product_analytics_rfm_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Продуктовая аналитика: Методы сегментации клиентов и целевой аудитории\n",
    "\n",
    "Обсудим, зачем необходимо сегментировать целевую аудиторию. Поговорим об особенностях сегментации в B2B и B2C продуктах. Познакомимся с популярными критериями выделения сегментов и методами сегментации: описательными (модель 5W, Khramatrix и др.) и аналитическими (RFM, когорты, k-means и др.)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Проект. RFM анализ\n",
    "\n",
    "**Скачайте датасет и проведите RFM анализ. В каждом подсегменте поделите пользователей на 4 класса. Отсчитывайте количество дней, прошедших с момента последней покупки, с максимальной даты покупки в датасете.**\n",
    "\n",
    "- Какое максимальное кол-во покупок было совершено одним пользователем?\n",
    "- Какая верхняя граница у суммы покупок у пользователей с классом 4 в подсегменте М? (Другими словами: пользователи, у которых сумма покупок от 0 до Х попадают в 4 класс в подсегменте М)\n",
    "- Какая нижняя граница у количества покупок у пользователей с классом 1 в подсегменте F?\n",
    "- Какая верхняя граница у количества покупок у пользователей с классом 2 в подсегменте R?\n",
    "- Сколько пользователей попало в сегмент 111?\n",
    "- Сколько пользователей попало в сегмент 311?\n",
    "- В каком RFM-сегменте самое большое кол-во пользователей?\n",
    "- В каком RFM-сегменте самое маленькое кол-во пользователей?\n",
    "- Какое количество пользователей попало в самый малочисленный сегмент?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/tljh/user/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3063: DtypeWarning: Columns (1) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "project = pd.read_csv('/mnt/HC_Volume_18315164/home-jupyter/jupyter-a-eremina/product_analytics/RFM_ht_data.csv', sep = ',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>InvoiceNo</th>\n",
       "      <th>CustomerCode</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C0011810010001</td>\n",
       "      <td>19067290</td>\n",
       "      <td>2020-09-01</td>\n",
       "      <td>1716.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C0011810010017</td>\n",
       "      <td>13233933</td>\n",
       "      <td>2020-09-01</td>\n",
       "      <td>1489.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C0011810010020</td>\n",
       "      <td>99057968</td>\n",
       "      <td>2020-09-01</td>\n",
       "      <td>151.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C0011810010021</td>\n",
       "      <td>80007276</td>\n",
       "      <td>2020-09-01</td>\n",
       "      <td>146.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C0011810010024</td>\n",
       "      <td>13164076</td>\n",
       "      <td>2020-09-01</td>\n",
       "      <td>104.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        InvoiceNo CustomerCode InvoiceDate   Amount\n",
       "0  C0011810010001     19067290  2020-09-01  1716.00\n",
       "1  C0011810010017     13233933  2020-09-01  1489.74\n",
       "2  C0011810010020     99057968  2020-09-01   151.47\n",
       "3  C0011810010021     80007276  2020-09-01   146.72\n",
       "4  C0011810010024     13164076  2020-09-01   104.00"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "project.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(332730, 4)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "project.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Приводим данные в нужный формат:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "project['InvoiceDate']=pd.to_datetime(project['InvoiceDate'])\n",
    "project['CustomerCode']=project['CustomerCode'].astype(str)\n",
    "project['InvoiceNo']=project['InvoiceNo'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "last_date=project['InvoiceDate'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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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>CustomerCode</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>89388</th>\n",
       "      <td>19057820</td>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44594</th>\n",
       "      <td>13215452</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10347</th>\n",
       "      <td>13032521</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97077</th>\n",
       "      <td>19080880</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119951</th>\n",
       "      <td>99003061</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58910</th>\n",
       "      <td>13272861</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58911</th>\n",
       "      <td>13272871</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58913</th>\n",
       "      <td>13272875</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58914</th>\n",
       "      <td>13272878</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61866</th>\n",
       "      <td>14000778</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>123733 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       CustomerCode  size\n",
       "89388      19057820   204\n",
       "44594      13215452   113\n",
       "10347      13032521   106\n",
       "97077      19080880    99\n",
       "119951     99003061    90\n",
       "...             ...   ...\n",
       "58910      13272861     1\n",
       "58911      13272871     1\n",
       "58913      13272875     1\n",
       "58914      13272878     1\n",
       "61866      14000778     1\n",
       "\n",
       "[123733 rows x 2 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "project.groupby('CustomerCode', as_index=False).size().sort_values('size', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerCode</th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>02213019</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>1609.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02213042</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>9685.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>02213071</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>415.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>02213088</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>305.00</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>02213092</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>1412.88</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>123728</th>\n",
       "      <td>99099927</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>961.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123729</th>\n",
       "      <td>99099936</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1521.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123730</th>\n",
       "      <td>99099959</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1444.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123731</th>\n",
       "      <td>99099963</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>3018.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123732</th>\n",
       "      <td>99099972</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4577.37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>123733 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       CustomerCode  recency  frequency  monetary_value\n",
       "0          02213019       19          1         1609.20\n",
       "1          02213042       22          3         9685.48\n",
       "2          02213071       29          1          415.00\n",
       "3          02213088       23          1          305.00\n",
       "4          02213092       25          1         1412.88\n",
       "...             ...      ...        ...             ...\n",
       "123728     99099927       10          1          961.10\n",
       "123729     99099936        0          1         1521.78\n",
