[분석실무]1.웹주문분석.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 웹 주문 수 분석\n",
    "* customer_master.csv : 쇼핑몰 사이트 고객 정보\n",
    "* item_master.csv : 상품 데이터(상품명, 가격)\n",
    "* transaction_1.csv, transaction_1.csv : 구매내역 데이터\n",
    "* transaction_detail_1.csv, transaction_detail_1.csv : 구매내역 상세 데이터"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 데이터 읽기"
   ]
  },
  {
   "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>customer_id</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>registration_date</th>\n",
       "      <th>email</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>birth</th>\n",
       "      <th>pref</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IK152942</td>\n",
       "      <td>김서준</td>\n",
       "      <td>2019-01-01 0:25</td>\n",
       "      <td>hirata_yuujirou@example.com</td>\n",
       "      <td>M</td>\n",
       "      <td>29</td>\n",
       "      <td>1990-06-10</td>\n",
       "      <td>대전광역시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TS808488</td>\n",
       "      <td>김예준</td>\n",
       "      <td>2019-01-01 1:13</td>\n",
       "      <td>tamura_shiori@example.com</td>\n",
       "      <td>F</td>\n",
       "      <td>33</td>\n",
       "      <td>1986-05-20</td>\n",
       "      <td>인천광역시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AS834628</td>\n",
       "      <td>김도윤</td>\n",
       "      <td>2019-01-01 2:00</td>\n",
       "      <td>hisano_yuki@example.com</td>\n",
       "      <td>F</td>\n",
       "      <td>63</td>\n",
       "      <td>1956-01-02</td>\n",
       "      <td>광주광역시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AS345469</td>\n",
       "      <td>김시우</td>\n",
       "      <td>2019-01-01 4:48</td>\n",
       "      <td>tsuruoka_kaoru@example.com</td>\n",
       "      <td>M</td>\n",
       "      <td>74</td>\n",
       "      <td>1945-03-25</td>\n",
       "      <td>인천광역시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GD892565</td>\n",
       "      <td>김주원</td>\n",
       "      <td>2019-01-01 4:54</td>\n",
       "      <td>oouchi_takashi@example.com</td>\n",
       "      <td>M</td>\n",
       "      <td>54</td>\n",
       "      <td>1965-08-05</td>\n",
       "      <td>울산광역시</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  customer_id customer_name registration_date                        email  \\\n",
       "0    IK152942           김서준   2019-01-01 0:25  hirata_yuujirou@example.com   \n",
       "1    TS808488           김예준   2019-01-01 1:13    tamura_shiori@example.com   \n",
       "2    AS834628           김도윤   2019-01-01 2:00      hisano_yuki@example.com   \n",
       "3    AS345469           김시우   2019-01-01 4:48   tsuruoka_kaoru@example.com   \n",
       "4    GD892565           김주원   2019-01-01 4:54   oouchi_takashi@example.com   \n",
       "\n",
       "  gender  age       birth   pref  \n",
       "0      M   29  1990-06-10  대전광역시  \n",
       "1      F   33  1986-05-20  인천광역시  \n",
       "2      F   63  1956-01-02  광주광역시  \n",
       "3      M   74  1945-03-25  인천광역시  \n",
       "4      M   54  1965-08-05  울산광역시  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "customer_master = pd.read_csv('customer_master.csv')\n",
    "customer_master.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_id</th>\n",
       "      <th>item_name</th>\n",
       "      <th>item_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S001</td>\n",
       "      <td>PC-A</td>\n",
       "      <td>50000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S002</td>\n",
       "      <td>PC-B</td>\n",
       "      <td>85000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S003</td>\n",
       "      <td>PC-C</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S004</td>\n",
       "      <td>PC-D</td>\n",
       "      <td>180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S005</td>\n",
       "      <td>PC-E</td>\n",
       "      <td>210000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "  item_id item_name  item_price\n",
       "0    S001      PC-A       50000\n",
       "1    S002      PC-B       85000\n",
       "2    S003      PC-C      120000\n",
       "3    S004      PC-D      180000\n",
       "4    S005      PC-E      210000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_master = pd.read_csv('item_master.csv')\n",
    "item_master.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>price</th>\n",
       "      <th>payment_date</th>\n",
       "      <th>customer_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>T0000000113</td>\n",
       "      <td>210000</td>\n",
       "      <td>2019-02-01 01:36:57</td>\n",
       "      <td>PL563502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>T0000000114</td>\n",
       "      <td>50000</td>\n",
       "      <td>2019-02-01 01:37:23</td>\n",
