Ecommerce 분석 - 김건형.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>index</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>TOTAL_ORDERS</th>\n",
       "      <th>REVENUE</th>\n",
       "      <th>AVERAGE_ORDER_VALUE</th>\n",
       "      <th>CARRIAGE_REVENUE</th>\n",
       "      <th>AVERAGESHIPPING</th>\n",
       "      <th>FIRST_ORDER_DATE</th>\n",
       "      <th>LATEST_ORDER_DATE</th>\n",
       "      <th>AVGDAYSBETWEENORDERS</th>\n",
       "      <th>...</th>\n",
       "      <th>WEEK3_DAY16_DAY23_REVENUE</th>\n",
       "      <th>WEEK4_DAY24_DAY31_REVENUE</th>\n",
       "      <th>TIME_0000_0600_ORDERS</th>\n",
       "      <th>TIME_0601_1200_ORDERS</th>\n",
       "      <th>TIME_1200_1800_ORDERS</th>\n",
       "      <th>TIME_1801_2359_ORDERS</th>\n",
       "      <th>TIME_0000_0600_REVENUE</th>\n",
       "      <th>TIME_0601_1200_REVENUE</th>\n",
       "      <th>TIME_1200_1800_REVENUE</th>\n",
       "      <th>TIME_1801_2359_REVENUE</th>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2354</td>\n",
       "      <td>124</td>\n",
       "      <td>11986.54</td>\n",
       "      <td>96.67</td>\n",
       "      <td>529.59</td>\n",
       "      <td>4.27</td>\n",
       "      <td>2016-12-30</td>\n",
       "      <td>2021-10-24</td>\n",
       "      <td>14.19</td>\n",
       "      <td>...</td>\n",
       "      <td>2592.18</td>\n",
       "      <td>2409.71</td>\n",
       "      <td>12</td>\n",
       "      <td>61</td>\n",
       "      <td>37</td>\n",
       "      <td>14</td>\n",
       "      <td>1067.92</td>\n",
       "      <td>5674.65</td>\n",
       "      <td>3916.53</td>\n",
       "      <td>1327.44</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2361</td>\n",
       "      <td>82</td>\n",
       "      <td>11025.96</td>\n",
       "      <td>134.46</td>\n",
       "      <td>97.92</td>\n",
       "      <td>1.19</td>\n",
       "      <td>2018-03-31</td>\n",
       "      <td>2021-10-24</td>\n",
       "      <td>15.89</td>\n",
       "      <td>...</td>\n",
       "      <td>2807.66</td>\n",
       "      <td>4106.19</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>24</td>\n",
       "      <td>44</td>\n",
       "      <td>30.25</td>\n",
       "      <td>3143.69</td>\n",
       "      <td>2205.47</td>\n",
       "      <td>5646.55</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2415</td>\n",
       "      <td>43</td>\n",
       "      <td>7259.69</td>\n",
       "      <td>168.83</td>\n",
       "      <td>171.69</td>\n",
       "      <td>3.99</td>\n",
       "      <td>2017-11-30</td>\n",
       "      <td>2021-10-24</td>\n",
       "      <td>33.12</td>\n",
       "      <td>...</td>\n",
       "      <td>713.94</td>\n",
       "      <td>2861.90</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>707.98</td>\n",
       "      <td>2560.91</td>\n",
       "      <td>3990.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2427</td>\n",
       "      <td>44</td>\n",
       "      <td>6992.27</td>\n",
       "      <td>158.92</td>\n",
       "      <td>92.82</td>\n",
       "      <td>2.11</td>\n",
       "      <td>2019-04-09</td>\n",
       "      <td>2021-10-24</td>\n",
       "      <td>21.11</td>\n",
       "      <td>...</td>\n",
       "      <td>997.02</td>\n",
       "      <td>1260.08</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1908.99</td>\n",
       "      <td>2025.05</td>\n",
       "      <td>3058.23</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2456</td>\n",
       "      <td>55</td>\n",
       "      <td>6263.44</td>\n",
       "      <td>113.88</td>\n",
       "      <td>179.04</td>\n",
       "      <td>3.26</td>\n",
       "      <td>2020-10-23</td>\n",
       "      <td>2021-10-24</td>\n",
       "      <td>6.65</td>\n",
       "      <td>...</td>\n",
       "      <td>2725.66</td>\n",
       "      <td>768.46</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>27</td>\n",
       "      <td>13</td>\n",
       "      <td>501.34</td>\n",
       "      <td>1197.83</td>\n",
       "      <td>3004.77</td>\n",
       "      <td>1559.50</td>\n",
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       "<p>5 rows × 41 columns</p>\n",
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      ],
      "text/plain": [
       "   index  CustomerID  TOTAL_ORDERS   REVENUE  AVERAGE_ORDER_VALUE  \\\n",
       "0      0        2354           124  11986.54                96.67   \n",
       "1      1        2361            82  11025.96               134.46   \n",
       "2      2        2415            43   7259.69               168.83   \n",
       "3      3        2427            44   6992.27               158.92   \n",
       "4      4        2456            55   6263.44               113.88   \n",
       "\n",
       "   CARRIAGE_REVENUE  AVERAGESHIPPING FIRST_ORDER_DATE LATEST_ORDER_DATE  \\\n",
       "0            529.59             4.27       2016-12-30        2021-10-24   \n",
       "1             97.92             1.19       2018-03-31        2021-10-24   \n",
       "2            171.69             3.99       2017-11-30        2021-10-24   \n",
       "3             92.82             2.11       2019-04-09        2021-10-24   \n",
       "4            179.04             3.26       2020-10-23        2021-10-24   \n",
       "\n",
       "   AVGDAYSBETWEENORDERS  ...  WEEK3_DAY16_DAY23_REVENUE  \\\n",
       "0                 14.19  ...                    2592.18   \n",
       "1                 15.89  ...                    2807.66   \n",
       "2                 33.12  ...                     713.94   \n",
       "3                 21.11  ...                     997.02   \n",
       "4                  6.65  ...                    2725.66   \n",
       "\n",
       "   WEEK4_DAY24_DAY31_REVENUE  TIME_0000_0600_ORDERS  TIME_0601_1200_ORDERS  \\\n",
       "0                    2409.71                     12                     61   \n",
       "1                    4106.19                      1                     13   \n",
       "2                    2861.90                      0                      6   \n",
       "3                    1260.08                      0                     10   \n",
       "4                     768.46                      6                      9   \n",
       "\n",
       "   TIME_1200_1800_ORDERS  TIME_1801_2359_ORDERS  TIME_0000_0600_REVENUE  \\\n",
       "0                     37                     14                 1067.92   \n",
       "1                     24                     44                   30.25   \n",
       "2                     12                     25                    0.00   \n",
       "3                     13                     21                    0.00   \n",
       "4                     27                     13                  501.34   \n",
       "\n",
       "   TIME_0601_1200_REVENUE  TIME_1200_1800_REVENUE  TIME_1801_2359_REVENUE  \n",
       "0                 5674.65                 3916.53                 1327.44  \n",
       "1                 3143.69                 2205.47                 5646.55  \n",
       "2                  707.98                 2560.91                 3990.80  \n",
       "3                 1908.99                 2025.05                 3058.23  \n",
       "4                 1197.83                 3004.77                 1559.50  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('datasets/e_commerce_sales.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5000 entries, 0 to 4999\n",
      "Data columns (total 41 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   index                      5000 non-null   int64  \n",
      " 1   CustomerID                 5000 non-null   int64  \n",
      " 2   TOTAL_ORDERS               5000 non-null   int64  \n",
      " 3   REVENUE                    5000 non-null   float64\n",
      " 4   AVERAGE_ORDER_VALUE        5000 non-null   float64\n",
      " 5   CARRIAGE_REVENUE           5000 non-null   float64\n",
      " 6   AVERAGESHIPPING            5000 non-null   float64\n",
      " 7   FIRST_ORDER_DATE           5000 non-null   object \n",
      " 8   LATEST_ORDER_DATE          5000 non-null   object \n",
      " 9   AVGDAYSBETWEENORDERS       5000 non-null   float64\n",
      " 10  DAYSSINCELASTORDER         5000 non-null   int64  \n",
      " 11  MONDAY_ORDERS              5000 non-null   int64  \n",
      " 12  TUESDAY_ORDERS             5000 non-null   int64  \n",
      " 13  WEDNESDAY_ORDERS           5000 non-null   int64  \n",
      " 14  THURSDAY_ORDERS            5000 non-null   int64  \n",
      " 15  FRIDAY_ORDERS              5000 non-null   int64  \n",
      " 16  SATURDAY_ORDERS            5000 non-null   int64  \n",
      " 17  SUNDAY_ORDERS              5000 non-null   int64  \n",
      " 18  MONDAY_REVENUE             5000 non-null   float64\n",
      " 19  TUESDAY_REVENUE            5000 non-null   float64\n",
      " 20  WEDNESDAY_REVENUE          5000 non-null   float64\n",
      " 21  THURSDAY_REVENUE           5000 non-null   float64\n",
      " 22  FRIDAY_REVENUE             5000 non-null   float64\n",
      " 23  SATURDAY_REVENUE           5000 non-null   float64\n",
      " 24  SUNDAY_REVENUE             5000 non-null   float64\n",
      " 25  WEEK1_DAY01_DAY07_ORDERS   5000 non-null   int64  \n",
      " 26  WEEK2_DAY08_DAY15_ORDERS   5000 non-null   int64  \n",
      " 27  WEEK3_DAY16_DAY23_ORDERS   5000 non-null   int64  \n",
      " 28  WEEK4_DAY24_DAY31_ORDERS   5000 non-null   int64  \n",
      " 29  WEEK1_DAY01_DAY07_REVENUE  5000 non-null   float64\n",
      " 30  WEEK2_DAY08_DAY15_REVENUE  5000 non-null   float64\n",
      " 31  WEEK3_DAY16_DAY23_REVENUE  5000 non-null   float64\n",
      " 32  WEEK4_DAY24_DAY31_REVENUE  5000 non-null   float64\n",
