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MyFirstNotebook.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": "# The code was removed by Watson Studio for sharing."
},
{
"cell_type": "code",
"execution_count": 5,
"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>customer_id</th>\n <th>customer_name</th>\n <th>registration_date</th>\n <th>customer_name_kana</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>\u5e73\u7530 \u88d5\u6b21\u90ce</td>\n <td>2019-01-01 00:25:33</td>\n <td>\u3072\u3089\u305f \u3086\u3046\u3058\u308d\u3046</td>\n <td>hirata_yuujirou@example.com</td>\n <td>M</td>\n <td>29</td>\n <td>1990/6/10</td>\n <td>\u77f3\u5ddd\u770c</td>\n </tr>\n <tr>\n <th>1</th>\n <td>TS808488</td>\n <td>\u7530\u6751 \u8a69\u7e54</td>\n <td>2019-01-01 01:13:45</td>\n <td>\u305f\u3080\u3089 \u3057\u304a\u308a</td>\n <td>tamura_shiori@example.com</td>\n <td>F</td>\n <td>33</td>\n <td>1986/5/20</td>\n <td>\u6771\u4eac\u90fd</td>\n </tr>\n <tr>\n <th>2</th>\n <td>AS834628</td>\n <td>\u4e45\u91ce \u7531\u6a39</td>\n <td>2019-01-01 02:00:14</td>\n <td>\u3072\u3055\u306e \u3086\u304d</td>\n <td>hisano_yuki@example.com</td>\n <td>F</td>\n <td>63</td>\n <td>1956/1/2</td>\n <td>\u8328\u57ce\u770c</td>\n </tr>\n <tr>\n <th>3</th>\n <td>AS345469</td>\n <td>\u9db4\u5ca1 \u85ab</td>\n <td>2019-01-01 04:48:22</td>\n <td>\u3064\u308b\u304a\u304b \u304b\u304a\u308b</td>\n <td>tsuruoka_kaoru@example.com</td>\n <td>M</td>\n <td>74</td>\n <td>1945/3/25</td>\n <td>\u6771\u4eac\u90fd</td>\n </tr>\n <tr>\n <th>4</th>\n <td>GD892565</td>\n <td>\u5927\u5185 \u9ad8\u53f2</td>\n <td>2019-01-01 04:54:51</td>\n <td>\u304a\u304a\u3046\u3061 \u305f\u304b\u3057</td>\n <td>oouchi_takashi@example.com</td>\n <td>M</td>\n <td>54</td>\n <td>1965/8/5</td>\n <td>\u5343\u8449\u770c</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " customer_id customer_name registration_date customer_name_kana \\\n0 IK152942 \u5e73\u7530 \u88d5\u6b21\u90ce 2019-01-01 00:25:33 \u3072\u3089\u305f \u3086\u3046\u3058\u308d\u3046 \n1 TS808488 \u7530\u6751 \u8a69\u7e54 2019-01-01 01:13:45 \u305f\u3080\u3089 \u3057\u304a\u308a \n2 AS834628 \u4e45\u91ce \u7531\u6a39 2019-01-01 02:00:14 \u3072\u3055\u306e \u3086\u304d \n3 AS345469 \u9db4\u5ca1 \u85ab 2019-01-01 04:48:22 \u3064\u308b\u304a\u304b \u304b\u304a\u308b \n4 GD892565 \u5927\u5185 \u9ad8\u53f2 2019-01-01 04:54:51 \u304a\u304a\u3046\u3061 \u305f\u304b\u3057 \n\n email gender age birth pref \n0 hirata_yuujirou@example.com M 29 1990/6/10 \u77f3\u5ddd\u770c \n1 tamura_shiori@example.com F 33 1986/5/20 \u6771\u4eac\u90fd \n2 hisano_yuki@example.com F 63 1956/1/2 \u8328\u57ce\u770c \n3 tsuruoka_kaoru@example.com M 74 1945/3/25 \u6771\u4eac\u90fd \n4 oouchi_takashi@example.com M 54 1965/8/5 \u5343\u8449\u770c "
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "cs_master.head()"
},
{
"cell_type": "code",
"execution_count": 6,
"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>age</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>count</th>\n <td>5000.000000</td>\n </tr>\n <tr>\n <th>mean</th>\n <td>50.053200</td>\n </tr>\n <tr>\n <th>std</th>\n <td>17.338607</td>\n </tr>\n <tr>\n <th>min</th>\n <td>20.000000</td>\n </tr>\n <tr>\n <th>25%</th>\n <td>35.000000</td>\n </tr>\n <tr>\n <th>50%</th>\n <td>50.000000</td>\n </tr>\n <tr>\n <th>75%</th>\n <td>65.000000</td>\n </tr>\n <tr>\n <th>max</th>\n <td>80.000000</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " age\ncount 5000.000000\nmean 50.053200\nstd 17.338607\nmin 20.000000\n25% 35.000000\n50% 50.000000\n75% 65.000000\nmax 80.000000"
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "cs_master.describe()"
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>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 </tr>\n <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</table>\n</div>",
"text/plain": " item_id item_name item_price\n0 S001 PC-A 50000\n1 S002 PC-B 85000\n2 S003 PC-C 120000\n3 S004 PC-D 180000\n4 S005 PC-E 210000"
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "item_master = client_3f9adee0f7984d12adf1b452a714a874.get_object(Bucket='practice-donotdelete-pr-i0yoa12mpgwzrt',Key='item_master.csv')['Body']\nitem_master = pd.read_csv(item_master)\nitem_master.head()"
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": " transaction_id price payment_date customer_id\n0 T0000000113 210000 2019-02-01 01:36:57 PL563502\n1 T0000000114 50000 2019-02-01 01:37:23 HD678019\n2 T0000000115 120000 2019-02-01 02:34:19 HD298120\n3 T0000000116 210000 2019-02-01 02:47:23 IK452215\n4 T0000000117 170000 2019-02-01 04:33:46 PL542865\n"
},
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>price</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>count</th>\n <td>5000.000000</td>\n </tr>\n <tr>\n <th>mean</th>\n <td>142248.000000</td>\n </tr>\n <tr>\n <th>std</th>\n <td>77256.327922</td>\n </tr>\n <tr>\n <th>min</th>\n <td>50000.000000</td>\n </tr>\n <tr>\n <th>25%</th>\n <td>85000.000000</td>\n </tr>\n <tr>\n <th>50%</th>\n <td>120000.000000</td>\n </tr>\n <tr>\n <th>75%</th>\n <td>210000.000000</td>\n </tr>\n <tr>\n <th>max</th>\n <td>750000.000000</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " price\ncount 5000.000000\nmean 142248.000000\nstd 77256.327922\nmin 50000.000000\n25% 85000.000000\n50% 120000.000000\n75% 210000.000000\nmax 750000.000000"
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "transaction_1 = client_3f9adee0f7984d12adf1b452a714a874.get_object(Bucket='practice-donotdelete-pr-i0yoa12mpgwzrt',Key='transaction_1.csv')['Body']\ntransaction_1 = pd.read_csv(transaction_1)\nprint(transaction_1.head())\ntransaction_1.describe()"
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": " transaction_id price payment_date customer_id\n0 T0000005113 295000 2019-06-15 07:20:27 TS169261\n1 T0000005114 50000 2019-06-15 07:35:47 HI599892\n2 T0000005115 85000 2019-06-15 07:56:36 HI421757\n3 T0000005116 50000 2019-06-15 08:40:55 OA386378\n4 T0000005117 120000 2019-06-15 08:44:23 TS506913\n"
},
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>price</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>count</th>\n <td>1786.000000</td>\n </tr>\n <tr>\n <th>mean</th>\n <td>145517.917133</td>\n </tr>\n <tr>\n <th>std</th>\n <td>78518.123750</td>\n </tr>\n <tr>\n <th>min</th>\n <td>50000.000000</td>\n </tr>\n <tr>\n <th>25%</th>\n <td>85000.000000</td>\n </tr>\n <tr>\n <th>50%</th>\n <td>120000.000000</td>\n </tr>\n <tr>\n <th>75%</th>\n <td>210000.000000</td>\n </tr>\n <tr>\n <th>max</th>\n <td>570000.000000</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " price\ncount 1786.000000\nmean 145517.917133\nstd 78518.123750\nmin 50000.000000\n25% 85000.000000\n50% 120000.000000\n75% 210000.000000\nmax 570000.000000"
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "transaction_2 = client_3f9adee0f7984d12adf1b452a714a874.get_object(Bucket='practice-donotdelete-pr-i0yoa12mpgwzrt',Key='transaction_2.csv')['Body']\ntransaction_2 = pd.read_csv(transaction_2)\nprint(transaction_2.head())\ntransaction_2.describe()\n"
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": " transaction_id price payment_date customer_id\n0 T0000000113 210000 2019-02-01 01:36:57 PL563502\n1 T0000000114 50000 2019-02-01 01:37:23 HD678019\n2 T0000000115 120000 2019-02-01 02:34:19 HD298120\n3 T0000000116 210000 2019-02-01 02:47:23 IK452215\n4 T0000000117 170000 2019-02-01 04:33:46 PL542865\n"
},
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>price</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>count</th>\n <td>6786.000000</td>\n </tr>\n <tr>\n <th>mean</th>\n <td>143108.605953</td>\n </tr>\n <tr>\n <th>std</th>\n <td>77597.965237</td>\n </tr>\n <tr>\n <th>min</th>\n <td>50000.000000</td>\n </tr>\n <tr>\n <th>25%</th>\n <td>85000.000000</td>\n </tr>\n <tr>\n <th>50%</th>\n <td>120000.000000</td>\n </tr>\n <tr>\n <th>75%</th>\n <td>210000.000000</td>\n </tr>\n <tr>\n <th>max</th>\n <td>750000.000000</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " price\ncount 6786.000000\nmean 143108.605953\nstd 77597.965237\nmin 50000.000000\n25% 85000.000000\n50% 120000.000000\n75% 210000.000000\nmax 750000.000000"
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "## \u30e6\u30cb\u30aa\u30f3\ntransaction = pd.concat([transaction_1, transaction_2], ignore_index=True)\nprint(transaction.head())\ntransaction.describe()"
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": " detail_id transaction_id item_id quantity\n0 0 T0000000113 S005 1\n1 1 T0000000114 S001 1\n2 2 T0000000115 S003 1\n3 3 T0000000116 S005 1\n4 4 T0000000117 S002 2\n"
},
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>detail_id</th>\n <th>quantity</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>count</th>\n <td>5000.000000</td>\n <td>5000.000000</td>\n </tr>\n <tr>\n <th>mean</th>\n <td>2499.500000</td>\n <td>1.199200</td>\n </tr>\n <tr>\n <th>std</th>\n <td>1443.520003</td>\n <td>0.513393</td>\n </tr>\n <tr>\n <th>min</th>\n <td>0.000000</td>\n <td>1.000000</td>\n </tr>\n <tr>\n <th>25%</th>\n <td>1249.750000</td>\n <td>1.000000</td>\n </tr>\n <tr>\n <th>50%</th>\n <td>2499.500000</td>\n <td>1.000000</td>\n </tr>\n <tr>\n <th>75%</th>\n <td>3749.250000</td>\n <td>1.000000</td>\n </tr>\n <tr>\n <th>max</th>\n <td>4999.000000</td>\n <td>4.000000</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " detail_id quantity\ncount 5000.000000 5000.000000\nmean 2499.500000 1.199200\nstd 1443.520003 0.513393\nmin 0.000000 1.000000\n25% 1249.750000 1.000000\n50% 2499.500000 1.000000\n75% 3749.250000 1.000000\nmax 4999.000000 4.000000"
