sns.lineplot(data=orders_count_day, x = 'week_day', y = 'purchase_orders_count', label = 'purchase_orders_count')\n",
    "sns.lineplot(data=orders_count_day, x = 'week_day', y = 'approved_orders_count', label = 'approved_orders_count')\n",
    "ax.set_title('Orders counts by week day', fontsize = 15)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В основном пользователи делают заказ и оплачивают его в один день, выбивается только воскресенье, понедельник и вторник.\n",
    "Видим, что во вторник идет большой скачок по количеству оплат, к концу недели наблюдаем спад"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Сколько у каждого из пользователей в среднем покупок в неделю (по месяцам)? (покупка = заказ)\n",
    "\n",
    "**Не стоит забывать, что внутри месяца может быть не целое количество недель. \n",
    "Например, в ноябре 2021 года 4,28 недели. И внутри метрики это нужно учесть.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Нас интересуют пользователи и их заказы, объединим все датафреймы в новый\n",
    "customers_orders_items = customers_orders.merge(order_items, on = 'order_id')\n",
    "\n",
    "# Берем из даты покупки только месяц (берем дату подтверждения оплаты)\n",
    "customers_orders_items['order_approved_month'] = customers_orders_items.order_approved_at.dt.strftime('%Y-%m')\n",
    "\n",
    "# И добавим еще столбец с количеством недель в каждом месяце\n",
    "customers_orders_items['weeks_count'] = customers_orders_items.order_approved_at.dt.days_in_month / 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_unique_id</th>\n",
       "      <th>order_approved_month</th>\n",
       "      <th>weeks_count</th>\n",
       "      <th>sum_order_count</th>\n",
       "      <th>mean_orders_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>75622</th>\n",
       "      <td>c8460e4251689ba205045f3ea17884a1</td>\n",
       "      <td>2018-08</td>\n",
       "      <td>4.428571</td>\n",
       "      <td>24</td>\n",
       "      <td>5.419355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74018</th>\n",
       "      <td>c402f431464c72e27330a67f7b94d4fb</td>\n",
       "      <td>2018-02</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>20</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26182</th>\n",
       "      <td>4546caea018ad8c692964e3382debd19</td>\n",
       "      <td>2017-07</td>\n",
       "      <td>4.428571</td>\n",
       "      <td>21</td>\n",
       "      <td>4.741935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39911</th>\n",
       "      <td>698e1cf81d01a3d389d96145f7fa6df8</td>\n",
       "      <td>2017-08</td>\n",
       "      <td>4.428571</td>\n",
       "      <td>20</td>\n",
       "      <td>4.516129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5818</th>\n",
       "      <td>0f5ac8d5c31de21d2f25e24be15bbffb</td>\n",
       "      <td>2017-11</td>\n",
       "      <td>4.285714</td>\n",
       "      <td>18</td>\n",
       "      <td>4.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41707</th>\n",
       "      <td>6e461ec96c9aad81128aca921cfe901d</td>\n",
       "      <td>2018-03</td>\n",
       "      <td>4.428571</td>\n",
       "      <td>1</td>\n",
       "      <td>0.225806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41708</th>\n",
       "      <td>6e46cf8edbb20e52f1eae047019255c4</td>\n",
       "      <td>2018-08</td>\n",
       "      <td>4.428571</td>\n",
       "      <td>1</td>\n",
       "      <td>0.225806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41709</th>\n",
       "      <td>6e46d472c691147917d485c533476bdf</td>\n",
       "      <td>2018-01</td>\n",
       "      <td>4.428571</td>\n",
       "      <td>1</td>\n",
       "      <td>0.225806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41711</th>\n",
       "      <td>6e4779d59935139d8410a45bc141bf8f</td>\n",
       "      <td>2018-05</td>\n",
       "      <td>4.428571</td>\n",
       "      <td>1</td>\n",
       "      <td>0.225806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96539</th>\n",
       "      <td>ffffd2657e2aad2907e67c3e9daecbeb</td>\n",
       "      <td>2017-05</td>\n",
       "      <td>4.428571</td>\n",
       "      <td>1</td>\n",
       "      <td>0.225806</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96540 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     customer_unique_id order_approved_month  weeks_count  \\\n",
       "75622  c8460e4251689ba205045f3ea17884a1              2018-08     4.428571   \n",
       "74018  c402f431464c72e27330a67f7b94d4fb              2018-02     4.000000   \n",
       "26182  4546caea018ad8c692964e3382debd19              2017-07     4.428571   \n",
       "39911  698e1cf81d01a3d389d96145f7fa6df8              2017-08     4.428571   \n",
       "5818   0f5ac8d5c31de21d2f25e24be15bbffb              2017-11     4.285714   \n",
       "...                                 ...                  ...          ...   \n",
       "41707  6e461ec96c9aad81128aca921cfe901d              2018-03     4.428571   \n",
       "41708  6e46cf8edbb20e52f1eae047019255c4              2018-08     4.428571   \n",
       "41709  6e46d472c691147917d485c533476bdf              2018-01     4.428571   \n",
       "41711  6e4779d59935139d8410a45bc141bf8f              2018-05     4.428571   \n",
       "96539  ffffd2657e2aad2907e67c3e9daecbeb              2017-05     4.428571   \n",
       "\n",
       "       sum_order_count  mean_orders_count  \n",
       "75622               24           5.419355  \n",
       "74018               20           5.000000  \n",
       "26182               21           4.741935  \n",
       "39911               20           4.516129  \n",
       "5818                18           4.200000  \n",
       "...                ...                ...  \n",
       "41707                1           0.225806  \n",
       "41708                1           0.225806  \n",
       "41709                1           0.225806  \n",
       "41711                1           0.225806  \n",
       "96539                1           0.225806  \n",
       "\n",
       "[96540 rows x 5 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Считаем среднее число покупок в неделю с разбивкой по месяцам у пользователей.\n",
    "# Выбираем заказы нужного статуса, группируем по уникальным пользователям, месяцам и числу недель.\n",
    "# Считаем число покупок в каждой группе.\n",
    "# Снова группипуем данные и суммируем количества заказов\n",
    "\n",
    "approved_week_count = customers_orders_items.query('order_status == [\"processing\", \"shipped\", \"delivered\"]') \\\n",
    "    .groupby(['customer_unique_id', 'order_approved_month', 'weeks_count'], as_index = False) \\\n",
    "    .agg({'order_status' : 'count'}) \\\n",
    "    .groupby(['customer_unique_id', 'order_approved_month', 'weeks_count'], as_index = False) \\\n",
    "    .agg({'order_status' : 'sum'}) \\\n",
    "    .rename(columns = {'order_status':'sum_order_count'}) \\\n",
    "    \n",
    "\n",
    "# И наконец-то находим среднее количество заказов в неделю, выведем их по убыванию\n",
    "approved_week_count['mean_orders_count'] = approved_week_count.sum_order_count / approved_week_count.weeks_count\n",