      " 33  TIME_0000_0600_ORDERS      5000 non-null   int64  \n",
      " 34  TIME_0601_1200_ORDERS      5000 non-null   int64  \n",
      " 35  TIME_1200_1800_ORDERS      5000 non-null   int64  \n",
      " 36  TIME_1801_2359_ORDERS      5000 non-null   int64  \n",
      " 37  TIME_0000_0600_REVENUE     5000 non-null   float64\n",
      " 38  TIME_0601_1200_REVENUE     5000 non-null   float64\n",
      " 39  TIME_1200_1800_REVENUE     5000 non-null   float64\n",
      " 40  TIME_1801_2359_REVENUE     5000 non-null   float64\n",
      "dtypes: float64(20), int64(19), object(2)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 첫 주문일, 최근 주문 일자 형식 변환 (기준일 : 2021-10-25)\n",
    "from datetime import datetime\n",
    "\n",
    "data['FIRST_ORDER_DATE'] = (datetime.strptime('2021-10-25', '%Y-%m-%d') - pd.to_datetime(data['FIRST_ORDER_DATE'])).dt.days\n",
    "data['LATEST_ORDER_DATE'] = (datetime.strptime('2021-10-25', '%Y-%m-%d') - pd.to_datetime(data['LATEST_ORDER_DATE'])).dt.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_corr = data.drop(['FIRST_ORDER_DATE', 'LATEST_ORDER_DATE'], axis=1).corr()\n",
    "sns.heatmap(data_corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_info = data[['CustomerID', 'TOTAL_ORDERS', 'REVENUE', 'AVERAGE_ORDER_VALUE', 'CARRIAGE_REVENUE', 'AVERAGESHIPPING', 'FIRST_ORDER_DATE', 'LATEST_ORDER_DATE',\n",
    "                'AVGDAYSBETWEENORDERS', 'DAYSSINCELASTORDER']]\n",
    "\n",
    "df_weekday = data[['CustomerID', 'MONDAY_ORDERS', 'TUESDAY_ORDERS', 'WEDNESDAY_ORDERS', 'THURSDAY_ORDERS', 'FRIDAY_ORDERS', 'SATURDAY_ORDERS', 'SUNDAY_ORDERS',\n",
    "                 'MONDAY_REVENUE', 'TUESDAY_REVENUE', 'WEDNESDAY_REVENUE', 'THURSDAY_REVENUE', 'FRIDAY_REVENUE', 'SATURDAY_REVENUE', 'SUNDAY_REVENUE']]\n",
    "\n",
    "df_week = data[['CustomerID', 'WEEK1_DAY01_DAY07_ORDERS', 'WEEK2_DAY08_DAY15_ORDERS', 'WEEK3_DAY16_DAY23_ORDERS', 'WEEK4_DAY24_DAY31_ORDERS',\n",
    "                'WEEK1_DAY01_DAY07_REVENUE', 'WEEK2_DAY08_DAY15_REVENUE', 'WEEK3_DAY16_DAY23_REVENUE', 'WEEK4_DAY24_DAY31_REVENUE']]\n",
    "\n",
    "df_time = data[['CustomerID', 'TIME_0000_0600_ORDERS', 'TIME_0601_1200_ORDERS', 'TIME_1200_1800_ORDERS', 'TIME_1801_2359_ORDERS',\n",
    "                'TIME_0000_0600_REVENUE', 'TIME_0601_1200_REVENUE', 'TIME_1200_1800_REVENUE', 'TIME_1801_2359_REVENUE']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>CustomerID</th>\n",
       "      <th>TOTAL_ORDERS</th>\n",
       "      <th>REVENUE</th>\n",
       "      <th>AVERAGE_ORDER_VALUE</th>\n",
       "      <th>CARRIAGE_REVENUE</th>\n",
       "      <th>AVERAGESHIPPING</th>\n",
       "      <th>FIRST_ORDER_DATE</th>\n",
       "      <th>LATEST_ORDER_DATE</th>\n",
       "      <th>AVGDAYSBETWEENORDERS</th>\n",
       "      <th>DAYSSINCELASTORDER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2354</td>\n",
       "      <td>124</td>\n",
       "      <td>11986.54</td>\n",
       "      <td>96.67</td>\n",
       "      <td>529.59</td>\n",
       "      <td>4.27</td>\n",
       "      <td>1760</td>\n",
       "      <td>1</td>\n",
       "      <td>14.19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2361</td>\n",
       "      <td>82</td>\n",
       "      <td>11025.96</td>\n",
       "      <td>134.46</td>\n",
       "      <td>97.92</td>\n",
       "      <td>1.19</td>\n",
       "      <td>1304</td>\n",
       "      <td>1</td>\n",
       "      <td>15.89</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2415</td>\n",
       "      <td>43</td>\n",
       "      <td>7259.69</td>\n",
       "      <td>168.83</td>\n",
       "      <td>171.69</td>\n",
       "      <td>3.99</td>\n",
       "      <td>1425</td>\n",
       "      <td>1</td>\n",
       "      <td>33.12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2427</td>\n",
       "      <td>44</td>\n",
       "      <td>6992.27</td>\n",
       "      <td>158.92</td>\n",
       "      <td>92.82</td>\n",
       "      <td>2.11</td>\n",
       "      <td>930</td>\n",
       "      <td>1</td>\n",
       "      <td>21.11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2456</td>\n",
       "      <td>55</td>\n",
       "      <td>6263.44</td>\n",
       "      <td>113.88</td>\n",
       "      <td>179.04</td>\n",
       "      <td>3.26</td>\n",
       "      <td>367</td>\n",
       "      <td>1</td>\n",
       "      <td>6.65</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  TOTAL_ORDERS   REVENUE  AVERAGE_ORDER_VALUE  CARRIAGE_REVENUE  \\\n",
       "0        2354           124  11986.54                96.67            529.59   \n",
       "1        2361            82  11025.96               134.46             97.92   \n",
       "2        2415            43   7259.69               168.83            171.69   \n",
       "3        2427            44   6992.27               158.92             92.82   \n",
       "4        2456            55   6263.44               113.88            179.04   \n",
       "\n",
       "   AVERAGESHIPPING  FIRST_ORDER_DATE  LATEST_ORDER_DATE  AVGDAYSBETWEENORDERS  \\\n",
       "0             4.27              1760                  1                 14.19   \n",
       "1             1.19              1304                  1                 15.89   \n",
       "2             3.99              1425                  1                 33.12   \n",
       "3             2.11               930                  1                 21.11   \n",
       "4             3.26               367                  1                  6.65   \n",
       "\n",
       "   DAYSSINCELASTORDER  \n",
       "0                   1  \n",
       "1                   1  \n",
       "2                   1  \n",
       "3                   1  \n",
       "4                   1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_info.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### df_info 가설 :  \n",
    "1. (TOTAL_ORDERS)주문 수가 많은 사람은 총 구매액 (REVENUE)가 높을 것이다.  \n",
    "2. (DAYSSINCELASTORDER)미지막 주문으로부터 ##일 이상 지난 사람은 주문 수(TOTAL_ORDERS)가 적을 것이다.  \n",
    "3. (AVGDAYSBETWEENORDERS) 평균 주문 간격이 짧은 사람은 주문 수(TOTAL_ORDERS)가 많을 것이다.  \n",
    "4. 평균 배송 금액이 높을 수록 총 주문 수는 낮을 것이다.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>MONDAY_ORDERS</th>\n",
       "      <th>TUESDAY_ORDERS</th>\n",
       "      <th>WEDNESDAY_ORDERS</th>\n",
       "      <th>THURSDAY_ORDERS</th>\n",
       "      <th>FRIDAY_ORDERS</th>\n",
       "      <th>SATURDAY_ORDERS</th>\n",
       "      <th>SUNDAY_ORDERS</th>\n",
       "      <th>MONDAY_REVENUE</th>\n",
       "      <th>TUESDAY_REVENUE</th>\n",
       "      <th>WEDNESDAY_REVENUE</th>\n",
       "      <th>THURSDAY_REVENUE</th>\n",
       "      <th>FRIDAY_REVENUE</th>\n",
       "      <th>SATURDAY_REVENUE</th>\n",
       "      <th>SUNDAY_REVENUE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2354</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>1287.50</td>\n",
       "      <td>1054.15</td>\n",
       "      <td>2842.52</td>\n",
       "      <td>2133.79</td>\n",
       "      <td>2051.94</td>\n",
       "      <td>1496.33</td>\n",
       "      <td>1120.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2361</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>1253.08</td>\n",
       "      <td>2003.25</td>\n",
       "      <td>1819.75</td>\n",
       "      <td>1114.83</td>\n",
       "      <td>1791.86</td>\n",
       "      <td>1624.34</td>\n",
       "      <td>1418.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2415</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>698.95</td>\n",
       "      <td>504.97</td>\n",
       "      <td>197.97</td>\n",
       "      <td>1182.97</td>\n",
       "      <td>733.96</td>\n",
       "      <td>1223.96</td>\n",
       "      <td>2716.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2427</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1753.95</td>\n",
       "      <td>1400.23</td>\n",
       "      <td>570.48</td>\n",
       "      <td>1154.06</td>\n",
       "      <td>798.07</td>\n",
       "      <td>630.00</td>\n",
       "      <td>685.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2456</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>150.98</td>\n",
       "      <td>330.38</td>\n",
       "      <td>1090.23</td>\n",
       "      <td>563.63</td>\n",
       "      <td>1339.44</td>\n",
       "      <td>956.56</td>\n",
       "      <td>1832.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  MONDAY_ORDERS  TUESDAY_ORDERS  WEDNESDAY_ORDERS  \\\n",
       "0        2354             13              13                29   \n",
       "1        2361             11              13                10   \n",
       "2        2415              5               4                 3   \n",
       "3        2427             10               8                 5   \n",
       "4        2456              2               3                 8   \n",
       "\n",
       "   THURSDAY_ORDERS  FRIDAY_ORDERS  SATURDAY_ORDERS  SUNDAY_ORDERS  \\\n",
       "0               25             19               15             10   \n",
       "1               13             14               10             11   \n",
       "2                5              5                8             13   \n",
       "3                8              5                3              5   \n",
       "4                8             12               10             12   \n",
       "\n",
       "   MONDAY_REVENUE  TUESDAY_REVENUE  WEDNESDAY_REVENUE  THURSDAY_REVENUE  \\\n",
       "0         1287.50          1054.15            2842.52           2133.79   \n",
       "1         1253.08          2003.25            1819.75           1114.83   \n",
       "2          698.95           504.97             197.97           1182.97   \n",
       "3         1753.95          1400.23             570.48           1154.06   \n",