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "transaction_detail_1= client_3f9adee0f7984d12adf1b452a714a874.get_object(Bucket='practice-donotdelete-pr-i0yoa12mpgwzrt',Key='transaction_detail_1.csv')['Body']\ntransaction_detail_1 = pd.read_csv(transaction_detail_1)\nprint(transaction_detail_1.head())\ntransaction_detail_1.describe()"
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": " detail_id transaction_id item_id quantity\n0 5000 T0000004870 S002 3\n1 5001 T0000004871 S003 1\n2 5002 T0000004872 S001 2\n3 5003 T0000004873 S004 1\n4 5004 T0000004874 S003 2\n"
},
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>detail_id</th>\n <th>quantity</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>count</th>\n <td>2144.000000</td>\n <td>2144.000000</td>\n </tr>\n <tr>\n <th>mean</th>\n <td>6071.500000</td>\n <td>1.201493</td>\n </tr>\n <tr>\n <th>std</th>\n <td>619.063809</td>\n <td>0.514355</td>\n </tr>\n <tr>\n <th>min</th>\n <td>5000.000000</td>\n <td>1.000000</td>\n </tr>\n <tr>\n <th>25%</th>\n <td>5535.750000</td>\n <td>1.000000</td>\n </tr>\n <tr>\n <th>50%</th>\n <td>6071.500000</td>\n <td>1.000000</td>\n </tr>\n <tr>\n <th>75%</th>\n <td>6607.250000</td>\n <td>1.000000</td>\n </tr>\n <tr>\n <th>max</th>\n <td>7143.000000</td>\n <td>4.000000</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " detail_id quantity\ncount 2144.000000 2144.000000\nmean 6071.500000 1.201493\nstd 619.063809 0.514355\nmin 5000.000000 1.000000\n25% 5535.750000 1.000000\n50% 6071.500000 1.000000\n75% 6607.250000 1.000000\nmax 7143.000000 4.000000"
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "transaction_detail_2= client_3f9adee0f7984d12adf1b452a714a874.get_object(Bucket='practice-donotdelete-pr-i0yoa12mpgwzrt',Key='transaction_detail_2.csv')['Body']\ntransaction_detail_2 = pd.read_csv(transaction_detail_2)\nprint(transaction_detail_2.head())\ntransaction_detail_2.describe()"
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": "def show_stats(df):\n print(df.describe())\n \n return df.head()"
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": " detail_id quantity\ncount 7144.000000 7144.000000\nmean 3571.500000 1.199888\nstd 2062.439494 0.513647\nmin 0.000000 1.000000\n25% 1785.750000 1.000000\n50% 3571.500000 1.000000\n75% 5357.250000 1.000000\nmax 7143.000000 4.000000\n"
},
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>detail_id</th>\n <th>transaction_id</th>\n <th>item_id</th>\n <th>quantity</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 </tr>\n <tr>\n <th>1</th>\n <td>1</td>\n <td>T0000000114</td>\n <td>S001</td>\n <td>1</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 </tr>\n <tr>\n <th>3</th>\n <td>3</td>\n <td>T0000000116</td>\n <td>S005</td>\n <td>1</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 </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " detail_id transaction_id item_id quantity\n0 0 T0000000113 S005 1\n1 1 T0000000114 S001 1\n2 2 T0000000115 S003 1\n3 3 T0000000116 S005 1\n4 4 T0000000117 S002 2"
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "transaction_detail = pd.concat([transaction_detail_1, transaction_detail_2], ignore_index=True)\nshow_stats(transaction_detail)"
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": " price\ncount 6786.000000\nmean 143108.605953\nstd 77597.965237\nmin 50000.000000\n25% 85000.000000\n50% 120000.000000\n75% 210000.000000\nmax 750000.000000\n"
},
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>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 <td>2019-02-01 04:33:46</td>\n <td>PL542865</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " transaction_id price payment_date customer_id\n0 T0000000113 210000 2019-02-01 01:36:57 PL563502\n1 T0000000114 50000 2019-02-01 01:37:23 HD678019\n2 T0000000115 120000 2019-02-01 02:34:19 HD298120\n3 T0000000116 210000 2019-02-01 02:47:23 IK452215\n4 T0000000117 170000 2019-02-01 04:33:46 PL542865"
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "show_stats(transaction)"
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": " detail_id quantity\ncount 7144.000000 7144.000000\nmean 3571.500000 1.199888\nstd 2062.439494 0.513647\nmin 0.000000 1.000000\n25% 1785.750000 1.000000\n50% 3571.500000 1.000000\n75% 5357.250000 1.000000\nmax 7143.000000 4.000000\n"
},
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>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\n0 0 T0000000113 S005 1 2019-02-01 01:36:57 PL563502\n1 1 T0000000114 S001 1 2019-02-01 01:37:23 HD678019\n2 2 T0000000115 S003 1 2019-02-01 02:34:19 HD298120\n3 3 T0000000116 S005 1 2019-02-01 02:47:23 IK452215\n4 4 T0000000117 S002 2 2019-02-01 04:33:46 PL542865"
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "join_data = pd.merge(transaction_detail, transaction[[\"transaction_id\",\"payment_date\",\"customer_id\"]], on=\"transaction_id\",how=\"left\")\nshow_stats(join_data)"
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": " detail_id quantity age item_price\ncount 7144.000000 7144.000000 7144.000000 7144.000000\nmean 3571.500000 1.199888 50.265677 121698.628219\nstd 2062.439494 0.513647 17.190314 64571.311830\nmin 0.000000 1.000000 20.000000 50000.000000\n25% 1785.750000 1.000000 36.000000 50000.000000\n50% 3571.500000 1.000000 50.000000 102500.000000\n75% 5357.250000 1.000000 65.000000 187500.000000\nmax 7143.000000 4.000000 80.000000 210000.000000\n"
},
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>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>customer_name_kana</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>\u4e95\u672c \u82b3\u6b63</td>\n <td>2019-01-07 14:34:35</td>\n <td>\u3044\u3082\u3068 \u3088\u3057\u307e\u3055</td>\n <td>imoto_yoshimasa@example.com</td>\n <td>M</td>\n <td>30</td>\n <td>1989/7/15</td>\n <td>\u718a\u672c\u770c</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>\u4e09\u8239 \u516d\u90ce</td>\n <td>2019-01-27 18:00:11</td>\n <td>\u307f\u3075\u306d \u308d\u304f\u308d\u3046</td>\n <td>mifune_rokurou@example.com</td>\n <td>M</td>\n <td>73</td>\n <td>1945/11/29</td>\n <td>\u4eac\u90fd\u5e9c</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>\u5c71\u6839 \u5c0f\u96c1</td>\n <td>2019-01-11 08:16:02</td>\n <td>\u3084\u307e\u306d \u3053\u304c\u3093</td>\n <td>yamane_kogan@example.com</td>\n <td>M</td>\n <td>42</td>\n <td>1977/5/17</td>\n <td>\u8328\u57ce\u770c</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>\u6c60\u7530 \u83dc\u6458</td>\n <td>2019-01-10 05:07:38</td>\n <td>\u3044\u3051\u3060 \u306a\u3064\u307f</td>\n <td>ikeda_natsumi@example.com</td>\n <td>F</td>\n <td>47</td>\n <td>1972/3/17</td>\n <td>\u5175\u5eab\u770c</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>\u6817\u7530 \u61b2\u4e00</td>\n <td>2019-01-25 06:46:05</td>\n <td>\u304f\u308a\u305f \u3051\u3093\u3044\u3061</td>\n <td>kurita_kenichi@example.com</td>\n <td>M</td>\n <td>74</td>\n <td>1944/12/17</td>\n <td>\u9577\u5d0e\u770c</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 \\\n0 0 T0000000113 S005 1 2019-02-01 01:36:57 \n1 1 T0000000114 S001 1 2019-02-01 01:37:23 \n2 2 T0000000115 S003 1 2019-02-01 02:34:19 \n3 3 T0000000116 S005 1 2019-02-01 02:47:23 \n4 4 T0000000117 S002 2 2019-02-01 04:33:46 \n\n customer_id customer_name registration_date customer_name_kana \\\n0 PL563502 \u4e95\u672c \u82b3\u6b63 2019-01-07 14:34:35 \u3044\u3082\u3068 \u3088\u3057\u307e\u3055 \n1 HD678019 \u4e09\u8239 \u516d\u90ce 2019-01-27 18:00:11 \u307f\u3075\u306d \u308d\u304f\u308d\u3046 \n2 HD298120 \u5c71\u6839 \u5c0f\u96c1 2019-01-11 08:16:02 \u3084\u307e\u306d \u3053\u304c\u3093 \n3 IK452215 \u6c60\u7530 \u83dc\u6458 2019-01-10 05:07:38 \u3044\u3051\u3060 \u306a\u3064\u307f \n4 PL542865 \u6817\u7530 \u61b2\u4e00 2019-01-25 06:46:05 \u304f\u308a\u305f \u3051\u3093\u3044\u3061 \n\n email gender age birth pref item_name \\\n0 imoto_yoshimasa@example.com M 30 1989/7/15 \u718a\u672c\u770c PC-E \n1 mifune_rokurou@example.com M 73 1945/11/29 \u4eac\u90fd\u5e9c PC-A \n2 yamane_kogan@example.com M 42 1977/5/17 \u8328\u57ce\u770c PC-C \n3 ikeda_natsumi@example.com F 47 1972/3/17 \u5175\u5eab\u770c PC-E \n4 kurita_kenichi@example.com M 74 1944/12/17 \u9577\u5d0e\u770c PC-B \n\n item_price \n0 210000 \n1 50000 \n2 120000 \n3 210000 \n4 85000 "
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "join_data = pd.merge(join_data, cs_master, on=\"customer_id\", how=\"left\")\njoin_data = pd.merge(join_data, item_master, on=\"item_id\", how=\"left\")\n\nshow_stats(join_data)"
},
{
"cell_type": "code",
"execution_count": 18,
"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\n0 1 210000 210000\n1 1 50000 50000\n2 1 120000 120000\n3 1 210000 210000\n4 2 85000 170000"
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "join_data[\"price\"] = join_data[\"quantity\"] * join_data[\"item_price\"]\njoin_data[[\"quantity\",\"item_price\",\"price\"]].head()"
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "971135000\n971135000\n"
}
],
"source": "## \u691c\u7b97\n\nprint(join_data[\"price\"].sum())\nprint(transaction[\"price\"].sum())\n"