    "approved_week_count.sort_values('mean_orders_count', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Используя pandas, проведи когортный анализ пользователей. \n",
    "**В период с января по декабрь выяви когорту с самым высоким retention на 3й месяц.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Будем считать retention месяца как процент заказчиков, которые сделали повторный заказ в данный месяц. \n",
    "\n",
    "# Используем уже знакомый объединенный датафрейм с заказчиками и заказами customers_orders\n",
    "# Уберем пропущенные значения по столбцу с датой подтверждения оплаты (order_approved_at)\n",
    "customers_orders_retention = customers_orders.dropna(subset = ['order_approved_at'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/tljh/user/lib/python3.7/site-packages/pandas/core/generic.py:5165: 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": [
    "# Так как нас интересуют ежемесячные данные, приведём даты покупок к нужному формату\n",
    "customers_orders_retention.order_approved_at = customers_orders_retention.order_approved_at.dt.to_period('M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_unique_id</th>\n",
       "      <th>first_buy_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0000366f3b9a7992bf8c76cfdf3221e2</td>\n",
       "      <td>2018-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0000b849f77a49e4a4ce2b2a4ca5be3f</td>\n",
       "      <td>2018-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0000f46a3911fa3c0805444483337064</td>\n",
       "      <td>2017-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0000f6ccb0745a6a4b88665a16c9f078</td>\n",
       "      <td>2017-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0004aac84e0df4da2b147fca70cf8255</td>\n",
       "      <td>2017-11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 customer_unique_id first_buy_date\n",
       "0  0000366f3b9a7992bf8c76cfdf3221e2        2018-05\n",
       "1  0000b849f77a49e4a4ce2b2a4ca5be3f        2018-05\n",
       "2  0000f46a3911fa3c0805444483337064        2017-03\n",
       "3  0000f6ccb0745a6a4b88665a16c9f078        2017-10\n",
       "4  0004aac84e0df4da2b147fca70cf8255        2017-11"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Считаем дату первой покупки каждого заказчика \n",
    "first_buy_date = customers_orders_retention \\\n",
    "    .groupby('customer_unique_id', as_index = False) \\\n",
    "    .agg({'order_approved_at' : 'first'}) \\\n",
    "    .rename(columns = {'order_approved_at' : 'first_buy_date'})\n",
    "\n",
    "first_buy_date.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <th>order_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>first_buy_date</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",
       "      <td>00e7ee1b050b8499577073aeb2a297a1</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-05-16 15:05:35</td>\n",
       "      <td>2017-05</td>\n",
       "      <td>2017-05-23 10:47:57</td>\n",
       "      <td>2017-05-25 10:35:35</td>\n",
       "      <td>2017-06-05</td>\n",
       "      <td>2017-05</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",
       "      <td>29150127e6685892b6eab3eec79f59c7</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-01-12 20:48:24</td>\n",
       "      <td>2018-01</td>\n",
       "      <td>2018-01-15 17:14:59</td>\n",
       "      <td>2018-01-29 12:41:19</td>\n",
       "      <td>2018-02-06</td>\n",
       "      <td>2018-01</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",
       "      <td>b2059ed67ce144a36e2aa97d2c9e9ad2</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-05-19 16:07:45</td>\n",
       "      <td>2018-05</td>\n",
       "      <td>2018-06-11 14:31:00</td>\n",
       "      <td>2018-06-14 17:58:51</td>\n",
       "      <td>2018-06-13</td>\n",
       "      <td>2018-05</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",
       "      <td>951670f92359f4fe4a63112aa7306eba</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-03-13 16:06:38</td>\n",
       "      <td>2018-03</td>\n",
       "      <td>2018-03-27 23:22:42</td>\n",
       "      <td>2018-03-28 16:04:25</td>\n",
       "      <td>2018-04-10</td>\n",
       "      <td>2018-03</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",
       "      <td>6b7d50bd145f6fc7f33cebabd7e49d0f</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-07-29 09:51:30</td>\n",
       "      <td>2018-07</td>\n",
       "      <td>2018-07-30 15:16:00</td>\n",
       "      <td>2018-08-09 20:55:48</td>\n",
       "      <td>2018-08-15</td>\n",
       "      <td>2018-07</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   \n",
       "\n",
       "                           order_id order_status order_purchase_timestamp  \\\n",
       "0  00e7ee1b050b8499577073aeb2a297a1    delivered      2017-05-16 15:05:35   \n",
       "1  29150127e6685892b6eab3eec79f59c7    delivered      2018-01-12 20:48:24   \n",
       "2  b2059ed67ce144a36e2aa97d2c9e9ad2    delivered      2018-05-19 16:07:45   \n",
       "3  951670f92359f4fe4a63112aa7306eba    delivered      2018-03-13 16:06:38   \n",
       "4  6b7d50bd145f6fc7f33cebabd7e49d0f    delivered      2018-07-29 09:51:30   \n",
       "\n",
       "  order_approved_at order_delivered_carrier_date  \\\n",
       "0           2017-05          2017-05-23 10:47:57   \n",
       "1           2018-01          2018-01-15 17:14:59   \n",
       "2           2018-05          2018-06-11 14:31:00   \n",
       "3           2018-03          2018-03-27 23:22:42   \n",
       "4           2018-07          2018-07-30 15:16:00   \n",
       "\n",
       "  order_delivered_customer_date order_estimated_delivery_date first_buy_date  \n",
       "0           2017-05-25 10:35:35                    2017-06-05        2017-05  \n",
       "1           2018-01-29 12:41:19                    2018-02-06        2018-01  \n",
       "2           2018-06-14 17:58:51                    2018-06-13        2018-05  \n",
       "3           2018-03-28 16:04:25                    2018-04-10        2018-03  \n",
       "4           2018-08-09 20:55:48                    2018-08-15        2018-07  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Соединяем получившийся датафрейм с нашим изначальным\n",
    "customers_orders_retention_full = customers_orders_retention.merge(first_buy_date, on = 'customer_unique_id')\n",
    "customers_orders_retention_full.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\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>first_buy_date</th>\n",
       "      <th>order_approved_at</th>\n",
       "      <th>unique_customer_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01</td>\n",
       "      <td>2017-01</td>\n",
       "      <td>690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01</td>\n",
       "      <td>2017-02</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01</td>\n",
       "      <td>2017-04</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01</td>\n",
       "      <td>2017-06</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01</td>\n",
       "      <td>2017-07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>2017-10</td>\n",