       "4          150.98           330.38            1090.23            563.63   \n",
       "\n",
       "   FRIDAY_REVENUE  SATURDAY_REVENUE  SUNDAY_REVENUE  \n",
       "0         2051.94           1496.33         1120.31  \n",
       "1         1791.86           1624.34         1418.85  \n",
       "2          733.96           1223.96         2716.91  \n",
       "3          798.07            630.00          685.48  \n",
       "4         1339.44            956.56         1832.22  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_weekday.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>MONDAY_ORDERS</th>\n",
       "      <th>TUESDAY_ORDERS</th>\n",
       "      <th>WEDNESDAY_ORDERS</th>\n",
       "      <th>THURSDAY_ORDERS</th>\n",
       "      <th>FRIDAY_ORDERS</th>\n",
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       "      <th>SUNDAY_ORDERS</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2354</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2361</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2415</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2427</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2456</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  MONDAY_ORDERS  TUESDAY_ORDERS  WEDNESDAY_ORDERS  \\\n",
       "0        2354             13              13                29   \n",
       "1        2361             11              13                10   \n",
       "2        2415              5               4                 3   \n",
       "3        2427             10               8                 5   \n",
       "4        2456              2               3                 8   \n",
       "\n",
       "   THURSDAY_ORDERS  FRIDAY_ORDERS  SATURDAY_ORDERS  SUNDAY_ORDERS  \n",
       "0               25             19               15             10  \n",
       "1               13             14               10             11  \n",
       "2                5              5                8             13  \n",
       "3                8              5                3              5  \n",
       "4                8             12               10             12  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_weekday_order = df_weekday.iloc[:, :8]\n",
    "df_weekday_order.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>CustomerID</th>\n",
       "      <th>MONDAY_REVENUE</th>\n",
       "      <th>TUESDAY_REVENUE</th>\n",
       "      <th>WEDNESDAY_REVENUE</th>\n",
       "      <th>THURSDAY_REVENUE</th>\n",
       "      <th>FRIDAY_REVENUE</th>\n",
       "      <th>SATURDAY_REVENUE</th>\n",
       "      <th>SUNDAY_REVENUE</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>2354</td>\n",
       "      <td>1287.50</td>\n",
       "      <td>1054.15</td>\n",
       "      <td>2842.52</td>\n",
       "      <td>2133.79</td>\n",
       "      <td>2051.94</td>\n",
       "      <td>1496.33</td>\n",
       "      <td>1120.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2361</td>\n",
       "      <td>1253.08</td>\n",
       "      <td>2003.25</td>\n",
       "      <td>1819.75</td>\n",
       "      <td>1114.83</td>\n",
       "      <td>1791.86</td>\n",
       "      <td>1624.34</td>\n",
       "      <td>1418.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2415</td>\n",
       "      <td>698.95</td>\n",
       "      <td>504.97</td>\n",
       "      <td>197.97</td>\n",
       "      <td>1182.97</td>\n",
       "      <td>733.96</td>\n",
       "      <td>1223.96</td>\n",
       "      <td>2716.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2427</td>\n",
       "      <td>1753.95</td>\n",
       "      <td>1400.23</td>\n",
       "      <td>570.48</td>\n",
       "      <td>1154.06</td>\n",
       "      <td>798.07</td>\n",
       "      <td>630.00</td>\n",
       "      <td>685.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2456</td>\n",
       "      <td>150.98</td>\n",
       "      <td>330.38</td>\n",
       "      <td>1090.23</td>\n",
       "      <td>563.63</td>\n",
       "      <td>1339.44</td>\n",
       "      <td>956.56</td>\n",
       "      <td>1832.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  MONDAY_REVENUE  TUESDAY_REVENUE  WEDNESDAY_REVENUE  \\\n",
       "0        2354         1287.50          1054.15            2842.52   \n",
       "1        2361         1253.08          2003.25            1819.75   \n",
       "2        2415          698.95           504.97             197.97   \n",
       "3        2427         1753.95          1400.23             570.48   \n",
       "4        2456          150.98           330.38            1090.23   \n",
       "\n",
       "   THURSDAY_REVENUE  FRIDAY_REVENUE  SATURDAY_REVENUE  SUNDAY_REVENUE  \n",
       "0           2133.79         2051.94           1496.33         1120.31  \n",
       "1           1114.83         1791.86           1624.34         1418.85  \n",
       "2           1182.97          733.96           1223.96         2716.91  \n",
       "3           1154.06          798.07            630.00          685.48  \n",
       "4            563.63         1339.44            956.56         1832.22  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_weekday_revenue = df_weekday.iloc[:, [0, 8, 9, 10, 11, 12, 13, 14]]\n",
    "df_weekday_revenue.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[8145, 8772, 8990, 10670, 9731, 8417, 9627]\n",
      "[1076041, 1167552, 1178446, 1329748, 1252902, 1098210, 1304716]\n"
     ]
    }
   ],
   "source": [
    "sum_order_day = []\n",
    "sum_revenue_day = []\n",
    "for i in range(1, 8):\n",
    "    sum_order_day.append(df_weekday_order.iloc[:, i].sum())\n",
    "    sum_revenue_day.append(int(df_weekday_revenue.iloc[:, i].sum()))\n",
    "\n",
    "print(sum_order_day)\n",
    "print(sum_revenue_day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "weekday = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
    "\n",
    "f, axes = plt.subplots(1, 2)\n",
    "f.set_size_inches(15, 7)\n",
    "\n",
    "axes[0].bar(weekday, sum_order_day)\n",
    "axes[0].set_title('Order')\n",
    "\n",
    "axes[1].bar(weekday, sum_revenue_day)\n",
    "axes[1].set_title('Revenue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "주문과 매출의 요일별 양상은 똑같으며, 대략적으로    \n",
    "(목 -> (금, 일) -> 수 -> 화 -> (월, 토)) 순으로 활발함"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 요일 관련 가설  \n",
    "1. 주문 빈도 or 총 주문액 or 주문 횟수가 많은 사람은 특정 요일에 더 많이 구매하는 경향이 있다. (+ 역)"
   ]
  },
  {
   "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>CustomerID</th>\n",
       "      <th>WEEK1_DAY01_DAY07_ORDERS</th>\n",
       "      <th>WEEK2_DAY08_DAY15_ORDERS</th>\n",
       "      <th>WEEK3_DAY16_DAY23_ORDERS</th>\n",
       "      <th>WEEK4_DAY24_DAY31_ORDERS</th>\n",
       "      <th>WEEK1_DAY01_DAY07_REVENUE</th>\n",
       "      <th>WEEK2_DAY08_DAY15_REVENUE</th>\n",
       "      <th>WEEK3_DAY16_DAY23_REVENUE</th>\n",
       "      <th>WEEK4_DAY24_DAY31_REVENUE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2354</td>\n",
       "      <td>28</td>\n",
       "      <td>42</td>\n",
       "      <td>30</td>\n",
       "      <td>24</td>\n",
       "      <td>2685.37</td>\n",
       "      <td>4299.28</td>\n",
       "      <td>2592.18</td>\n",
       "      <td>2409.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2361</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>1336.09</td>\n",
       "      <td>2776.02</td>\n",
       "      <td>2807.66</td>\n",
       "      <td>4106.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2415</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>2299.93</td>\n",
       "      <td>1383.92</td>\n",
       "      <td>713.94</td>\n",
       "      <td>2861.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2427</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>2317.95</td>\n",
       "      <td>2417.22</td>\n",
       "      <td>997.02</td>\n",
       "      <td>1260.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2456</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>831.14</td>\n",
       "      <td>1938.18</td>\n",
       "      <td>2725.66</td>\n",
       "      <td>768.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  WEEK1_DAY01_DAY07_ORDERS  WEEK2_DAY08_DAY15_ORDERS  \\\n",
       "0        2354                        28                        42   \n",
       "1        2361                        18                        19   \n",
       "2        2415                         9                        11   \n",
       "3        2427                        12                        15   \n",
       "4        2456                        10                        18   \n",
       "\n",
       "   WEEK3_DAY16_DAY23_ORDERS  WEEK4_DAY24_DAY31_ORDERS  \\\n",
       "0                        30                        24   \n",
       "1                        19                        26   \n",
       "2                         6                        17   \n",
       "3                         9                         8   \n",
       "4                        21                         6   \n",
       "\n",
       "   WEEK1_DAY01_DAY07_REVENUE  WEEK2_DAY08_DAY15_REVENUE  \\\n",
       "0                    2685.37                    4299.28   \n",
       "1                    1336.09                    2776.02   \n",
       "2                    2299.93                    1383.92   \n",
       "3                    2317.95                    2417.22   \n",
       "4                     831.14                    1938.18   \n",
       "\n",
       "   WEEK3_DAY16_DAY23_REVENUE  WEEK4_DAY24_DAY31_REVENUE  \n",
       "0                    2592.18                    2409.71  \n",
       "1                    2807.66                    4106.19  \n",
       "2                     713.94                    2861.90  \n",
