hw2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4740630f-295e-4ffd-8654-c44994bdf1bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/allg.csv\n",
      "data/10_groups.csv\n",
      "data/bad_ids - bad_ids.csv.csv\n",
      "data/product_groups.csv\n",
      "data/09_groups.csv\n",
      "data/11_groups.csv\n"
     ]
    }
   ],
   "source": [
    "# data_path = Path('data').glob('*')\n",
    "# for i in data_path:\n",
    "#     print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4ece43f2-d4e0-426a-95f0-bef67e128f34",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>id_doc</th>\n",
       "      <th>id_order</th>\n",
       "      <th>id_card</th>\n",
       "      <th>id_tov</th>\n",
       "      <th>id_kontr</th>\n",
       "      <th>quantity</th>\n",
       "      <th>sum</th>\n",
       "      <th>is_green</th>\n",
       "      <th>id_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-09-01 17:01:25</td>\n",
       "      <td>1283228</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1538855</td>\n",
       "      <td>52</td>\n",
       "      <td>271</td>\n",
       "      <td>1.0</td>\n",
       "      <td>108.00</td>\n",
       "      <td>0</td>\n",
       "      <td>10004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-09-01 17:35:36</td>\n",
       "      <td>8873113</td>\n",
       "      <td>0.0</td>\n",
       "      <td>267307</td>\n",
       "      <td>52</td>\n",
       "      <td>271</td>\n",
       "      <td>1.0</td>\n",
       "      <td>108.00</td>\n",
       "      <td>0</td>\n",
       "      <td>10004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-09-01 19:19:21</td>\n",
       "      <td>12712899</td>\n",
       "      <td>0.0</td>\n",
       "      <td>610220</td>\n",
       "      <td>52</td>\n",
       "      <td>271</td>\n",
       "      <td>1.0</td>\n",
       "      <td>107.95</td>\n",
       "      <td>0</td>\n",
       "      <td>10004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-09-01 21:53:34</td>\n",
       "      <td>21535283</td>\n",
       "      <td>0.0</td>\n",
       "      <td>441497</td>\n",
       "      <td>52</td>\n",
       "      <td>271</td>\n",
       "      <td>1.0</td>\n",
       "      <td>108.00</td>\n",
       "      <td>0</td>\n",
       "      <td>10004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-09-01 18:42:31</td>\n",
       "      <td>642341</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1065358</td>\n",
       "      <td>61</td>\n",
       "      <td>97</td>\n",
       "      <td>2.0</td>\n",
       "      <td>88.00</td>\n",
       "      <td>0</td>\n",
       "      <td>10091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date    id_doc  id_order  id_card  id_tov  id_kontr  \\\n",
       "0  2020-09-01 17:01:25   1283228       0.0  1538855      52       271   \n",
       "1  2020-09-01 17:35:36   8873113       0.0   267307      52       271   \n",
       "2  2020-09-01 19:19:21  12712899       0.0   610220      52       271   \n",
       "3  2020-09-01 21:53:34  21535283       0.0   441497      52       271   \n",
       "4  2020-09-01 18:42:31    642341       0.0  1065358      61        97   \n",
       "\n",
       "   quantity     sum  is_green  id_group  \n",
       "0       1.0  108.00         0     10004  \n",
       "1       1.0  108.00         0     10004  \n",
       "2       1.0  107.95         0     10004  \n",
       "3       1.0  108.00         0     10004  \n",
       "4       2.0   88.00         0     10091  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# g_9 = pd.read_csv('data/09_groups.csv')\n",
    "# g_9.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4ede1ad8-c4a9-4640-b4e6-92b29ed2e474",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>id_doc</th>\n",
       "      <th>id_order</th>\n",
       "      <th>id_card</th>\n",
       "      <th>id_tov</th>\n",
       "      <th>id_kontr</th>\n",
       "      <th>quantity</th>\n",
       "      <th>sum</th>\n",
       "      <th>is_green</th>\n",
       "      <th>id_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-10-01 20:22:55</td>\n",
       "      <td>2035558</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1636749</td>\n",
       "      <td>52</td>\n",
       "      <td>339</td>\n",
       "      <td>1.0</td>\n",
       "      <td>107.90</td>\n",
       "      <td>0</td>\n",
       "      <td>10004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-10-01 20:54:12</td>\n",
       "      <td>13037327</td>\n",
       "      <td>0.0</td>\n",