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": " detail_id quantity age item_price price\ncount 7144.000000 7144.000000 7144.000000 7144.000000 7144.000000\nmean 3571.500000 1.199888 50.265677 121698.628219 135937.150056\nstd 2062.439494 0.513647 17.190314 64571.311830 68511.453297\nmin 0.000000 1.000000 20.000000 50000.000000 50000.000000\n25% 1785.750000 1.000000 36.000000 50000.000000 85000.000000\n50% 3571.500000 1.000000 50.000000 102500.000000 120000.000000\n75% 5357.250000 1.000000 65.000000 187500.000000 210000.000000\nmax 7143.000000 4.000000 80.000000 210000.000000 420000.000000\n"
},
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>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>customer_name_kana</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 <th>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>\u4e95\u672c \u82b3\u6b63</td>\n <td>2019-01-07 14:34:35</td>\n <td>\u3044\u3082\u3068 \u3088\u3057\u307e\u3055</td>\n <td>imoto_yoshimasa@example.com</td>\n <td>M</td>\n <td>30</td>\n <td>1989/7/15</td>\n <td>\u718a\u672c\u770c</td>\n <td>PC-E</td>\n <td>210000</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>\u4e09\u8239 \u516d\u90ce</td>\n <td>2019-01-27 18:00:11</td>\n <td>\u307f\u3075\u306d \u308d\u304f\u308d\u3046</td>\n <td>mifune_rokurou@example.com</td>\n <td>M</td>\n <td>73</td>\n <td>1945/11/29</td>\n <td>\u4eac\u90fd\u5e9c</td>\n <td>PC-A</td>\n <td>50000</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>\u5c71\u6839 \u5c0f\u96c1</td>\n <td>2019-01-11 08:16:02</td>\n <td>\u3084\u307e\u306d \u3053\u304c\u3093</td>\n <td>yamane_kogan@example.com</td>\n <td>M</td>\n <td>42</td>\n <td>1977/5/17</td>\n <td>\u8328\u57ce\u770c</td>\n <td>PC-C</td>\n <td>120000</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>\u6c60\u7530 \u83dc\u6458</td>\n <td>2019-01-10 05:07:38</td>\n <td>\u3044\u3051\u3060 \u306a\u3064\u307f</td>\n <td>ikeda_natsumi@example.com</td>\n <td>F</td>\n <td>47</td>\n <td>1972/3/17</td>\n <td>\u5175\u5eab\u770c</td>\n <td>PC-E</td>\n <td>210000</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>\u6817\u7530 \u61b2\u4e00</td>\n <td>2019-01-25 06:46:05</td>\n <td>\u304f\u308a\u305f \u3051\u3093\u3044\u3061</td>\n <td>kurita_kenichi@example.com</td>\n <td>M</td>\n <td>74</td>\n <td>1944/12/17</td>\n <td>\u9577\u5d0e\u770c</td>\n <td>PC-B</td>\n <td>85000</td>\n <td>170000</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " detail_id transaction_id item_id quantity payment_date \\\n0 0 T0000000113 S005 1 2019-02-01 01:36:57 \n1 1 T0000000114 S001 1 2019-02-01 01:37:23 \n2 2 T0000000115 S003 1 2019-02-01 02:34:19 \n3 3 T0000000116 S005 1 2019-02-01 02:47:23 \n4 4 T0000000117 S002 2 2019-02-01 04:33:46 \n\n customer_id customer_name registration_date customer_name_kana \\\n0 PL563502 \u4e95\u672c \u82b3\u6b63 2019-01-07 14:34:35 \u3044\u3082\u3068 \u3088\u3057\u307e\u3055 \n1 HD678019 \u4e09\u8239 \u516d\u90ce 2019-01-27 18:00:11 \u307f\u3075\u306d \u308d\u304f\u308d\u3046 \n2 HD298120 \u5c71\u6839 \u5c0f\u96c1 2019-01-11 08:16:02 \u3084\u307e\u306d \u3053\u304c\u3093 \n3 IK452215 \u6c60\u7530 \u83dc\u6458 2019-01-10 05:07:38 \u3044\u3051\u3060 \u306a\u3064\u307f \n4 PL542865 \u6817\u7530 \u61b2\u4e00 2019-01-25 06:46:05 \u304f\u308a\u305f \u3051\u3093\u3044\u3061 \n\n email gender age birth pref item_name \\\n0 imoto_yoshimasa@example.com M 30 1989/7/15 \u718a\u672c\u770c PC-E \n1 mifune_rokurou@example.com M 73 1945/11/29 \u4eac\u90fd\u5e9c PC-A \n2 yamane_kogan@example.com M 42 1977/5/17 \u8328\u57ce\u770c PC-C \n3 ikeda_natsumi@example.com F 47 1972/3/17 \u5175\u5eab\u770c PC-E \n4 kurita_kenichi@example.com M 74 1944/12/17 \u9577\u5d0e\u770c PC-B \n\n item_price price \n0 210000 210000 \n1 50000 50000 \n2 120000 120000 \n3 210000 210000 \n4 85000 170000 "
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "show_stats(join_data)"
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "detail_id int64\ntransaction_id object\nitem_id object\nquantity int64\npayment_date object\ncustomer_id object\ncustomer_name object\nregistration_date object\ncustomer_name_kana object\nemail object\ngender object\nage int64\nbirth object\npref object\nitem_name object\nitem_price int64\nprice int64\ndtype: object"
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "join_data.dtypes"
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\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\n0 2019-02-01 01:36:57 201902\n1 2019-02-01 01:37:23 201902\n2 2019-02-01 02:34:19 201902\n3 2019-02-01 02:47:23 201902\n4 2019-02-01 04:33:46 201902"
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "join_data[\"payment_date\"] = pd.to_datetime(join_data[\"payment_date\"])\njoin_data[\"payment_month\"] = join_data[\"payment_date\"].dt.strftime(\"%Y%m\")\njoin_data[[\"payment_date\", \"payment_month\"]].head()"
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "payment_month\n201902 160185000\n201903 160370000\n201904 160510000\n201905 155420000\n201906 164030000\n201907 170620000\nName: price, dtype: int64"
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "join_data.groupby(\"payment_month\").sum()[\"price\"]\n"
},
{
"cell_type": "code",
"execution_count": 24,
"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></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\npayment_month item_name \n201902 PC-A 24150000 483\n PC-B 25245000 297\n PC-C 19800000 165\n PC-D 31140000 173\n PC-E 59850000 285\n201903 PC-A 26000000 520\n PC-B 25500000 300\n PC-C 19080000 159\n PC-D 25740000 143\n PC-E 64050000 305\n201904 PC-A 25900000 518\n PC-B 23460000 276\n PC-C 21960000 183\n PC-D 24300000 135\n PC-E 64890000 309\n201905 PC-A 24850000 497\n PC-B 25330000 298\n PC-C 20520000 171\n PC-D 25920000 144\n PC-E 58800000 280\n201906 PC-A 26000000 520\n PC-B 23970000 282\n PC-C 21840000 182\n PC-D 28800000 160\n PC-E 63420000 302\n201907 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": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "join_data.groupby([\"payment_month\", \"item_name\"]).sum()[[\"price\", \"quantity\"]]"
},
{
"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 tr th {\n text-align: left;\n }\n\n .dataframe thead tr:last-of-type th {\n text-align: right;\n }\n</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 \\\npayment_month 201902 201903 201904 201905 201906 201907 \nitem_name \nPC-A 24150000 26000000 25900000 24850000 26000000 25250000 \nPC-B 25245000 25500000 23460000 25330000 23970000 28220000 \nPC-C 19800000 19080000 21960000 20520000 21840000 19440000 \nPC-D 31140000 25740000 24300000 25920000 28800000 26100000 \nPC-E 59850000 64050000 64890000 58800000 63420000 71610000 \n\n quantity \npayment_month 201902 201903 201904 201905 201906 201907 \nitem_name \nPC-A 483 520 518 497 520 505 \nPC-B 297 300 276 298 282 332 \nPC-C 165 159 183 171 182 162 \nPC-D 173 143 135 144 160 145 \nPC-E 285 305 309 280 302 341 "
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "pd.pivot_table(join_data, index='item_name', columns='payment_month', values=['price', 'quantity'], aggfunc='sum')"
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th>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\npayment_month \n201902 24150000 25245000 19800000 31140000 59850000\n201903 26000000 25500000 19080000 25740000 64050000\n201904 25900000 23460000 21960000 24300000 64890000\n201905 24850000 25330000 20520000 25920000 58800000\n201906 26000000 23970000 21840000 28800000 63420000"
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "graph_data = pd.pivot_table(join_data, index='payment_month', columns='item_name', values='price', aggfunc='sum')\ngraph_data.head()"
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "<matplotlib.legend.Legend at 0x7fdf8c02eac8>"
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "import matplotlib.pyplot as plt\n\nplt.plot(graph_data.index, graph_data[\"PC-A\"], label=\"PC-A\")\nplt.plot(graph_data.index, graph_data[\"PC-B\"], label=\"PC-B\")\nplt.plot(graph_data.index, graph_data[\"PC-C\"], label=\"PC-C\")\nplt.plot(graph_data.index, graph_data[\"PC-D\"], label=\"PC-D\")\nplt.plot(graph_data.index, graph_data[\"PC-E\"], label=\"PC-E\")\nplt.legend()"
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": " item_price\ncount 2612.000000\nmean 1296.401225\nstd 717.955460\nmin 100.000000\n25% 700.000000\n50% 1300.000000\n75% 1900.000000\nmax 2600.000000\n"
},
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>purchase_date</th>\n <th>item_name</th>\n <th>item_price</th>\n <th>customer_name</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2019-06-13 18:02:34</td>\n <td>\u5546\u54c1A</td>\n <td>100.0</td>\n <td>\u6df1\u4e95\u83dc\u3005\u7f8e</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2019-07-13 13:05:29</td>\n <td>\u5546 \u54c1 S</td>\n <td>NaN</td>\n <td>\u6d45\u7530\u8ce2\u4e8c</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2019-05-11 19:42:07</td>\n <td>\u5546 \u54c1 a</td>\n <td>NaN</td>\n <td>\u5357\u90e8\u6176\u4e8c</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2019-02-12 23:40:45</td>\n <td>\u5546\u54c1Z</td>\n <td>2600.0</td>\n <td>\u9ebb\u751f\u8389\u7dd2</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2019-04-22 03:09:35</td>\n <td>\u5546\u54c1a</td>\n <td>NaN</td>\n <td>\u5e73\u7530\u9244\u4e8c</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " purchase_date item_name item_price customer_name\n0 2019-06-13 18:02:34 \u5546\u54c1A 100.0 \u6df1\u4e95\u83dc\u3005\u7f8e\n1 2019-07-13 13:05:29 \u5546 \u54c1 S NaN \u6d45\u7530\u8ce2\u4e8c\n2 2019-05-11 19:42:07 \u5546 \u54c1 a NaN \u5357\u90e8\u6176\u4e8c\n3 2019-02-12 23:40:45 \u5546\u54c1Z 2600.0 \u9ebb\u751f\u8389\u7dd2\n4 2019-04-22 03:09:35 \u5546\u54c1a NaN \u5e73\u7530\u9244\u4e8c"