       "3                     997.02                    1260.08  \n",
       "4                    2725.66                     768.46  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_week.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[14989, 15313, 16150, 17900]\n",
      "[1893191, 2032978, 2109134, 2372314]\n"
     ]
    }
   ],
   "source": [
    "df_week_order = df_week.iloc[:, 1:5]\n",
    "df_week_revenue = df_week.iloc[:, 5:]\n",
    "\n",
    "sum_order_week = []\n",
    "sum_revenue_week = []\n",
    "\n",
    "for i in range(4):\n",
    "    sum_order_week.append(df_week_order.iloc[:, i].sum())\n",
    "    sum_revenue_week.append(int(df_week_revenue.iloc[:, i].sum()))\n",
    "print(sum_order_week)\n",
    "print(sum_revenue_week)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "week = ['Week1', 'Week2', 'Week3', 'Week4']\n",
    "\n",
    "f, axes = plt.subplots(1, 2)\n",
    "f.set_size_inches(15, 7)\n",
    "\n",
    "axes[0].bar(week, sum_order_week)\n",
    "axes[0].set_title('Order')\n",
    "\n",
    "axes[1].bar(week, sum_revenue_week)\n",
    "axes[1].set_title('Revenue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 주차별 가설  \n",
    "1. 목요일의 높은 매출은 마지막 주의 높은 매출 때문이다. (월급날?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>CustomerID</th>\n",
       "      <th>TIME_0000_0600_ORDERS</th>\n",
       "      <th>TIME_0601_1200_ORDERS</th>\n",
       "      <th>TIME_1200_1800_ORDERS</th>\n",
       "      <th>TIME_1801_2359_ORDERS</th>\n",
       "      <th>TIME_0000_0600_REVENUE</th>\n",
       "      <th>TIME_0601_1200_REVENUE</th>\n",
       "      <th>TIME_1200_1800_REVENUE</th>\n",
       "      <th>TIME_1801_2359_REVENUE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2354</td>\n",
       "      <td>12</td>\n",
       "      <td>61</td>\n",
       "      <td>37</td>\n",
       "      <td>14</td>\n",
       "      <td>1067.92</td>\n",
       "      <td>5674.65</td>\n",
       "      <td>3916.53</td>\n",
       "      <td>1327.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2361</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>24</td>\n",
       "      <td>44</td>\n",
       "      <td>30.25</td>\n",
       "      <td>3143.69</td>\n",
       "      <td>2205.47</td>\n",
       "      <td>5646.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2415</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>707.98</td>\n",
       "      <td>2560.91</td>\n",
       "      <td>3990.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2427</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1908.99</td>\n",
       "      <td>2025.05</td>\n",
       "      <td>3058.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2456</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>27</td>\n",
       "      <td>13</td>\n",
       "      <td>501.34</td>\n",
       "      <td>1197.83</td>\n",
       "      <td>3004.77</td>\n",
       "      <td>1559.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  TIME_0000_0600_ORDERS  TIME_0601_1200_ORDERS  \\\n",
       "0        2354                     12                     61   \n",
       "1        2361                      1                     13   \n",
       "2        2415                      0                      6   \n",
       "3        2427                      0                     10   \n",
       "4        2456                      6                      9   \n",
       "\n",
       "   TIME_1200_1800_ORDERS  TIME_1801_2359_ORDERS  TIME_0000_0600_REVENUE  \\\n",
       "0                     37                     14                 1067.92   \n",
       "1                     24                     44                   30.25   \n",
       "2                     12                     25                    0.00   \n",
       "3                     13                     21                    0.00   \n",
       "4                     27                     13                  501.34   \n",
       "\n",
       "   TIME_0601_1200_REVENUE  TIME_1200_1800_REVENUE  TIME_1801_2359_REVENUE  \n",
       "0                 5674.65                 3916.53                 1327.44  \n",
       "1                 3143.69                 2205.47                 5646.55  \n",
       "2                  707.98                 2560.91                 3990.80  \n",
       "3                 1908.99                 2025.05                 3058.23  \n",
       "4                 1197.83                 3004.77                 1559.50  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_time.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5144, 18731, 22170, 18307]\n",
      "[655313, 2434319, 2923658, 2394328]\n"
     ]
    }
   ],
   "source": [
    "df_time_order = df_time.iloc[:, 1:5]\n",
    "df_time_revenue = df_time.iloc[:, 5:]\n",
    "\n",
    "sum_order_time = []\n",
    "sum_revenue_time = []\n",
    "\n",
    "for i in range(4):\n",
    "    sum_order_time.append(df_time_order.iloc[:, i].sum())\n",
    "    sum_revenue_time.append(int(df_time_revenue.iloc[:, i].sum()))\n",
    "print(sum_order_time)\n",
    "print(sum_revenue_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time = ['0000~0600', '0601~1200', '1201~1800', '1801~2359']\n",
    "\n",
    "f, axes = plt.subplots(1, 2)\n",
    "f.set_size_inches(15, 7)\n",
    "\n",
    "axes[0].bar(time, sum_order_time)\n",
    "axes[0].set_title('Order')\n",
    "\n",
    "axes[1].bar(time, sum_revenue_time)\n",
    "axes[1].set_title('Revenue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 요약  \n",
    "### df_info 가설 :  \n",
    "1. (TOTAL_ORDERS)주문 수가 많은 사람은 총 구매액 (REVENUE)가 높을 것이다.  \n",
    "2. (DAYSSINCELASTORDER)마지막 주문으로부터 ##일 이상 지난 사람은 주문 수(TOTAL_ORDERS)가 적을 것이다.  \n",
    "3. (AVGDAYSBETWEENORDERS) 평균 주문 간격이 짧은 사람은 주문 수(TOTAL_ORDERS)가 많을 것이다.  \n",
    "4. 평균 배송 금액이 높을 수록 총 주문 수는 낮을 것이다.  \n",
    "\n",
    "### 요일별 가설\n",
    "주문과 매출의 요일별 양상은 똑같으며, 대략적으로    \n",
    "(목 -> (금, 일) -> 수 -> 화 -> (월, 토)) 순으로 활발함  \n",
    "1. 주문 빈도 or 총 주문액 or 주문 횟수가 많은 사람은 특정 요일에 더 많이 구매하는 경향이 있다. (+ 역)\n",
    "\n",
    "### 주차별 가설  \n",
    "1. 목요일의 높은 매출은 마지막 주의 높은 매출 때문이다. (월급날?)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 분석"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 귀무가설 : 주문 수가 많은 사람은 총 구매액이 높을 것이다.  \n",
    "상관분석"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              TOTAL_ORDERS   REVENUE\n",
      "TOTAL_ORDERS      1.000000  0.771996\n",
      "REVENUE           0.771996  1.000000\n",
      "---------------\n",
      "PearsonRResult(statistic=0.7719960094538744, pvalue=0.0)\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "df2 = data[['TOTAL_ORDERS', 'REVENUE']]\n",
    "\n",
    "\n",
    "print(df2.corr())\n",
    "print('---------------')\n",
    "print(stats.pearsonr(df2['REVENUE'], df2['TOTAL_ORDERS']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 상관계수 0.77에 유의확률 0.0으로, 주문 수와 총 구매액 사이에는 유의미한 상관관계가 있다고 할 수 있다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. 귀무가설 : 마지막 주문으로부터 n일 이상 지난 사람은 주문 수가 적을 것이다.  \n",
    "(마지막 주문으로부터 30일 이상 지난 사람들의 그룹, 마지막 주문으로부터 30일 이상 지나지 않은 사람들의 그룹)과 주문 수, 매출 과의 상관관계 분석  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_latest_under_30 = data.loc[data['LATEST_ORDER_DATE'] <= 30, ['TOTAL_ORDERS', 'REVENUE', 'LATEST_ORDER_DATE']]\n",
    "df_latest_up_30 = data.loc[data['LATEST_ORDER_DATE'] > 30, ['TOTAL_ORDERS', 'REVENUE', 'LATEST_ORDER_DATE']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "최근 주문 30일 미만 평균 주문 수 :  13.548709847288047\n",
      "최근 주문 30일 미만 평균 구매액 :  1699.5458135860977\n",
      "-------------------------------------------------------------\n",
      "최근 주문 30일 이상 평균 주문 수 :  12.45501451144792\n",
      "최근 주문 30일 이상 평균 구매액 :  1670.4874879071267\n"
     ]
    }
   ],
   "source": [
    "print('최근 주문 30일 미만 평균 주문 수 : ', df_latest_under_30['TOTAL_ORDERS'].mean())\n",
    "print('최근 주문 30일 미만 평균 구매액 : ', df_latest_under_30['REVENUE'].mean())\n",
    "print('-------------------------------------------------------------')\n",
    "print('최근 주문 30일 이상 평균 주문 수 : ',df_latest_up_30['TOTAL_ORDERS'].mean())\n",
    "print('최근 주문 30일 이상 평균 구매액 : ',df_latest_up_30['REVENUE'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TOTAL_ORDERS</th>\n",
       "      <th>REVENUE</th>\n",
       "      <th>LATEST_ORDER_DATE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>124</td>\n",
       "      <td>11986.54</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>82</td>\n",
       "      <td>11025.96</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>43</td>\n",
       "      <td>7259.69</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44</td>\n",
       "      <td>6992.27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "      <td>6263.44</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TOTAL_ORDERS   REVENUE  LATEST_ORDER_DATE\n",
       "0           124  11986.54                  1\n",
       "1            82  11025.96                  1\n",
       "2            43   7259.69                  1\n",
       "3            44   6992.27                  1\n",
       "4            55   6263.44                  1"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = data[['TOTAL_ORDERS', 'REVENUE', 'LATEST_ORDER_DATE']]\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/29/49d2_7gx7ml5z_vc76rgbnsm0000gn/T/ipykernel_50263/4113397229.py:5: 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",
      "  df3.loc[:, 'CATEGORY_LATEST_ORDER'] = pd.cut(df3['LATEST_ORDER_DATE'], bins=bins, labels=labels, right=False)\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>TOTAL_ORDERS</th>\n",
       "      <th>REVENUE</th>\n",
       "      <th>LATEST_ORDER_DATE</th>\n",
       "      <th>CATEGORY_LATEST_ORDER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>124</td>\n",
       "      <td>11986.54</td>\n",
       "      <td>1</td>\n",