       "      <td>2017-11</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>2017-10</td>\n",
       "      <td>2017-12</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>2017-11</td>\n",
       "      <td>2017-11</td>\n",
       "      <td>7022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>2017-11</td>\n",
       "      <td>2017-12</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>2017-12</td>\n",
       "      <td>2017-12</td>\n",
       "      <td>5584</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    first_buy_date order_approved_at  unique_customer_count\n",
       "0          2017-01           2017-01                    690\n",
       "1          2017-01           2017-02                      2\n",
       "2          2017-01           2017-04                      1\n",
       "3          2017-01           2017-06                      1\n",
       "4          2017-01           2017-07                      3\n",
       "..             ...               ...                    ...\n",
       "106        2017-10           2017-11                     14\n",
       "107        2017-10           2017-12                      8\n",
       "118        2017-11           2017-11                   7022\n",
       "119        2017-11           2017-12                     15\n",
       "130        2017-12           2017-12                   5584\n",
       "\n",
       "[71 rows x 3 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Отбираем покупки по статусу заказа и по дате (т.к. в условии задан период с января по декабрь, \n",
    "# в наших данных полный год только 2017, его и берем)\n",
    "# Далее группируем по датам первой покупки и остальных покупок и считаем количество уникальных customer_unique_id\n",
    "\n",
    "customers_orders_retention_full = customers_orders_retention_full \\\n",
    "    .query('order_status == [\"processing\", \"shipped\", \"delivered\"] and \"2017-01\" <= first_buy_date <= \"2017-12\" and \"2017-01\" <= order_approved_at <= \"2017-12\"') \\\n",
    "    .groupby(['first_buy_date', 'order_approved_at'], as_index = False) \\\n",
    "    .agg({'customer_unique_id' : 'nunique'}) \\\n",
    "    .rename(columns = {'customer_unique_id' : 'unique_customer_count'})\n",
    "\n",
    "customers_orders_retention_full = customers_orders_retention_full.query('order_approved_at >= first_buy_date')\n",
    "customers_orders_retention_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<PeriodArray>\n",
       "['2017-01', '2017-02', '2017-04', '2017-06', '2017-07', '2017-11', '2017-12',\n",
       " '2017-03', '2017-05', '2017-09', '2017-10', '2017-08']\n",
       "Length: 12, dtype: period[M]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Проверим, нужные ли у нас даты\n",
    "customers_orders_retention_full.order_approved_at.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_buy_date</th>\n",
       "      <th>order_approved_at</th>\n",
       "      <th>unique_customer_count</th>\n",
       "      <th>month_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01</td>\n",
       "      <td>2017-01</td>\n",
       "      <td>690</td>\n",
       "      <td>&lt;0 * MonthEnds&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01</td>\n",
       "      <td>2017-02</td>\n",
       "      <td>2</td>\n",
       "      <td>&lt;MonthEnd&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01</td>\n",
       "      <td>2017-04</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt;3 * MonthEnds&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01</td>\n",
       "      <td>2017-06</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt;5 * MonthEnds&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01</td>\n",
       "      <td>2017-07</td>\n",
       "      <td>3</td>\n",
       "      <td>&lt;6 * MonthEnds&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>2017-10</td>\n",
       "      <td>2017-11</td>\n",
       "      <td>14</td>\n",
       "      <td>&lt;MonthEnd&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>2017-10</td>\n",
       "      <td>2017-12</td>\n",
       "      <td>8</td>\n",
       "      <td>&lt;2 * MonthEnds&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>2017-11</td>\n",
       "      <td>2017-11</td>\n",
       "      <td>7022</td>\n",
       "      <td>&lt;0 * MonthEnds&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>2017-11</td>\n",
       "      <td>2017-12</td>\n",
       "      <td>15</td>\n",
       "      <td>&lt;MonthEnd&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>2017-12</td>\n",
       "      <td>2017-12</td>\n",
       "      <td>5584</td>\n",
       "      <td>&lt;0 * MonthEnds&gt;</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    first_buy_date order_approved_at  unique_customer_count      month_count\n",
       "0          2017-01           2017-01                    690  <0 * MonthEnds>\n",
       "1          2017-01           2017-02                      2       <MonthEnd>\n",
       "2          2017-01           2017-04                      1  <3 * MonthEnds>\n",
       "3          2017-01           2017-06                      1  <5 * MonthEnds>\n",
       "4          2017-01           2017-07                      3  <6 * MonthEnds>\n",
       "..             ...               ...                    ...              ...\n",
       "106        2017-10           2017-11                     14       <MonthEnd>\n",
       "107        2017-10           2017-12                      8  <2 * MonthEnds>\n",
       "118        2017-11           2017-11                   7022  <0 * MonthEnds>\n",
       "119        2017-11           2017-12                     15       <MonthEnd>\n",
       "130        2017-12           2017-12                   5584  <0 * MonthEnds>\n",
       "\n",
       "[71 rows x 4 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Посчитаем месяц покупки, начиная с месяца первой покупки (first_buy_date)\n",
    "customers_orders_retention_full['month_count'] = (customers_orders_retention_full.order_approved_at - customers_orders_retention_full.first_buy_date)\n",
    "customers_orders_retention_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_buy_date</th>\n",
       "      <th>order_approved_at</th>\n",
       "      <th>unique_customer_count</th>\n",
       "      <th>month_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01</td>\n",
       "      <td>2017-01</td>\n",
       "      <td>690</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01</td>\n",
       "      <td>2017-02</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01</td>\n",
       "      <td>2017-04</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01</td>\n",
       "      <td>2017-06</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01</td>\n",
       "      <td>2017-07</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>2017-10</td>\n",
       "      <td>2017-11</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>2017-10</td>\n",
       "      <td>2017-12</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>2017-11</td>\n",
       "      <td>2017-11</td>\n",
       "      <td>7022</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>2017-11</td>\n",
       "      <td>2017-12</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>2017-12</td>\n",
