Question_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9ac4d1f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load requred libraries\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "beda279d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>shop_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>order_amount</th>\n",
       "      <th>total_items</th>\n",
       "      <th>payment_method</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>746</td>\n",
       "      <td>224</td>\n",
       "      <td>2</td>\n",
       "      <td>cash</td>\n",
       "      <td>2017-03-13 12:36:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>92</td>\n",
       "      <td>925</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>cash</td>\n",
       "      <td>2017-03-03 17:38:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>44</td>\n",
       "      <td>861</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>cash</td>\n",
       "      <td>2017-03-14 4:23:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>935</td>\n",
       "      <td>156</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2017-03-26 12:43:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>883</td>\n",
       "      <td>156</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2017-03-01 4:35:11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  shop_id  user_id  order_amount  total_items payment_method  \\\n",
       "0         1       53      746           224            2           cash   \n",
       "1         2       92      925            90            1           cash   \n",
       "2         3       44      861           144            1           cash   \n",
       "3         4       18      935           156            1    credit_card   \n",
       "4         5       18      883           156            1    credit_card   \n",
       "\n",
       "            created_at  \n",
       "0  2017-03-13 12:36:56  \n",
       "1  2017-03-03 17:38:52  \n",
       "2   2017-03-14 4:23:56  \n",
       "3  2017-03-26 12:43:37  \n",
       "4   2017-03-01 4:35:11  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import csv \n",
    "df = pd.read_csv('Input/2019 Winter Data Science Intern Challenge Data Set.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c33f7ad9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\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>order_id</th>\n",
       "      <th>shop_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>order_amount</th>\n",
       "      <th>total_items</th>\n",
       "      <th>payment_method</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>4996</td>\n",
       "      <td>73</td>\n",
       "      <td>993</td>\n",
       "      <td>330</td>\n",
       "      <td>2</td>\n",
       "      <td>debit</td>\n",
       "      <td>2017-03-30 13:47:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>4997</td>\n",
       "      <td>48</td>\n",
       "      <td>789</td>\n",
       "      <td>234</td>\n",
       "      <td>2</td>\n",
       "      <td>cash</td>\n",
       "      <td>2017-03-16 20:36:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>4998</td>\n",
       "      <td>56</td>\n",
       "      <td>867</td>\n",
       "      <td>351</td>\n",
       "      <td>3</td>\n",
       "      <td>cash</td>\n",
       "      <td>2017-03-19 5:42:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>4999</td>\n",
       "      <td>60</td>\n",
       "      <td>825</td>\n",
       "      <td>354</td>\n",
       "      <td>2</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2017-03-16 14:51:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>5000</td>\n",
       "      <td>44</td>\n",
       "      <td>734</td>\n",
       "      <td>288</td>\n",
       "      <td>2</td>\n",
       "      <td>debit</td>\n",
       "      <td>2017-03-18 15:48:18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      order_id  shop_id  user_id  order_amount  total_items payment_method  \\\n",
       "4995      4996       73      993           330            2          debit   \n",
       "4996      4997       48      789           234            2           cash   \n",
       "4997      4998       56      867           351            3           cash   \n",
       "4998      4999       60      825           354            2    credit_card   \n",
       "4999      5000       44      734           288            2          debit   \n",
       "\n",
       "               created_at  \n",
       "4995  2017-03-30 13:47:17  \n",
       "4996  2017-03-16 20:36:16  \n",
       "4997   2017-03-19 5:42:42  \n",
       "4998  2017-03-16 14:51:18  \n",
       "4999  2017-03-18 15:48:18  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check data frame structure\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c44599e",
   "metadata": {},
   "source": [
    "# 1. Exploratory Data Analysis\n",
    "## 1.1 General Analysit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9ec34dc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>shop_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>order_amount</th>\n",
       "      <th>total_items</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2500.500000</td>\n",
       "      <td>50.078800</td>\n",
       "      <td>849.092400</td>\n",
       "      <td>3145.128000</td>\n",
       "      <td>8.78720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1443.520003</td>\n",
       "      <td>29.006118</td>\n",
       "      <td>87.798982</td>\n",
       "      <td>41282.539349</td>\n",
       "      <td>116.32032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>607.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1250.750000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>775.000000</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2500.500000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>849.000000</td>\n",
       "      <td>284.000000</td>\n",
       "      <td>2.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3750.250000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>925.000000</td>\n",
       "      <td>390.000000</td>\n",
       "      <td>3.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5000.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>704000.000000</td>\n",
       "      <td>2000.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          order_id      shop_id      user_id   order_amount  total_items\n",
       "count  5000.000000  5000.000000  5000.000000    5000.000000   5000.00000\n",
       "mean   2500.500000    50.078800   849.092400    3145.128000      8.78720\n",
       "std    1443.520003    29.006118    87.798982   41282.539349    116.32032\n",
       "min       1.000000     1.000000   607.000000      90.000000      1.00000\n",
       "25%    1250.750000    24.000000   775.000000     163.000000      1.00000\n",
       "50%    2500.500000    50.000000   849.000000     284.000000      2.00000\n",
       "75%    3750.250000    75.000000   925.000000     390.000000      3.00000\n",
       "max    5000.000000   100.000000   999.000000  704000.000000   2000.00000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "68fda016",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5000 entries, 0 to 4999\n",
      "Data columns (total 7 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   order_id        5000 non-null   int64 \n",
      " 1   shop_id         5000 non-null   int64 \n",
      " 2   user_id         5000 non-null   int64 \n",
      " 3   order_amount    5000 non-null   int64 \n",
      " 4   total_items     5000 non-null   int64 \n",
      " 5   payment_method  5000 non-null   object\n",
      " 6   created_at      5000 non-null   object\n",
      "dtypes: int64(5), object(2)\n",