       "      <td>267261</td>\n",
       "      <td>52</td>\n",
       "      <td>339</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80.04</td>\n",
       "      <td>0</td>\n",
       "      <td>10004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-10-01 21:25:59</td>\n",
       "      <td>1430590</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1331395</td>\n",
       "      <td>61</td>\n",
       "      <td>379</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31.92</td>\n",
       "      <td>1</td>\n",
       "      <td>10091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-10-01 13:31:01</td>\n",
       "      <td>3504660</td>\n",
       "      <td>0.0</td>\n",
       "      <td>641525</td>\n",
       "      <td>61</td>\n",
       "      <td>97</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55.98</td>\n",
       "      <td>0</td>\n",
       "      <td>10091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-10-01 16:32:06</td>\n",
       "      <td>11024438</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1325852</td>\n",
       "      <td>61</td>\n",
       "      <td>97</td>\n",
       "      <td>4.0</td>\n",
       "      <td>176.00</td>\n",
       "      <td>0</td>\n",
       "      <td>10091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date    id_doc  id_order  id_card  id_tov  id_kontr  \\\n",
       "0  2020-10-01 20:22:55   2035558       0.0  1636749      52       339   \n",
       "1  2020-10-01 20:54:12  13037327       0.0   267261      52       339   \n",
       "2  2020-10-01 21:25:59   1430590       0.0  1331395      61       379   \n",
       "3  2020-10-01 13:31:01   3504660       0.0   641525      61        97   \n",
       "4  2020-10-01 16:32:06  11024438       0.0  1325852      61        97   \n",
       "\n",
       "   quantity     sum  is_green  id_group  \n",
       "0       1.0  107.90         0     10004  \n",
       "1       1.0   80.04         0     10004  \n",
       "2       1.0   31.92         1     10091  \n",
       "3       1.0   55.98         0     10091  \n",
       "4       4.0  176.00         0     10091  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# g_10 = pd.read_csv('./data/10_groups.csv')\n",
    "# g_10.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c19b3e39-b4b9-4d70-9209-8b27b4b86daa",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>id_doc</th>\n",
       "      <th>id_order</th>\n",
       "      <th>id_card</th>\n",
       "      <th>id_tov</th>\n",
       "      <th>id_kontr</th>\n",
       "      <th>quantity</th>\n",
       "      <th>sum</th>\n",
       "      <th>is_green</th>\n",
       "      <th>id_group</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-11-01 15:29:01</td>\n",
       "      <td>3121751</td>\n",
       "      <td>0.0</td>\n",
       "      <td>332351</td>\n",
       "      <td>52</td>\n",
       "      <td>271</td>\n",
       "      <td>1.0</td>\n",
       "      <td>107.98</td>\n",
       "      <td>0</td>\n",
       "      <td>10004</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-11-01 12:41:10</td>\n",
       "      <td>22047017</td>\n",
       "      <td>8293317.0</td>\n",
       "      <td>1875438</td>\n",
       "      <td>52</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1.0</td>\n",
       "      <td>108.00</td>\n",
       "      <td>0</td>\n",
       "      <td>10004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-11-01 13:17:09</td>\n",
       "      <td>6419825</td>\n",
       "      <td>0.0</td>\n",
       "      <td>563112</td>\n",
       "      <td>61</td>\n",
       "      <td>379</td>\n",
       "      <td>2.0</td>\n",
       "      <td>112.00</td>\n",
       "      <td>0</td>\n",
       "      <td>10091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-11-01 12:31:31</td>\n",
       "      <td>10284541</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1841710</td>\n",
       "      <td>61</td>\n",
       "      <td>379</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55.91</td>\n",
       "      <td>0</td>\n",
       "      <td>10091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-11-01 13:06:15</td>\n",
       "      <td>10424967</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1304277</td>\n",
       "      <td>61</td>\n",
       "      <td>271</td>\n",
       "      <td>2.0</td>\n",
       "      <td>87.80</td>\n",
       "      <td>0</td>\n",
       "      <td>10091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                  date    id_doc   id_order  id_card  id_tov  id_kontr  \\\n",