       "      <td>2017-12</td>\n",
       "      <td>5584</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    first_buy_date order_approved_at  unique_customer_count  month_count\n",
       "0          2017-01           2017-01                    690            0\n",
       "1          2017-01           2017-02                      2            1\n",
       "2          2017-01           2017-04                      1            3\n",
       "3          2017-01           2017-06                      1            5\n",
       "4          2017-01           2017-07                      3            6\n",
       "..             ...               ...                    ...          ...\n",
       "106        2017-10           2017-11                     14            1\n",
       "107        2017-10           2017-12                      8            2\n",
       "118        2017-11           2017-11                   7022            0\n",
       "119        2017-11           2017-12                     15            1\n",
       "130        2017-12           2017-12                   5584            0\n",
       "\n",
       "[71 rows x 4 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Формат полученных данных нам не подходит (содержат лишний атрибут), попробуем его изменить\n",
    "# Нам нужно только значение атрибута для каждой строки датафрейма. Мы можем добиться этого, используя attrgetter:\n",
    "customers_orders_retention_full['month_count'] = customers_orders_retention_full['month_count'].apply(attrgetter(\"n\"))\n",
    "customers_orders_retention_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>month_count</th>\n",
       "      <th>0</th>\n",
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       "      <th>2</th>\n",
       "      <th>3</th>\n",
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       "      <th>10</th>\n",
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       "    <tr>\n",
       "      <th>first_buy_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01</th>\n",
       "      <td>690.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02</th>\n",
       "      <td>1637.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03</th>\n",
       "      <td>2537.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04</th>\n",
       "      <td>2253.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05</th>\n",
       "      <td>3464.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06</th>\n",
       "      <td>3068.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07</th>\n",
       "      <td>3744.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08</th>\n",
       "      <td>4093.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09</th>\n",
       "      <td>4040.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10</th>\n",
       "      <td>4321.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11</th>\n",
       "      <td>7022.0</td>\n",
       "      <td>15.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12</th>\n",
       "      <td>5584.0</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "month_count         0     1     2    3     4    5    6    7    8    9    10  \\\n",
       "first_buy_date                                                                \n",
       "2017-01          690.0   2.0   NaN  1.0   NaN  1.0  3.0  NaN  NaN  NaN  1.0   \n",
       "2017-02         1637.0   1.0   2.0  1.0   5.0  1.0  NaN  1.0  1.0  1.0  1.0   \n",
       "2017-03         2537.0   8.0   7.0  5.0   1.0  2.0  2.0  4.0  3.0  3.0  NaN   \n",
       "2017-04         2253.0   9.0   NaN  2.0   4.0  2.0  5.0  3.0  4.0  NaN  NaN   \n",
       "2017-05         3464.0  12.0   7.0  4.0   6.0  7.0  6.0  2.0  NaN  NaN  NaN   \n",
       "2017-06         3068.0   6.0   4.0  8.0   3.0  6.0  7.0  NaN  NaN  NaN  NaN   \n",
       "2017-07         3744.0  12.0   7.0  4.0   6.0  4.0  NaN  NaN  NaN  NaN  NaN   \n",
       "2017-08         4093.0  12.0  11.0  6.0  10.0  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "2017-09         4040.0  13.0   6.0  6.0   NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "2017-10         4321.0  14.0   8.0  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "2017-11         7022.0  15.0   NaN  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "2017-12         5584.0   NaN   NaN  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "\n",
       "month_count      11  \n",
       "first_buy_date       \n",
       "2017-01         1.0  \n",
       "2017-02         NaN  \n",
       "2017-03         NaN  \n",
       "2017-04         NaN  \n",
       "2017-05         NaN  \n",
       "2017-06         NaN  \n",
       "2017-07         NaN  \n",
       "2017-08         NaN  \n",
       "2017-09         NaN  \n",
       "2017-10         NaN  \n",
       "2017-11         NaN  \n",
       "2017-12         NaN  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Создадим итоговую pivot таблицу с полученными данными\n",
    "customers_orders_retention_pivot = customers_orders_retention_full.pivot(index = 'first_buy_date', columns = 'month_count', values = 'unique_customer_count')\n",
    "customers_orders_retention_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>month_count</th>\n",
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       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_buy_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.002899</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001449</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001449</td>\n",
       "      <td>0.004348</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001449</td>\n",
       "      <td>0.001449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000611</td>\n",
       "      <td>0.001222</td>\n",
       "      <td>0.000611</td>\n",
       "      <td>0.003054</td>\n",
       "      <td>0.000611</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000611</td>\n",
       "      <td>0.000611</td>\n",
       "      <td>0.000611</td>\n",
       "      <td>0.000611</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003153</td>\n",
       "      <td>0.002759</td>\n",
       "      <td>0.001971</td>\n",
       "      <td>0.000394</td>\n",
       "      <td>0.000788</td>\n",
       "      <td>0.000788</td>\n",
       "      <td>0.001577</td>\n",
       "      <td>0.001182</td>\n",
       "      <td>0.001182</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000888</td>\n",
       "      <td>0.001775</td>\n",
       "      <td>0.000888</td>\n",
       "      <td>0.002219</td>\n",
       "      <td>0.001332</td>\n",
       "      <td>0.001775</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003464</td>\n",
       "      <td>0.002021</td>\n",
       "      <td>0.001155</td>\n",
       "      <td>0.001732</td>\n",
       "      <td>0.002021</td>\n",
       "      <td>0.001732</td>\n",
       "      <td>0.000577</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.001956</td>\n",
       "      <td>0.001304</td>\n",
       "      <td>0.002608</td>\n",
       "      <td>0.000978</td>\n",
       "      <td>0.001956</td>\n",