       "      <td>0_to_30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>82</td>\n",
       "      <td>11025.96</td>\n",
       "      <td>1</td>\n",
       "      <td>0_to_30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>43</td>\n",
       "      <td>7259.69</td>\n",
       "      <td>1</td>\n",
       "      <td>0_to_30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44</td>\n",
       "      <td>6992.27</td>\n",
       "      <td>1</td>\n",
       "      <td>0_to_30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "      <td>6263.44</td>\n",
       "      <td>1</td>\n",
       "      <td>0_to_30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TOTAL_ORDERS   REVENUE  LATEST_ORDER_DATE CATEGORY_LATEST_ORDER\n",
       "0           124  11986.54                  1               0_to_30\n",
       "1            82  11025.96                  1               0_to_30\n",
       "2            43   7259.69                  1               0_to_30\n",
       "3            44   6992.27                  1               0_to_30\n",
       "4            55   6263.44                  1               0_to_30"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 최근 주문일을 범주형으로 나눈다.\n",
    "bins= [0, 30, 50, 100, 300]\n",
    "labels= ['0_to_30', '31_to_50', '51_to_100', '100_to_max']\n",
    "\n",
    "df3.loc[:, 'CATEGORY_LATEST_ORDER'] = pd.cut(df3['LATEST_ORDER_DATE'], bins=bins, labels=labels, right=False)\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\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>Source</th>\n",
       "      <th>ddof1</th>\n",
       "      <th>ddof2</th>\n",
       "      <th>F</th>\n",
       "      <th>p-unc</th>\n",
       "      <th>np2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CATEGORY_LATEST_ORDER</td>\n",
       "      <td>3</td>\n",
       "      <td>1228.883169</td>\n",
       "      <td>175.037324</td>\n",
       "      <td>1.752829e-94</td>\n",
       "      <td>0.090484</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Source  ddof1        ddof2           F         p-unc  \\\n",
       "0  CATEGORY_LATEST_ORDER      3  1228.883169  175.037324  1.752829e-94   \n",
       "\n",
       "        np2  \n",
       "0  0.090484  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 분산분석\n",
    "import scipy as sp\n",
    "import numpy as np\n",
    "import pingouin as pg\n",
    "import scikit_posthocs\n",
    "\n",
    "pg.welch_anova(dv='TOTAL_ORDERS', between='CATEGORY_LATEST_ORDER', data=df3)\n"
   ]
  },
  {
   "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",
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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>0_to_30</th>\n",
       "      <th>31_to_50</th>\n",
       "      <th>51_to_100</th>\n",
       "      <th>100_to_max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0_to_30</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>6.936829e-21</td>\n",
       "      <td>1.816683e-18</td>\n",
       "      <td>5.283196e-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31_to_50</th>\n",
       "      <td>6.936829e-21</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>7.296804e-02</td>\n",
       "      <td>1.861459e-55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51_to_100</th>\n",
       "      <td>1.816683e-18</td>\n",
       "      <td>7.296804e-02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>9.550562e-67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100_to_max</th>\n",
       "      <td>5.283196e-28</td>\n",
       "      <td>1.861459e-55</td>\n",
       "      <td>9.550562e-67</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0_to_30      31_to_50     51_to_100    100_to_max\n",
       "0_to_30     1.000000e+00  6.936829e-21  1.816683e-18  5.283196e-28\n",
       "31_to_50    6.936829e-21  1.000000e+00  7.296804e-02  1.861459e-55\n",
       "51_to_100   1.816683e-18  7.296804e-02  1.000000e+00  9.550562e-67\n",
       "100_to_max  5.283196e-28  1.861459e-55  9.550562e-67  1.000000e+00"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 사후분석\n",
    "scikit_posthocs.posthoc_scheffe(df3, val_col='TOTAL_ORDERS', group_col='CATEGORY_LATEST_ORDER')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Source</th>\n",
       "      <th>ddof1</th>\n",
       "      <th>ddof2</th>\n",
       "      <th>F</th>\n",
       "      <th>p-unc</th>\n",
       "      <th>np2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CATEGORY_LATEST_ORDER</td>\n",
       "      <td>3</td>\n",
       "      <td>1215.86179</td>\n",
       "      <td>93.503042</td>\n",
       "      <td>1.881926e-54</td>\n",
       "      <td>0.061298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Source  ddof1       ddof2          F         p-unc       np2\n",
       "0  CATEGORY_LATEST_ORDER      3  1215.86179  93.503042  1.881926e-54  0.061298"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pg.welch_anova(dv='REVENUE', between='CATEGORY_LATEST_ORDER', data=df3)"
   ]
  },
  {
   "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>0_to_30</th>\n",
       "      <th>31_to_50</th>\n",
       "      <th>51_to_100</th>\n",
       "      <th>100_to_max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0_to_30</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>8.117008e-16</td>\n",
       "      <td>1.068395e-18</td>\n",
       "      <td>3.839694e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31_to_50</th>\n",
       "      <td>8.117008e-16</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>5.578711e-01</td>\n",
       "      <td>1.496206e-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51_to_100</th>\n",
       "      <td>1.068395e-18</td>\n",
       "      <td>5.578711e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.854920e-47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100_to_max</th>\n",
       "      <td>3.839694e-12</td>\n",
       "      <td>1.496206e-34</td>\n",
       "      <td>1.854920e-47</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0_to_30      31_to_50     51_to_100    100_to_max\n",
       "0_to_30     1.000000e+00  8.117008e-16  1.068395e-18  3.839694e-12\n",
       "31_to_50    8.117008e-16  1.000000e+00  5.578711e-01  1.496206e-34\n",
       "51_to_100   1.068395e-18  5.578711e-01  1.000000e+00  1.854920e-47\n",
       "100_to_max  3.839694e-12  1.496206e-34  1.854920e-47  1.000000e+00"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scikit_posthocs.posthoc_scheffe(df3, val_col='REVENUE', group_col='CATEGORY_LATEST_ORDER')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.493899204244032\n",
      "20.511627906976745\n",
      "18.41886269070735\n",
      "9.06341463414634\n",
      "--------------------------------------------------------\n",
      "1690.4002015915119\n",
      "2666.657587209302\n",
      "2484.0033425797506\n",
      "1225.8137560975608\n"
     ]
    }
   ],
   "source": [
    "print(df3.loc[df3['CATEGORY_LATEST_ORDER'] == '0_to_30', 'TOTAL_ORDERS'].mean())\n",
    "print(df3.loc[df3['CATEGORY_LATEST_ORDER'] == '31_to_50', 'TOTAL_ORDERS'].mean())\n",
    "print(df3.loc[df3['CATEGORY_LATEST_ORDER'] == '51_to_100', 'TOTAL_ORDERS'].mean())\n",
    "print(df3.loc[df3['CATEGORY_LATEST_ORDER'] == '100_to_max', 'TOTAL_ORDERS'].mean())\n",
    "print('--------------------------------------------------------')\n",
    "print(df3.loc[df3['CATEGORY_LATEST_ORDER'] == '0_to_30', 'REVENUE'].mean())\n",
    "print(df3.loc[df3['CATEGORY_LATEST_ORDER'] == '31_to_50', 'REVENUE'].mean())\n",
    "print(df3.loc[df3['CATEGORY_LATEST_ORDER'] == '51_to_100', 'REVENUE'].mean())\n",
    "print(df3.loc[df3['CATEGORY_LATEST_ORDER'] == '100_to_max', 'REVENUE'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "분산분석 결과, p-value의 값은 0.01 미만으로 각 범주 간 총 주문 수에는 유의한 차이가 있다고 볼 수 있다.  \n",
    "사후분석 결과, '31_to_50' 그룹과 '51_to_100'그룹을 제외하고는 (유의확률 0.07) 모두 그룹 간 유의한 차이가 있다고 볼 수 있다.  \n",
    "각 그룹 별 평균 주문 수와 주문액을 살펴보면, 예상과 다르게 가장 최근 30일 이내로 주문한 그룹의 총 주문 수와 매출액이 적은 편인 것을 볼 수 있다.  \n",
    "신규 유입, 단발성 구매 등을 이유로 추론할 수 있을 듯하다.  \n",
    "분산분석과 사후분석, 그리고 각 범주별 평균을 모두 종합해봤을 때, 최근 주문일을 기준으로 한 주요 고객층은 31~100일 구간의 고객이라고 할 수 있다.  \n",
    "  \n",
    "  그러나 최근 구매한 고객 중 단발성 구매인 고객과 단발성 구매 후 장기 미사용 고객을 제외시켜야 구매가 활발한 충성고객을 더욱 잘 반영한 통계가 나올 듯하다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df3['LATEST_ORDER_DATE'], bins=range(0, 220, 5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TOTAL_ORDERS             2715\n",
       "REVENUE                  2715\n",
       "LATEST_ORDER_DATE        2715\n",
       "CATEGORY_LATEST_ORDER    2715\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 먼저, 총 주문 수가 10회 이하인 사람을 추려낸다.\n",
    "df3.loc[df3['TOTAL_ORDERS'] <= 10].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "538.0836206261511"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.loc[df3['TOTAL_ORDERS'] <= 10]['REVENUE'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>TOTAL_ORDERS</th>\n",
       "      <th>REVENUE</th>\n",
       "      <th>LATEST_ORDER_DATE</th>\n",
       "      <th>CATEGORY_LATEST_ORDER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>124</td>\n",
       "      <td>11986.54</td>\n",
       "      <td>1</td>\n",
       "      <td>0_to_30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>82</td>\n",
       "      <td>11025.96</td>\n",
       "      <td>1</td>\n",
       "      <td>0_to_30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>43</td>\n",
       "      <td>7259.69</td>\n",
       "      <td>1</td>\n",
       "      <td>0_to_30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44</td>\n",
       "      <td>6992.27</td>\n",
       "      <td>1</td>\n",
       "      <td>0_to_30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "      <td>6263.44</td>\n",
       "      <td>1</td>\n",