       "      <td>HD678019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>T0000000115</td>\n",
       "      <td>120000</td>\n",
       "      <td>2019-02-01 02:34:19</td>\n",
       "      <td>HD298120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>T0000000116</td>\n",
       "      <td>210000</td>\n",
       "      <td>2019-02-01 02:47:23</td>\n",
       "      <td>IK452215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>T0000000117</td>\n",
       "      <td>170000</td>\n",
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      "text/plain": [
       "  transaction_id   price         payment_date customer_id\n",
       "0    T0000000113  210000  2019-02-01 01:36:57    PL563502\n",
       "1    T0000000114   50000  2019-02-01 01:37:23    HD678019\n",
       "2    T0000000115  120000  2019-02-01 02:34:19    HD298120\n",
       "3    T0000000116  210000  2019-02-01 02:47:23    IK452215\n",
       "4    T0000000117  170000  2019-02-01 04:33:46    PL542865"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transaction_1 = pd.read_csv('transaction_1.csv')\n",
    "transaction_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>detail_id</th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>quantity</th>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>T0000000114</td>\n",
       "      <td>S001</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>T0000000115</td>\n",
       "      <td>S003</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>T0000000117</td>\n",
       "      <td>S002</td>\n",
       "      <td>2</td>\n",
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      "text/plain": [
       "   detail_id transaction_id item_id  quantity\n",
       "0          0    T0000000113    S005         1\n",
       "1          1    T0000000114    S001         1\n",
       "2          2    T0000000115    S003         1\n",
       "3          3    T0000000116    S005         1\n",
       "4          4    T0000000117    S002         2"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transaction_detail_1 = pd.read_csv('transaction_detail_1.csv')\n",
    "transaction_detail_1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 데이터 결합\n",
    "* 데이터 전체를 파악하는 것이 중요\n",
    "* 상세한 데이터를 기준으로 맞추어 데이터를 결합/가공"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>price</th>\n",
       "      <th>payment_date</th>\n",
       "      <th>customer_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>T0000000113</td>\n",
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       "      <td>PL563502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>T0000000114</td>\n",
       "      <td>50000</td>\n",
       "      <td>2019-02-01 01:37:23</td>\n",
       "      <td>HD678019</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>T0000000115</td>\n",
       "      <td>120000</td>\n",
       "      <td>2019-02-01 02:34:19</td>\n",
       "      <td>HD298120</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>T0000000116</td>\n",
       "      <td>210000</td>\n",
       "      <td>2019-02-01 02:47:23</td>\n",
       "      <td>IK452215</td>\n",
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       "      <th>4</th>\n",
       "      <td>T0000000117</td>\n",
       "      <td>170000</td>\n",
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       "      <td>PL542865</td>\n",
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      ],
      "text/plain": [
       "  transaction_id   price         payment_date customer_id\n",
       "0    T0000000113  210000  2019-02-01 01:36:57    PL563502\n",
       "1    T0000000114   50000  2019-02-01 01:37:23    HD678019\n",
       "2    T0000000115  120000  2019-02-01 02:34:19    HD298120\n",
       "3    T0000000116  210000  2019-02-01 02:47:23    IK452215\n",
       "4    T0000000117  170000  2019-02-01 04:33:46    PL542865"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# transaction_1, transaction_2 결합\n",
    "transaction_2 = pd.read_csv('transaction_2.csv')\n",
    "\n",
    "transaction = pd.concat([transaction_1, transaction_2], ignore_index=True)\n",
    "transaction.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 4)\n",
      "(1786, 4)\n",
      "(6786, 4)\n"
     ]
    }
   ],
   "source": [
    "print(transaction_1.shape)\n",
    "print(transaction_2.shape)\n",
    "print(transaction.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>detail_id</th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>quantity</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>T0000000113</td>\n",
       "      <td>S005</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>T0000000114</td>\n",
       "      <td>S001</td>\n",
       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>T0000000115</td>\n",
       "      <td>S003</td>\n",
       "      <td>1</td>\n",