       "      <td>0.002282</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003205</td>\n",
       "      <td>0.001870</td>\n",
       "      <td>0.001068</td>\n",
       "      <td>0.001603</td>\n",
       "      <td>0.001068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.002932</td>\n",
       "      <td>0.002688</td>\n",
       "      <td>0.001466</td>\n",
       "      <td>0.002443</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003218</td>\n",
       "      <td>0.001485</td>\n",
       "      <td>0.001485</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003240</td>\n",
       "      <td>0.001851</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.002136</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>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": [
       "month_count      0         1         2         3         4         5   \\\n",
       "first_buy_date                                                          \n",
       "2017-01         1.0  0.002899       NaN  0.001449       NaN  0.001449   \n",
       "2017-02         1.0  0.000611  0.001222  0.000611  0.003054  0.000611   \n",
       "2017-03         1.0  0.003153  0.002759  0.001971  0.000394  0.000788   \n",
       "2017-04         1.0  0.003995       NaN  0.000888  0.001775  0.000888   \n",
       "2017-05         1.0  0.003464  0.002021  0.001155  0.001732  0.002021   \n",
       "2017-06         1.0  0.001956  0.001304  0.002608  0.000978  0.001956   \n",
       "2017-07         1.0  0.003205  0.001870  0.001068  0.001603  0.001068   \n",
       "2017-08         1.0  0.002932  0.002688  0.001466  0.002443       NaN   \n",
       "2017-09         1.0  0.003218  0.001485  0.001485       NaN       NaN   \n",
       "2017-10         1.0  0.003240  0.001851       NaN       NaN       NaN   \n",
       "2017-11         1.0  0.002136       NaN       NaN       NaN       NaN   \n",
       "2017-12         1.0       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "month_count           6         7         8         9         10        11  \n",
       "first_buy_date                                                              \n",
       "2017-01         0.004348       NaN       NaN       NaN  0.001449  0.001449  \n",
       "2017-02              NaN  0.000611  0.000611  0.000611  0.000611       NaN  \n",
       "2017-03         0.000788  0.001577  0.001182  0.001182       NaN       NaN  \n",
       "2017-04         0.002219  0.001332  0.001775       NaN       NaN       NaN  \n",
       "2017-05         0.001732  0.000577       NaN       NaN       NaN       NaN  \n",
       "2017-06         0.002282       NaN       NaN       NaN       NaN       NaN  \n",
       "2017-07              NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "2017-08              NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "2017-09              NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "2017-10              NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "2017-11              NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "2017-12              NaN       NaN       NaN       NaN       NaN       NaN  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Нормализуем данные первого столбца на единицу\n",
    "first = customers_orders_retention_pivot[0]\n",
    "\n",
    "# И рассчитаем возвращаемость\n",
    "retention = customers_orders_retention_pivot.divide(first, axis = 0)\n",
    "retention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2017-06'], dtype='period[M]', name='first_buy_date', freq='M')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Находим когорту с самым высоким retention на 3й месяц (будем считать именно третий индекс, точку отсчета в расчет не берем)\n",
    "retention[retention[3] == retention[3].max()].index\n",
    "\n",
    "# Максимальный retention когорты на 3й месяц 2017-06"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Визуализируем полученные данные при помощи тепловой карты\n",
    "fig, ax = plt.subplots(figsize = (10, 8))\n",
    "sns.heatmap(data = retention, vmin = 0.0, vmax = 0.004, cmap = 'crest', annot = True, linewidth = 0.5)\n",
    "ax.set_yticklabels(retention.index)\n",
    "ax.xaxis.tick_top()\n",
    "fig = ax.get_figure()\n",
    "ax.set_title('Customers retention 2017 year\\n', fontsize = 15)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видим, что большинство пользователей делают 1 заказ. Возвращаемость ожидаемо максимальна в первые месяцы"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Используя python, построй RFM-сегментацию пользователей, чтобы качественно оценить свою аудиторию. \n",
    "В кластеризации можешь выбрать следующие метрики: \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. \n",
    "Для каждого RFM-сегмента построй границы метрик recency, frequency и monetary для интерпретации этих кластеров. \n",
    "\n",
    "Пример такого описания: RFM-сегмент 132 (recency=1, frequency=3, monetary=2) имеет границы метрик recency от 130 до 500 дней, \n",
    "frequency от 2 до 5 заказов в неделю, monetary от 1780 до 3560 рублей в неделю. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Используем наш \"строенный\" датафрейм со всеми данными. Для начала сразу удалим пропуски и оставим нужные статусы\n",
    "customers_orders_items_RFM = customers_orders_items.dropna().query('order_status == [\"processing\", \"shipped\", \"delivered\"]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Будем считать, что текущая дата - это максимальная дата покупки + 1 день\n",
    "now = customers_orders_items_RFM.order_approved_at.max() + timedelta(days=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <th>order_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",
       "      <th>order_approved_month</th>\n",
       "      <th>weeks_count</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",
       "      <td>00e7ee1b050b8499577073aeb2a297a1</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-05-16 15:05:35</td>\n",
       "      <td>2017-05-16 15:22:12</td>\n",
       "      <td>2017-05-23 10:47:57</td>\n",
       "      <td>2017-05-25 10:35:35</td>\n",
       "      <td>2017-06-05</td>\n",
       "      <td>1</td>\n",
       "      <td>a9516a079e37a9c9c36b9b78b10169e8</td>\n",
       "      <td>7c67e1448b00f6e969d365cea6b010ab</td>\n",
       "      <td>2017-05-22 15:22:12</td>\n",
       "      <td>124.99</td>\n",
       "      <td>21.88</td>\n",
       "      <td>2017-05</td>\n",
       "      <td>4.428571</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",
       "      <td>29150127e6685892b6eab3eec79f59c7</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-01-12 20:48:24</td>\n",
       "      <td>2018-01-12 20:58:32</td>\n",
       "      <td>2018-01-15 17:14:59</td>\n",
       "      <td>2018-01-29 12:41:19</td>\n",
       "      <td>2018-02-06</td>\n",
       "      <td>1</td>\n",
       "      <td>4aa6014eceb682077f9dc4bffebc05b0</td>\n",
       "      <td>b8bc237ba3788b23da09c0f1f3a3288c</td>\n",
       "      <td>2018-01-18 20:58:32</td>\n",
       "      <td>289.00</td>\n",
       "      <td>46.48</td>\n",
       "      <td>2018-01</td>\n",
       "      <td>4.428571</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",