      "memory usage: 273.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5d2c74e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the average price of each order\n",
    "df['average_price'] = df.order_amount/df.total_items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fa5317a6",
   "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>order_id</th>\n",
       "      <th>shop_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>order_amount</th>\n",
       "      <th>total_items</th>\n",
       "      <th>payment_method</th>\n",
       "      <th>created_at</th>\n",
       "      <th>average_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>746</td>\n",
       "      <td>224</td>\n",
       "      <td>2</td>\n",
       "      <td>cash</td>\n",
       "      <td>2017-03-13 12:36:56</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>92</td>\n",
       "      <td>925</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>cash</td>\n",
       "      <td>2017-03-03 17:38:52</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>44</td>\n",
       "      <td>861</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>cash</td>\n",
       "      <td>2017-03-14 4:23:56</td>\n",
       "      <td>144.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>935</td>\n",
       "      <td>156</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2017-03-26 12:43:37</td>\n",
       "      <td>156.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>883</td>\n",
       "      <td>156</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2017-03-01 4:35:11</td>\n",
       "      <td>156.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  shop_id  user_id  order_amount  total_items payment_method  \\\n",
       "0         1       53      746           224            2           cash   \n",
       "1         2       92      925            90            1           cash   \n",
       "2         3       44      861           144            1           cash   \n",
       "3         4       18      935           156            1    credit_card   \n",
       "4         5       18      883           156            1    credit_card   \n",
       "\n",
       "            created_at  average_price  \n",
       "0  2017-03-13 12:36:56          112.0  \n",
       "1  2017-03-03 17:38:52           90.0  \n",
       "2   2017-03-14 4:23:56          144.0  \n",
       "3  2017-03-26 12:43:37          156.0  \n",
       "4   2017-03-01 4:35:11          156.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1cfe0c0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3145.128"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate the mean of order amount\n",
    "df.order_amount.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e1fde232",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "284.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate the median of order amount\n",
    "df.order_amount.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f37c6c1",
   "metadata": {},
   "source": [
    "## 1.2 Average Price Outlier Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2bdeb974",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   90.,    94.,   101.,   111.,   112.,   114.,   116.,   117.,\n",
       "         118.,   122.,   127.,   128.,   129.,   130.,   131.,   132.,\n",
       "         133.,   134.,   136.,   138.,   140.,   142.,   144.,   145.,\n",
       "         146.,   147.,   148.,   149.,   153.,   154.,   155.,   156.,\n",
       "         158.,   160.,   161.,   162.,   163.,   164.,   165.,   166.,\n",
       "         168.,   169.,   171.,   172.,   173.,   176.,   177.,   178.,\n",
       "         181.,   184.,   187.,   190.,   193.,   195.,   196.,   201.,\n",
       "         352., 25725.])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the unique value of average price\n",
    "np.sort(df['average_price'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "786660f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 25725 is an outlier from average price\n",
    "df.boxplot(column = 'average_price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5816176e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter the outlier on average price\n",
    "df_average_price_outlier = df[df['average_price'] > 500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2476cbd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>shop_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>order_amount</th>\n",
       "      <th>total_items</th>\n",
       "      <th>payment_method</th>\n",
       "      <th>created_at</th>\n",
       "      <th>average_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>161</td>\n",
       "      <td>78</td>\n",
       "      <td>990</td>\n",
       "      <td>25725</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2017-03-12 5:56:57</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>491</td>\n",
       "      <td>78</td>\n",
       "      <td>936</td>\n",
       "      <td>51450</td>\n",
       "      <td>2</td>\n",
       "      <td>debit</td>\n",
       "      <td>2017-03-26 17:08:19</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>494</td>\n",
       "      <td>78</td>\n",
       "      <td>983</td>\n",
       "      <td>51450</td>\n",
       "      <td>2</td>\n",
       "      <td>cash</td>\n",
       "      <td>2017-03-16 21:39:35</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>512</td>\n",
       "      <td>78</td>\n",
       "      <td>967</td>\n",
       "      <td>51450</td>\n",
       "      <td>2</td>\n",
       "      <td>cash</td>\n",
       "      <td>2017-03-09 7:23:14</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>618</td>\n",
       "      <td>78</td>\n",
       "      <td>760</td>\n",
       "      <td>51450</td>\n",
       "      <td>2</td>\n",
       "      <td>cash</td>\n",
       "      <td>2017-03-18 11:18:42</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691</th>\n",
       "      <td>692</td>\n",
       "      <td>78</td>\n",
       "      <td>878</td>\n",
       "      <td>154350</td>\n",
       "      <td>6</td>\n",
       "      <td>debit</td>\n",
       "      <td>2017-03-27 22:51:43</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1056</th>\n",
       "      <td>1057</td>\n",
       "      <td>78</td>\n",
       "      <td>800</td>\n",
       "      <td>25725</td>\n",
       "      <td>1</td>\n",
       "      <td>debit</td>\n",
       "      <td>2017-03-15 10:16:45</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1193</th>\n",
       "      <td>1194</td>\n",
       "      <td>78</td>\n",
       "      <td>944</td>\n",
       "      <td>25725</td>\n",
       "      <td>1</td>\n",
       "      <td>debit</td>\n",
       "      <td>2017-03-16 16:38:26</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1204</th>\n",
       "      <td>1205</td>\n",
       "      <td>78</td>\n",
       "      <td>970</td>\n",
       "      <td>25725</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2017-03-17 22:32:21</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1259</th>\n",
       "      <td>1260</td>\n",
       "      <td>78</td>\n",
       "      <td>775</td>\n",
       "      <td>77175</td>\n",
       "      <td>3</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2017-03-27 9:27:20</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1384</th>\n",
       "      <td>1385</td>\n",
       "      <td>78</td>\n",
       "      <td>867</td>\n",
       "      <td>25725</td>\n",
       "      <td>1</td>\n",
       "      <td>cash</td>\n",
       "      <td>2017-03-17 16:38:06</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1419</th>\n",
       "      <td>1420</td>\n",
       "      <td>78</td>\n",