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       "      <th>3</th>\n",
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       "      <td>S005</td>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>T0000000117</td>\n",
       "      <td>S002</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   detail_id transaction_id item_id  quantity\n",
       "0          0    T0000000113    S005         1\n",
       "1          1    T0000000114    S001         1\n",
       "2          2    T0000000115    S003         1\n",
       "3          3    T0000000116    S005         1\n",
       "4          4    T0000000117    S002         2"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# transaction_detail\n",
    "transaction_detail_2 = pd.read_csv('transaction_detail_2.csv')\n",
    "\n",
    "transaction_detail = pd.concat([transaction_detail_1, transaction_detail_2], ignore_index=True)\n",
    "transaction_detail.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 4)\n",
      "(2144, 4)\n",
      "(7144, 4)\n"
     ]
    }
   ],
   "source": [
    "print(transaction_detail_1.shape)\n",
    "print(transaction_detail_2.shape)\n",
    "print(transaction_detail.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 매출 데이터 결합(join)\n",
    "* transaction_detail 기준\n",
    "* 데이터 join시 부족한 컬럼은 무엇인지, 공통되는 데이터 컬럼은 무엇인지 파악    \n",
    "  추가 : payment_date, customer_id / 공통 : transaction_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>detail_id</th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>payment_date</th>\n",
       "      <th>customer_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>T0000000113</td>\n",
       "      <td>S005</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-02-01 01:36:57</td>\n",
       "      <td>PL563502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>T0000000114</td>\n",
       "      <td>S001</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-02-01 01:37:23</td>\n",
       "      <td>HD678019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>T0000000115</td>\n",
       "      <td>S003</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-02-01 02:34:19</td>\n",
       "      <td>HD298120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>T0000000116</td>\n",
       "      <td>S005</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-02-01 02:47:23</td>\n",
       "      <td>IK452215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>T0000000117</td>\n",
       "      <td>S002</td>\n",
       "      <td>2</td>\n",
       "      <td>2019-02-01 04:33:46</td>\n",
       "      <td>PL542865</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   detail_id transaction_id item_id  quantity         payment_date customer_id\n",
       "0          0    T0000000113    S005         1  2019-02-01 01:36:57    PL563502\n",
       "1          1    T0000000114    S001         1  2019-02-01 01:37:23    HD678019\n",
       "2          2    T0000000115    S003         1  2019-02-01 02:34:19    HD298120\n",
       "3          3    T0000000116    S005         1  2019-02-01 02:47:23    IK452215\n",
       "4          4    T0000000117    S002         2  2019-02-01 04:33:46    PL542865"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "join_data = pd.merge(transaction_detail, transaction[['transaction_id', 'payment_date', 'customer_id']],\n",
    "                    on = 'transaction_id', how='left')\n",
    "join_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7144, 4)\n",
      "(6786, 4)\n",
      "(7144, 6)\n"
     ]
    }
   ],
   "source": [
    "print(transaction_detail.shape)\n",
    "print(transaction.shape)\n",
    "print(join_data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 마스터데이터 결합(join)\n",
    "* 추가 : customer_master, item_master / 공통 : customer_id, item_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>detail_id</th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>payment_date</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>registration_date</th>\n",
       "      <th>email</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>birth</th>\n",
       "      <th>pref</th>\n",
       "      <th>item_name</th>\n",
       "      <th>item_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>T0000000113</td>\n",
       "      <td>S005</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-02-01 01:36:57</td>\n",
       "      <td>PL563502</td>\n",
       "      <td>김태경</td>\n",
       "      <td>2019-01-07 14:34</td>\n",
       "      <td>imoto_yoshimasa@example.com</td>\n",
       "      <td>M</td>\n",
       "      <td>30</td>\n",
       "      <td>1989-07-15</td>\n",
       "      <td>대전광역시</td>\n",
       "      <td>PC-E</td>\n",
       "      <td>210000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>T0000000114</td>\n",