       "      <td>b2059ed67ce144a36e2aa97d2c9e9ad2</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-05-19 16:07:45</td>\n",
       "      <td>2018-05-20 16:19:10</td>\n",
       "      <td>2018-06-11 14:31:00</td>\n",
       "      <td>2018-06-14 17:58:51</td>\n",
       "      <td>2018-06-13</td>\n",
       "      <td>1</td>\n",
       "      <td>bd07b66896d6f1494f5b86251848ced7</td>\n",
       "      <td>7c67e1448b00f6e969d365cea6b010ab</td>\n",
       "      <td>2018-06-05 16:19:10</td>\n",
       "      <td>139.94</td>\n",
       "      <td>17.79</td>\n",
       "      <td>2018-05</td>\n",
       "      <td>4.428571</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",
       "      <td>951670f92359f4fe4a63112aa7306eba</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-03-13 16:06:38</td>\n",
       "      <td>2018-03-13 17:29:19</td>\n",
       "      <td>2018-03-27 23:22:42</td>\n",
       "      <td>2018-03-28 16:04:25</td>\n",
       "      <td>2018-04-10</td>\n",
       "      <td>1</td>\n",
       "      <td>a5647c44af977b148e0a3a4751a09e2e</td>\n",
       "      <td>7c67e1448b00f6e969d365cea6b010ab</td>\n",
       "      <td>2018-03-27 16:31:16</td>\n",
       "      <td>149.94</td>\n",
       "      <td>23.36</td>\n",
       "      <td>2018-03</td>\n",
       "      <td>4.428571</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",
       "      <td>6b7d50bd145f6fc7f33cebabd7e49d0f</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-07-29 09:51:30</td>\n",
       "      <td>2018-07-29 10:10:09</td>\n",
       "      <td>2018-07-30 15:16:00</td>\n",
       "      <td>2018-08-09 20:55:48</td>\n",
       "      <td>2018-08-15</td>\n",
       "      <td>1</td>\n",
       "      <td>9391a573abe00141c56e38d84d7d5b3b</td>\n",
       "      <td>4a3ca9315b744ce9f8e9374361493884</td>\n",
       "      <td>2018-07-31 10:10:09</td>\n",
       "      <td>230.00</td>\n",
       "      <td>22.25</td>\n",
       "      <td>2018-07</td>\n",
       "      <td>4.428571</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   \n",
       "\n",
       "                           order_id order_status order_purchase_timestamp  \\\n",
       "0  00e7ee1b050b8499577073aeb2a297a1    delivered      2017-05-16 15:05:35   \n",
       "1  29150127e6685892b6eab3eec79f59c7    delivered      2018-01-12 20:48:24   \n",
       "2  b2059ed67ce144a36e2aa97d2c9e9ad2    delivered      2018-05-19 16:07:45   \n",
       "3  951670f92359f4fe4a63112aa7306eba    delivered      2018-03-13 16:06:38   \n",
       "4  6b7d50bd145f6fc7f33cebabd7e49d0f    delivered      2018-07-29 09:51:30   \n",
       "\n",
       "    order_approved_at order_delivered_carrier_date  \\\n",
       "0 2017-05-16 15:22:12          2017-05-23 10:47:57   \n",
       "1 2018-01-12 20:58:32          2018-01-15 17:14:59   \n",
       "2 2018-05-20 16:19:10          2018-06-11 14:31:00   \n",
       "3 2018-03-13 17:29:19          2018-03-27 23:22:42   \n",
       "4 2018-07-29 10:10:09          2018-07-30 15:16:00   \n",
       "\n",
       "  order_delivered_customer_date order_estimated_delivery_date  order_item_id  \\\n",
       "0           2017-05-25 10:35:35                    2017-06-05              1   \n",
       "1           2018-01-29 12:41:19                    2018-02-06              1   \n",
       "2           2018-06-14 17:58:51                    2018-06-13              1   \n",
       "3           2018-03-28 16:04:25                    2018-04-10              1   \n",
       "4           2018-08-09 20:55:48                    2018-08-15              1   \n",
       "\n",
       "                         product_id                         seller_id  \\\n",
       "0  a9516a079e37a9c9c36b9b78b10169e8  7c67e1448b00f6e969d365cea6b010ab   \n",
       "1  4aa6014eceb682077f9dc4bffebc05b0  b8bc237ba3788b23da09c0f1f3a3288c   \n",
       "2  bd07b66896d6f1494f5b86251848ced7  7c67e1448b00f6e969d365cea6b010ab   \n",
       "3  a5647c44af977b148e0a3a4751a09e2e  7c67e1448b00f6e969d365cea6b010ab   \n",
       "4  9391a573abe00141c56e38d84d7d5b3b  4a3ca9315b744ce9f8e9374361493884   \n",
       "\n",
       "  shipping_limit_date   price  freight_value order_approved_month  weeks_count  \n",
       "0 2017-05-22 15:22:12  124.99          21.88              2017-05     4.428571  \n",
       "1 2018-01-18 20:58:32  289.00          46.48              2018-01     4.428571  \n",
       "2 2018-06-05 16:19:10  139.94          17.79              2018-05     4.428571  \n",
       "3 2018-03-27 16:31:16  149.94          23.36              2018-03     4.428571  \n",
       "4 2018-07-31 10:10:09  230.00          22.25              2018-07     4.428571  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_orders_items_RFM.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_unique_id</th>\n",
       "      <th>R</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0000366f3b9a7992bf8c76cfdf3221e2</td>\n",
       "      <td>112</td>\n",
       "      <td>1</td>\n",
       "      <td>129.90</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0000b849f77a49e4a4ce2b2a4ca5be3f</td>\n",
       "      <td>114</td>\n",
       "      <td>1</td>\n",
       "      <td>18.90</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0000f46a3911fa3c0805444483337064</td>\n",
       "      <td>537</td>\n",
       "      <td>1</td>\n",
       "      <td>69.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0000f6ccb0745a6a4b88665a16c9f078</td>\n",
       "      <td>321</td>\n",
       "      <td>1</td>\n",
       "      <td>25.99</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0004aac84e0df4da2b147fca70cf8255</td>\n",
       "      <td>288</td>\n",
       "      <td>1</td>\n",
       "      <td>180.00</td>\n",
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       "      <th>...</th>\n",
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       "      <th>93331</th>\n",
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       "      <td>1570.00</td>\n",
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       "    <tr>\n",
       "      <th>93332</th>\n",
       "      <td>fffea47cd6d3cc0a88bd621562a9d061</td>\n",
       "      <td>262</td>\n",
       "      <td>1</td>\n",
       "      <td>64.89</td>\n",
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       "    <tr>\n",
       "      <th>93333</th>\n",
       "      <td>ffff371b4d645b6ecea244b27531430a</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>93335</th>\n",
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       "      <td>483</td>\n",
       "      <td>1</td>\n",
       "      <td>56.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>93336 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     customer_unique_id    R  F        M\n",
       "0      0000366f3b9a7992bf8c76cfdf3221e2  112  1   129.90\n",
       "1      0000b849f77a49e4a4ce2b2a4ca5be3f  114  1    18.90\n",
       "2      0000f46a3911fa3c0805444483337064  537  1    69.00\n",
       "3      0000f6ccb0745a6a4b88665a16c9f078  321  1    25.99\n",
       "4      0004aac84e0df4da2b147fca70cf8255  288  1   180.00\n",
       "...                                 ...  ... ..      ...\n",
       "93331  fffcf5a5ff07b0908bd4e2dbc735a684  447  2  1570.00\n",