       "0  2020-11-01 15:29:01   3121751        0.0   332351      52       271   \n",
       "1  2020-11-01 12:41:10  22047017  8293317.0  1875438      52     -9999   \n",
       "2  2020-11-01 13:17:09   6419825        0.0   563112      61       379   \n",
       "3  2020-11-01 12:31:31  10284541        0.0  1841710      61       379   \n",
       "4  2020-11-01 13:06:15  10424967        0.0  1304277      61       271   \n",
       "\n",
       "   quantity     sum  is_green  id_group  \n",
       "0       1.0  107.98         0     10004  \n",
       "1       1.0  108.00         0     10004  \n",
       "2       2.0  112.00         0     10091  \n",
       "3       1.0   55.91         0     10091  \n",
       "4       2.0   87.80         0     10091  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# g_11 = pd.read_csv('./data/11_groups.csv')\n",
    "# g_11.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f8ce8fa2-61ff-415c-8ad8-c8a480f1e4d3",
   "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>id_tov</th>\n",
       "      <th>id_group</th>\n",
       "      <th>name_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52</td>\n",
       "      <td>10004</td>\n",
       "      <td>Кисломолочные продукты</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>75</td>\n",
       "      <td>10004</td>\n",
       "      <td>Кисломолочные продукты</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>77</td>\n",
       "      <td>10004</td>\n",
       "      <td>Кисломолочные продукты</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>143</td>\n",
       "      <td>10004</td>\n",
       "      <td>Кисломолочные продукты</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151</td>\n",
       "      <td>10004</td>\n",
       "      <td>Кисломолочные продукты</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id_tov  id_group              name_group\n",
       "0      52     10004  Кисломолочные продукты\n",
       "1      75     10004  Кисломолочные продукты\n",
       "2      77     10004  Кисломолочные продукты\n",
       "3     143     10004  Кисломолочные продукты\n",
       "4     151     10004  Кисломолочные продукты"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# p_g = pd.read_csv('./data/product_groups.csv', delimiter=';', encoding='windows-1251')\n",
    "# p_g.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "52ec50cd-0fc9-41ec-9bc2-b35b580a21ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# g = pd.concat([g_9, g_10, g_11])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b1dde5fa-ada1-488b-9e21-6442c0db2388",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len g before deleetion of bad ids 134720639\n",
      "len g after deleetion of bad ids 122718354\n"
     ]
    }
   ],
   "source": [
    "# print(f'len g before deleetion of bad ids {len(g)}')\n",
    "# bad = pd.read_csv('data/bad_ids - bad_ids.csv.csv')\n",
    "# g = g[~g['id_card'].isin(bad['id_card'])]\n",
    "# print(f'len g after deleetion of bad ids {len(g)}')\n",
    "# g.to_csv('data/allg.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd97ce10-e84c-4bec-82be-771e31192c7f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4c3a85a-45d3-427d-b6c0-390b31c0ec26",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "510308e1-d47c-4c5e-955e-b24a4b64c3cd",
   "metadata": {},
   "source": [
    "# Task 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14159c91-2505-4c31-af3b-bfe811a7cbaf",
   "metadata": {},
   "source": [
    "## H0: there is no dependency between avarage money spent in the order and buying discounted products  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8991982e-44dc-46d1-bf8a-f158a0291339",
   "metadata": {},
   "source": [
    "## H1: there is some dependency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "72b7d6d8-e2a9-448e-ae04-8cda3a0e5b9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import ttest_ind, pearsonr, f_oneway\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c7b95f72-6966-484c-a755-b867b5561946",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42100858\n",
      "CPU times: user 26.3 s, sys: 4.97 s, total: 31.3 s\n",
      "Wall time: 37.8 s\n"
     ]
    },
    {
     "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>date</th>\n",
       "      <th>id_doc</th>\n",
       "      <th>id_order</th>\n",
       "      <th>id_card</th>\n",
       "      <th>id_tov</th>\n",
       "      <th>id_kontr</th>\n",
       "      <th>quantity</th>\n",
       "      <th>sum</th>\n",