       "      <td>S001</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-02-01 01:37:23</td>\n",
       "      <td>HD678019</td>\n",
       "      <td>김영웅</td>\n",
       "      <td>2019-01-27 18:00</td>\n",
       "      <td>mifune_rokurou@example.com</td>\n",
       "      <td>M</td>\n",
       "      <td>73</td>\n",
       "      <td>1945-11-29</td>\n",
       "      <td>서울특별시</td>\n",
       "      <td>PC-A</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>T0000000115</td>\n",
       "      <td>S003</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-02-01 02:34:19</td>\n",
       "      <td>HD298120</td>\n",
       "      <td>김강현</td>\n",
       "      <td>2019-01-11 8:16</td>\n",
       "      <td>yamane_kogan@example.com</td>\n",
       "      <td>M</td>\n",
       "      <td>42</td>\n",
       "      <td>1977-05-17</td>\n",
       "      <td>광주광역시</td>\n",
       "      <td>PC-C</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>T0000000116</td>\n",
       "      <td>S005</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-02-01 02:47:23</td>\n",
       "      <td>IK452215</td>\n",
       "      <td>김주한</td>\n",
       "      <td>2019-01-10 5:07</td>\n",
       "      <td>ikeda_natsumi@example.com</td>\n",
       "      <td>F</td>\n",
       "      <td>47</td>\n",
       "      <td>1972-03-17</td>\n",
       "      <td>인천광역시</td>\n",
       "      <td>PC-E</td>\n",
       "      <td>210000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>T0000000117</td>\n",
       "      <td>S002</td>\n",
       "      <td>2</td>\n",
       "      <td>2019-02-01 04:33:46</td>\n",
       "      <td>PL542865</td>\n",
       "      <td>김영빈</td>\n",
       "      <td>2019-01-25 6:46</td>\n",
       "      <td>kurita_kenichi@example.com</td>\n",
       "      <td>M</td>\n",
       "      <td>74</td>\n",
       "      <td>1944-12-17</td>\n",
       "      <td>광주광역시</td>\n",
       "      <td>PC-B</td>\n",
       "      <td>85000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   detail_id transaction_id item_id  quantity         payment_date  \\\n",
       "0          0    T0000000113    S005         1  2019-02-01 01:36:57   \n",
       "1          1    T0000000114    S001         1  2019-02-01 01:37:23   \n",
       "2          2    T0000000115    S003         1  2019-02-01 02:34:19   \n",
       "3          3    T0000000116    S005         1  2019-02-01 02:47:23   \n",
       "4          4    T0000000117    S002         2  2019-02-01 04:33:46   \n",
       "\n",
       "  customer_id customer_name registration_date                        email  \\\n",
       "0    PL563502           김태경  2019-01-07 14:34  imoto_yoshimasa@example.com   \n",
       "1    HD678019           김영웅  2019-01-27 18:00   mifune_rokurou@example.com   \n",
       "2    HD298120           김강현   2019-01-11 8:16     yamane_kogan@example.com   \n",
       "3    IK452215           김주한   2019-01-10 5:07    ikeda_natsumi@example.com   \n",
       "4    PL542865           김영빈   2019-01-25 6:46   kurita_kenichi@example.com   \n",
       "\n",
       "  gender  age       birth   pref item_name  item_price  \n",
       "0      M   30  1989-07-15  대전광역시      PC-E      210000  \n",
       "1      M   73  1945-11-29  서울특별시      PC-A       50000  \n",
       "2      M   42  1977-05-17  광주광역시      PC-C      120000  \n",
       "3      F   47  1972-03-17  인천광역시      PC-E      210000  \n",
       "4      M   74  1944-12-17  광주광역시      PC-B       85000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "join_data = pd.merge(join_data, customer_master, on='customer_id', how='left')\n",
    "join_data = pd.merge(join_data, item_master, on='item_id', how='left')\n",
    "join_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. 필요 데이터 생성\n",
    "* price = quantity * item_price"
   ]
  },
  {
   "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>quantity</th>\n",
       "      <th>item_price</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>210000</td>\n",
       "      <td>210000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>50000</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>120000</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>210000</td>\n",
       "      <td>210000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>85000</td>\n",
       "      <td>170000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   quantity  item_price   price\n",
       "0         1      210000  210000\n",
       "1         1       50000   50000\n",
       "2         1      120000  120000\n",
       "3         1      210000  210000\n",
       "4         2       85000  170000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "join_data['price'] = join_data['quantity'] * join_data['item_price']\n",
    "join_data[['quantity', 'item_price', 'price']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. 계산 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "971135000\n",