       "93332  fffea47cd6d3cc0a88bd621562a9d061  262  1    64.89\n",
       "93333  ffff371b4d645b6ecea244b27531430a  568  1    89.90\n",
       "93334  ffff5962728ec6157033ef9805bacc48  119  1   115.00\n",
       "93335  ffffd2657e2aad2907e67c3e9daecbeb  483  1    56.99\n",
       "\n",
       "[93336 rows x 4 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Найдем сразу все требуемые метрики\n",
    "# R - время от последней покупки пользователя до текущей даты\n",
    "# F - суммарное количество покупок у пользователя за всё время (покупка = заказ)\n",
    "# M - сумма покупок за всё время\n",
    "rfm = customers_orders_items_RFM.groupby('customer_unique_id', as_index = False) \\\n",
    "    .agg({'order_approved_at': lambda x:(now - x.max()).days, 'order_id': 'count', 'price': 'sum'}) \\\n",
    "    .rename(columns = {'order_approved_at': 'R', 'order_id': 'F', 'price': 'M'})\n",
    "\n",
    "rfm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Recency (Давность) — Как давно клиент совершил покупку?\n",
    "- Frequency (Частота) — Как часто они совершают покупки?\n",
    "- Monetary Value (Денежная ценность) — Сколько они тратят? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>R</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.1</th>\n",
       "      <td>42.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.98</td>\n",
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       "    <tr>\n",
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       "      <td>69.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.7</th>\n",
       "      <td>312.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>0.9</th>\n",
       "      <td>465.0</td>\n",
       "      <td>2.0</td>\n",
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       "    </tr>\n",
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      ],
      "text/plain": [
       "         R    F       M\n",
       "0.1   42.0  1.0   25.98\n",
       "0.4  177.0  1.0   69.90\n",
       "0.7  312.0  1.0  139.00\n",
       "0.9  465.0  2.0  279.99"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Чтобы распределить пользователей по кластерам, используем квантили — составление частей на основе доступных значений \n",
    "# Нам нужно выявить явных \"фаворитов\" и \"аутсайдеров\", поэтому используем более крайние значения квантилей\n",
    "quantiles = rfm.quantile(q=[0.1, 0.4, 0.7, 0.9])\n",
    "quantiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     81728\n",
       "2      8795\n",
       "3      1646\n",
       "4       621\n",
       "5       241\n",
       "6       190\n",
       "7        45\n",
       "8        16\n",
       "10       11\n",
       "11       11\n",
       "9        10\n",
       "12        9\n",
       "14        3\n",
       "15        3\n",
       "13        2\n",
       "20        2\n",
       "21        1\n",
       "18        1\n",
       "24        1\n",
       "Name: F, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Из-за того, что клиенты в основном совершают 1 заказ, почти все квантили для метрики F получаются одинаковые\n",
    "# Будем использовать собственную кластеризацию для всех метрик, с оглядкой на полученные квантили\n",
    "rfm.F.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>93336.000000</td>\n",
       "      <td>93336.000000</td>\n",
       "      <td>93336.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>237.486093</td>\n",
       "      <td>1.180391</td>\n",
       "      <td>141.621663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>152.569986</td>\n",
       "      <td>0.620913</td>\n",
       "      <td>215.714143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>114.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>47.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>219.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>89.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>346.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>154.692500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>714.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>13440.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  R             F             M\n",
       "count  93336.000000  93336.000000  93336.000000\n",
       "mean     237.486093      1.180391    141.621663\n",
       "std      152.569986      0.620913    215.714143\n",
       "min        1.000000      1.000000      0.850000\n",
       "25%      114.000000      1.000000     47.650000\n",
       "50%      219.000000      1.000000     89.700000\n",
       "75%      346.000000      1.000000    154.692500\n",
       "max      714.000000     24.000000  13440.000000"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Посмотрим на экстремальные значения метрик \n",
    "rfm.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_unique_id</th>\n",
       "      <th>R</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>R_categ</th>\n",
       "      <th>F_categ</th>\n",
       "      <th>M_categ</th>\n",
       "      <th>RFM_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0000366f3b9a7992bf8c76cfdf3221e2</td>\n",
       "      <td>112</td>\n",
       "      <td>1</td>\n",
       "      <td>129.90</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0000b849f77a49e4a4ce2b2a4ca5be3f</td>\n",
       "      <td>114</td>\n",
       "      <td>1</td>\n",
       "      <td>18.90</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0000f46a3911fa3c0805444483337064</td>\n",
       "      <td>537</td>\n",
       "      <td>1</td>\n",
       "      <td>69.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0000f6ccb0745a6a4b88665a16c9f078</td>\n",
       "      <td>321</td>\n",
       "      <td>1</td>\n",
       "      <td>25.99</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0004aac84e0df4da2b147fca70cf8255</td>\n",
       "      <td>288</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": [
       "                 customer_unique_id    R  F       M R_categ F_categ M_categ  \\\n",
       "0  0000366f3b9a7992bf8c76cfdf3221e2  112  1  129.90       3       1       3   \n",
       "1  0000b849f77a49e4a4ce2b2a4ca5be3f  114  1   18.90       3       1       1   \n",
       "2  0000f46a3911fa3c0805444483337064  537  1   69.00       1       1       2   \n",
       "3  0000f6ccb0745a6a4b88665a16c9f078  321  1   25.99       2       1       2   \n",
       "4  0004aac84e0df4da2b147fca70cf8255  288  1  180.00       2       1       4   \n",
       "\n",
       "  RFM_score  \n",
       "0       313  \n",
       "1       311  \n",
       "2       112  \n",
       "3       212  \n",
       "4       214  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Делим данные на сегменты при помощи pd.cut\n",
    "rfm['R_categ'] = pd.cut(rfm.R, bins = [0, 32, 184, 367, 715], \n",
    "                                     labels = ['4', '3', '2', '1'])\n",
    "rfm['F_categ'] = pd.cut(rfm.F, bins = [0, 1, 3, 6, 25], \n",
    "                                     labels = ['1', '2', '3', '4'])\n",
    "rfm['M_categ'] = pd.cut(rfm.M, bins = [0, 20, 70, 140, 13450], \n",
    "                                     labels = ['1', '2', '3', '4'])\n",
    "\n",
    "# Меняем тип данных на строковый\n",