       "123730     99099959        8          2         1444.56\n",
       "123731     99099963       19          1         3018.91\n",
       "123732     99099972        0          2         4577.37\n",
       "\n",
       "[123733 rows x 4 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Создаём RFM-таблицу\n",
    "\n",
    "rfm_table=project.groupby('CustomerCode',as_index=False).agg({'InvoiceDate':lambda x: (last_date-x.max()).days,\n",
    "                                         'InvoiceNo':lambda x:len(x),\n",
    "                                         'Amount':lambda x:x.sum()})\n",
    "\n",
    "\n",
    "rfm_table['InvoiceDate'] = rfm_table['InvoiceDate'].astype(int)\n",
    "\n",
    "\n",
    "rfm_table=rfm_table.rename(columns={'InvoiceDate':'recency',\n",
    "                         'InvoiceNo':'frequency',\n",
    "                         'Amount':'monetary_value'})\n",
    "\n",
    "rfm_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>765.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1834.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4008.84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      recency  frequency  monetary_value\n",
       "0.25      2.0        1.0          765.00\n",
       "0.50      8.0        2.0         1834.48\n",
       "0.75     16.0        3.0         4008.84"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Рассчитаем квантили\n",
    "\n",
    "quantiles=rfm_table.quantile(q=(0.25,0.5,0.75))\n",
    "quantiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Разобьём пользователей на классы\n",
    "\n",
    "def Rclass(value, parameter_name, quantiles_table):\n",
    "    if value <= quantiles_table[parameter_name][0.25]:\n",
    "        return 1\n",
    "    elif value <= quantiles_table[parameter_name][0.5]:\n",
    "        return 2\n",
    "    elif value <= quantiles_table[parameter_name][0.75]:\n",
    "        return 3\n",
    "    else:\n",
    "        return 4\n",
    "    \n",
    "def FMclass(value, parameter_name, quantiles_table):\n",
    "    if value <= quantiles_table[parameter_name][0.25]:\n",
    "        return 4\n",
    "    elif value <= quantiles_table[parameter_name][0.5]:\n",
    "        return 3\n",
    "    elif value <= quantiles_table[parameter_name][0.75]:\n",
    "        return 2\n",
    "    else:\n",
    "        return 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfm_segmentation=rfm_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfm_segmentation['R_quantile']=rfm_segmentation['recency'].apply(Rclass,args=('recency', quantiles))\n",
    "rfm_segmentation['F_quantile']=rfm_segmentation['frequency'].apply(FMclass,args=('frequency', quantiles))\n",
    "rfm_segmentation['M_quantile']=rfm_segmentation['monetary_value'].apply(FMclass,args=('monetary_value', quantiles))\n",
    "rfm_segmentation['RFM_class']=rfm_segmentation['R_quantile'].map(str)\\\n",
    "                             +rfm_segmentation['F_quantile'].map(str)\\\n",
    "                             +rfm_segmentation['M_quantile'].map(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerCode</th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary_value</th>\n",
       "      <th>R_quantile</th>\n",
       "      <th>F_quantile</th>\n",
       "      <th>M_quantile</th>\n",
       "      <th>RFM_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>02213019</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>1609.20</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02213042</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>9685.48</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>02213071</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>415.00</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>02213088</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>305.00</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>02213092</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>1412.88</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>443</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CustomerCode  recency  frequency  monetary_value  R_quantile  F_quantile  \\\n",
       "0     02213019       19          1         1609.20           4           4   \n",
       "1     02213042       22          3         9685.48           4           2   \n",
       "2     02213071       29          1          415.00           4           4   \n",
       "3     02213088       23          1          305.00           4           4   \n",
       "4     02213092       25          1         1412.88           4           4   \n",
       "\n",
       "   M_quantile RFM_class  \n",
       "0           3       443  \n",
       "1           1       421  \n",
       "2           4       444  \n",
       "3           4       444  \n",
       "4           3       443  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm_segmentation.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Какое максимальное кол-во покупок было совершено одним пользователем?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "204"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm_segmentation.frequency.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Какая верхняя граница у суммы покупок у пользователей с классом 4 в подсегменте М? (Другими словами: пользователи, у которых сумма покупок от 0 до Х попадают в 4 класс в подсегменте М)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "765.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm_segmentation.query('M_quantile == 4').monetary_value.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Какая нижняя граница у количества покупок у пользователей с классом 1 в подсегменте F?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm_segmentation.query('F_quantile == 1').frequency.min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Какое максимальное количество дней может пройти с момента последней покупки для того, чтобы пользователь попал в класс 2 в подсегменте R?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm_segmentation.query('R_quantile == 2').recency.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сколько пользователей попало в сегмент 111?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9705"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm_segmentation.query('RFM_class == \"111\"').shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сколько пользователей попало в сегмент 311?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1609"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm_segmentation.query('RFM_class == \"311\"').shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В каком RFM-сегменте самое большое кол-во пользователей?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'444'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm_segmentation.groupby('RFM_class').size().idxmax()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В каком RFM-сегменте самое маленькое кол-во пользователей?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'414'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm_segmentation.groupby('RFM_class').size().idxmin()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Какое количество пользователей попало в самый малочисленный сегмент?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm_segmentation.groupby('RFM_class').size().min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# heatmap\n",
    "\n",
    "rfm_viz = rfm_segmentation.pivot_table(\n",
    "                        index='R_quantile', \n",
    "                        columns='F_quantile', \n",
    "                        values='monetary_value', \n",
    "                        aggfunc=np.median).applymap(int)\n",
    "sns.heatmap(rfm_viz, cmap=\"YlGnBu\", annot=True, fmt=\".0f\", linewidths=4.15, annot_kws={\"size\": 10},yticklabels=4);"
   ]
  }
 ],
 "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
}