      "971135000\n"
     ]
    }
   ],
   "source": [
    "print(join_data['price'].sum())\n",
    "print(transaction['price'].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "join_data['price'].sum() == transaction['price'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. 통계량 파악\n",
    "* 데이터 분석 시 먼저 파악해야할 숫자    \n",
    "  결측치의 개수, 전체 숫자"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "detail_id            0\n",
       "transaction_id       0\n",
       "item_id              0\n",
       "quantity             0\n",
       "payment_date         0\n",
       "customer_id          0\n",
       "customer_name        0\n",
       "registration_date    0\n",
       "email                0\n",
       "gender               0\n",
       "age                  0\n",
       "birth                0\n",
       "pref                 0\n",
       "item_name            0\n",
       "item_price           0\n",
       "price                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "join_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>detail_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>age</th>\n",
       "      <th>item_price</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7144.000000</td>\n",
       "      <td>7144.000000</td>\n",
       "      <td>7144.000000</td>\n",
       "      <td>7144.000000</td>\n",
       "      <td>7144.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3571.500000</td>\n",
       "      <td>1.199888</td>\n",
       "      <td>50.265677</td>\n",
       "      <td>121698.628219</td>\n",
       "      <td>135937.150056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2062.439494</td>\n",
       "      <td>0.513647</td>\n",
       "      <td>17.190314</td>\n",
       "      <td>64571.311830</td>\n",
       "      <td>68511.453297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1785.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>85000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3571.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>102500.000000</td>\n",
       "      <td>120000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5357.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>187500.000000</td>\n",
       "      <td>210000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7143.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>210000.000000</td>\n",
       "      <td>420000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         detail_id     quantity          age     item_price          price\n",
       "count  7144.000000  7144.000000  7144.000000    7144.000000    7144.000000\n",
       "mean   3571.500000     1.199888    50.265677  121698.628219  135937.150056\n",
       "std    2062.439494     0.513647    17.190314   64571.311830   68511.453297\n",
       "min       0.000000     1.000000    20.000000   50000.000000   50000.000000\n",
       "25%    1785.750000     1.000000    36.000000   50000.000000   85000.000000\n",
       "50%    3571.500000     1.000000    50.000000  102500.000000  120000.000000\n",
       "75%    5357.250000     1.000000    65.000000  187500.000000  210000.000000\n",
       "max    7143.000000     4.000000    80.000000  210000.000000  420000.000000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "join_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 추가 확인 : 데이터의 기간"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-02-01 01:36:57\n",
      "2019-07-31 23:41:38\n"
     ]
    }
   ],
   "source": [
    "print(join_data['payment_date'].min())\n",
    "print(join_data['payment_date'].max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. 월별 데이터 집계"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 각 컬럼의 데이터타입 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "detail_id             int64\n",
       "transaction_id       object\n",
       "item_id              object\n",
       "quantity              int64\n",
       "payment_date         object\n",
       "customer_id          object\n",
       "customer_name        object\n",
       "registration_date    object\n",
       "email                object\n",
       "gender               object\n",
       "age                   int64\n",
       "birth                object\n",
       "pref                 object\n",
       "item_name            object\n",
       "item_price            int64\n",
       "price                 int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "join_data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* payment_date의 타입 변경 : object -> datetime"
   ]
  },
  {
   "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",
       "        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>payment_date</th>\n",