       "      <th>is_green</th>\n",
       "      <th>id_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-09-01 17:01:25</td>\n",
       "      <td>1283228</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1538855</td>\n",
       "      <td>52</td>\n",
       "      <td>271</td>\n",
       "      <td>1.0</td>\n",
       "      <td>108.00</td>\n",
       "      <td>0</td>\n",
       "      <td>10004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-09-01 17:35:36</td>\n",
       "      <td>8873113</td>\n",
       "      <td>0.0</td>\n",
       "      <td>267307</td>\n",
       "      <td>52</td>\n",
       "      <td>271</td>\n",
       "      <td>1.0</td>\n",
       "      <td>108.00</td>\n",
       "      <td>0</td>\n",
       "      <td>10004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-09-01 19:19:21</td>\n",
       "      <td>12712899</td>\n",
       "      <td>0.0</td>\n",
       "      <td>610220</td>\n",
       "      <td>52</td>\n",
       "      <td>271</td>\n",
       "      <td>1.0</td>\n",
       "      <td>107.95</td>\n",
       "      <td>0</td>\n",
       "      <td>10004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-09-01 21:53:34</td>\n",
       "      <td>21535283</td>\n",
       "      <td>0.0</td>\n",
       "      <td>441497</td>\n",
       "      <td>52</td>\n",
       "      <td>271</td>\n",
       "      <td>1.0</td>\n",
       "      <td>108.00</td>\n",
       "      <td>0</td>\n",
       "      <td>10004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-09-01 18:42:31</td>\n",
       "      <td>642341</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1065358</td>\n",
       "      <td>61</td>\n",
       "      <td>97</td>\n",
       "      <td>2.0</td>\n",
       "      <td>88.00</td>\n",
       "      <td>0</td>\n",
       "      <td>10091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date    id_doc  id_order  id_card  id_tov  id_kontr  \\\n",
       "0  2020-09-01 17:01:25   1283228       0.0  1538855      52       271   \n",
       "1  2020-09-01 17:35:36   8873113       0.0   267307      52       271   \n",
       "2  2020-09-01 19:19:21  12712899       0.0   610220      52       271   \n",
       "3  2020-09-01 21:53:34  21535283       0.0   441497      52       271   \n",
       "4  2020-09-01 18:42:31    642341       0.0  1065358      61        97   \n",
       "\n",
       "   quantity     sum  is_green  id_group  \n",
       "0       1.0  108.00         0     10004  \n",
       "1       1.0  108.00         0     10004  \n",
       "2       1.0  107.95         0     10004  \n",
       "3       1.0  108.00         0     10004  \n",
       "4       2.0   88.00         0     10091  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# g = pd.read_csv('data/allg.csv')\n",
    "g = pd.read_csv('data/09_groups.csv') # works 20 years, I gave up\n",
    "print(len(g))\n",
    "g.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "728cce1e-adea-48dd-9c85-66613ba4cb9b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5.84 s, sys: 276 ms, total: 6.12 s\n",
      "Wall time: 6.11 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "avg_spent = g[['sum', 'id_doc']].groupby(\"id_doc\").mean()['sum'].to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ed4025ec-7ed3-42ee-94c9-8a34508fdce0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 6.08 s, sys: 304 ms, total: 6.39 s\n",
      "Wall time: 6.38 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "sum_spent = g[['sum', 'id_doc']].groupby(\"id_doc\").sum()['sum'].to_list()\n",
    "# lets also try to check dependance on total order sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3afb40a0-95d9-4936-894d-1cf9632e4053",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 6.4 s, sys: 91.2 ms, total: 6.49 s\n",
      "Wall time: 6.49 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "disc = np.array(g[['id_doc', 'is_green']].groupby(\"id_doc\")['is_green'].sum().to_list())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8d73f15e-4056-4f55-aca2-ac9da3c7e64d",
   "metadata": {},
   "outputs": [],
   "source": [
    "n = np.array(g[['sum', 'id_doc']].groupby(\"id_doc\").count()['sum'].to_list())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "13062d97-47cc-405a-b9e4-0d4742d25ed4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 20.4 ms, sys: 37 µs, total: 20.4 ms\n",