       "      <td>912</td>\n",
       "      <td>25725</td>\n",
       "      <td>1</td>\n",
       "      <td>cash</td>\n",
       "      <td>2017-03-30 12:23:43</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1452</th>\n",
       "      <td>1453</td>\n",
       "      <td>78</td>\n",
       "      <td>812</td>\n",
       "      <td>25725</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2017-03-17 18:09:54</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1529</th>\n",
       "      <td>1530</td>\n",
       "      <td>78</td>\n",
       "      <td>810</td>\n",
       "      <td>51450</td>\n",
       "      <td>2</td>\n",
       "      <td>cash</td>\n",
       "      <td>2017-03-29 7:12:01</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2270</th>\n",
       "      <td>2271</td>\n",
       "      <td>78</td>\n",
       "      <td>855</td>\n",
       "      <td>25725</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2017-03-14 23:58:22</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2452</th>\n",
       "      <td>2453</td>\n",
       "      <td>78</td>\n",
       "      <td>709</td>\n",
       "      <td>51450</td>\n",
       "      <td>2</td>\n",
       "      <td>cash</td>\n",
       "      <td>2017-03-27 11:04:04</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2492</th>\n",
       "      <td>2493</td>\n",
       "      <td>78</td>\n",
       "      <td>834</td>\n",
       "      <td>102900</td>\n",
       "      <td>4</td>\n",
       "      <td>debit</td>\n",
       "      <td>2017-03-04 4:37:34</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2495</th>\n",
       "      <td>2496</td>\n",
       "      <td>78</td>\n",
       "      <td>707</td>\n",
       "      <td>51450</td>\n",
       "      <td>2</td>\n",
       "      <td>cash</td>\n",
       "      <td>2017-03-26 4:38:52</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2512</th>\n",
       "      <td>2513</td>\n",
       "      <td>78</td>\n",
       "      <td>935</td>\n",
       "      <td>51450</td>\n",
       "      <td>2</td>\n",
       "      <td>debit</td>\n",
       "      <td>2017-03-18 18:57:13</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2548</th>\n",
       "      <td>2549</td>\n",
       "      <td>78</td>\n",
       "      <td>861</td>\n",
       "      <td>25725</td>\n",
       "      <td>1</td>\n",
       "      <td>cash</td>\n",
       "      <td>2017-03-17 19:36:00</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2564</th>\n",
       "      <td>2565</td>\n",
       "      <td>78</td>\n",
       "      <td>915</td>\n",
       "      <td>77175</td>\n",
       "      <td>3</td>\n",
       "      <td>debit</td>\n",
       "      <td>2017-03-25 1:19:35</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2690</th>\n",
       "      <td>2691</td>\n",
       "      <td>78</td>\n",
       "      <td>962</td>\n",
       "      <td>77175</td>\n",
       "      <td>3</td>\n",
       "      <td>debit</td>\n",
       "      <td>2017-03-22 7:33:25</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2773</th>\n",
       "      <td>2774</td>\n",
       "      <td>78</td>\n",
       "      <td>890</td>\n",
       "      <td>25725</td>\n",
       "      <td>1</td>\n",
       "      <td>cash</td>\n",
       "      <td>2017-03-26 10:36:43</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2818</th>\n",
       "      <td>2819</td>\n",
       "      <td>78</td>\n",
       "      <td>869</td>\n",
       "      <td>51450</td>\n",
       "      <td>2</td>\n",
       "      <td>debit</td>\n",
       "      <td>2017-03-17 6:25:51</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2821</th>\n",
       "      <td>2822</td>\n",
       "      <td>78</td>\n",
       "      <td>814</td>\n",
       "      <td>51450</td>\n",
       "      <td>2</td>\n",
       "      <td>cash</td>\n",
       "      <td>2017-03-02 17:13:25</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2906</th>\n",
       "      <td>2907</td>\n",
       "      <td>78</td>\n",
       "      <td>817</td>\n",
       "      <td>77175</td>\n",
       "      <td>3</td>\n",
       "      <td>debit</td>\n",
       "      <td>2017-03-16 3:45:46</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2922</th>\n",
       "      <td>2923</td>\n",
       "      <td>78</td>\n",
       "      <td>740</td>\n",
       "      <td>25725</td>\n",
       "      <td>1</td>\n",
       "      <td>debit</td>\n",
       "      <td>2017-03-12 20:10:58</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3085</th>\n",
       "      <td>3086</td>\n",
       "      <td>78</td>\n",
       "      <td>910</td>\n",
       "      <td>25725</td>\n",
       "      <td>1</td>\n",
       "      <td>cash</td>\n",
       "      <td>2017-03-26 1:59:27</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3101</th>\n",
       "      <td>3102</td>\n",
       "      <td>78</td>\n",
       "      <td>855</td>\n",
       "      <td>51450</td>\n",
       "      <td>2</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2017-03-21 5:10:34</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3151</th>\n",
       "      <td>3152</td>\n",
       "      <td>78</td>\n",
       "      <td>745</td>\n",
       "      <td>25725</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2017-03-18 13:13:07</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3167</th>\n",
       "      <td>3168</td>\n",
       "      <td>78</td>\n",
       "      <td>927</td>\n",
       "      <td>51450</td>\n",
       "      <td>2</td>\n",
       "      <td>cash</td>\n",
       "      <td>2017-03-12 12:23:08</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3403</th>\n",
       "      <td>3404</td>\n",
       "      <td>78</td>\n",
       "      <td>928</td>\n",
       "      <td>77175</td>\n",
       "      <td>3</td>\n",
       "      <td>debit</td>\n",
       "      <td>2017-03-16 9:45:05</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3440</th>\n",
       "      <td>3441</td>\n",
       "      <td>78</td>\n",
       "      <td>982</td>\n",
       "      <td>25725</td>\n",
       "      <td>1</td>\n",
       "      <td>debit</td>\n",
       "      <td>2017-03-19 19:02:54</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3705</th>\n",
       "      <td>3706</td>\n",
       "      <td>78</td>\n",
       "      <td>828</td>\n",
       "      <td>51450</td>\n",
       "      <td>2</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2017-03-14 20:43:15</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3724</th>\n",
       "      <td>3725</td>\n",
       "      <td>78</td>\n",
       "      <td>766</td>\n",
       "      <td>77175</td>\n",
       "      <td>3</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2017-03-16 14:13:26</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3780</th>\n",
       "      <td>3781</td>\n",
       "      <td>78</td>\n",
       "      <td>889</td>\n",
       "      <td>25725</td>\n",
       "      <td>1</td>\n",
       "      <td>cash</td>\n",
       "      <td>2017-03-11 21:14:50</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4040</th>\n",
       "      <td>4041</td>\n",
       "      <td>78</td>\n",
       "      <td>852</td>\n",
       "      <td>25725</td>\n",
       "      <td>1</td>\n",
       "      <td>cash</td>\n",
       "      <td>2017-03-02 14:31:12</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4079</th>\n",
       "      <td>4080</td>\n",
       "      <td>78</td>\n",
       "      <td>946</td>\n",
       "      <td>51450</td>\n",
       "      <td>2</td>\n",
       "      <td>cash</td>\n",
       "      <td>2017-03-20 21:14:00</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4192</th>\n",
       "      <td>4193</td>\n",
