05_02_begin.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ee7e7699",
   "metadata": {},
   "source": [
    "# Grouping data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "29452d05",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dc791129",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2240 entries, 0 to 2239\n",
      "Data columns (total 29 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   ID                   2240 non-null   int64  \n",
      " 1   Year_Birth           2240 non-null   int64  \n",
      " 2   Education            2240 non-null   object \n",
      " 3   Marital_Status       2240 non-null   object \n",
      " 4   Income               2216 non-null   float64\n",
      " 5   Kidhome              2240 non-null   int64  \n",
      " 6   Teenhome             2240 non-null   int64  \n",
      " 7   Dt_Customer          2240 non-null   object \n",
      " 8   Recency              2240 non-null   int64  \n",
      " 9   MntWines             2240 non-null   int64  \n",
      " 10  MntFruits            2240 non-null   int64  \n",
      " 11  MntMeatProducts      2240 non-null   int64  \n",
      " 12  MntFishProducts      2240 non-null   int64  \n",
      " 13  MntSweetProducts     2240 non-null   int64  \n",
      " 14  MntGoldProds         2240 non-null   int64  \n",
      " 15  NumDealsPurchases    2240 non-null   int64  \n",
      " 16  NumWebPurchases      2240 non-null   int64  \n",
      " 17  NumCatalogPurchases  2240 non-null   int64  \n",
      " 18  NumStorePurchases    2240 non-null   int64  \n",
      " 19  NumWebVisitsMonth    2240 non-null   int64  \n",
      " 20  AcceptedCmp3         2240 non-null   int64  \n",
      " 21  AcceptedCmp4         2240 non-null   int64  \n",
      " 22  AcceptedCmp5         2240 non-null   int64  \n",
      " 23  AcceptedCmp1         2240 non-null   int64  \n",
      " 24  AcceptedCmp2         2240 non-null   int64  \n",
      " 25  Complain             2240 non-null   int64  \n",
      " 26  Z_CostContact        2240 non-null   int64  \n",
      " 27  Z_Revenue            2240 non-null   int64  \n",
      " 28  Response             2240 non-null   int64  \n",
      "dtypes: float64(1), int64(25), object(3)\n",
      "memory usage: 507.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"./data/marketing_campaign.csv\", sep='\\t')\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a39d6a11",
   "metadata": {},
   "source": [
    "## Average amount of each product bought by each group of customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "178450b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "amt_bought = [\n",
    "    'MntWines',\n",
    "    'MntFruits',\n",
    "    'MntMeatProducts',\n",
    "    'MntFishProducts',\n",
    "    'MntSweetProducts',\n",
    "    'MntGoldProds'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f886b74a",
   "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>MntWines</th>\n",
       "      <th>MntFruits</th>\n",
       "      <th>MntMeatProducts</th>\n",
       "      <th>MntFishProducts</th>\n",
       "      <th>MntSweetProducts</th>\n",
       "      <th>MntGoldProds</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marital_Status</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>Absurd</th>\n",
       "      <td>355.500000</td>\n",
       "      <td>84.500000</td>\n",
       "      <td>312.500000</td>\n",
       "      <td>205.500000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>204.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alone</th>\n",
       "      <td>184.666667</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>26.333333</td>\n",
       "      <td>7.666667</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Divorced</th>\n",
       "      <td>324.844828</td>\n",
       "      <td>27.426724</td>\n",
       "      <td>150.206897</td>\n",
       "      <td>35.043103</td>\n",
       "      <td>26.818966</td>\n",
       "      <td>46.288793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Married</th>\n",
       "      <td>299.480324</td>\n",
       "      <td>25.734954</td>\n",
       "      <td>160.681713</td>\n",
       "      <td>35.380787</td>\n",
       "      <td>26.701389</td>\n",
       "      <td>42.822917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Single</th>\n",
       "      <td>288.331250</td>\n",
       "      <td>26.835417</td>\n",
       "      <td>182.108333</td>\n",
       "      <td>38.216667</td>\n",
       "      <td>27.262500</td>\n",
       "      <td>43.729167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Together</th>\n",
       "      <td>306.825862</td>\n",
       "      <td>25.350000</td>\n",
       "      <td>168.103448</td>\n",
       "      <td>38.991379</td>\n",
       "      <td>26.122414</td>\n",
       "      <td>42.994828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Widow</th>\n",
       "      <td>369.272727</td>\n",
       "      <td>33.090909</td>\n",
       "      <td>189.285714</td>\n",
       "      <td>51.389610</td>\n",
       "      <td>39.012987</td>\n",
       "      <td>56.766234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOLO</th>\n",
       "      <td>322.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>42.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  MntWines  MntFruits  MntMeatProducts  MntFishProducts  \\\n",
       "Marital_Status                                                            \n",
       "Absurd          355.500000  84.500000       312.500000       205.500000   \n",
       "Alone           184.666667   4.000000        26.333333         7.666667   \n",
       "Divorced        324.844828  27.426724       150.206897        35.043103   \n",
       "Married         299.480324  25.734954       160.681713        35.380787   \n",
       "Single          288.331250  26.835417       182.108333        38.216667   \n",
       "Together        306.825862  25.350000       168.103448        38.991379   \n",
       "Widow           369.272727  33.090909       189.285714        51.389610   \n",
       "YOLO            322.000000   3.000000        50.000000         4.000000   \n",
       "\n",
       "                MntSweetProducts  MntGoldProds  \n",
       "Marital_Status                                  \n",
       "Absurd                 30.500000    204.000000  \n",
       "Alone                   7.000000     27.000000  \n",
       "Divorced               26.818966     46.288793  \n",
       "Married                26.701389     42.822917  \n",
       "Single                 27.262500     43.729167  \n",
       "Together               26.122414     42.994828  \n",
       "Widow                  39.012987     56.766234  \n",
       "YOLO                    3.000000     42.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Marital_Status']).mean()[amt_bought]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e1c894c",
   "metadata": {},
   "source": [
    "## Grouping by multiple columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3bf469b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b9fb703",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73a62425",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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