      "Wall time: 19.7 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "disc_nrmd = disc / n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "151544fd-d849-4676-aa3e-a697eaa299d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=4024.795575945475, pvalue=0.0)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttest_ind(avg_spent, disc_nrmd)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92a72902-8576-49e1-9005-c8285ba82eec",
   "metadata": {},
   "source": [
    "reject H0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c919f5e3-51e9-4c4b-80b4-5ea3c41cc0c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=2579.6329517009312, pvalue=0.0)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttest_ind(sum_spent, disc_nrmd)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eb34fcc-c6a6-406a-9486-061995188bcb",
   "metadata": {},
   "source": [
    "reject H0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9305ffa2-9e74-4d15-8115-2d550879047c",
   "metadata": {},
   "source": [
    "we can run Pearson test to understand if dependance is direct or inverse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "503b4268-aab6-44d0-8514-7bbfa2f5b39b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.14385746125694876, 0.0)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pearsonr(avg_spent, disc_nrmd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "391ab81f-0aa1-4498-8f5e-3c3218558cae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.1162956497020839, 0.0)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pearsonr(sum_spent, disc_nrmd)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3e1fec9-63af-4caa-99c9-2f448e0f5c42",
   "metadata": {},
   "source": [
    "looks like inverse dependance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63708dbf-fd85-48e4-b9a8-2ed5cd53ca28",
   "metadata": {},
   "source": [
    "# Task 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "339d9577-0ac6-4935-92bf-cbfa219ec3be",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g.date = pd.to_datetime(g.date, format='%Y-%m-%d %H:%M:%S')\n",
    "rpd = g.groupby(g.date.dt.day)['id_doc'].count()\n",
    "plt.figure(figsize=(25, 15))\n",
    "plt.locator_params(axis='x', nbins=31)\n",
    "plt.plot(rpd)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1143fd86-4c63-47bd-931f-4b27e9d9c89e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5b5ce9cc-051d-41bb-97cd-fa3d0f3de33b",
   "metadata": {},
   "source": [
    "looks like at 5th, 6th, 19th, 20th local minimums and then it increasesrpd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6b3f9b0-cd71-4b37-aaf8-b5acbe70acb3",
   "metadata": {},
   "source": [
    "### lets ANOVA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45e7146d-8386-4b2c-9418-d1d60ce81089",
   "metadata": {},
   "source": [
    "## H0 : shopping in afterwage days same as all other days"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19c6b982-bde2-4639-b717-5207e00ef89e",
   "metadata": {},
   "source": [
    "## H1 : differnet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a842b917-da58-4637-8e16-35e7bc8a03a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "1     1386381\n",
       "2     1403567\n",
       "3     1406574\n",
       "4     1394242\n",
       "5     1167874\n",
       "6     1279774\n",
       "7     1490998\n",
       "8     1343796\n",
       "9     1455905\n",
       "10    1424965\n",
       "11    1406188\n",
       "12    1200989\n",
       "13    1289206\n",
       "14    1490892\n",
       "15    1436508\n",
       "16    1446709\n",
       "17    1295384\n",
       "18    1559868\n",
       "19    1288153\n",
       "20    1330407\n",
       "21    1492300\n",
       "22    1474016\n",
       "23    1467160\n",
       "24    1458965\n",
       "25    1493372\n",
       "26    1247927\n",
       "27    1346850\n",
       "28    1541365\n",
       "29    1538009\n",
       "30    1542514\n",
       "Name: id_doc, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rpd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "32eebf1f-5e4f-47d5-b243-84c85fa7d943",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "            18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30],\n",
       "           dtype='int64', name='date')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rpd.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8e16dcd0-925d-459d-a382-ab203c3f4b3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=0.8483356604781856, pvalue=0.3648917659121198)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ad = rpd.index.to_list()\n",
    "interesting, leftover = ad[0:6]+ad[19:26], ad[6:18] + ad[25:]\n",
    "f_oneway(leftover, interesting)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "698fcc95-7277-4fcb-8f55-1d0ce2a19768",