       "      <td>78</td>\n",
       "      <td>787</td>\n",
       "      <td>77175</td>\n",
       "      <td>3</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2017-03-18 9:25:32</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4311</th>\n",
       "      <td>4312</td>\n",
       "      <td>78</td>\n",
       "      <td>960</td>\n",
       "      <td>51450</td>\n",
       "      <td>2</td>\n",
       "      <td>debit</td>\n",
       "      <td>2017-03-01 3:02:10</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4412</th>\n",
       "      <td>4413</td>\n",
       "      <td>78</td>\n",
       "      <td>756</td>\n",
       "      <td>51450</td>\n",
       "      <td>2</td>\n",
       "      <td>debit</td>\n",
       "      <td>2017-03-02 4:13:39</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4420</th>\n",
       "      <td>4421</td>\n",
       "      <td>78</td>\n",
       "      <td>969</td>\n",
       "      <td>77175</td>\n",
       "      <td>3</td>\n",
       "      <td>debit</td>\n",
       "      <td>2017-03-09 15:21:35</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4505</th>\n",
       "      <td>4506</td>\n",
       "      <td>78</td>\n",
       "      <td>866</td>\n",
       "      <td>25725</td>\n",
       "      <td>1</td>\n",
       "      <td>debit</td>\n",
       "      <td>2017-03-22 22:06:01</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4584</th>\n",
       "      <td>4585</td>\n",
       "      <td>78</td>\n",
       "      <td>997</td>\n",
       "      <td>25725</td>\n",
       "      <td>1</td>\n",
       "      <td>cash</td>\n",
       "      <td>2017-03-25 21:48:44</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4715</th>\n",
       "      <td>4716</td>\n",
       "      <td>78</td>\n",
       "      <td>818</td>\n",
       "      <td>77175</td>\n",
       "      <td>3</td>\n",
       "      <td>debit</td>\n",
       "      <td>2017-03-05 5:10:44</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4918</th>\n",
       "      <td>4919</td>\n",
       "      <td>78</td>\n",
       "      <td>823</td>\n",
       "      <td>25725</td>\n",
       "      <td>1</td>\n",
       "      <td>cash</td>\n",
       "      <td>2017-03-15 13:26:46</td>\n",
       "      <td>25725.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      order_id  shop_id  user_id  order_amount  total_items payment_method  \\\n",
       "160        161       78      990         25725            1    credit_card   \n",
       "490        491       78      936         51450            2          debit   \n",
       "493        494       78      983         51450            2           cash   \n",
       "511        512       78      967         51450            2           cash   \n",
       "617        618       78      760         51450            2           cash   \n",
       "691        692       78      878        154350            6          debit   \n",
       "1056      1057       78      800         25725            1          debit   \n",
       "1193      1194       78      944         25725            1          debit   \n",
       "1204      1205       78      970         25725            1    credit_card   \n",
       "1259      1260       78      775         77175            3    credit_card   \n",
       "1384      1385       78      867         25725            1           cash   \n",
       "1419      1420       78      912         25725            1           cash   \n",
       "1452      1453       78      812         25725            1    credit_card   \n",
       "1529      1530       78      810         51450            2           cash   \n",
       "2270      2271       78      855         25725            1    credit_card   \n",
       "2452      2453       78      709         51450            2           cash   \n",
       "2492      2493       78      834        102900            4          debit   \n",
       "2495      2496       78      707         51450            2           cash   \n",
       "2512      2513       78      935         51450            2          debit   \n",
       "2548      2549       78      861         25725            1           cash   \n",
       "2564      2565       78      915         77175            3          debit   \n",
       "2690      2691       78      962         77175            3          debit   \n",
       "2773      2774       78      890         25725            1           cash   \n",
       "2818      2819       78      869         51450            2          debit   \n",
       "2821      2822       78      814         51450            2           cash   \n",
       "2906      2907       78      817         77175            3          debit   \n",
       "2922      2923       78      740         25725            1          debit   \n",
       "3085      3086       78      910         25725            1           cash   \n",
       "3101      3102       78      855         51450            2    credit_card   \n",
       "3151      3152       78      745         25725            1    credit_card   \n",
       "3167      3168       78      927         51450            2           cash   \n",
       "3403      3404       78      928         77175            3          debit   \n",
       "3440      3441       78      982         25725            1          debit   \n",
       "3705      3706       78      828         51450            2    credit_card   \n",
       "3724      3725       78      766         77175            3    credit_card   \n",
       "3780      3781       78      889         25725            1           cash   \n",
       "4040      4041       78      852         25725            1           cash   \n",
       "4079      4080       78      946         51450            2           cash   \n",
       "4192      4193       78      787         77175            3    credit_card   \n",
       "4311      4312       78      960         51450            2          debit   \n",
       "4412      4413       78      756         51450            2          debit   \n",
       "4420      4421       78      969         77175            3          debit   \n",
       "4505      4506       78      866         25725            1          debit   \n",
       "4584      4585       78      997         25725            1           cash   \n",
       "4715      4716       78      818         77175            3          debit   \n",
       "4918      4919       78      823         25725            1           cash   \n",
       "\n",
       "               created_at  average_price  \n",
       "160    2017-03-12 5:56:57        25725.0  \n",
       "490   2017-03-26 17:08:19        25725.0  \n",
       "493   2017-03-16 21:39:35        25725.0  \n",
       "511    2017-03-09 7:23:14        25725.0  \n",
       "617   2017-03-18 11:18:42        25725.0  \n",
       "691   2017-03-27 22:51:43        25725.0  \n",
       "1056  2017-03-15 10:16:45        25725.0  \n",
       "1193  2017-03-16 16:38:26        25725.0  \n",
       "1204  2017-03-17 22:32:21        25725.0  \n",
       "1259   2017-03-27 9:27:20        25725.0  \n",
       "1384  2017-03-17 16:38:06        25725.0  \n",
       "1419  2017-03-30 12:23:43        25725.0  \n",
       "1452  2017-03-17 18:09:54        25725.0  \n",
       "1529   2017-03-29 7:12:01        25725.0  \n",
       "2270  2017-03-14 23:58:22        25725.0  \n",
       "2452  2017-03-27 11:04:04        25725.0  \n",
       "2492   2017-03-04 4:37:34        25725.0  \n",
       "2495   2017-03-26 4:38:52        25725.0  \n",
       "2512  2017-03-18 18:57:13        25725.0  \n",
       "2548  2017-03-17 19:36:00        25725.0  \n",
       "2564   2017-03-25 1:19:35        25725.0  \n",
       "2690   2017-03-22 7:33:25        25725.0  \n",