       "      <th>payment_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-02-01 01:36:57</td>\n",
       "      <td>201902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-02-01 01:37:23</td>\n",
       "      <td>201902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-02-01 02:34:19</td>\n",
       "      <td>201902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-02-01 02:47:23</td>\n",
       "      <td>201902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-02-01 04:33:46</td>\n",
       "      <td>201902</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         payment_date payment_month\n",
       "0 2019-02-01 01:36:57        201902\n",
       "1 2019-02-01 01:37:23        201902\n",
       "2 2019-02-01 02:34:19        201902\n",
       "3 2019-02-01 02:47:23        201902\n",
       "4 2019-02-01 04:33:46        201902"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "join_data['payment_date'] = pd.to_datetime(join_data['payment_date'])\n",
    "join_data['payment_month'] = join_data['payment_date'].dt.strftime('%Y%m')\n",
    "join_data[['payment_date', 'payment_month']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 월별 매출 합계"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "payment_month\n",
       "201902    160185000\n",
       "201903    160370000\n",
       "201904    160510000\n",
       "201905    155420000\n",
       "201906    164030000\n",
       "201907    170620000\n",
       "Name: price, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "join_data.groupby('payment_month').sum()['price']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. 월별, 상품별 데이터 집계"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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></th>\n",
       "      <th>price</th>\n",
       "      <th>quantity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>payment_month</th>\n",
       "      <th>item_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">201902</th>\n",
       "      <th>PC-A</th>\n",
       "      <td>24150000</td>\n",
       "      <td>483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC-B</th>\n",
       "      <td>25245000</td>\n",
       "      <td>297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC-C</th>\n",
       "      <td>19800000</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC-D</th>\n",
       "      <td>31140000</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC-E</th>\n",
       "      <td>59850000</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">201903</th>\n",
       "      <th>PC-A</th>\n",
       "      <td>26000000</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC-B</th>\n",
       "      <td>25500000</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC-C</th>\n",
       "      <td>19080000</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC-D</th>\n",
       "      <td>25740000</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC-E</th>\n",
       "      <td>64050000</td>\n",
       "      <td>305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">201904</th>\n",
       "      <th>PC-A</th>\n",
       "      <td>25900000</td>\n",
       "      <td>518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC-B</th>\n",
       "      <td>23460000</td>\n",
       "      <td>276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC-C</th>\n",
       "      <td>21960000</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC-D</th>\n",
       "      <td>24300000</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC-E</th>\n",
       "      <td>64890000</td>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">201905</th>\n",
       "      <th>PC-A</th>\n",
       "      <td>24850000</td>\n",
       "      <td>497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC-B</th>\n",
       "      <td>25330000</td>\n",
       "      <td>298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC-C</th>\n",
       "      <td>20520000</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC-D</th>\n",
       "      <td>25920000</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC-E</th>\n",
       "      <td>58800000</td>\n",
       "      <td>280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">201906</th>\n",
       "      <th>PC-A</th>\n",
       "      <td>26000000</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC-B</th>\n",
       "      <td>23970000</td>\n",
       "      <td>282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC-C</th>\n",
       "      <td>21840000</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC-D</th>\n",
       "      <td>28800000</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC-E</th>\n",
       "      <td>63420000</td>\n",
       "      <td>302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">201907</th>\n",
       "      <th>PC-A</th>\n",
       "      <td>25250000</td>\n",