       "      <td>0_to_30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TOTAL_ORDERS   REVENUE  LATEST_ORDER_DATE CATEGORY_LATEST_ORDER\n",
       "0           124  11986.54                  1               0_to_30\n",
       "1            82  11025.96                  1               0_to_30\n",
       "2            43   7259.69                  1               0_to_30\n",
       "3            44   6992.27                  1               0_to_30\n",
       "4            55   6263.44                  1               0_to_30"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3_order_more_10 = df3.loc[df3['TOTAL_ORDERS'] > 10]\n",
    "df3_order_more_10.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df3_order_more_10['LATEST_ORDER_DATE'], bins=range(0, 220, 5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1885\n",
      "344\n",
      "721\n",
      "2050\n",
      "-----------------------------\n",
      "858\n",
      "276\n",
      "517\n",
      "634\n"
     ]
    }
   ],
   "source": [
    "print(df3.loc[df3['CATEGORY_LATEST_ORDER'] == '0_to_30']['CATEGORY_LATEST_ORDER'].count())\n",
    "print(df3.loc[df3['CATEGORY_LATEST_ORDER'] == '31_to_50']['CATEGORY_LATEST_ORDER'].count())\n",
    "print(df3.loc[df3['CATEGORY_LATEST_ORDER'] == '51_to_100']['CATEGORY_LATEST_ORDER'].count())\n",
    "print(df3.loc[df3['CATEGORY_LATEST_ORDER'] == '100_to_max']['CATEGORY_LATEST_ORDER'].count())\n",
    "print('-----------------------------')\n",
    "print(df3_order_more_10.loc[df3_order_more_10['CATEGORY_LATEST_ORDER'] == '0_to_30']['CATEGORY_LATEST_ORDER'].count())\n",
    "print(df3_order_more_10.loc[df3_order_more_10['CATEGORY_LATEST_ORDER'] == '31_to_50']['CATEGORY_LATEST_ORDER'].count())\n",
    "print(df3_order_more_10.loc[df3_order_more_10['CATEGORY_LATEST_ORDER'] == '51_to_100']['CATEGORY_LATEST_ORDER'].count())\n",
    "print(df3_order_more_10.loc[df3_order_more_10['CATEGORY_LATEST_ORDER'] == '100_to_max']['CATEGORY_LATEST_ORDER'].count())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "주문 수가 10회 이하인 사람들 중 대부분은 신규 유입 또는 장기 미사용 고객인 것을 볼 수 있다.  \n",
    "다시 분산분석, 사후분석 진행"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>ddof1</th>\n",
       "      <th>ddof2</th>\n",
       "      <th>F</th>\n",
       "      <th>p-unc</th>\n",
       "      <th>np2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CATEGORY_LATEST_ORDER</td>\n",
       "      <td>3</td>\n",
       "      <td>955.808488</td>\n",
       "      <td>14.343587</td>\n",
       "      <td>3.798107e-09</td>\n",
       "      <td>0.014898</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Source  ddof1       ddof2          F         p-unc       np2\n",
       "0  CATEGORY_LATEST_ORDER      3  955.808488  14.343587  3.798107e-09  0.014898"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pg.welch_anova(dv='TOTAL_ORDERS', between='CATEGORY_LATEST_ORDER', data=df3_order_more_10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>0_to_30</th>\n",
       "      <th>31_to_50</th>\n",
       "      <th>51_to_100</th>\n",
       "      <th>100_to_max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0_to_30</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.930862</td>\n",
       "      <td>0.373785</td>\n",
       "      <td>3.877329e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31_to_50</th>\n",
       "      <td>9.308615e-01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.921046</td>\n",
       "      <td>6.203126e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51_to_100</th>\n",
       "      <td>3.737848e-01</td>\n",
       "      <td>0.921046</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.005155e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100_to_max</th>\n",
       "      <td>3.877329e-07</td>\n",
       "      <td>0.006203</td>\n",
       "      <td>0.009005</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0_to_30  31_to_50  51_to_100    100_to_max\n",
       "0_to_30     1.000000e+00  0.930862   0.373785  3.877329e-07\n",
       "31_to_50    9.308615e-01  1.000000   0.921046  6.203126e-03\n",
       "51_to_100   3.737848e-01  0.921046   1.000000  9.005155e-03\n",
       "100_to_max  3.877329e-07  0.006203   0.009005  1.000000e+00"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scikit_posthocs.posthoc_scheffe(df3_order_more_10, val_col='TOTAL_ORDERS', group_col='CATEGORY_LATEST_ORDER')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\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>Source</th>\n",
       "      <th>ddof1</th>\n",
       "      <th>ddof2</th>\n",
       "      <th>F</th>\n",
       "      <th>p-unc</th>\n",
       "      <th>np2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CATEGORY_LATEST_ORDER</td>\n",
       "      <td>3</td>\n",
       "      <td>941.542335</td>\n",
       "      <td>0.306896</td>\n",
       "      <td>0.820424</td>\n",
       "      <td>0.000459</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Source  ddof1       ddof2         F     p-unc       np2\n",
       "0  CATEGORY_LATEST_ORDER      3  941.542335  0.306896  0.820424  0.000459"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pg.welch_anova(dv='REVENUE', between='CATEGORY_LATEST_ORDER', data=df3_order_more_10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0_to_30</th>\n",
       "      <th>31_to_50</th>\n",
       "      <th>51_to_100</th>\n",
       "      <th>100_to_max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0_to_30</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.995921</td>\n",
       "      <td>0.947396</td>\n",
       "      <td>0.972793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31_to_50</th>\n",
       "      <td>0.995921</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.997243</td>\n",
       "      <td>0.951359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51_to_100</th>\n",
       "      <td>0.947396</td>\n",
       "      <td>0.997243</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.805919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100_to_max</th>\n",
       "      <td>0.972793</td>\n",
       "      <td>0.951359</td>\n",
       "      <td>0.805919</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             0_to_30  31_to_50  51_to_100  100_to_max\n",
       "0_to_30     1.000000  0.995921   0.947396    0.972793\n",
       "31_to_50    0.995921  1.000000   0.997243    0.951359\n",
       "51_to_100   0.947396  0.997243   1.000000    0.805919\n",
       "100_to_max  0.972793  0.951359   0.805919    1.000000"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scikit_posthocs.posthoc_scheffe(df3_order_more_10, val_col='REVENUE', group_col='CATEGORY_LATEST_ORDER')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24.726107226107224\n",
      "24.184782608695652\n",
      "23.572533849129595\n",
      "21.20504731861199\n",
      "--------------------------------------------------------\n",
      "3034.1287762237766\n",
      "3071.9951449275363\n",
      "3107.7430367504835\n",
      "2979.2837066246057\n"
     ]
    }
   ],
   "source": [
    "print(df3_order_more_10.loc[df3_order_more_10['CATEGORY_LATEST_ORDER'] == '0_to_30', 'TOTAL_ORDERS'].mean())\n",
    "print(df3_order_more_10.loc[df3_order_more_10['CATEGORY_LATEST_ORDER'] == '31_to_50', 'TOTAL_ORDERS'].mean())\n",
    "print(df3_order_more_10.loc[df3_order_more_10['CATEGORY_LATEST_ORDER'] == '51_to_100', 'TOTAL_ORDERS'].mean())\n",
    "print(df3_order_more_10.loc[df3_order_more_10['CATEGORY_LATEST_ORDER'] == '100_to_max', 'TOTAL_ORDERS'].mean())\n",
    "print('--------------------------------------------------------')\n",
    "print(df3_order_more_10.loc[df3_order_more_10['CATEGORY_LATEST_ORDER'] == '0_to_30', 'REVENUE'].mean())\n",
    "print(df3_order_more_10.loc[df3_order_more_10['CATEGORY_LATEST_ORDER'] == '31_to_50', 'REVENUE'].mean())\n",
    "print(df3_order_more_10.loc[df3_order_more_10['CATEGORY_LATEST_ORDER'] == '51_to_100', 'REVENUE'].mean())\n",
    "print(df3_order_more_10.loc[df3_order_more_10['CATEGORY_LATEST_ORDER'] == '100_to_max', 'REVENUE'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "주문 수 10회 이상인 고객들로만 분석을 시행한 결과, 분산분석에서는 그룹 간 주문 수의 차이가 유의하다고는 나오지만 사후분석 결과 30일 이내 주문한 고객 그룹과 100일 이상 미주문 고객들 그룹을 제외하고는 그룹 간 유의한 차이가 없다고 결론을 내릴 수 있다.  \n",
    "또한, 주문 수가 아닌 매출을 종속변수로 설정 시 확실하게 그룹 간 유의한 차이가 없음을 볼 수 있다.  \n",
    "각 그룹의 평균 주문 수와 매출을 봐도 그룹간 주문 수, 주문액 평균의 유의한 차이가 있다고 볼 수는 없다.  \n",
    "\n",
    "### 결론 : 최근 주문일과 주문 수, 구매액과는 유의한 차이가 없다.  \n",
    "따라서, 최근 주문일로는 활성화 고객과 비활성화 고객을 구분하는 용도 정도로 쓸 수 있을 것이다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 요일별 분석  \n",
    "가장 활발한 목요일, 그리고 유의미하게 활봘한 (목, 금, 일) 그룹에 대해서 각각 분석을 진행한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TOTAL_ORDERS</th>\n",
       "      <th>REVENUE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5000.00000</td>\n",
       "      <td>5000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.87040</td>\n",
       "      <td>1681.523840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.67988</td>\n",
       "      <td>1998.618678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>38.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.00000</td>\n",
       "      <td>315.097500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.00000</td>\n",
       "      <td>966.725000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>20.00000</td>\n",
       "      <td>2493.072500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>156.00000</td>\n",
       "      <td>34847.400000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       TOTAL_ORDERS       REVENUE\n",
       "count    5000.00000   5000.000000\n",
       "mean       12.87040   1681.523840\n",
       "std        12.67988   1998.618678\n",
       "min         1.00000     38.500000\n",
       "25%         3.00000    315.097500\n",
       "50%         8.00000    966.725000\n",
       "75%        20.00000   2493.072500\n",