       "2773  2017-03-26 10:36:43        25725.0  \n",
       "2818   2017-03-17 6:25:51        25725.0  \n",
       "2821  2017-03-02 17:13:25        25725.0  \n",
       "2906   2017-03-16 3:45:46        25725.0  \n",
       "2922  2017-03-12 20:10:58        25725.0  \n",
       "3085   2017-03-26 1:59:27        25725.0  \n",
       "3101   2017-03-21 5:10:34        25725.0  \n",
       "3151  2017-03-18 13:13:07        25725.0  \n",
       "3167  2017-03-12 12:23:08        25725.0  \n",
       "3403   2017-03-16 9:45:05        25725.0  \n",
       "3440  2017-03-19 19:02:54        25725.0  \n",
       "3705  2017-03-14 20:43:15        25725.0  \n",
       "3724  2017-03-16 14:13:26        25725.0  \n",
       "3780  2017-03-11 21:14:50        25725.0  \n",
       "4040  2017-03-02 14:31:12        25725.0  \n",
       "4079  2017-03-20 21:14:00        25725.0  \n",
       "4192   2017-03-18 9:25:32        25725.0  \n",
       "4311   2017-03-01 3:02:10        25725.0  \n",
       "4412   2017-03-02 4:13:39        25725.0  \n",
       "4420  2017-03-09 15:21:35        25725.0  \n",
       "4505  2017-03-22 22:06:01        25725.0  \n",
       "4584  2017-03-25 21:48:44        25725.0  \n",
       "4715   2017-03-05 5:10:44        25725.0  \n",
       "4918  2017-03-15 13:26:46        25725.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the structure of data frame\n",
    "df_average_price_outlier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52b42357",
   "metadata": {},
   "source": [
    "## 1.3 total_items Outlier Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "276e97da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   2,    1,    3, 2000,    4,    5,    6,    8], dtype=int64)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the items number of each order\n",
    "df.total_items.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d05dd609",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# total items 2000 is an outlier\n",
    "df.boxplot(column = 'total_items')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c6ea3c5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>shop_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>order_amount</th>\n",
       "      <th>total_items</th>\n",
       "      <th>payment_method</th>\n",
       "      <th>created_at</th>\n",
       "      <th>average_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>42</td>\n",
       "      <td>607</td>\n",
       "      <td>704000</td>\n",
       "      <td>2000</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2017-03-07 4:00:00</td>\n",
       "      <td>352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>61</td>\n",
       "      <td>42</td>\n",
       "      <td>607</td>\n",
       "      <td>704000</td>\n",
       "      <td>2000</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2017-03-04 4:00:00</td>\n",
       "      <td>352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>520</th>\n",
       "      <td>521</td>\n",
       "      <td>42</td>\n",
       "      <td>607</td>\n",
       "      <td>704000</td>\n",
       "      <td>2000</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2017-03-02 4:00:00</td>\n",
       "      <td>352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>1105</td>\n",
       "      <td>42</td>\n",
       "      <td>607</td>\n",
       "      <td>704000</td>\n",
       "      <td>2000</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2017-03-24 4:00:00</td>\n",
       "      <td>352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1362</th>\n",
       "      <td>1363</td>\n",
       "      <td>42</td>\n",
       "      <td>607</td>\n",
       "      <td>704000</td>\n",
       "      <td>2000</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2017-03-15 4:00:00</td>\n",
       "      <td>352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1436</th>\n",
       "      <td>1437</td>\n",
       "      <td>42</td>\n",
       "      <td>607</td>\n",
       "      <td>704000</td>\n",
       "      <td>2000</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2017-03-11 4:00:00</td>\n",
       "      <td>352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1562</th>\n",
       "      <td>1563</td>\n",
       "      <td>42</td>\n",
       "      <td>607</td>\n",
       "      <td>704000</td>\n",
       "      <td>2000</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2017-03-19 4:00:00</td>\n",
       "      <td>352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1602</th>\n",
       "      <td>1603</td>\n",
       "      <td>42</td>\n",
       "      <td>607</td>\n",
       "      <td>704000</td>\n",
       "      <td>2000</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2017-03-17 4:00:00</td>\n",
       "      <td>352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2153</th>\n",
       "      <td>2154</td>\n",
       "      <td>42</td>\n",
       "      <td>607</td>\n",
       "      <td>704000</td>\n",
       "      <td>2000</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2017-03-12 4:00:00</td>\n",
       "      <td>352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2297</th>\n",
       "      <td>2298</td>\n",
       "      <td>42</td>\n",
       "      <td>607</td>\n",
       "      <td>704000</td>\n",
       "      <td>2000</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2017-03-07 4:00:00</td>\n",
       "      <td>352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2835</th>\n",
       "      <td>2836</td>\n",
       "      <td>42</td>\n",
       "      <td>607</td>\n",
       "      <td>704000</td>\n",
       "      <td>2000</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2017-03-28 4:00:00</td>\n",
       "      <td>352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2969</th>\n",
       "      <td>2970</td>\n",
       "      <td>42</td>\n",
       "      <td>607</td>\n",
       "      <td>704000</td>\n",
       "      <td>2000</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2017-03-28 4:00:00</td>\n",
       "      <td>352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3332</th>\n",
       "      <td>3333</td>\n",
       "      <td>42</td>\n",
       "      <td>607</td>\n",
       "      <td>704000</td>\n",
       "      <td>2000</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2017-03-24 4:00:00</td>\n",
       "      <td>352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4056</th>\n",
       "      <td>4057</td>\n",
       "      <td>42</td>\n",
       "      <td>607</td>\n",
       "      <td>704000</td>\n",
       "      <td>2000</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2017-03-28 4:00:00</td>\n",
       "      <td>352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4646</th>\n",
       "      <td>4647</td>\n",
       "      <td>42</td>\n",
       "      <td>607</td>\n",
       "      <td>704000</td>\n",
       "      <td>2000</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2017-03-02 4:00:00</td>\n",
       "      <td>352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4868</th>\n",
       "      <td>4869</td>\n",
       "      <td>42</td>\n",
       "      <td>607</td>\n",
       "      <td>704000</td>\n",
       "      <td>2000</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2017-03-22 4:00:00</td>\n",
       "      <td>352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4882</th>\n",
       "      <td>4883</td>\n",
       "      <td>42</td>\n",
       "      <td>607</td>\n",
       "      <td>704000</td>\n",
       "      <td>2000</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2017-03-25 4:00:00</td>\n",
       "      <td>352.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      order_id  shop_id  user_id  order_amount  total_items payment_method  \\\n",