       "      <td>505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC-B</th>\n",
       "      <td>28220000</td>\n",
       "      <td>332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC-C</th>\n",
       "      <td>19440000</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC-D</th>\n",
       "      <td>26100000</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC-E</th>\n",
       "      <td>71610000</td>\n",
       "      <td>341</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            price  quantity\n",
       "payment_month item_name                    \n",
       "201902        PC-A       24150000       483\n",
       "              PC-B       25245000       297\n",
       "              PC-C       19800000       165\n",
       "              PC-D       31140000       173\n",
       "              PC-E       59850000       285\n",
       "201903        PC-A       26000000       520\n",
       "              PC-B       25500000       300\n",
       "              PC-C       19080000       159\n",
       "              PC-D       25740000       143\n",
       "              PC-E       64050000       305\n",
       "201904        PC-A       25900000       518\n",
       "              PC-B       23460000       276\n",
       "              PC-C       21960000       183\n",
       "              PC-D       24300000       135\n",
       "              PC-E       64890000       309\n",
       "201905        PC-A       24850000       497\n",
       "              PC-B       25330000       298\n",
       "              PC-C       20520000       171\n",
       "              PC-D       25920000       144\n",
       "              PC-E       58800000       280\n",
       "201906        PC-A       26000000       520\n",
       "              PC-B       23970000       282\n",
       "              PC-C       21840000       182\n",
       "              PC-D       28800000       160\n",
       "              PC-E       63420000       302\n",
       "201907        PC-A       25250000       505\n",
       "              PC-B       28220000       332\n",
       "              PC-C       19440000       162\n",
       "              PC-D       26100000       145\n",
       "              PC-E       71610000       341"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "join_data.groupby(['payment_month', 'item_name']).sum()[['price', 'quantity']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"6\" halign=\"left\">price</th>\n",
       "      <th colspan=\"6\" halign=\"left\">quantity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>payment_month</th>\n",
       "      <th>201902</th>\n",
       "      <th>201903</th>\n",
       "      <th>201904</th>\n",
       "      <th>201905</th>\n",
       "      <th>201906</th>\n",
       "      <th>201907</th>\n",
       "      <th>201902</th>\n",
       "      <th>201903</th>\n",
       "      <th>201904</th>\n",
       "      <th>201905</th>\n",
       "      <th>201906</th>\n",
       "      <th>201907</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>PC-A</th>\n",
       "      <td>24150000</td>\n",
       "      <td>26000000</td>\n",
       "      <td>25900000</td>\n",
       "      <td>24850000</td>\n",
       "      <td>26000000</td>\n",
       "      <td>25250000</td>\n",
       "      <td>483</td>\n",
       "      <td>520</td>\n",
       "      <td>518</td>\n",
       "      <td>497</td>\n",
       "      <td>520</td>\n",
       "      <td>505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC-B</th>\n",
       "      <td>25245000</td>\n",
       "      <td>25500000</td>\n",
       "      <td>23460000</td>\n",
       "      <td>25330000</td>\n",
       "      <td>23970000</td>\n",
       "      <td>28220000</td>\n",
       "      <td>297</td>\n",
       "      <td>300</td>\n",
       "      <td>276</td>\n",
       "      <td>298</td>\n",
       "      <td>282</td>\n",
       "      <td>332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC-C</th>\n",
       "      <td>19800000</td>\n",
       "      <td>19080000</td>\n",
       "      <td>21960000</td>\n",
       "      <td>20520000</td>\n",
       "      <td>21840000</td>\n",
       "      <td>19440000</td>\n",
       "      <td>165</td>\n",
       "      <td>159</td>\n",
       "      <td>183</td>\n",
       "      <td>171</td>\n",
       "      <td>182</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC-D</th>\n",
       "      <td>31140000</td>\n",
       "      <td>25740000</td>\n",
       "      <td>24300000</td>\n",
       "      <td>25920000</td>\n",
       "      <td>28800000</td>\n",
       "      <td>26100000</td>\n",
       "      <td>173</td>\n",
       "      <td>143</td>\n",
       "      <td>135</td>\n",
       "      <td>144</td>\n",
       "      <td>160</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC-E</th>\n",