       "max       156.00000  34847.400000"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['TOTAL_ORDERS', 'REVENUE']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "상위 그룹을 주문 수 총 주문 수 또는 구매액이 상위 10%에 드는 고객들로 설정한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TOTAL_ORDERS      28.000\n",
       "REVENUE         3899.444\n",
       "Name: 0.9, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['TOTAL_ORDERS', 'REVENUE']].quantile(q=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "      <th>index</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>TOTAL_ORDERS</th>\n",
       "      <th>REVENUE</th>\n",
       "      <th>AVERAGE_ORDER_VALUE</th>\n",
       "      <th>CARRIAGE_REVENUE</th>\n",
       "      <th>AVERAGESHIPPING</th>\n",
       "      <th>FIRST_ORDER_DATE</th>\n",
       "      <th>LATEST_ORDER_DATE</th>\n",
       "      <th>AVGDAYSBETWEENORDERS</th>\n",
       "      <th>...</th>\n",
       "      <th>WEEK3_DAY16_DAY23_REVENUE</th>\n",
       "      <th>WEEK4_DAY24_DAY31_REVENUE</th>\n",
       "      <th>TIME_0000_0600_ORDERS</th>\n",
       "      <th>TIME_0601_1200_ORDERS</th>\n",
       "      <th>TIME_1200_1800_ORDERS</th>\n",
       "      <th>TIME_1801_2359_ORDERS</th>\n",
       "      <th>TIME_0000_0600_REVENUE</th>\n",
       "      <th>TIME_0601_1200_REVENUE</th>\n",
       "      <th>TIME_1200_1800_REVENUE</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>count</th>\n",
       "      <td>754.000000</td>\n",
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       "      <td>754.000000</td>\n",
       "      <td>754.000000</td>\n",
       "      <td>754.000000</td>\n",
       "      <td>754.000000</td>\n",
       "      <td>754.000000</td>\n",
       "      <td>754.000000</td>\n",
       "      <td>754.000000</td>\n",
       "      <td>754.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>754.000000</td>\n",
       "      <td>754.000000</td>\n",
       "      <td>754.000000</td>\n",
       "      <td>754.000000</td>\n",
       "      <td>754.000000</td>\n",
       "      <td>754.000000</td>\n",
       "      <td>754.000000</td>\n",
       "      <td>754.000000</td>\n",
       "      <td>754.000000</td>\n",
       "      <td>754.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2185.233422</td>\n",
       "      <td>3043.531830</td>\n",
       "      <td>33.415119</td>\n",
       "      <td>4929.109324</td>\n",
       "      <td>173.075239</td>\n",
       "      <td>108.481127</td>\n",
       "      <td>3.251751</td>\n",
       "      <td>1290.851459</td>\n",
       "      <td>58.327586</td>\n",
       "      <td>44.207268</td>\n",
       "      <td>...</td>\n",
       "      <td>1267.908395</td>\n",
       "      <td>1328.757387</td>\n",
       "      <td>2.766578</td>\n",
       "      <td>9.558355</td>\n",
       "      <td>11.513263</td>\n",
       "      <td>9.576923</td>\n",
       "      <td>386.671724</td>\n",
       "      <td>1424.186963</td>\n",
       "      <td>1701.227706</td>\n",
       "      <td>1417.022931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>889.113763</td>\n",
       "      <td>1846.878847</td>\n",
       "      <td>15.468902</td>\n",
       "      <td>2854.899842</td>\n",
       "      <td>123.204859</td>\n",
       "      <td>64.129341</td>\n",
       "      <td>1.289394</td>\n",
       "      <td>707.056004</td>\n",
       "      <td>51.855762</td>\n",
       "      <td>34.705435</td>\n",
       "      <td>...</td>\n",
       "      <td>1069.441247</td>\n",
       "      <td>943.278513</td>\n",
       "      <td>3.868852</td>\n",
       "      <td>7.265953</td>\n",
       "      <td>7.065090</td>\n",
       "      <td>6.817098</td>\n",
       "      <td>646.043133</td>\n",
       "      <td>1471.010010</td>\n",
       "      <td>1282.707357</td>\n",
       "      <td>1253.869429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2333.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>532.690000</td>\n",
       "      <td>11.220000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.640000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1503.250000</td>\n",
       "      <td>2521.250000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>3443.555000</td>\n",
       "      <td>96.687500</td>\n",
       "      <td>66.892500</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>679.750000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>18.805000</td>\n",
       "      <td>...</td>\n",
       "      <td>658.435000</td>\n",
       "      <td>728.070000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>655.717500</td>\n",
       "      <td>891.600000</td>\n",
       "      <td>614.317500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2118.500000</td>\n",
       "      <td>2709.500000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>4319.395000</td>\n",
       "      <td>135.840000</td>\n",
       "      <td>99.815000</td>\n",
       "      <td>3.290000</td>\n",
       "      <td>1267.000000</td>\n",
       "      <td>41.500000</td>\n",
       "      <td>36.065000</td>\n",
       "      <td>...</td>\n",
       "      <td>1025.330000</td>\n",
       "      <td>1162.425000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>178.475000</td>\n",
       "      <td>1130.590000</td>\n",
       "      <td>1443.460000</td>\n",
       "      <td>1118.730000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2901.750000</td>\n",
       "      <td>3045.500000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>5451.762500</td>\n",
       "      <td>210.430000</td>\n",
       "      <td>137.567500</td>\n",
       "      <td>4.177500</td>\n",
       "      <td>1737.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>56.910000</td>\n",
       "      <td>...</td>\n",
       "      <td>1569.485000</td>\n",
       "      <td>1681.377500</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>497.775000</td>\n",
       "      <td>1829.742500</td>\n",
       "      <td>2190.522500</td>\n",
       "      <td>1813.895000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3984.000000</td>\n",
       "      <td>39212.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>34847.400000</td>\n",
       "      <td>1294.000000</td>\n",
       "      <td>529.590000</td>\n",
       "      <td>13.610000</td>\n",
       "      <td>2822.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>233.330000</td>\n",
       "      <td>...</td>\n",
       "      <td>12946.220000</td>\n",
       "      <td>8274.970000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>7700.240000</td>\n",
       "      <td>23832.770000</td>\n",
       "      <td>17749.010000</td>\n",
       "      <td>12995.020000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             index    CustomerID  TOTAL_ORDERS       REVENUE  \\\n",
       "count   754.000000    754.000000    754.000000    754.000000   \n",
       "mean   2185.233422   3043.531830     33.415119   4929.109324   \n",
       "std     889.113763   1846.878847     15.468902   2854.899842   \n",
       "min       0.000000   2333.000000      5.000000    532.690000   \n",
       "25%    1503.250000   2521.250000     25.000000   3443.555000   \n",
       "50%    2118.500000   2709.500000     31.000000   4319.395000   \n",
       "75%    2901.750000   3045.500000     39.000000   5451.762500   \n",
       "max    3984.000000  39212.000000    156.000000  34847.400000   \n",
       "\n",
       "       AVERAGE_ORDER_VALUE  CARRIAGE_REVENUE  AVERAGESHIPPING  \\\n",
       "count           754.000000        754.000000       754.000000   \n",
       "mean            173.075239        108.481127         3.251751   \n",
       "std             123.204859         64.129341         1.289394   \n",
       "min              11.220000          0.000000         0.000000   \n",
       "25%              96.687500         66.892500         2.400000   \n",
       "50%             135.840000         99.815000         3.290000   \n",
       "75%             210.430000        137.567500         4.177500   \n",
       "max            1294.000000        529.590000        13.610000   \n",
       "\n",
       "       FIRST_ORDER_DATE  LATEST_ORDER_DATE  AVGDAYSBETWEENORDERS  ...  \\\n",
       "count        754.000000         754.000000            754.000000  ...   \n",
       "mean        1290.851459          58.327586             44.207268  ...   \n",
       "std          707.056004          51.855762             34.705435  ...   \n",
       "min           90.000000           1.000000              1.640000  ...   \n",
       "25%          679.750000          14.000000             18.805000  ...   \n",
       "50%         1267.000000          41.500000             36.065000  ...   \n",
       "75%         1737.000000          96.000000             56.910000  ...   \n",
       "max         2822.000000         205.000000            233.330000  ...   \n",
       "\n",
       "       WEEK3_DAY16_DAY23_REVENUE  WEEK4_DAY24_DAY31_REVENUE  \\\n",
       "count                 754.000000                 754.000000   \n",
       "mean                 1267.908395                1328.757387   \n",
       "std                  1069.441247                 943.278513   \n",
       "min                     0.000000                   0.000000   \n",
       "25%                   658.435000                 728.070000   \n",
       "50%                  1025.330000                1162.425000   \n",
       "75%                  1569.485000                1681.377500   \n",
       "max                 12946.220000                8274.970000   \n",
       "\n",
       "       TIME_0000_0600_ORDERS  TIME_0601_1200_ORDERS  TIME_1200_1800_ORDERS  \\\n",
       "count             754.000000             754.000000             754.000000   \n",