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "\nbody = client_3f9adee0f7984d12adf1b452a714a874.get_object(Bucket='practice-donotdelete-pr-i0yoa12mpgwzrt',Key='uriage.csv')['Body']\n# add missing __iter__ method, so pandas accepts body as file-like object\nif not hasattr(body, \"__iter__\"): body.__iter__ = types.MethodType( __iter__, body )\n\nuriage_data = pd.read_csv(body)\nshow_stats(uriage_data)"
},
{
"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></th>\n <th>\u9867\u5ba2\u540d</th>\n <th>\u304b\u306a</th>\n <th>\u5730\u57df</th>\n <th>\u30e1\u30fc\u30eb\u30a2\u30c9\u30ec\u30b9</th>\n <th>\u767b\u9332\u65e5</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>\u9808\u8cc0\u3072\u3068\u307f</td>\n <td>\u3059\u304c \u3072\u3068\u307f</td>\n <td>H\u5e02</td>\n <td>suga_hitomi@example.com</td>\n <td>2018/01/04</td>\n </tr>\n <tr>\n <th>1</th>\n <td>\u5ca1\u7530\u3000 \u654f\u4e5f</td>\n <td>\u304a\u304b\u3060 \u3068\u3057\u3084</td>\n <td>E\u5e02</td>\n <td>okada_toshiya@example.com</td>\n <td>42782</td>\n </tr>\n <tr>\n <th>2</th>\n <td>\u82b3\u8cc0 \u5e0c</td>\n <td>\u306f\u304c \u306e\u305e\u307f</td>\n <td>A\u5e02</td>\n <td>haga_nozomi@example.com</td>\n <td>2018/01/07</td>\n </tr>\n <tr>\n <th>3</th>\n <td>\u837b\u91ce \u611b</td>\n <td>\u304a\u304e\u306e \u3042\u3044</td>\n <td>F\u5e02</td>\n <td>ogino_ai@example.com</td>\n <td>42872</td>\n </tr>\n <tr>\n <th>4</th>\n <td>\u6817\u7530 \u61b2\u4e00</td>\n <td>\u304f\u308a\u305f \u3051\u3093\u3044\u3061</td>\n <td>E\u5e02</td>\n <td>kurita_kenichi@example.com</td>\n <td>43127</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " \u9867\u5ba2\u540d \u304b\u306a \u5730\u57df \u30e1\u30fc\u30eb\u30a2\u30c9\u30ec\u30b9 \u767b\u9332\u65e5\n0 \u9808\u8cc0\u3072\u3068\u307f \u3059\u304c \u3072\u3068\u307f H\u5e02 suga_hitomi@example.com 2018/01/04\n1 \u5ca1\u7530\u3000 \u654f\u4e5f \u304a\u304b\u3060 \u3068\u3057\u3084 E\u5e02 okada_toshiya@example.com 42782\n2 \u82b3\u8cc0 \u5e0c \u306f\u304c \u306e\u305e\u307f A\u5e02 haga_nozomi@example.com 2018/01/07\n3 \u837b\u91ce \u611b \u304a\u304e\u306e \u3042\u3044 F\u5e02 ogino_ai@example.com 42872\n4 \u6817\u7530 \u61b2\u4e00 \u304f\u308a\u305f \u3051\u3093\u3044\u3061 E\u5e02 kurita_kenichi@example.com 43127"
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "\nbody = client_3f9adee0f7984d12adf1b452a714a874.get_object(Bucket='practice-donotdelete-pr-i0yoa12mpgwzrt',Key='kokyaku_daicho.xlsx')['Body']\n# add missing __iter__ method, so pandas accepts body as file-like object\nif not hasattr(body, \"__iter__\"): body.__iter__ = types.MethodType( __iter__, body )\n\nkokyaku_data = pd.read_excel(body)\nkokyaku_data.head()\n"
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "0 \u5546\u54c1A\n1 \u5546 \u54c1 S\n2 \u5546 \u54c1 a\n3 \u5546\u54c1Z\n4 \u5546\u54c1a\nName: item_name, dtype: object"
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "uriage_data[\"item_name\"].head()"
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "0 100.0\n1 NaN\n2 NaN\n3 2600.0\n4 NaN\nName: item_price, dtype: float64"
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "uriage_data[\"item_price\"].head()"
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "99\n"
}
],
"source": "print(len(pd.unique(uriage_data[\"item_name\"])))"
},
{
"cell_type": "code",
"execution_count": 33,
"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>purchase_date</th>\n <th>item_name</th>\n <th>item_price</th>\n <th>customer_name</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2019-06-13 18:02:34</td>\n <td>\u5546\u54c1A</td>\n <td>100.0</td>\n <td>\u6df1\u4e95\u83dc\u3005\u7f8e</td>\n </tr>\n <tr>\n <th>1748</th>\n <td>2019-05-19 20:22:22</td>\n <td>\u5546\u54c1A</td>\n <td>100.0</td>\n <td>\u677e\u5ddd\u7dbe\u5973</td>\n </tr>\n <tr>\n <th>223</th>\n <td>2019-06-25 08:13:20</td>\n <td>\u5546\u54c1A</td>\n <td>100.0</td>\n <td>\u677f\u6a4b\u9686</td>\n </tr>\n <tr>\n <th>1742</th>\n <td>2019-06-13 16:03:17</td>\n <td>\u5546\u54c1A</td>\n <td>100.0</td>\n <td>\u5c0f\u5e73\u967d\u5b50</td>\n </tr>\n <tr>\n <th>1738</th>\n <td>2019-02-10 00:28:43</td>\n <td>\u5546\u54c1A</td>\n <td>100.0</td>\n <td>\u677e\u7530\u6d69\u6b63</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " purchase_date item_name item_price customer_name\n0 2019-06-13 18:02:34 \u5546\u54c1A 100.0 \u6df1\u4e95\u83dc\u3005\u7f8e\n1748 2019-05-19 20:22:22 \u5546\u54c1A 100.0 \u677e\u5ddd\u7dbe\u5973\n223 2019-06-25 08:13:20 \u5546\u54c1A 100.0 \u677f\u6a4b\u9686\n1742 2019-06-13 16:03:17 \u5546\u54c1A 100.0 \u5c0f\u5e73\u967d\u5b50\n1738 2019-02-10 00:28:43 \u5546\u54c1A 100.0 \u677e\u7530\u6d69\u6b63"
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "uriage_data[\"item_name\"] = uriage_data[\"item_name\"].str.upper()\nuriage_data[\"item_name\"] = uriage_data[\"item_name\"].str.replace(\"\u3000\", \"\")\nuriage_data[\"item_name\"] = uriage_data[\"item_name\"].str.replace(\" \", \"\")\nuriage_data.sort_values(by=[\"item_name\"], ascending=True).head()\n"
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "['\u5546\u54c1A' '\u5546\u54c1B' '\u5546\u54c1C' '\u5546\u54c1D' '\u5546\u54c1E' '\u5546\u54c1F' '\u5546\u54c1G' '\u5546\u54c1H' '\u5546\u54c1I' '\u5546\u54c1J' '\u5546\u54c1K' '\u5546\u54c1L'\n '\u5546\u54c1M' '\u5546\u54c1N' '\u5546\u54c1O' '\u5546\u54c1P' '\u5546\u54c1Q' '\u5546\u54c1R' '\u5546\u54c1S' '\u5546\u54c1T' '\u5546\u54c1U' '\u5546\u54c1V' '\u5546\u54c1W' '\u5546\u54c1X'\n '\u5546\u54c1Y' '\u5546\u54c1Z']\n"
}
],
"source": "print(uriage_data.sort_values(by=[\"item_name\"], ascending=True)['item_name'].unique())"
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "purchase_date False\nitem_name False\nitem_price True\ncustomer_name False\ndtype: bool"
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "uriage_data.isnull().any(axis=0)"
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": "/opt/conda/envs/Python36/lib/python3.6/site-packages/pandas/core/indexing.py:190: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n self._setitem_with_indexer(indexer, value)\n"
},
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>purchase_date</th>\n <th>item_name</th>\n <th>item_price</th>\n <th>customer_name</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2019-06-13 18:02:34</td>\n <td>\u5546\u54c1A</td>\n <td>100.0</td>\n <td>\u6df1\u4e95\u83dc\u3005\u7f8e</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2019-07-13 13:05:29</td>\n <td>\u5546\u54c1S</td>\n <td>1900.0</td>\n <td>\u6d45\u7530\u8ce2\u4e8c</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2019-05-11 19:42:07</td>\n <td>\u5546\u54c1A</td>\n <td>100.0</td>\n <td>\u5357\u90e8\u6176\u4e8c</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2019-02-12 23:40:45</td>\n <td>\u5546\u54c1Z</td>\n <td>2600.0</td>\n <td>\u9ebb\u751f\u8389\u7dd2</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2019-04-22 03:09:35</td>\n <td>\u5546\u54c1A</td>\n <td>100.0</td>\n <td>\u5e73\u7530\u9244\u4e8c</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " purchase_date item_name item_price customer_name\n0 2019-06-13 18:02:34 \u5546\u54c1A 100.0 \u6df1\u4e95\u83dc\u3005\u7f8e\n1 2019-07-13 13:05:29 \u5546\u54c1S 1900.0 \u6d45\u7530\u8ce2\u4e8c\n2 2019-05-11 19:42:07 \u5546\u54c1A 100.0 \u5357\u90e8\u6176\u4e8c\n3 2019-02-12 23:40:45 \u5546\u54c1Z 2600.0 \u9ebb\u751f\u8389\u7dd2\n4 2019-04-22 03:09:35 \u5546\u54c1A 100.0 \u5e73\u7530\u9244\u4e8c"
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "## \u6b20\u640d\u5024\u88dc\u5b8c\nflg_is_null = uriage_data[\"item_price\"].isnull()\nfor trg in list(uriage_data.loc[flg_is_null, \"item_name\"].unique()):\n price = uriage_data.loc[(~flg_is_null) & (uriage_data[\"item_name\"]==trg), \"item_price\"].max()\n uriage_data[\"item_price\"].loc[(flg_is_null) & (uriage_data[\"item_name\"]==trg)] = price\nuriage_data.head()"
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "purchase_date False\nitem_name False\nitem_price False\ncustomer_name False\ndtype: bool\n"
}
],
"source": "print(uriage_data.isnull().any(axis=0))"
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "\u5546\u54c1A\u306e\u6700\u5927\u5024100.0\u306e\u6700\u521d\u5024:100.0\n\u5546\u54c1B\u306e\u6700\u5927\u5024200.0\u306e\u6700\u521d\u5024:200.0\n\u5546\u54c1C\u306e\u6700\u5927\u5024300.0\u306e\u6700\u521d\u5024:300.0\n\u5546\u54c1D\u306e\u6700\u5927\u5024400.0\u306e\u6700\u521d\u5024:400.0\n\u5546\u54c1E\u306e\u6700\u5927\u5024500.0\u306e\u6700\u521d\u5024:500.0\n\u5546\u54c1F\u306e\u6700\u5927\u5024600.0\u306e\u6700\u521d\u5024:600.0\n\u5546\u54c1G\u306e\u6700\u5927\u5024700.0\u306e\u6700\u521d\u5024:700.0\n\u5546\u54c1H\u306e\u6700\u5927\u5024800.0\u306e\u6700\u521d\u5024:800.0\n\u5546\u54c1I\u306e\u6700\u5927\u5024900.0\u306e\u6700\u521d\u5024:900.0\n\u5546\u54c1J\u306e\u6700\u5927\u50241000.0\u306e\u6700\u521d\u5024:1000.0\n\u5546\u54c1K\u306e\u6700\u5927\u50241100.0\u306e\u6700\u521d\u5024:1100.0\n\u5546\u54c1L\u306e\u6700\u5927\u50241200.0\u306e\u6700\u521d\u5024:1200.0\n\u5546\u54c1M\u306e\u6700\u5927\u50241300.0\u306e\u6700\u521d\u5024:1300.0\n\u5546\u54c1N\u306e\u6700\u5927\u50241400.0\u306e\u6700\u521d\u5024:1400.0\n\u5546\u54c1O\u306e\u6700\u5927\u50241500.0\u306e\u6700\u521d\u5024:1500.0\n\u5546\u54c1P\u306e\u6700\u5927\u50241600.0\u306e\u6700\u521d\u5024:1600.0\n\u5546\u54c1Q\u306e\u6700\u5927\u50241700.0\u306e\u6700\u521d\u5024:1700.0\n\u5546\u54c1R\u306e\u6700\u5927\u50241800.0\u306e\u6700\u521d\u5024:1800.0\n\u5546\u54c1S\u306e\u6700\u5927\u50241900.0\u306e\u6700\u521d\u5024:1900.0\n\u5546\u54c1T\u306e\u6700\u5927\u50242000.0\u306e\u6700\u521d\u5024:2000.0\n\u5546\u54c1U\u306e\u6700\u5927\u50242100.0\u306e\u6700\u521d\u5024:2100.0\n\u5546\u54c1V\u306e\u6700\u5927\u50242200.0\u306e\u6700\u521d\u5024:2200.0\n\u5546\u54c1W\u306e\u6700\u5927\u50242300.0\u306e\u6700\u521d\u5024:2300.0\n\u5546\u54c1X\u306e\u6700\u5927\u50242400.0\u306e\u6700\u521d\u5024:2400.0\n\u5546\u54c1Y\u306e\u6700\u5927\u50242500.0\u306e\u6700\u521d\u5024:2500.0\n\u5546\u54c1Z\u306e\u6700\u5927\u50242600.0\u306e\u6700\u521d\u5024:2600.0\n"