       "      <td>59850000</td>\n",
       "      <td>64050000</td>\n",
       "      <td>64890000</td>\n",
       "      <td>58800000</td>\n",
       "      <td>63420000</td>\n",
       "      <td>71610000</td>\n",
       "      <td>285</td>\n",
       "      <td>305</td>\n",
       "      <td>309</td>\n",
       "      <td>280</td>\n",
       "      <td>302</td>\n",
       "      <td>341</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  price                                                    \\\n",
       "payment_month    201902    201903    201904    201905    201906    201907   \n",
       "item_name                                                                   \n",
       "PC-A           24150000  26000000  25900000  24850000  26000000  25250000   \n",
       "PC-B           25245000  25500000  23460000  25330000  23970000  28220000   \n",
       "PC-C           19800000  19080000  21960000  20520000  21840000  19440000   \n",
       "PC-D           31140000  25740000  24300000  25920000  28800000  26100000   \n",
       "PC-E           59850000  64050000  64890000  58800000  63420000  71610000   \n",
       "\n",
       "              quantity                                     \n",
       "payment_month   201902 201903 201904 201905 201906 201907  \n",
       "item_name                                                  \n",
       "PC-A               483    520    518    497    520    505  \n",
       "PC-B               297    300    276    298    282    332  \n",
       "PC-C               165    159    183    171    182    162  \n",
       "PC-D               173    143    135    144    160    145  \n",
       "PC-E               285    305    309    280    302    341  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(join_data, index='item_name', columns='payment_month', values=['price', 'quantity'], aggfunc='sum')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10. 상품별 매출 추이 가시화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>item_name</th>\n",
       "      <th>PC-A</th>\n",
       "      <th>PC-B</th>\n",
       "      <th>PC-C</th>\n",
       "      <th>PC-D</th>\n",
       "      <th>PC-E</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>payment_month</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>201902</th>\n",
       "      <td>24150000</td>\n",
       "      <td>25245000</td>\n",
       "      <td>19800000</td>\n",
       "      <td>31140000</td>\n",
       "      <td>59850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201903</th>\n",
       "      <td>26000000</td>\n",
       "      <td>25500000</td>\n",
       "      <td>19080000</td>\n",
       "      <td>25740000</td>\n",
       "      <td>64050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201904</th>\n",
       "      <td>25900000</td>\n",
       "      <td>23460000</td>\n",
       "      <td>21960000</td>\n",
       "      <td>24300000</td>\n",
       "      <td>64890000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201905</th>\n",
       "      <td>24850000</td>\n",
       "      <td>25330000</td>\n",
       "      <td>20520000</td>\n",
       "      <td>25920000</td>\n",
       "      <td>58800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201906</th>\n",
       "      <td>26000000</td>\n",
       "      <td>23970000</td>\n",
       "      <td>21840000</td>\n",
       "      <td>28800000</td>\n",
       "      <td>63420000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "item_name          PC-A      PC-B      PC-C      PC-D      PC-E\n",
       "payment_month                                                  \n",
       "201902         24150000  25245000  19800000  31140000  59850000\n",
       "201903         26000000  25500000  19080000  25740000  64050000\n",
       "201904         25900000  23460000  21960000  24300000  64890000\n",
       "201905         24850000  25330000  20520000  25920000  58800000\n",
       "201906         26000000  23970000  21840000  28800000  63420000"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph_data = pd.pivot_table(join_data, index='payment_month', columns='item_name', values='price', aggfunc='sum')\n",
    "graph_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x14dd3a11f88>"
      ]
     },
     "execution_count": 30,
     "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": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.plot(list(graph_data.index), graph_data['PC-A'], label='PC-A')\n",
    "plt.plot(list(graph_data.index), graph_data['PC-B'], label='PC-B')\n",
    "plt.plot(list(graph_data.index), graph_data['PC-C'], label='PC-C')\n",
    "plt.plot(list(graph_data.index), graph_data['PC-D'], label='PC-D')\n",
    "plt.plot(list(graph_data.index), graph_data['PC-E'], label='PC-E')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}