   "metadata": {},
   "source": [
    "p value big, fail to reject H0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac7f9e5c-5b16-4671-b174-4c3190893eca",
   "metadata": {},
   "source": [
    "# Task 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "527d44bf-a54d-4735-a22a-224891703fa3",
   "metadata": {},
   "source": [
    "## freq buyers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c740859f-3b99-4e76-afee-dcae32f08689",
   "metadata": {},
   "outputs": [],
   "source": [
    "g = pd.read_csv('data/09_groups.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "b8e8c104-79c2-4fd3-bd5d-1b907ed2f505",
   "metadata": {},
   "outputs": [],
   "source": [
    "g.date = pd.to_datetime(g.date, format='%Y-%m-%d %H:%M:%S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "a8aac2c2-2035-45a5-9e4f-40de7438d374",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of purchases')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cr = df.groupby('id_card')['id_doc'].count()\n",
    "plt.hist(cr, bins=100)\n",
    "plt.xlabel('Number of customers')\n",
    "plt.ylabel('Number of purchases')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bae36999-05fe-4f56-bde9-43001e51897e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "80171045-8fef-4524-99e3-255e9e38792f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of purchases')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "freqs = (1 / cr[cr > 0] * 30).round()\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.hist(freqs, bins=30)\n",
    "plt.xlabel('Number of days between two purchases (on average)')\n",
    "plt.ylabel('Number of purchases')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b775444d-76b5-49e3-83de-08f7e48e0a3c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "66a5501f-c580-4f65-a21a-ec98f638cac9",
   "metadata": {},
   "source": [
    "## Average purchase sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "d0996e36-b56a-48d4-9d2b-71224c9ec08b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id_card\n",
       "0    3607.20\n",
       "1     147.00\n",
       "2    1485.00\n",
       "3    4393.38\n",
       "4    3905.12\n",
       "Name: sum, dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aps = g.groupby('id_card')['sum'].sum()\n",
    "aps.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "8b0aead6-9222-488f-8cc9-e706deb9f00e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "plt.ylabel('Number of customers')\n",
    "plt.xlabel('Purchase sum')\n",
    "plt.hist(aps // 100 * 100, bins=30);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "1bf3abc4-837c-425b-8de0-7fd8f1e2d0dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "plt.ylabel('Number of customers')\n",
    "plt.xlabel('Purchase sum')\n",
    "plt.hist(aps[aps < 40000] // 100 * 100, bins=30);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42af2f80-59ae-4eca-85b5-ae893711ee57",
   "metadata": {},
   "source": [
    "Most of the customers make purchases less than 1000 rubles\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d77c7653-d2da-4b4b-b2b7-02b42104d6f2",
   "metadata": {},
   "source": [
    "## Usual cart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "001988cb-fec6-43fc-bccb-d61ac5ef75ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ccpg = g.groupby('id_group')['id_card'].unique().apply(len)\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.hist(ccpg.sort_values(ascending=False)[:30], bins=20)\n",
    "plt.ylabel('Groups count')\n",
    "plt.ylabel('Number of customers');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "df5c14e4-4ad5-4c78-8641-83c95a78f4a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='id_group'>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ccpg = g.groupby(by=['id_group'])['id_card'].unique().apply(len)\n",
    "ccpg = ccpg.sort_values(ascending=False)\n",
    "ccpg[:20].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "a6d2f9af-756b-41ac-a080-8cd37771a3a6",
   "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>id_tov</th>\n",
       "      <th>id_group</th>\n",
       "      <th>name_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52</td>\n",
       "      <td>10004</td>\n",
       "      <td>Кисломолочные продукты</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>75</td>\n",
       "      <td>10004</td>\n",
       "      <td>Кисломолочные продукты</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>77</td>\n",
       "      <td>10004</td>\n",