       "15          16       42      607        704000         2000    credit_card   \n",
       "60          61       42      607        704000         2000    credit_card   \n",
       "520        521       42      607        704000         2000    credit_card   \n",
       "1104      1105       42      607        704000         2000    credit_card   \n",
       "1362      1363       42      607        704000         2000    credit_card   \n",
       "1436      1437       42      607        704000         2000    credit_card   \n",
       "1562      1563       42      607        704000         2000    credit_card   \n",
       "1602      1603       42      607        704000         2000    credit_card   \n",
       "2153      2154       42      607        704000         2000    credit_card   \n",
       "2297      2298       42      607        704000         2000    credit_card   \n",
       "2835      2836       42      607        704000         2000    credit_card   \n",
       "2969      2970       42      607        704000         2000    credit_card   \n",
       "3332      3333       42      607        704000         2000    credit_card   \n",
       "4056      4057       42      607        704000         2000    credit_card   \n",
       "4646      4647       42      607        704000         2000    credit_card   \n",
       "4868      4869       42      607        704000         2000    credit_card   \n",
       "4882      4883       42      607        704000         2000    credit_card   \n",
       "\n",
       "              created_at  average_price  \n",
       "15    2017-03-07 4:00:00          352.0  \n",
       "60    2017-03-04 4:00:00          352.0  \n",
       "520   2017-03-02 4:00:00          352.0  \n",
       "1104  2017-03-24 4:00:00          352.0  \n",
       "1362  2017-03-15 4:00:00          352.0  \n",
       "1436  2017-03-11 4:00:00          352.0  \n",
       "1562  2017-03-19 4:00:00          352.0  \n",
       "1602  2017-03-17 4:00:00          352.0  \n",
       "2153  2017-03-12 4:00:00          352.0  \n",
       "2297  2017-03-07 4:00:00          352.0  \n",
       "2835  2017-03-28 4:00:00          352.0  \n",
       "2969  2017-03-28 4:00:00          352.0  \n",
       "3332  2017-03-24 4:00:00          352.0  \n",
       "4056  2017-03-28 4:00:00          352.0  \n",
       "4646  2017-03-02 4:00:00          352.0  \n",
       "4868  2017-03-22 4:00:00          352.0  \n",
       "4882  2017-03-25 4:00:00          352.0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter out the outlier on total items\n",
    "df_total_items_outlier = df[df['total_items'] > 10]\n",
    "df_total_items_outlier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c693b53",
   "metadata": {},
   "source": [
    "## 1.4 Dataset after removing outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0ebc12ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove the outlier\n",
    "df_cleaned = df.loc[df['total_items'] < 10,].loc[df['average_price'] < 500, ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "866be633",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 4937 entries, 0 to 4999\n",
      "Data columns (total 8 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   order_id        4937 non-null   int64  \n",
      " 1   shop_id         4937 non-null   int64  \n",
      " 2   user_id         4937 non-null   int64  \n",
      " 3   order_amount    4937 non-null   int64  \n",
      " 4   total_items     4937 non-null   int64  \n",
      " 5   payment_method  4937 non-null   object \n",
      " 6   created_at      4937 non-null   object \n",
      " 7   average_price   4937 non-null   float64\n",
      "dtypes: float64(1), int64(5), object(2)\n",
      "memory usage: 347.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# check data frame info\n",
    "df_cleaned.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6ef8957c",
   "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",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>shop_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>order_amount</th>\n",
       "      <th>total_items</th>\n",
       "      <th>average_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4937.000000</td>\n",
       "      <td>4937.000000</td>\n",
       "      <td>4937.000000</td>\n",
       "      <td>4937.000000</td>\n",
       "      <td>4937.000000</td>\n",
       "      <td>4937.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2499.551347</td>\n",
       "      <td>49.846465</td>\n",
       "      <td>849.752279</td>\n",
       "      <td>302.580514</td>\n",
       "      <td>1.994734</td>\n",
       "      <td>151.788536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1444.069407</td>\n",
       "      <td>29.061131</td>\n",
       "      <td>86.840313</td>\n",
       "      <td>160.804912</td>\n",
       "      <td>0.982821</td>\n",
       "      <td>29.034215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1248.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>775.000000</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>132.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2497.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>850.000000</td>\n",
       "      <td>284.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>153.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3751.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>925.000000</td>\n",
       "      <td>387.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>166.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5000.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>1760.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>352.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          order_id      shop_id      user_id  order_amount  total_items  \\\n",
       "count  4937.000000  4937.000000  4937.000000   4937.000000  4937.000000   \n",
       "mean   2499.551347    49.846465   849.752279    302.580514     1.994734   \n",
       "std    1444.069407    29.061131    86.840313    160.804912     0.982821   \n",
       "min       1.000000     1.000000   700.000000     90.000000     1.000000   \n",
       "25%    1248.000000    24.000000   775.000000    163.000000     1.000000   \n",
       "50%    2497.000000    50.000000   850.000000    284.000000     2.000000   \n",
       "75%    3751.000000    74.000000   925.000000    387.000000     3.000000   \n",
       "max    5000.000000   100.000000   999.000000   1760.000000     8.000000   \n",
       "\n",
       "       average_price  \n",
       "count    4937.000000  \n",
       "mean      151.788536  \n",
       "std        29.034215  \n",
       "min        90.000000  \n",
       "25%       132.000000  \n",
       "50%       153.000000  \n",
       "75%       166.000000  \n",
       "max       352.000000  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cleaned.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d87329b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "302.58051448247926"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate the mean after removing outliers\n",
    "df_cleaned.order_amount.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d2ed6d3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "284.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate the median after removing outliers\n",
    "df_cleaned.order_amount.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c214ab51",
   "metadata": {},
   "source": [
    "## After removing outliers, the median of order_amount is very close to the mean of order amount."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7885c5f6",
   "metadata": {},