    "rfm['R_categ'] = rfm['R_categ'].astype(str)\n",
    "rfm['F_categ'] = rfm['F_categ'].astype(str)\n",
    "rfm['M_categ'] = rfm['M_categ'].astype(str)\n",
    "\n",
    "\n",
    "# И соединяем оценку\n",
    "rfm['RFM_score'] = rfm['R_categ'] + rfm['F_categ'] + rfm['M_categ']\n",
    "rfm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Итак, для оценок использовались следующие границы метрик:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**R - время от последней покупки пользователя до текущей даты**\n",
    "- 1: больше года\n",
    "- 2: от полугода до года (6-12 месяцев)\n",
    "- 3: 1-6 месяцев\n",
    "- 4: 0-1 месяц\n",
    "\n",
    "(первая цифра не включительно)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Эти значения удобнее использовать и ими оперировать, чем значениями рассчитанных ранее квантилей (но они близки друг к другу). Чем меньше срок от последней покупки - тем выше оценка"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**F - суммарное количество покупок у пользователя за всё время (покупка = заказ, т.к. нам нужна частота покупок)**\n",
    "- 1: 1 заказ\n",
    "- 2: 2-3 заказа\n",
    "- 3: 4-6 заказов\n",
    "- 4: 7 и более заказов"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Значения выбраны интуитивно, глядя на распределение rfm.F.value_counts(). Чем больше заказов - тем выше оценка"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**M - сумма покупок за всё время**\n",
    "- 1: 0-20\n",
    "- 2: 20-70\n",
    "- 3: 70-140\n",
    "- 4: больше 140\n",
    "\n",
    "(первая цифра не включительно)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Значения подобраны близкие к значениям рассчитанных ранее квантилей. Чем больше сумма - тем выше оценка"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>RFM_score</th>\n",
       "      <th>customer_count</th>\n",
       "      <th>customer_per</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>212</td>\n",
       "      <td>11294</td>\n",
       "      <td>12.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>312</td>\n",
       "      <td>10382</td>\n",
       "      <td>11.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>213</td>\n",
       "      <td>8438</td>\n",
       "      <td>9.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>313</td>\n",
       "      <td>8284</td>\n",
       "      <td>8.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>214</td>\n",
       "      <td>7541</td>\n",
       "      <td>8.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RFM_score  customer_count  customer_per\n",
       "15       212           11294         12.10\n",
       "28       312           10382         11.12\n",
       "16       213            8438          9.04\n",
       "29       313            8284          8.88\n",
       "17       214            7541          8.08"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Рассчитаем число пользователей с группировкой по оценкам и их процентное содержание\n",
    "rfm_count = rfm.groupby('RFM_score', as_index = False) \\\n",
    "    .agg({'customer_unique_id' : 'count'}) \\\n",
    "    .rename(columns = {'customer_unique_id' : 'customer_count'}) \\\n",
    "    .sort_values('customer_count', ascending = False) \n",
    "\n",
    "rfm_count['customer_per'] = round((rfm_count['customer_count'] / rfm_count['customer_count'].sum()*100), 2)\n",
    "\n",
    "rfm_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Графическое представление распределения RFM_score\n",
    "fig, ax = plt.subplots(figsize = (20, 15))\n",
    "sns.barplot(data = rfm_count, x = 'RFM_score', y = 'customer_per')\n",
    "ax.set_title('RFM Score\\n', fontsize = 15)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Пользователи \"111\" покупали давно (более года назад), один раз и на маленькую сумму (до 20). О таких можно забыть.\n",
    "Пользователи \"444\" покупают часто (больше 6 раз), на большую сумму (больше 140) и последняя покупка была недавно (до месяца). \n",
    "Таких клиентов стоит беречь и следить за изменениями по ним.\n",
    "Пользователи \"144\", \"244\", \"134\", \"133\" и т.д. - лояльные, но потеряли активность, надо напомнить о себе.\n",
    "\n",
    "Основная доля клиентов состоит в группах RFM \"212\" и \"312\" (последняя покупка от 1 до 6 месяцев назад, количество один, \n",
    "суммарный объем покупки 20-70).\n",
    "Мы имеем дело с минимальными показателями retention, и невысоким средним чеком. \n",
    "\n",
    "Лучшие клиенты с RFM \"444\" составляют около 0.01 %"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>RFM_score</th>\n",
       "      <th>R</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>111</td>\n",
       "      <td>466.601210</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.925750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>112</td>\n",
       "      <td>463.399799</td>\n",
       "      <td>1.0</td>\n",
       "      <td>46.060514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>113</td>\n",
       "      <td>464.013535</td>\n",
       "      <td>1.0</td>\n",
       "      <td>103.785536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>114</td>\n",
       "      <td>466.271583</td>\n",
       "      <td>1.0</td>\n",
       "      <td>333.677795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>121</td>\n",
       "      <td>458.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.214118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  RFM_score           R    F           M\n",
       "0       111  466.601210  1.0   15.925750\n",
       "1       112  463.399799  1.0   46.060514\n",
       "2       113  464.013535  1.0  103.785536\n",
       "3       114  466.271583  1.0  333.677795\n",
       "4       121  458.000000  2.0   16.214118"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Вычислим среднее значение метрик во всех сегментах и посмотрим на их распределение по сегментам\n",
    "rfm_mean = rfm.groupby('RFM_score', as_index = False) \\\n",
    "    .agg({'R' : 'mean', 'F' : 'mean', 'M' : 'mean'})\n",
    "rfm_mean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (15, 10))\n",
    "sns.barplot(data = rfm_mean, x = 'RFM_score', y = 'R')\n",
    "ax.set_title('Mean R score\\n', fontsize = 15)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На графике видим довольно равномерное уменьшение времени от последней покупки пользователя до текущей даты при увеличении \n",
    "первого числа сегмента (чем меньше дней, тем больше число)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (15, 10))\n",
    "sns.barplot(data = rfm_mean, x = 'RFM_score', y = 'F')\n",
    "ax.set_title('Mean F score\\n', fontsize = 15)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Здесь у нас возрастает второе число в оценке, поэтому график имеет ступенькообразный вид. Максимальное количество заказов было у очень давних покупателей"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (15, 10))\n",
    "sns.barplot(data = rfm_mean, x = 'RFM_score', y = 'M')\n",
    "ax.set_title('Mean M score\\n', fontsize = 15)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Здесь также наблюдаем ступенькообразный вид графика"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}