}
],
"source": "for trg in list(uriage_data[\"item_name\"].sort_values().unique()):\n print(trg + \"\u306e\u6700\u5927\u5024\" + str(uriage_data.loc[uriage_data[\"item_name\"]==trg][\"item_price\"].max()) \n + \"\u306e\u6700\u521d\u5024:\" + str(uriage_data.loc[uriage_data[\"item_name\"]==trg][\"item_price\"].min(skipna=False)))"
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": " \u9867\u5ba2\u540d \u304b\u306a \u5730\u57df \u30e1\u30fc\u30eb\u30a2\u30c9\u30ec\u30b9 \u767b\u9332\u65e5\ncount 200 200 200 200 200\nunique 200 200 8 200 174\ntop \u4e38\u5c71 \u5149\u81e3 \u304f\u307e\u3044 \u306e\u308a\u3072\u3068 A\u5e02 ishida_ikue@example.com 2017/01/30\nfreq 1 1 31 1 4\n"
},
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>\u9867\u5ba2\u540d</th>\n <th>\u304b\u306a</th>\n <th>\u5730\u57df</th>\n <th>\u30e1\u30fc\u30eb\u30a2\u30c9\u30ec\u30b9</th>\n <th>\u767b\u9332\u65e5</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>\u9808\u8cc0\u3072\u3068\u307f</td>\n <td>\u3059\u304c \u3072\u3068\u307f</td>\n <td>H\u5e02</td>\n <td>suga_hitomi@example.com</td>\n <td>2018/01/04</td>\n </tr>\n <tr>\n <th>1</th>\n <td>\u5ca1\u7530\u3000 \u654f\u4e5f</td>\n <td>\u304a\u304b\u3060 \u3068\u3057\u3084</td>\n <td>E\u5e02</td>\n <td>okada_toshiya@example.com</td>\n <td>42782</td>\n </tr>\n <tr>\n <th>2</th>\n <td>\u82b3\u8cc0 \u5e0c</td>\n <td>\u306f\u304c \u306e\u305e\u307f</td>\n <td>A\u5e02</td>\n <td>haga_nozomi@example.com</td>\n <td>2018/01/07</td>\n </tr>\n <tr>\n <th>3</th>\n <td>\u837b\u91ce \u611b</td>\n <td>\u304a\u304e\u306e \u3042\u3044</td>\n <td>F\u5e02</td>\n <td>ogino_ai@example.com</td>\n <td>42872</td>\n </tr>\n <tr>\n <th>4</th>\n <td>\u6817\u7530 \u61b2\u4e00</td>\n <td>\u304f\u308a\u305f \u3051\u3093\u3044\u3061</td>\n <td>E\u5e02</td>\n <td>kurita_kenichi@example.com</td>\n <td>43127</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " \u9867\u5ba2\u540d \u304b\u306a \u5730\u57df \u30e1\u30fc\u30eb\u30a2\u30c9\u30ec\u30b9 \u767b\u9332\u65e5\n0 \u9808\u8cc0\u3072\u3068\u307f \u3059\u304c \u3072\u3068\u307f H\u5e02 suga_hitomi@example.com 2018/01/04\n1 \u5ca1\u7530\u3000 \u654f\u4e5f \u304a\u304b\u3060 \u3068\u3057\u3084 E\u5e02 okada_toshiya@example.com 42782\n2 \u82b3\u8cc0 \u5e0c \u306f\u304c \u306e\u305e\u307f A\u5e02 haga_nozomi@example.com 2018/01/07\n3 \u837b\u91ce \u611b \u304a\u304e\u306e \u3042\u3044 F\u5e02 ogino_ai@example.com 42872\n4 \u6817\u7530 \u61b2\u4e00 \u304f\u308a\u305f \u3051\u3093\u3044\u3061 E\u5e02 kurita_kenichi@example.com 43127"
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "show_stats(kokyaku_data)"
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "0 \u9808\u8cc0\u3072\u3068\u307f\n1 \u5ca1\u7530\u3000 \u654f\u4e5f\n2 \u82b3\u8cc0 \u5e0c\n3 \u837b\u91ce \u611b\n4 \u6817\u7530 \u61b2\u4e00\nName: \u9867\u5ba2\u540d, dtype: object"
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "kokyaku_data[\"\u9867\u5ba2\u540d\"].head()"
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "0 \u9808\u8cc0\u3072\u3068\u307f\n1 \u5ca1\u7530\u654f\u4e5f\n2 \u82b3\u8cc0\u5e0c\n3 \u837b\u91ce\u611b\n4 \u6817\u7530\u61b2\u4e00\nName: \u9867\u5ba2\u540d, dtype: object"
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "kokyaku_data[\"\u9867\u5ba2\u540d\"] = kokyaku_data[\"\u9867\u5ba2\u540d\"].str.replace(\"\u3000\", \"\")\nkokyaku_data[\"\u9867\u5ba2\u540d\"] = kokyaku_data[\"\u9867\u5ba2\u540d\"].str.replace(\" \", \"\")\nkokyaku_data[\"\u9867\u5ba2\u540d\"].head()"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "\u65e5\u4ed8\u306e\u63fa\u308c\u3092\u88dc\u6b63\u3059\u308b"
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "22"
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "flg_is_serial = kokyaku_data[\"\u767b\u9332\u65e5\"].astype(\"str\").str.isdigit()\nflg_is_serial.sum()"
},
{
"cell_type": "code",
"execution_count": 48,
"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>\u9867\u5ba2\u540d</th>\n <th>\u304b\u306a</th>\n <th>\u5730\u57df</th>\n <th>\u30e1\u30fc\u30eb\u30a2\u30c9\u30ec\u30b9</th>\n <th>\u767b\u9332\u65e5</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>\u9808\u8cc0\u3072\u3068\u307f</td>\n <td>\u3059\u304c \u3072\u3068\u307f</td>\n <td>H\u5e02</td>\n <td>suga_hitomi@example.com</td>\n <td>2018/01/04</td>\n </tr>\n <tr>\n <th>1</th>\n <td>\u5ca1\u7530\u654f\u4e5f</td>\n <td>\u304a\u304b\u3060 \u3068\u3057\u3084</td>\n <td>E\u5e02</td>\n <td>okada_toshiya@example.com</td>\n <td>42782</td>\n </tr>\n <tr>\n <th>2</th>\n <td>\u82b3\u8cc0\u5e0c</td>\n <td>\u306f\u304c \u306e\u305e\u307f</td>\n <td>A\u5e02</td>\n <td>haga_nozomi@example.com</td>\n <td>2018/01/07</td>\n </tr>\n <tr>\n <th>3</th>\n <td>\u837b\u91ce\u611b</td>\n <td>\u304a\u304e\u306e \u3042\u3044</td>\n <td>F\u5e02</td>\n <td>ogino_ai@example.com</td>\n <td>42872</td>\n </tr>\n <tr>\n <th>4</th>\n <td>\u6817\u7530\u61b2\u4e00</td>\n <td>\u304f\u308a\u305f \u3051\u3093\u3044\u3061</td>\n <td>E\u5e02</td>\n <td>kurita_kenichi@example.com</td>\n <td>43127</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " \u9867\u5ba2\u540d \u304b\u306a \u5730\u57df \u30e1\u30fc\u30eb\u30a2\u30c9\u30ec\u30b9 \u767b\u9332\u65e5\n0 \u9808\u8cc0\u3072\u3068\u307f \u3059\u304c \u3072\u3068\u307f H\u5e02 suga_hitomi@example.com 2018/01/04\n1 \u5ca1\u7530\u654f\u4e5f \u304a\u304b\u3060 \u3068\u3057\u3084 E\u5e02 okada_toshiya@example.com 42782\n2 \u82b3\u8cc0\u5e0c \u306f\u304c \u306e\u305e\u307f A\u5e02 haga_nozomi@example.com 2018/01/07\n3 \u837b\u91ce\u611b \u304a\u304e\u306e \u3042\u3044 F\u5e02 ogino_ai@example.com 42872\n4 \u6817\u7530\u61b2\u4e00 \u304f\u308a\u305f \u3051\u3093\u3044\u3061 E\u5e02 kurita_kenichi@example.com 43127"
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "kokyaku_data.head()"
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "1 2017-02-18\n3 2017-05-19\n4 2018-01-29\n21 2017-07-06\n27 2017-06-17\n47 2017-01-08\n49 2017-07-15\n53 2017-04-10\n76 2018-03-31\n80 2018-01-12\n99 2017-06-01\n114 2018-06-05\n118 2018-01-31\n122 2018-04-18\n139 2017-05-27\n143 2017-03-26\n155 2017-01-21\n172 2018-03-24\n179 2017-01-10\n183 2017-07-26\n186 2018-07-15\n192 2018-06-10\nName: \u767b\u9332\u65e5, dtype: datetime64[ns]"
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "fromSerial = pd.to_timedelta(kokyaku_data.loc[flg_is_serial, \"\u767b\u9332\u65e5\"].astype(\"float\"), unit=\"D\") + pd.to_datetime(\"1900/01/01\")\nfromSerial"
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "0 2018-01-04\n2 2018-01-07\n5 2017-06-20\n6 2018-06-11\n7 2017-05-19\n8 2018-02-12\n9 2017-07-05\n10 2018-03-31\n11 2017-04-22\n12 2018-03-09\n13 2017-03-13\n14 2018-01-24\n15 2017-06-09\n16 2018-05-02\n17 2017-02-05\n18 2018-07-10\n19 2017-07-08\n20 2018-03-12\n22 2018-02-28\n23 2017-05-16\n24 2018-01-29\n25 2017-03-17\n26 2018-06-28\n28 2018-05-21\n29 2017-03-12\n30 2018-07-01\n31 2017-01-30\n32 2018-02-06\n33 2017-01-30\n34 2018-04-21\n ... \n165 2017-02-18\n166 2018-03-04\n167 2017-03-29\n168 2018-07-02\n169 2017-06-28\n170 2018-05-27\n171 2017-02-22\n173 2017-02-05\n174 2018-01-09\n175 2017-06-12\n176 2018-02-14\n177 2017-04-14\n178 2018-03-15\n180 2018-07-19\n181 2017-05-23\n182 2018-05-01\n184 2018-05-24\n185 2017-04-08\n187 2017-05-09\n188 2018-07-22\n189 2017-07-30\n190 2018-06-14\n191 2017-01-28\n193 2017-05-05\n194 2018-02-27\n195 2017-06-20\n196 2018-06-20\n197 2017-04-29\n198 2019-04-19\n199 2019-04-23\nName: \u767b\u9332\u65e5, Length: 178, dtype: datetime64[ns]"
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "fromString = pd.to_datetime(kokyaku_data.loc[~flg_is_serial, \"\u767b\u9332\u65e5\"])\nfromString"
},
{
"cell_type": "code",
"execution_count": 51,