   "source": [
    "# 2. Explore if there is any difference for different payment methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1b21fa75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['cash', 'credit_card', 'debit'], dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check payment methods\n",
    "df_cleaned.payment_method.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "69eabece",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>shop_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>order_amount</th>\n",
       "      <th>total_items</th>\n",
       "      <th>created_at</th>\n",
       "      <th>average_price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>payment_method</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cash</th>\n",
       "      <td>1576</td>\n",
       "      <td>1576</td>\n",
       "      <td>1576</td>\n",
       "      <td>1576</td>\n",
       "      <td>1576</td>\n",
       "      <td>1576</td>\n",
       "      <td>1576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>credit_card</th>\n",
       "      <td>1708</td>\n",
       "      <td>1708</td>\n",
       "      <td>1708</td>\n",
       "      <td>1708</td>\n",
       "      <td>1708</td>\n",
       "      <td>1708</td>\n",
       "      <td>1708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>debit</th>\n",
       "      <td>1653</td>\n",
       "      <td>1653</td>\n",
       "      <td>1653</td>\n",
       "      <td>1653</td>\n",
       "      <td>1653</td>\n",
       "      <td>1653</td>\n",
       "      <td>1653</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                order_id  shop_id  user_id  order_amount  total_items  \\\n",
       "payment_method                                                          \n",
       "cash                1576     1576     1576          1576         1576   \n",
       "credit_card         1708     1708     1708          1708         1708   \n",
       "debit               1653     1653     1653          1653         1653   \n",
       "\n",
       "                created_at  average_price  \n",
       "payment_method                             \n",
       "cash                  1576           1576  \n",
       "credit_card           1708           1708  \n",
       "debit                 1653           1653  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The order amounts for three different payment methods are basically the same\n",
    "df_cleaned.groupby('payment_method').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "71115190",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter data with different payment methods\n",
    "df_cleaned_cash = df_cleaned[df_cleaned.payment_method == 'cash']\n",
    "df_cleaned_debit = df_cleaned[df_cleaned.payment_method == 'debit']\n",
    "df_cleaned_credit = df_cleaned[df_cleaned.payment_method == 'credit_card']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e44df5b1",
   "metadata": {},
   "source": [
    "## 2.1 data frame info check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2d91984e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1576 entries, 0 to 4997\n",
      "Data columns (total 8 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   order_id        1576 non-null   int64  \n",
      " 1   shop_id         1576 non-null   int64  \n",
      " 2   user_id         1576 non-null   int64  \n",
      " 3   order_amount    1576 non-null   int64  \n",
      " 4   total_items     1576 non-null   int64  \n",
      " 5   payment_method  1576 non-null   object \n",
      " 6   created_at      1576 non-null   object \n",
      " 7   average_price   1576 non-null   float64\n",
      "dtypes: float64(1), int64(5), object(2)\n",
      "memory usage: 110.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df_cleaned_cash.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b7a1959e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1653 entries, 8 to 4999\n",
      "Data columns (total 8 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   order_id        1653 non-null   int64  \n",
      " 1   shop_id         1653 non-null   int64  \n",
      " 2   user_id         1653 non-null   int64  \n",
      " 3   order_amount    1653 non-null   int64  \n",
      " 4   total_items     1653 non-null   int64  \n",
      " 5   payment_method  1653 non-null   object \n",
      " 6   created_at      1653 non-null   object \n",
      " 7   average_price   1653 non-null   float64\n",
      "dtypes: float64(1), int64(5), object(2)\n",
      "memory usage: 116.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df_cleaned_debit.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3bb13f39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1708 entries, 3 to 4998\n",
      "Data columns (total 8 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   order_id        1708 non-null   int64  \n",
      " 1   shop_id         1708 non-null   int64  \n",
      " 2   user_id         1708 non-null   int64  \n",
      " 3   order_amount    1708 non-null   int64  \n",
      " 4   total_items     1708 non-null   int64  \n",
      " 5   payment_method  1708 non-null   object \n",
      " 6   created_at      1708 non-null   object \n",
      " 7   average_price   1708 non-null   float64\n",
      "dtypes: float64(1), int64(5), object(2)\n",
      "memory usage: 120.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df_cleaned_credit.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "401d9a42",
   "metadata": {},
   "source": [
    "## 2.2 data frame value check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4f602392",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>shop_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>order_amount</th>\n",
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       "      <th>average_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1576.000000</td>\n",
       "      <td>1576.000000</td>\n",
       "      <td>1576.000000</td>\n",
       "      <td>1576.000000</td>\n",
       "      <td>1576.000000</td>\n",
       "      <td>1576.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2519.574239</td>\n",
       "      <td>50.541244</td>\n",
       "      <td>850.016497</td>\n",
       "      <td>297.974619</td>\n",
       "      <td>1.968909</td>\n",
       "      <td>151.074873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1416.172165</td>\n",
       "      <td>29.089782</td>\n",
       "      <td>85.843584</td>\n",
       "      <td>160.289720</td>\n",
       "      <td>0.961968</td>\n",
       "      <td>28.269937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1334.500000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>777.000000</td>\n",
       "      <td>161.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>132.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2527.500000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>853.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>153.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3719.250000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>922.000000</td>\n",
       "      <td>386.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>165.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4998.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>1760.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>352.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          order_id      shop_id      user_id  order_amount  total_items  \\\n",
       "count  1576.000000  1576.000000  1576.000000   1576.000000  1576.000000   \n",
       "mean   2519.574239    50.541244   850.016497    297.974619     1.968909   \n",
       "std    1416.172165    29.089782    85.843584    160.289720     0.961968   \n",