       "      <td>Кисломолочные продукты</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>143</td>\n",
       "      <td>10004</td>\n",
       "      <td>Кисломолочные продукты</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151</td>\n",
       "      <td>10004</td>\n",
       "      <td>Кисломолочные продукты</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id_tov  id_group              name_group\n",
       "0      52     10004  Кисломолочные продукты\n",
       "1      75     10004  Кисломолочные продукты\n",
       "2      77     10004  Кисломолочные продукты\n",
       "3     143     10004  Кисломолочные продукты\n",
       "4     151     10004  Кисломолочные продукты"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_g = pd.read_csv('./data/product_groups.csv', delimiter=';', encoding='windows-1251')\n",
    "p_g.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "00ecd0fd-0ab5-4e86-affc-8af97749326c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/torchok4/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\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",
      "  \n",
      "/opt/conda/envs/torchok4/lib/python3.7/site-packages/ipykernel_launcher.py:3: 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",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "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>id_group</th>\n",
       "      <th>name_group</th>\n",
       "      <th>occurence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10004</td>\n",
       "      <td>Кисломолочные продукты</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>10005</td>\n",
       "      <td>Творог</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>10008</td>\n",
       "      <td>Торты, пирожные, муссы, суфле</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>960</th>\n",
       "      <td>10033</td>\n",
       "      <td>Выпечка, печенье, вафли, пряники</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1032</th>\n",
       "      <td>10034</td>\n",
       "      <td>Леденцы, мармелад, халва, зефир, восточные сла...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id_group                                         name_group  occurence\n",
       "0        10004                             Кисломолочные продукты        NaN\n",
       "43       10005                                             Творог        NaN\n",
       "93       10008                      Торты, пирожные, муссы, суфле        NaN\n",
       "960      10033                   Выпечка, печенье, вафли, пряники        NaN\n",
       "1032     10034  Леденцы, мармелад, халва, зефир, восточные сла...        NaN"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ig = p_g[p_g.id_group.isin(ccpg[:20].index)]\n",
    "ig.drop_duplicates('id_group', 'first', inplace=True)\n",
    "ig['occurence'] = ccpg[:20]\n",
    "ig.sort_values('occurence', ascending=False)\n",
    "ig[['id_group', 'name_group', 'occurence']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "034572c7-b71e-402b-8da8-74d154419a5e",
   "metadata": {},
   "source": [
    "Most of the clients buy \"Кисломолочные продукты\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae3cb488-e122-4ca1-93db-fc55419ee337",
   "metadata": {},
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5aefe3a-0bc0-43e5-88b6-7541125987cd",
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   "source": []
  },
  {
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   "execution_count": null,
   "id": "2135cb77-da1c-4a34-a12f-3a883952295e",
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   "id": "cb2b4458-1211-48d4-a677-9c5aec4f594d",
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  {
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   "source": []
  },
  {
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   "execution_count": null,
   "id": "a288c684-bd9d-482c-8dfa-bb5d88ed5e95",
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   "execution_count": null,
   "id": "6b44d62c-e914-4d86-b458-2990f8ecbd35",
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  {
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   "id": "ec59afcb-699d-403e-b73c-863fee51337e",
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   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "id": "9cf691cb-754b-4c82-942a-78219b37e3b7",
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31632b7e-2fb5-4e1e-9055-37bcf335b1d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  }
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