"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>\u9867\u5ba2\u540d</th>\n <th>\u304b\u306a</th>\n <th>\u5730\u57df</th>\n <th>\u30e1\u30fc\u30eb\u30a2\u30c9\u30ec\u30b9</th>\n <th>\u767b\u9332\u65e5</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>\u9808\u8cc0\u3072\u3068\u307f</td>\n <td>\u3059\u304c \u3072\u3068\u307f</td>\n <td>H\u5e02</td>\n <td>suga_hitomi@example.com</td>\n <td>2018-01-04</td>\n </tr>\n <tr>\n <th>1</th>\n <td>\u5ca1\u7530\u654f\u4e5f</td>\n <td>\u304a\u304b\u3060 \u3068\u3057\u3084</td>\n <td>E\u5e02</td>\n <td>okada_toshiya@example.com</td>\n <td>2017-02-18</td>\n </tr>\n <tr>\n <th>2</th>\n <td>\u82b3\u8cc0\u5e0c</td>\n <td>\u306f\u304c \u306e\u305e\u307f</td>\n <td>A\u5e02</td>\n <td>haga_nozomi@example.com</td>\n <td>2018-01-07</td>\n </tr>\n <tr>\n <th>3</th>\n <td>\u837b\u91ce\u611b</td>\n <td>\u304a\u304e\u306e \u3042\u3044</td>\n <td>F\u5e02</td>\n <td>ogino_ai@example.com</td>\n <td>2017-05-19</td>\n </tr>\n <tr>\n <th>4</th>\n <td>\u6817\u7530\u61b2\u4e00</td>\n <td>\u304f\u308a\u305f \u3051\u3093\u3044\u3061</td>\n <td>E\u5e02</td>\n <td>kurita_kenichi@example.com</td>\n <td>2018-01-29</td>\n </tr>\n <tr>\n <th>5</th>\n <td>\u6885\u6ca2\u9ebb\u7dd2</td>\n <td>\u3046\u3081\u3056\u308f \u307e\u304a</td>\n <td>A\u5e02</td>\n <td>umezawa_mao@example.com</td>\n <td>2017-06-20</td>\n </tr>\n <tr>\n <th>6</th>\n <td>\u76f8\u539f\u3072\u3068\u308a</td>\n <td>\u3042\u3044\u306f\u3089 \u3072\u3068\u308a</td>\n <td>H\u5e02</td>\n <td>aihara_hitori@example.com</td>\n <td>2018-06-11</td>\n </tr>\n <tr>\n <th>7</th>\n <td>\u65b0\u6751\u4e08\u53f2</td>\n <td>\u306b\u3044\u3080\u3089 \u305f\u3051\u3057</td>\n <td>B\u5e02</td>\n <td>niimura_takeshi@example.com</td>\n <td>2017-05-19</td>\n </tr>\n <tr>\n <th>8</th>\n <td>\u77f3\u5ddd\u307e\u3055\u307f</td>\n <td>\u3044\u3057\u304b\u308f \u307e\u3055\u307f</td>\n <td>G\u5e02</td>\n <td>ishikawa_masami@example.com</td>\n <td>2018-02-12</td>\n </tr>\n <tr>\n <th>9</th>\n <td>\u5c0f\u6817\u6b63\u7fa9</td>\n <td>\u304a\u3050\u308a \u307e\u3055\u3088\u3057</td>\n <td>G\u5e02</td>\n <td>oguri_masayoshi@example.com</td>\n <td>2017-07-05</td>\n </tr>\n <tr>\n <th>10</th>\n <td>\u5927\u5009\u6643\u53f8</td>\n <td>\u304a\u304a\u304f\u3089 \u3053\u3046\u3058</td>\n <td>E\u5e02</td>\n <td>ookura_kouji@example.com</td>\n <td>2018-03-31</td>\n </tr>\n <tr>\n <th>11</th>\n <td>\u90a3\u9808\u84bc\u752b</td>\n <td>\u306a\u3059 \u305d\u3046\u3059\u3051</td>\n <td>A\u5e02</td>\n <td>nasu_sousuke@example.com</td>\n <td>2017-04-22</td>\n </tr>\n <tr>\n <th>12</th>\n <td>\u6e05\u6c34\u88d5\u6b21\u90ce</td>\n <td>\u3057\u307f\u305a \u3086\u3046\u3058\u308d\u3046</td>\n <td>C\u5e02</td>\n <td>shimizu_yuujirou@example.com</td>\n <td>2018-03-09</td>\n </tr>\n <tr>\n <th>13</th>\n <td>\u698a\u539f\u3057\u307c\u308a</td>\n <td>\u3055\u304b\u304d\u3070\u3089 \u3057\u307c\u308a</td>\n <td>D\u5e02</td>\n <td>sakakibara_shibori@example.com</td>\n <td>2017-03-13</td>\n </tr>\n <tr>\n <th>14</th>\n <td>\u9ad8\u6ca2\u7f8e\u54b2</td>\n <td>\u305f\u304b\u3055\u308f \u307f\u3055\u304d</td>\n <td>E\u5e02</td>\n <td>takasawa_misaki@example.com</td>\n <td>2018-01-24</td>\n </tr>\n <tr>\n <th>15</th>\n <td>\u5ddd\u5cf6\u53cb\u4ee5\u4e43</td>\n <td>\u304b\u308f\u3057\u307e \u3086\u3044\u306e</td>\n <td>G\u5e02</td>\n <td>kawashima_yuino@example.com</td>\n <td>2017-06-09</td>\n </tr>\n <tr>\n <th>16</th>\n <td>\u5510\u6ca2\u666f\u5b50</td>\n <td>\u304b\u3089\u3055\u308f \u3051\u3044\u3053</td>\n <td>D\u5e02</td>\n <td>karasawa_keiko@example.com</td>\n <td>2018-05-02</td>\n </tr>\n <tr>\n <th>17</th>\n <td>\u7a32\u7530\u5c06\u4e5f</td>\n <td>\u3044\u306a\u3060 \u307e\u3055\u3084</td>\n <td>G\u5e02</td>\n <td>inada_masaya@example.com</td>\n <td>2017-02-05</td>\n </tr>\n <tr>\n <th>18</th>\n <td>\u79cb\u8449\u3042\u304d</td>\n <td>\u3042\u304d\u3070 \u3042\u304d</td>\n <td>H\u5e02</td>\n <td>akiba_aki@example.com</td>\n <td>2018-07-10</td>\n </tr>\n <tr>\n <th>19</th>\n <td>\u897f\u8107\u793c\u5b50</td>\n <td>\u306b\u3057\u308f\u304d \u308c\u3044\u3053</td>\n <td>H\u5e02</td>\n <td>nishiwaki_reiko@example.com</td>\n <td>2017-07-08</td>\n </tr>\n <tr>\n <th>20</th>\n <td>\u5185\u6751\u307e\u3055\u307f</td>\n <td>\u3046\u3061\u3080\u3089 \u307e\u3055\u307f</td>\n <td>A\u5e02</td>\n <td>uchimura_masami@example.com</td>\n <td>2018-03-12</td>\n </tr>\n <tr>\n <th>21</th>\n <td>\u9032\u85e4\u77ac</td>\n <td>\u3057\u3093\u3069\u3046 \u3057\u3085\u3093</td>\n <td>A\u5e02</td>\n <td>shinndou_shun@example.com</td>\n <td>2017-07-06</td>\n </tr>\n <tr>\n <th>22</th>\n <td>\u5c0f\u53e3\u8c4a</td>\n <td>\u304a\u3050\u3061 \u3086\u305f\u304b</td>\n <td>H\u5e02</td>\n <td>oguchi_yutaka@example.com</td>\n <td>2018-02-28</td>\n </tr>\n <tr>\n <th>23</th>\n <td>\u7b39\u539f\u3057\u307c\u308a</td>\n <td>\u3055\u3055\u306f\u3089 \u3057\u307c\u308a</td>\n <td>E\u5e02</td>\n <td>sasahara_shibori@example.com</td>\n <td>2017-05-16</td>\n </tr>\n <tr>\n <th>24</th>\n <td>\u5ca9\u4f50\u5b5d\u592a\u90ce</td>\n <td>\u3044\u308f\u3055 \u3053\u3046\u305f\u308d\u3046</td>\n <td>D\u5e02</td>\n <td>iwasa_koutarou@example.com</td>\n <td>2018-01-29</td>\n </tr>\n <tr>\n <th>25</th>\n <td>\u6cb3\u6751\u7531\u6a39</td>\n <td>\u304b\u308f\u3080\u3089 \u3086\u304d</td>\n <td>C\u5e02</td>\n <td>kawamura_yuki@example.com</td>\n <td>2017-03-17</td>\n </tr>\n <tr>\n <th>26</th>\n <td>\u83c5\u539f\u8aa0\u6cbb</td>\n <td>\u3059\u304c\u308f\u3089 \u305b\u3044\u3058</td>\n <td>C\u5e02</td>\n <td>sugawara_seiji@example.com</td>\n <td>2018-06-28</td>\n </tr>\n <tr>\n <th>27</th>\n <td>\u698e\u672c\u85ab</td>\n <td>\u3048\u306e\u3082\u3068 \u304b\u304a\u308b</td>\n <td>C\u5e02</td>\n <td>enomoto_kaoru@example.com</td>\n <td>2017-06-17</td>\n </tr>\n <tr>\n <th>28</th>\n <td>\u9ad8\u68a8\u7d50\u8863</td>\n <td>\u305f\u304b\u306a\u3057 \u3086\u3044</td>\n <td>C\u5e02</td>\n <td>takanashi_yui@example.com</td>\n <td>2018-05-21</td>\n </tr>\n <tr>\n <th>29</th>\n <td>\u9db4\u5ca1\u85ab</td>\n <td>\u3064\u308b\u304a\u304b \u304b\u304a\u308b</td>\n <td>F\u5e02</td>\n <td>tsuruoka_kaoru@example.com</td>\n <td>2017-03-12</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>170</th>\n <td>\u5ca9\u4e95\u8389\u7dd2</td>\n <td>\u3044\u308f\u3044 \u308a\u304a</td>\n <td>F\u5e02</td>\n <td>iwai_rio@example.com</td>\n <td>2018-05-27</td>\n </tr>\n <tr>\n <th>171</th>\n <td>\u5927\u5d0e\u30d2\u30ab\u30eb</td>\n <td>\u304a\u304a\u3055\u304d \u3072\u304b\u308b</td>\n <td>E\u5e02</td>\n <td>oosaki_hikaru@example.com</td>\n <td>2017-02-22</td>\n </tr>\n <tr>\n <th>172</th>\n <td>\u77e2\u6ca2\u6075\u68a8\u9999</td>\n <td>\u3084\u3056\u308f \u3048\u308a\u304b</td>\n <td>G\u5e02</td>\n <td>yazawa_erika@example.com</td>\n <td>2018-03-24</td>\n </tr>\n <tr>\n <th>173</th>\n <td>\u77f3\u7530\u90c1\u6075</td>\n <td>\u3044\u3057\u3060 \u3044\u304f\u3048</td>\n <td>B\u5e02</td>\n <td>ishida_ikue@example.com</td>\n <td>2017-02-05</td>\n </tr>\n <tr>\n <th>174</th>\n <td>\u837b\u91ce\u611b\u83dc</td>\n <td>\u304a\u304e\u306e \u3042\u3044\u306a</td>\n <td>B\u5e02</td>\n <td>ogino_aina@example.com</td>\n <td>2018-01-09</td>\n </tr>\n <tr>\n <th>175</th>\n <td>\u9999\u690e\u512a\u4e00</td>\n <td>\u304b\u3057\u3044 \u3086\u3046\u3044\u3061</td>\n <td>H\u5e02</td>\n <td>kashii_yuuichi@example.com</td>\n <td>2017-06-12</td>\n </tr>\n <tr>\n <th>176</th>\n <td>\u9ec4\u5ddd\u7530\u535a\u4e4b</td>\n <td>\u304d\u304b\u308f\u3060 \u3072\u308d\u3086\u304d</td>\n <td>C\u5e02</td>\n <td>kikawada_hiroyuki@example.com</td>\n <td>2018-02-14</td>\n </tr>\n <tr>\n <th>177</th>\n <td>\u4e95\u6751\u4fca\u4e8c</td>\n <td>\u3044\u3080\u3089 \u3057\u3085\u3093\u3058</td>\n <td>D\u5e02</td>\n <td>imura_shunji@example.com</td>\n <td>2017-04-14</td>\n </tr>\n <tr>\n <th>178</th>\n <td>\u690d\u6728\u6c99\u77e5\u7d75</td>\n <td>\u3046\u3048\u304d \u3055\u3061\u3048</td>\n <td>F\u5e02</td>\n <td>ueki_sachie@example.com</td>\n <td>2018-03-15</td>\n </tr>\n <tr>\n <th>179</th>\n <td>\u5c0f\u677e\u96bc\u58eb</td>\n <td>\u3053\u307e\u3064 \u3057\u3085\u3093\u3058</td>\n <td>C\u5e02</td>\n <td>komatsu_shunji@example.com</td>\n <td>2017-01-10</td>\n </tr>\n <tr>\n <th>180</th>\n <td>\u677e\u6751\u8061</td>\n <td>\u307e\u3064\u3080\u3089 \u3055\u3068\u3057</td>\n <td>G\u5e02</td>\n <td>matsumura_satoshi@example.com</td>\n <td>2018-07-19</td>\n </tr>\n <tr>\n <th>181</th>\n <td>\u5e73\u8cc0\u4e00\u54c9</td>\n <td>\u3072\u3089\u304c \u304b\u305a\u3084</td>\n <td>B\u5e02</td>\n <td>hiraga_kazuya@example.com</td>\n <td>2017-05-23</td>\n </tr>\n <tr>\n <th>182</th>\n <td>\u624b\u585a\u9032</td>\n <td>\u3066\u3065\u304b \u3059\u3059\u3080</td>\n <td>A\u5e02</td>\n <td>teduka_susumu@example.com</td>\n <td>2018-05-01</td>\n </tr>\n <tr>\n <th>183</th>\n <td>\u78ef\u91ce\u5e0c</td>\n <td>\u3044\u305d\u306e \u306e\u305e\u307f</td>\n <td>B\u5e02</td>\n <td>isono_nozomi@example.com</td>\n <td>2017-07-26</td>\n </tr>\n <tr>\n <th>184</th>\n <td>\u767d\u4e95\u4fca\u4e8c</td>\n <td>\u3057\u3089\u3044 \u3057\u3085\u3093\u3058</td>\n <td>H\u5e02</td>\n <td>shirai_shunji@example.com</td>\n <td>2018-05-24</td>\n </tr>\n <tr>\n <th>185</th>\n <td>\u7b39\u5ddd\u7167\u751f</td>\n <td>\u3055\u3055\u304c\u308f \u3066\u308b\u304a</td>\n <td>F\u5e02</td>\n <td>sasagawa_teruo@example.com</td>\n <td>2017-04-08</td>\n </tr>\n <tr>\n <th>186</th>\n <td>\u82a6\u7530\u535a\u4e4b</td>\n <td>\u3042\u3057\u3060 \u3072\u308d\u3086\u304d</td>\n <td>E\u5e02</td>\n <td>ashida_hiroyuki@example.com</td>\n <td>2018-07-15</td>\n </tr>\n <tr>\n <th>187</th>\n <td>\u5927\u5730\u793c\u5b50</td>\n <td>\u304a\u304a\u3061 \u308c\u3044\u3053</td>\n <td>E\u5e02</td>\n <td>oochi_reiko@example.com</td>\n <td>2017-05-09</td>\n </tr>\n <tr>\n <th>188</th>\n <td>\u5c0f\u753a\u77ac</td>\n <td>\u3053\u307e\u3061 \u3057\u3085\u3093</td>\n <td>D\u5e02</td>\n <td>komachi_shun@example.com</td>\n <td>2018-07-22</td>\n </tr>\n <tr>\n <th>189</th>\n <td>\u6c34\u91ce\u30e1\u30a4\u30b5</td>\n <td>\u307f\u305a\u306e \u3081\u3044\u3055</td>\n <td>D\u5e02</td>\n <td>mizuno_meisa@example.com</td>\n <td>2017-07-30</td>\n </tr>\n <tr>\n <th>190</th>\n <td>\u677f\u6a4b\u9686</td>\n <td>\u3044\u305f\u3070\u3057 \u305f\u304b\u3057</td>\n <td>B\u5e02</td>\n <td>itabashi_takashi@example.com</td>\n <td>2018-06-14</td>\n </tr>\n <tr>\n <th>191</th>\n <td>\u7dbe\u702c\u4fca\u4ecb</td>\n <td>\u3042\u3084\u305b \u3057\u3085\u3093\u3059\u3051</td>\n <td>B\u5e02</td>\n <td>ayase_shunsuke@example.com</td>\n <td>2017-01-28</td>\n </tr>\n <tr>\n <th>192</th>\n <td>\u548c\u6cc9\u76f4\u4eba</td>\n <td>\u308f\u3044\u305a\u307f \u306a\u304a\u3068</td>\n <td>H\u5e02</td>\n <td>waizumi_naoto@example.com</td>\n <td>2018-06-10</td>\n </tr>\n <tr>\n <th>193</th>\n <td>\u5800\u5317\u96c5\u5f66</td>\n <td>\u307b\u308a\u304d\u305f \u307e\u3055\u3072\u3053</td>\n <td>H\u5e02</td>\n <td>horikita_masahiko@example.com</td>\n <td>2017-05-05</td>\n </tr>\n <tr>\n <th>194</th>\n <td>\u5510\u6ca2\u6dbc</td>\n <td>\u304b\u3089\u3055\u308f \u308a\u3087\u3046</td>\n <td>H\u5e02</td>\n <td>karasawa_ryou@example.com</td>\n <td>2018-02-27</td>\n </tr>\n <tr>\n <th>195</th>\n <td>\u5ddd\u4e0a\u308a\u3048</td>\n <td>\u304b\u308f\u304b\u307f \u308a\u3048</td>\n <td>G\u5e02</td>\n <td>kawakami_rie@example.com</td>\n <td>2017-06-20</td>\n </tr>\n <tr>\n <th>196</th>\n <td>\u5c0f\u677e\u5b63\u8863</td>\n <td>\u3053\u307e\u3064 \u3068\u3057\u3048</td>\n <td>E\u5e02</td>\n <td>komatsu_toshie@example.com</td>\n <td>2018-06-20</td>\n </tr>\n <tr>\n <th>197</th>\n <td>\u767d\u9ce5\u308a\u3048</td>\n <td>\u3057\u3089\u3068\u308a \u308a\u3048</td>\n <td>F\u5e02</td>\n <td>shiratori_rie@example.com</td>\n <td>2017-04-29</td>\n </tr>\n <tr>\n <th>198</th>\n <td>\u5927\u897f\u9686\u4e4b\u4ecb</td>\n <td>\u304a\u304a\u306b\u3057 \u308a\u3085\u3046\u306e\u3059\u3051</td>\n <td>H\u5e02</td>\n <td>oonishi_ryuunosuke@example.com</td>\n <td>2019-04-19</td>\n </tr>\n <tr>\n <th>199</th>\n <td>\u798f\u4e95\u7f8e\u5e0c</td>\n <td>\u3075\u304f\u3044 \u307f\u304d</td>\n <td>D\u5e02</td>\n <td>fukui_miki1@example.com</td>\n <td>2019-04-23</td>\n </tr>\n </tbody>\n</table>\n<p>200 rows \u00d7 5 columns</p>\n</div>",
"text/plain": " \u9867\u5ba2\u540d \u304b\u306a \u5730\u57df \u30e1\u30fc\u30eb\u30a2\u30c9\u30ec\u30b9 \u767b\u9332\u65e5\n0 \u9808\u8cc0\u3072\u3068\u307f \u3059\u304c \u3072\u3068\u307f H\u5e02 suga_hitomi@example.com 2018-01-04\n1 \u5ca1\u7530\u654f\u4e5f \u304a\u304b\u3060 \u3068\u3057\u3084 E\u5e02 okada_toshiya@example.com 2017-02-18\n2 \u82b3\u8cc0\u5e0c \u306f\u304c \u306e\u305e\u307f A\u5e02 haga_nozomi@example.com 2018-01-07\n3 \u837b\u91ce\u611b \u304a\u304e\u306e \u3042\u3044 F\u5e02 ogino_ai@example.com 2017-05-19\n4 \u6817\u7530\u61b2\u4e00 \u304f\u308a\u305f \u3051\u3093\u3044\u3061 E\u5e02 kurita_kenichi@example.com 2018-01-29\n5 \u6885\u6ca2\u9ebb\u7dd2 \u3046\u3081\u3056\u308f \u307e\u304a A\u5e02 umezawa_mao@example.com 2017-06-20\n6 \u76f8\u539f\u3072\u3068\u308a \u3042\u3044\u306f\u3089 \u3072\u3068\u308a H\u5e02 aihara_hitori@example.com 2018-06-11\n7 \u65b0\u6751\u4e08\u53f2 \u306b\u3044\u3080\u3089 \u305f\u3051\u3057 B\u5e02 niimura_takeshi@example.com 2017-05-19\n8 \u77f3\u5ddd\u307e\u3055\u307f \u3044\u3057\u304b\u308f \u307e\u3055\u307f G\u5e02 ishikawa_masami@example.com 2018-02-12\n9 \u5c0f\u6817\u6b63\u7fa9 \u304a\u3050\u308a \u307e\u3055\u3088\u3057 G\u5e02 oguri_masayoshi@example.com 2017-07-05\n10 \u5927\u5009\u6643\u53f8 \u304a\u304a\u304f\u3089 \u3053\u3046\u3058 E\u5e02 ookura_kouji@example.com 2018-03-31\n11 \u90a3\u9808\u84bc\u752b \u306a\u3059 \u305d\u3046\u3059\u3051 A\u5e02 nasu_sousuke@example.com 2017-04-22\n12 \u6e05\u6c34\u88d5\u6b21\u90ce \u3057\u307f\u305a \u3086\u3046\u3058\u308d\u3046 C\u5e02 shimizu_yuujirou@example.com 2018-03-09\n13 \u698a\u539f\u3057\u307c\u308a \u3055\u304b\u304d\u3070\u3089 \u3057\u307c\u308a D\u5e02 sakakibara_shibori@example.com 2017-03-13\n14 \u9ad8\u6ca2\u7f8e\u54b2 \u305f\u304b\u3055\u308f \u307f\u3055\u304d E\u5e02 takasawa_misaki@example.com 2018-01-24\n15 \u5ddd\u5cf6\u53cb\u4ee5\u4e43 \u304b\u308f\u3057\u307e \u3086\u3044\u306e G\u5e02 kawashima_yuino@example.com 2017-06-09\n16 \u5510\u6ca2\u666f\u5b50 \u304b\u3089\u3055\u308f \u3051\u3044\u3053 D\u5e02 karasawa_keiko@example.com 2018-05-02\n17 \u7a32\u7530\u5c06\u4e5f \u3044\u306a\u3060 \u307e\u3055\u3084 G\u5e02 inada_masaya@example.com 2017-02-05\n18 \u79cb\u8449\u3042\u304d \u3042\u304d\u3070 \u3042\u304d H\u5e02 akiba_aki@example.com 2018-07-10\n19 \u897f\u8107\u793c\u5b50 \u306b\u3057\u308f\u304d \u308c\u3044\u3053 H\u5e02 nishiwaki_reiko@example.com 2017-07-08\n20 \u5185\u6751\u307e\u3055\u307f \u3046\u3061\u3080\u3089 \u307e\u3055\u307f A\u5e02 uchimura_masami@example.com 2018-03-12\n21 \u9032\u85e4\u77ac \u3057\u3093\u3069\u3046 \u3057\u3085\u3093 A\u5e02 shinndou_shun@example.com 2017-07-06\n22 \u5c0f\u53e3\u8c4a \u304a\u3050\u3061 \u3086\u305f\u304b H\u5e02 oguchi_yutaka@example.com 2018-02-28\n23 \u7b39\u539f\u3057\u307c\u308a \u3055\u3055\u306f\u3089 \u3057\u307c\u308a E\u5e02 sasahara_shibori@example.com 2017-05-16\n24 \u5ca9\u4f50\u5b5d\u592a\u90ce \u3044\u308f\u3055 \u3053\u3046\u305f\u308d\u3046 D\u5e02 iwasa_koutarou@example.com 2018-01-29\n25 \u6cb3\u6751\u7531\u6a39 \u304b\u308f\u3080\u3089 \u3086\u304d C\u5e02 kawamura_yuki@example.com 2017-03-17\n26 \u83c5\u539f\u8aa0\u6cbb \u3059\u304c\u308f\u3089 \u305b\u3044\u3058 C\u5e02 sugawara_seiji@example.com 2018-06-28\n27 \u698e\u672c\u85ab \u3048\u306e\u3082\u3068 \u304b\u304a\u308b C\u5e02 enomoto_kaoru@example.com 2017-06-17\n28 \u9ad8\u68a8\u7d50\u8863 \u305f\u304b\u306a\u3057 \u3086\u3044 C\u5e02 takanashi_yui@example.com 2018-05-21\n29 \u9db4\u5ca1\u85ab \u3064\u308b\u304a\u304b \u304b\u304a\u308b F\u5e02 tsuruoka_kaoru@example.com 2017-03-12\n.. ... ... .. ... ...\n170 \u5ca9\u4e95\u8389\u7dd2 \u3044\u308f\u3044 \u308a\u304a F\u5e02 iwai_rio@example.com 2018-05-27\n171 \u5927\u5d0e\u30d2\u30ab\u30eb \u304a\u304a\u3055\u304d \u3072\u304b\u308b E\u5e02 oosaki_hikaru@example.com 2017-02-22\n172 \u77e2\u6ca2\u6075\u68a8\u9999 \u3084\u3056\u308f \u3048\u308a\u304b G\u5e02 yazawa_erika@example.com 2018-03-24\n173 \u77f3\u7530\u90c1\u6075 \u3044\u3057\u3060 \u3044\u304f\u3048 B\u5e02 ishida_ikue@example.com 2017-02-05\n174 \u837b\u91ce\u611b\u83dc \u304a\u304e\u306e \u3042\u3044\u306a B\u5e02 ogino_aina@example.com 2018-01-09\n175 \u9999\u690e\u512a\u4e00 \u304b\u3057\u3044 \u3086\u3046\u3044\u3061 H\u5e02 kashii_yuuichi@example.com 2017-06-12\n176 \u9ec4\u5ddd\u7530\u535a\u4e4b \u304d\u304b\u308f\u3060 \u3072\u308d\u3086\u304d C\u5e02 kikawada_hiroyuki@example.com 2018-02-14\n177 \u4e95\u6751\u4fca\u4e8c \u3044\u3080\u3089 \u3057\u3085\u3093\u3058 D\u5e02 imura_shunji@example.com 2017-04-14\n178 \u690d\u6728\u6c99\u77e5\u7d75 \u3046\u3048\u304d \u3055\u3061\u3048 F\u5e02 ueki_sachie@example.com 2018-03-15\n179 \u5c0f\u677e\u96bc\u58eb \u3053\u307e\u3064 \u3057\u3085\u3093\u3058 C\u5e02 komatsu_shunji@example.com 2017-01-10\n180 \u677e\u6751\u8061 \u307e\u3064\u3080\u3089 \u3055\u3068\u3057 G\u5e02 matsumura_satoshi@example.com 2018-07-19\n181 \u5e73\u8cc0\u4e00\u54c9 \u3072\u3089\u304c \u304b\u305a\u3084 B\u5e02 hiraga_kazuya@example.com 2017-05-23\n182 \u624b\u585a\u9032 \u3066\u3065\u304b \u3059\u3059\u3080 A\u5e02 teduka_susumu@example.com 2018-05-01\n183 \u78ef\u91ce\u5e0c \u3044\u305d\u306e \u306e\u305e\u307f B\u5e02 isono_nozomi@example.com 2017-07-26\n184 \u767d\u4e95\u4fca\u4e8c \u3057\u3089\u3044 \u3057\u3085\u3093\u3058 H\u5e02 shirai_shunji@example.com 2018-05-24\n185 \u7b39\u5ddd\u7167\u751f \u3055\u3055\u304c\u308f \u3066\u308b\u304a F\u5e02 sasagawa_teruo@example.com 2017-04-08\n186 \u82a6\u7530\u535a\u4e4b \u3042\u3057\u3060 \u3072\u308d\u3086\u304d E\u5e02 ashida_hiroyuki@example.com 2018-07-15\n187 \u5927\u5730\u793c\u5b50 \u304a\u304a\u3061 \u308c\u3044\u3053 E\u5e02 oochi_reiko@example.com 2017-05-09\n188 \u5c0f\u753a\u77ac \u3053\u307e\u3061 \u3057\u3085\u3093 D\u5e02 komachi_shun@example.com 2018-07-22\n189 \u6c34\u91ce\u30e1\u30a4\u30b5 \u307f\u305a\u306e \u3081\u3044\u3055 D\u5e02 mizuno_meisa@example.com 2017-07-30\n190 \u677f\u6a4b\u9686 \u3044\u305f\u3070\u3057 \u305f\u304b\u3057 B\u5e02 itabashi_takashi@example.com 2018-06-14\n191 \u7dbe\u702c\u4fca\u4ecb \u3042\u3084\u305b \u3057\u3085\u3093\u3059\u3051 B\u5e02 ayase_shunsuke@example.com 2017-01-28\n192 \u548c\u6cc9\u76f4\u4eba \u308f\u3044\u305a\u307f \u306a\u304a\u3068 H\u5e02 waizumi_naoto@example.com 2018-06-10\n193 \u5800\u5317\u96c5\u5f66 \u307b\u308a\u304d\u305f \u307e\u3055\u3072\u3053 H\u5e02 horikita_masahiko@example.com 2017-05-05\n194 \u5510\u6ca2\u6dbc \u304b\u3089\u3055\u308f \u308a\u3087\u3046 H\u5e02 karasawa_ryou@example.com 2018-02-27\n195 \u5ddd\u4e0a\u308a\u3048 \u304b\u308f\u304b\u307f \u308a\u3048 G\u5e02 kawakami_rie@example.com 2017-06-20\n196 \u5c0f\u677e\u5b63\u8863 \u3053\u307e\u3064 \u3068\u3057\u3048 E\u5e02 komatsu_toshie@example.com 2018-06-20\n197 \u767d\u9ce5\u308a\u3048 \u3057\u3089\u3068\u308a \u308a\u3048 F\u5e02 shiratori_rie@example.com 2017-04-29\n198 \u5927\u897f\u9686\u4e4b\u4ecb \u304a\u304a\u306b\u3057 \u308a\u3085\u3046\u306e\u3059\u3051 H\u5e02 oonishi_ryuunosuke@example.com 2019-04-19\n199 \u798f\u4e95\u7f8e\u5e0c \u3075\u304f\u3044 \u307f\u304d D\u5e02 fukui_miki1@example.com 2019-04-23\n\n[200 rows x 5 columns]"
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "kokyaku_data[\"\u767b\u9332\u65e5\"] = pd.concat([fromSerial, fromString])\nkokyaku_data"
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "200\n"
},
{
"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": "kokyaku_data[\"\u767b\u9332\u5e74\u6708\"] = kokyaku_data[\"\u767b\u9332\u65e5\"].dt.strftime(\"%Y%m\")\nrslt = kokyaku_data.groupby(\"\u767b\u9332\u5e74\u6708\").count()[\"\u9867\u5ba2\u540d\"]\nrslt.plot()\nprint(len(kokyaku_data))"