       "min       1.000000     1.000000   700.000000     90.000000     1.000000   \n",
       "25%    1334.500000    25.000000   777.000000    161.000000     1.000000   \n",
       "50%    2527.500000    51.000000   853.000000    280.000000     2.000000   \n",
       "75%    3719.250000    75.000000   922.000000    386.000000     3.000000   \n",
       "max    4998.000000   100.000000   999.000000   1760.000000     6.000000   \n",
       "\n",
       "       average_price  \n",
       "count    1576.000000  \n",
       "mean      151.074873  \n",
       "std        28.269937  \n",
       "min        90.000000  \n",
       "25%       132.000000  \n",
       "50%       153.000000  \n",
       "75%       165.000000  \n",
       "max       352.000000  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cleaned_cash.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "cf6193c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>shop_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>order_amount</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1653.000000</td>\n",
       "      <td>1653.000000</td>\n",
       "      <td>1653.000000</td>\n",
       "      <td>1653.000000</td>\n",
       "      <td>1653.000000</td>\n",
       "      <td>1653.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2475.166969</td>\n",
       "      <td>49.114338</td>\n",
       "      <td>850.114338</td>\n",
       "      <td>308.176044</td>\n",
       "      <td>2.025408</td>\n",
       "      <td>152.526921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1443.040267</td>\n",
       "      <td>29.013391</td>\n",
       "      <td>86.394989</td>\n",
       "      <td>162.934006</td>\n",
       "      <td>0.991774</td>\n",
       "      <td>30.956484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1198.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>776.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>131.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2439.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>849.000000</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>153.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3723.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>924.000000</td>\n",
       "      <td>390.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>169.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5000.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>1408.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>352.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "          order_id      shop_id      user_id  order_amount  total_items  \\\n",
       "count  1653.000000  1653.000000  1653.000000   1653.000000  1653.000000   \n",
       "mean   2475.166969    49.114338   850.114338    308.176044     2.025408   \n",
       "std    1443.040267    29.013391    86.394989    162.934006     0.991774   \n",
       "min       9.000000     1.000000   700.000000     90.000000     1.000000   \n",
       "25%    1198.000000    23.000000   776.000000    165.000000     1.000000   \n",
       "50%    2439.000000    50.000000   849.000000    288.000000     2.000000   \n",
       "75%    3723.000000    73.000000   924.000000    390.000000     3.000000   \n",
       "max    5000.000000   100.000000   999.000000   1408.000000     8.000000   \n",
       "\n",
       "       average_price  \n",
       "count    1653.000000  \n",
       "mean      152.526921  \n",
       "std        30.956484  \n",
       "min        90.000000  \n",
       "25%       131.000000  \n",
       "50%       153.000000  \n",
       "75%       169.000000  \n",
       "max       352.000000  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cleaned_debit.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4a1ef717",
   "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>order_id</th>\n",
       "      <th>shop_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>order_amount</th>\n",
       "      <th>total_items</th>\n",
       "      <th>average_price</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1708.000000</td>\n",
       "      <td>1708.000000</td>\n",
       "      <td>1708.000000</td>\n",
       "      <td>1708.000000</td>\n",
       "      <td>1708.000000</td>\n",
       "      <td>1708.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2504.675059</td>\n",
       "      <td>49.913934</td>\n",
       "      <td>849.158080</td>\n",
       "      <td>301.415105</td>\n",
       "      <td>1.988876</td>\n",
       "      <td>151.732436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1470.818005</td>\n",
       "      <td>29.081188</td>\n",
       "      <td>88.221314</td>\n",
       "      <td>159.131464</td>\n",
       "      <td>0.992883</td>\n",
       "      <td>27.780910</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>90.000000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1202.500000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>774.000000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>133.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2527.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>847.000000</td>\n",
       "      <td>284.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>153.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3802.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>928.000000</td>\n",
       "      <td>386.250000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>166.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4999.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>1056.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>352.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "          order_id      shop_id      user_id  order_amount  total_items  \\\n",
       "count  1708.000000  1708.000000  1708.000000   1708.000000  1708.000000   \n",
       "mean   2504.675059    49.913934   849.158080    301.415105     1.988876   \n",
       "std    1470.818005    29.081188    88.221314    159.131464     0.992883   \n",
       "min       4.000000     1.000000   700.000000     90.000000     1.000000   \n",
       "25%    1202.500000    24.000000   774.000000    162.000000     1.000000   \n",
       "50%    2527.000000    49.000000   847.000000    284.000000     2.000000   \n",
       "75%    3802.000000    75.000000   928.000000    386.250000     3.000000   \n",
       "max    4999.000000   100.000000   999.000000   1056.000000     6.000000   \n",
       "\n",
       "       average_price  \n",
       "count    1708.000000  \n",
       "mean      151.732436  \n",
       "std        27.780910  \n",
       "min        90.000000  \n",
       "25%       133.000000  \n",
       "50%       153.000000  \n",
       "75%       166.500000  \n",
       "max       352.000000  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cleaned_credit.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad7155c1",
   "metadata": {},
   "source": [
    "## There is much difference on order amount for three payment methods, except the outlier (shop id: 42) with the payment method of credit card"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1de679a9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5235590b",
   "metadata": {},
   "source": [
    "## Same methodologies can be used to check which shop is most ordered and earns the most revenue as well as which user places the most order and spends the most money"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44612bdf",
   "metadata": {},
   "source": [
    "## R can also be used to analyze this in the same method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ecc458eb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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