       "mean                2.766578               9.558355              11.513263   \n",
       "std                 3.868852               7.265953               7.065090   \n",
       "min                 0.000000               0.000000               0.000000   \n",
       "25%                 0.000000               5.000000               7.000000   \n",
       "50%                 1.000000               8.000000              10.000000   \n",
       "75%                 4.000000              13.000000              15.000000   \n",
       "max                34.000000              61.000000              58.000000   \n",
       "\n",
       "       TIME_1801_2359_ORDERS  TIME_0000_0600_REVENUE  TIME_0601_1200_REVENUE  \\\n",
       "count             754.000000              754.000000              754.000000   \n",
       "mean                9.576923              386.671724             1424.186963   \n",
       "std                 6.817098              646.043133             1471.010010   \n",
       "min                 0.000000                0.000000                0.000000   \n",
       "25%                 5.000000                0.000000              655.717500   \n",
       "50%                 8.000000              178.475000             1130.590000   \n",
       "75%                13.000000              497.775000             1829.742500   \n",
       "max                64.000000             7700.240000            23832.770000   \n",
       "\n",
       "       TIME_1200_1800_REVENUE  TIME_1801_2359_REVENUE  \n",
       "count              754.000000              754.000000  \n",
       "mean              1701.227706             1417.022931  \n",
       "std               1282.707357             1253.869429  \n",
       "min                  0.000000                0.000000  \n",
       "25%                891.600000              614.317500  \n",
       "50%               1443.460000             1118.730000  \n",
       "75%               2190.522500             1813.895000  \n",
       "max              17749.010000            12995.020000  \n",
       "\n",
       "[8 rows x 41 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vip = data.loc[(data['TOTAL_ORDERS'] >= 28) | (data['REVENUE'] >= 3899)]\n",
    "df_not_vip = data.loc[(data['TOTAL_ORDERS'] < 28) & (data['REVENUE'] < 3899)]\n",
    "df_vip.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_vip_weekday_order = df_vip.iloc[:, 11:18].sum()\n",
    "df_vip_weekday_revenue = df_vip.iloc[:, 18:25].sum()\n",
    "\n",
    "df_not_vip_weekday_order = df_not_vip.iloc[:, 11:18].sum()\n",
    "df_not_vip_weekday_revenue = df_not_vip.iloc[:, 18:25].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MONDAY_ORDERS', 'TUESDAY_ORDERS', 'WEDNESDAY_ORDERS',\n",
       "       'THURSDAY_ORDERS', 'FRIDAY_ORDERS', 'SATURDAY_ORDERS', 'SUNDAY_ORDERS'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vip_weekday_order.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(754, 41)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vip.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4246, 41)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_not_vip.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MONDAY_ORDERS       3241\n",
      "TUESDAY_ORDERS      3660\n",
      "WEDNESDAY_ORDERS    3710\n",
      "THURSDAY_ORDERS     3755\n",
      "FRIDAY_ORDERS       3911\n",
      "SATURDAY_ORDERS     3424\n",
      "SUNDAY_ORDERS       3494\n",
      "dtype: int64\n",
      "------------------\n",
      "MONDAY_REVENUE       484940.72\n",
      "TUESDAY_REVENUE      556564.27\n",
      "WEDNESDAY_REVENUE    554865.99\n",
      "THURSDAY_REVENUE     540417.31\n",
      "FRIDAY_REVENUE       551950.22\n",
      "SATURDAY_REVENUE     497897.93\n",
      "SUNDAY_REVENUE       529911.99\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(df_vip_weekday_order)\n",
    "print('------------------')\n",
    "print(df_vip_weekday_revenue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 3000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(1, 2)\n",
    "f.set_size_inches(30, 7)\n",
    "\n",
    "axes[0].bar(df_vip_weekday_order.index, df_vip_weekday_order)\n",
    "axes[0].set_title('Order')\n",
    "\n",
    "axes[1].bar(df_vip_weekday_revenue.index, df_vip_weekday_revenue)\n",
    "axes[1].set_title('Revenue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MONDAY_ORDERS       4904\n",
      "TUESDAY_ORDERS      5112\n",
      "WEDNESDAY_ORDERS    5280\n",
      "THURSDAY_ORDERS     6915\n",
      "FRIDAY_ORDERS       5820\n",
      "SATURDAY_ORDERS     4993\n",
      "SUNDAY_ORDERS       6133\n",
      "dtype: int64\n",
      "------------------\n",
      "MONDAY_REVENUE       591100.96\n",
      "TUESDAY_REVENUE      610987.88\n",
      "WEDNESDAY_REVENUE    623580.48\n",
      "THURSDAY_REVENUE     789331.67\n",
      "FRIDAY_REVENUE       700952.55\n",
      "SATURDAY_REVENUE     600312.57\n",
      "SUNDAY_REVENUE       774804.66\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(df_not_vip_weekday_order)\n",
    "print('------------------')\n",
    "print(df_not_vip_weekday_revenue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 3000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(1, 2)\n",
    "f.set_size_inches(30, 7)\n",
    "\n",
    "axes[0].bar(df_not_vip_weekday_order.index, df_not_vip_weekday_order)\n",
    "axes[0].set_title('Order')\n",
    "\n",
    "axes[1].bar(df_not_vip_weekday_revenue.index, df_not_vip_weekday_revenue)\n",
    "axes[1].set_title('Revenue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "시각화 자료를 보면, 상위 그룹의 경우 (금, 토, 일)주말보다는 평일에 더욱 활발한 경향이 있지만 뚜렷한 차이는 보이지 않는다.  \n",
    "반면 비 상위 그룹의 경우 (목, 금, 일)요일에 뚜렷하게 활발한 차이를 보인다.  \n",
    "  \n",
    "  상위 그룹과 비 상위 그룹에서, (목, 금, 일)요일의 평균 주문수와 구매액, 이외 요일의 평균 주문수와 구매액이 총 주문 수와 구매액에 영향을 미치는지 다중회귀분석을 진행한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import statsmodels.formula.api as smf\n",
    "from patsy import dmatrices\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "VIF 값이 모두 2.02로 다중공선성 문제 X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 요거 원본 데이터서 'mean_TFS'와 'mean_MTWS' order와 revenue로 추가한 후 vip와 not_vip 나누기\n",
    "\n",
    "df_vip.loc[:, 'mean_order_TFS'] = (df_vip['THURSDAY_ORDERS'] + df_vip['FRIDAY_ORDERS'] + df_vip['SATURDAY_ORDERS']) / 3\n",
    "df_vip.loc[:, 'mean_order_MTWS'] = (df_vip['MONDAY_ORDERS'] + df_vip['TUESDAY_ORDERS'] + df_vip['WEDNESDAY_ORDERS'] + df_vip['SATURDAY_ORDERS']) / 4\n",
    "\n",
    "df_not_vip.loc[:, 'mean_order_TFS'] = (df_not_vip['THURSDAY_ORDERS'] + df_not_vip['FRIDAY_ORDERS'] + df_not_vip['SATURDAY_ORDERS']) / 3\n",
    "df_not_vip.loc[:, 'mean_order_MTWS'] = (df_not_vip['MONDAY_ORDERS'] + df_not_vip['TUESDAY_ORDERS'] + df_not_vip['WEDNESDAY_ORDERS'] + df_not_vip['SATURDAY_ORDERS']) / 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 요일, 주차, 시간 모두 p-value를 따지는 통계적 회귀분석 보다는, 머신러닝의 회귀분석을 이용해 회귀선을 만들고 식을 통해 결론 도출하기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "상위 10% 고객을 제외한 데이터에서 주차별, 시간대별 추이 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_not_vip_week = df_not_vip[['CustomerID', 'WEEK1_DAY01_DAY07_ORDERS', 'WEEK2_DAY08_DAY15_ORDERS', 'WEEK3_DAY16_DAY23_ORDERS', 'WEEK4_DAY24_DAY31_ORDERS',\n",
    "                'WEEK1_DAY01_DAY07_REVENUE', 'WEEK2_DAY08_DAY15_REVENUE', 'WEEK3_DAY16_DAY23_REVENUE', 'WEEK4_DAY24_DAY31_REVENUE']]\n",
    "\n",
    "df_not_vip_time = df_not_vip[['CustomerID', 'TIME_0000_0600_ORDERS', 'TIME_0601_1200_ORDERS', 'TIME_1200_1800_ORDERS', 'TIME_1801_2359_ORDERS',\n",
    "                'TIME_0000_0600_REVENUE', 'TIME_0601_1200_REVENUE', 'TIME_1200_1800_REVENUE', 'TIME_1801_2359_REVENUE']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9449, 8975, 9614, 11119]\n",
      "[1085169, 1082338, 1153131, 1370431]\n"
     ]
    }
   ],
   "source": [
    "df_not_vip_week_order = df_not_vip_week.iloc[:, 1:5]\n",
    "df_not_vip_week_revenue = df_not_vip_week.iloc[:, 5:]\n",
    "\n",
    "sum_order_not_vip_week = []\n",
    "sum_revenue_not_vip_week = []\n",
    "\n",
    "for i in range(4):\n",
    "    sum_order_not_vip_week.append(df_not_vip_week_order.iloc[:, i].sum())\n",
    "    sum_revenue_not_vip_week.append(int(df_not_vip_week_revenue.iloc[:, i].sum()))\n",
    "print(sum_order_not_vip_week)\n",
    "print(sum_revenue_not_vip_week)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3058, 11524, 13489, 11086]\n",
      "[363762, 1360482, 1640932, 1325893]\n"
     ]
    }
   ],
   "source": [
    "df_not_vip_time_order = df_not_vip_time.iloc[:, 1:5]\n",
    "df_not_vip_time_revenue = df_not_vip_time.iloc[:, 5:]\n",
    "\n",
    "sum_not_vip_order_time = []\n",
    "sum_not_vip_revenue_time = []\n",
    "\n",
    "for i in range(4):\n",
    "    sum_not_vip_order_time.append(df_not_vip_time_order.iloc[:, i].sum())\n",
    "    sum_not_vip_revenue_time.append(int(df_not_vip_time_revenue.iloc[:, i].sum()))\n",
    "print(sum_not_vip_order_time)\n",
    "print(sum_not_vip_revenue_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 상위 10% 제외한 고객들의 주차별 주문량, 구매액\n",
    "week = ['Week1', 'Week2', 'Week3', 'Week4']\n",
    "\n",
    "f, axes = plt.subplots(1, 2)\n",
    "f.set_size_inches(15, 7)\n",
    "\n",
    "axes[0].bar(week, sum_order_not_vip_week)\n",
    "axes[0].set_title('Order')\n",
    "\n",
    "axes[1].bar(week, sum_revenue_not_vip_week)\n",
    "axes[1].set_title('Revenue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 상위 10% 고객들을 제외한 시간대별 고객의 주문량, 구매액\n",
    "time = ['0000~0600', '0601~1200', '1201~1800', '1801~2359']\n",
    "\n",
    "f, axes = plt.subplots(1, 2)\n",
    "f.set_size_inches(15, 7)\n",
    "\n",
    "axes[0].bar(time, sum_not_vip_order_time)\n",
    "axes[0].set_title('Order')\n",
    "\n",
    "axes[1].bar(time, sum_not_vip_revenue_time)\n",
    "axes[1].set_title('Revenue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}