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "0"
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "flg_is_serial = kokyaku_data[\"\u767b\u9332\u65e5\"].astype(\"str\").str.isdigit()\nflg_is_serial.sum()"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "2\u3064\u306e\u30c7\u30fc\u30bf\u3092\u7d50\u5408"
},
{
"cell_type": "code",
"execution_count": 55,
"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>purchase_date</th>\n <th>item_name</th>\n <th>item_price</th>\n <th>\u9867\u5ba2\u540d</th>\n <th>\u304b\u306a</th>\n <th>\u5730\u57df</th>\n <th>\u30e1\u30fc\u30eb\u30a2\u30c9\u30ec\u30b9</th>\n <th>\u767b\u9332\u65e5</th>\n <th>\u767b\u9332\u5e74\u6708</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2019-06-13 18:02:34</td>\n <td>\u5546\u54c1A</td>\n <td>100.0</td>\n <td>\u6df1\u4e95\u83dc\u3005\u7f8e</td>\n <td>\u3075\u304b\u3044 \u306a\u306a\u307f</td>\n <td>C\u5e02</td>\n <td>fukai_nanami@example.com</td>\n <td>2017-01-26</td>\n <td>201701</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2019-07-13 13:05:29</td>\n <td>\u5546\u54c1S</td>\n <td>1900.0</td>\n <td>\u6d45\u7530\u8ce2\u4e8c</td>\n <td>\u3042\u3055\u3060 \u3051\u3093\u3058</td>\n <td>C\u5e02</td>\n <td>asada_kenji@example.com</td>\n <td>2018-04-07</td>\n <td>201804</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2019-05-11 19:42:07</td>\n <td>\u5546\u54c1A</td>\n <td>100.0</td>\n <td>\u5357\u90e8\u6176\u4e8c</td>\n <td>\u306a\u3093\u3076 \u3051\u3044\u3058</td>\n <td>A\u5e02</td>\n <td>nannbu_keiji@example.com</td>\n <td>2018-06-19</td>\n <td>201806</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2019-02-12 23:40:45</td>\n <td>\u5546\u54c1Z</td>\n <td>2600.0</td>\n <td>\u9ebb\u751f\u8389\u7dd2</td>\n <td>\u3042\u305d\u3046 \u308a\u304a</td>\n <td>D\u5e02</td>\n <td>asou_rio@example.com</td>\n <td>2018-07-22</td>\n <td>201807</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2019-04-22 03:09:35</td>\n <td>\u5546\u54c1A</td>\n <td>100.0</td>\n <td>\u5e73\u7530\u9244\u4e8c</td>\n <td>\u3072\u3089\u305f \u3066\u3064\u3058</td>\n <td>D\u5e02</td>\n <td>hirata_tetsuji@example.com</td>\n <td>2017-06-07</td>\n <td>201706</td>\n </tr>\n <tr>\n <th>5</th>\n <td>2019-03-20 19:16:01</td>\n <td>\u5546\u54c1S</td>\n <td>1900.0</td>\n <td>\u5800\u6c5f\u4f51</td>\n <td>\u307b\u308a\u3048 \u305f\u3059\u304f</td>\n <td>H\u5e02</td>\n <td>horie_tasuku@example.com</td>\n <td>2018-05-14</td>\n <td>201805</td>\n </tr>\n <tr>\n <th>6</th>\n <td>2019-05-18 19:16:53</td>\n <td>\u5546\u54c1A</td>\n <td>100.0</td>\n <td>\u6df1\u4e95\u7167\u751f</td>\n <td>\u3075\u304b\u3044 \u3066\u308b\u304a</td>\n <td>A\u5e02</td>\n <td>fukai_teruo@example.com</td>\n <td>2018-02-21</td>\n <td>201802</td>\n </tr>\n <tr>\n <th>7</th>\n <td>2019-04-18 00:14:21</td>\n <td>\u5546\u54c1V</td>\n <td>2200.0</td>\n <td>\u7267\u7530\u73b2\u90a3</td>\n <td>\u307e\u304d\u305f \u308c\u306a</td>\n <td>A\u5e02</td>\n <td>makita_rena@example.com</td>\n <td>2017-05-13</td>\n <td>201705</td>\n </tr>\n <tr>\n <th>8</th>\n <td>2019-01-10 15:51:01</td>\n <td>\u5546\u54c1O</td>\n <td>1500.0</td>\n <td>\u5800\u5317\u96c5\u5f66</td>\n <td>\u307b\u308a\u304d\u305f \u307e\u3055\u3072\u3053</td>\n <td>H\u5e02</td>\n <td>horikita_masahiko@example.com</td>\n <td>2017-05-05</td>\n <td>201705</td>\n </tr>\n <tr>\n <th>9</th>\n <td>2019-01-28 10:47:03</td>\n <td>\u5546\u54c1A</td>\n <td>100.0</td>\n <td>\u5927\u5730\u793c\u5b50</td>\n <td>\u304a\u304a\u3061 \u308c\u3044\u3053</td>\n <td>E\u5e02</td>\n <td>oochi_reiko@example.com</td>\n <td>2017-05-09</td>\n <td>201705</td>\n </tr>\n <tr>\n <th>10</th>\n <td>2019-06-21 01:54:35</td>\n <td>\u5546\u54c1U</td>\n <td>2100.0</td>\n <td>\u77e2\u90e8\u60c7</td>\n <td>\u3084\u3079 \u3058\u3085\u3093</td>\n <td>A\u5e02</td>\n <td>yabe_jun@example.com</td>\n <td>2018-05-20</td>\n <td>201805</td>\n </tr>\n <tr>\n <th>11</th>\n <td>2019-06-08 11:32:25</td>\n <td>\u5546\u54c1L</td>\n <td>1200.0</td>\n <td>\u5ca1\u7530\u654f\u4e5f</td>\n <td>\u304a\u304b\u3060 \u3068\u3057\u3084</td>\n <td>E\u5e02</td>\n <td>okada_toshiya@example.com</td>\n <td>2017-02-18</td>\n <td>201702</td>\n </tr>\n <tr>\n <th>12</th>\n <td>2019-04-08 02:00:44</td>\n <td>\u5546\u54c1V</td>\n <td>2200.0</td>\n <td>\u6d45\u898b\u5e83\u53f8</td>\n <td>\u3042\u3055\u307f \u3053\u3046\u3058</td>\n <td>D\u5e02</td>\n <td>asami_kouji@example.com</td>\n <td>2018-06-05</td>\n <td>201806</td>\n </tr>\n <tr>\n <th>13</th>\n <td>2019-06-19 09:50:52</td>\n <td>\u5546\u54c1O</td>\n <td>1500.0</td>\n <td>\u718a\u4e95\u61b2\u53f2</td>\n <td>\u304f\u307e\u3044 \u306e\u308a\u3072\u3068</td>\n <td>A\u5e02</td>\n <td>kumai_norihito@example.com</td>\n <td>2017-03-29</td>\n <td>201703</td>\n </tr>\n <tr>\n <th>14</th>\n <td>2019-06-11 12:57:24</td>\n <td>\u5546\u54c1A</td>\n <td>100.0</td>\n <td>\u9ec4\u5ddd\u7530\u535a\u4e4b</td>\n <td>\u304d\u304b\u308f\u3060 \u3072\u308d\u3086\u304d</td>\n <td>C\u5e02</td>\n <td>kikawada_hiroyuki@example.com</td>\n <td>2018-02-14</td>\n <td>201802</td>\n </tr>\n <tr>\n <th>15</th>\n <td>2019-04-21 00:11:43</td>\n <td>\u5546\u54c1C</td>\n <td>300.0</td>\n <td>\u5c3e\u5f62\u5c0f\u96c1</td>\n <td>\u304a\u304c\u305f \u3053\u304c\u3093</td>\n <td>B\u5e02</td>\n <td>ogata_kogan@example.com</td>\n <td>2017-03-15</td>\n <td>201703</td>\n </tr>\n <tr>\n <th>16</th>\n <td>2019-03-28 23:24:46</td>\n <td>\u5546\u54c1V</td>\n <td>2200.0</td>\n <td>\u795e\u539f\u7f8e\u5609</td>\n <td>\u304b\u3093\u3070\u3089 \u307f\u304b</td>\n <td>D\u5e02</td>\n <td>kannbara_mika@example.com</td>\n <td>2017-03-23</td>\n <td>201703</td>\n </tr>\n <tr>\n <th>17</th>\n <td>2019-04-06 12:00:53</td>\n <td>\u5546\u54c1I</td>\n <td>900.0</td>\n <td>\u82e5\u6749\u5fb9</td>\n <td>\u308f\u304b\u3059\u304e \u3068\u304a\u308b</td>\n <td>G\u5e02</td>\n <td>wakasugi_tohru@example.com</td>\n <td>2017-03-26</td>\n <td>201703</td>\n </tr>\n <tr>\n <th>18</th>\n <td>2019-07-16 05:55:57</td>\n <td>\u5546\u54c1R</td>\n <td>1800.0</td>\n <td>\u77f3\u6e21\u5c0f\u96c1</td>\n <td>\u3044\u3057\u308f\u305f\u308a \u3053\u304c\u3093</td>\n <td>D\u5e02</td>\n <td>ishiwatari_kogan@example.com</td>\n <td>2017-07-28</td>\n <td>201707</td>\n </tr>\n <tr>\n <th>19</th>\n <td>2019-07-03 07:04:05</td>\n <td>\u5546\u54c1X</td>\n <td>2400.0</td>\n <td>\u6771\u5149\u535a</td>\n <td>\u3072\u304c\u3057 \u307f\u3064\u3072\u308d</td>\n <td>A\u5e02</td>\n <td>higashi_mitsuhiro@example.com</td>\n <td>2018-02-06</td>\n <td>201802</td>\n </tr>\n <tr>\n <th>20</th>\n <td>2019-07-05 10:49:13</td>\n <td>\u5546\u54c1O</td>\n <td>1500.0</td>\n <td>\u65e5\u91ce\u590f\u5e0c</td>\n <td>\u3072\u306e \u306a\u3064\u304d</td>\n <td>A\u5e02</td>\n <td>hino_natsuki@example.com</td>\n <td>2017-05-23</td>\n <td>201705</td>\n </tr>\n <tr>\n <th>21</th>\n <td>2019-06-10 19:07:23</td>\n <td>\u5546\u54c1G</td>\n <td>700.0</td>\n <td>\u9ed2\u8c37\u9577\u5229</td>\n <td>\u304f\u308d\u305f\u306b \u306a\u304c\u3068\u3057</td>\n <td>A\u5e02</td>\n <td>kurotani_nagatoshi@example.com</td>\n <td>2017-04-27</td>\n <td>201704</td>\n </tr>\n <tr>\n <th>22</th>\n <td>2019-07-10 20:28:12</td>\n <td>\u5546\u54c1X</td>\n <td>2400.0</td>\n <td>\u7530\u8fba\u5149\u6d0b</td>\n <td>\u305f\u306a\u3079 \u307f\u3064\u3072\u308d</td>\n <td>A\u5e02</td>\n <td>tanabe_mitsuhiro@example.com</td>\n <td>2018-07-04</td>\n <td>201807</td>\n </tr>\n <tr>\n <th>23</th>\n <td>2019-07-10 12:44:10</td>\n <td>\u5546\u54c1R</td>\n <td>1800.0</td>\n <td>\u6238\u585a\u7f8e\u5e78</td>\n <td>\u3068\u3065\u304b \u307f\u3086\u304d</td>\n <td>H\u5e02</td>\n <td>toduka_miyuki@example.com</td>\n <td>2017-01-30</td>\n <td>201701</td>\n </tr>\n <tr>\n <th>24</th>\n <td>2019-02-14 01:30:09</td>\n <td>\u5546\u54c1P</td>\n <td>1600.0</td>\n <td>\u698a\u539f\u3057\u307c\u308a</td>\n <td>\u3055\u304b\u304d\u3070\u3089 \u3057\u307c\u308a</td>\n <td>D\u5e02</td>\n <td>sakakibara_shibori@example.com</td>\n <td>2017-03-13</td>\n <td>201703</td>\n </tr>\n <tr>\n <th>25</th>\n <td>2019-04-18 01:50:16</td>\n <td>\u5546\u54c1Q</td>\n <td>1700.0</td>\n <td>\u6d45\u7530\u8ce2\u4e8c</td>\n <td>\u3042\u3055\u3060 \u3051\u3093\u3058</td>\n <td>C\u5e02</td>\n <td>asada_kenji@example.com</td>\n <td>2018-04-07</td>\n <td>201804</td>\n </tr>\n <tr>\n <th>26</th>\n <td>2019-05-16 04:45:21</td>\n <td>\u5546\u54c1Y</td>\n <td>2500.0</td>\n <td>\u660e\u77f3\u5bb6\u660e</td>\n <td>\u3042\u304b\u3057\u3084 \u3042\u304d\u3089</td>\n <td>B\u5e02</td>\n <td>akashiya_akira@example.com</td>\n <td>2018-02-13</td>\n <td>201802</td>\n </tr>\n <tr>\n <th>27</th>\n <td>2019-05-26 10:58:00</td>\n <td>\u5546\u54c1P</td>\n <td>1600.0</td>\n <td>\u624b\u585a\u9032</td>\n <td>\u3066\u3065\u304b \u3059\u3059\u3080</td>\n <td>A\u5e02</td>\n <td>teduka_susumu@example.com</td>\n <td>2018-05-01</td>\n <td>201805</td>\n </tr>\n <tr>\n <th>28</th>\n <td>2019-01-04 12:05:04</td>\n <td>\u5546\u54c1P</td>\n <td>1600.0</td>\n <td>\u5800\u6c5f\u4f51</td>\n <td>\u307b\u308a\u3048 \u305f\u3059\u304f</td>\n <td>H\u5e02</td>\n <td>horie_tasuku@example.com</td>\n <td>2018-05-14</td>\n <td>201805</td>\n </tr>\n <tr>\n <th>29</th>\n <td>2019-02-11 18:32:05</td>\n <td>\u5546\u54c1R</td>\n <td>1800.0</td>\n <td>\u837b\u91ce\u611b</td>\n <td>\u304a\u304e\u306e \u3042\u3044</td>\n <td>F\u5e02</td>\n <td>ogino_ai@example.com</td>\n <td>2017-05-19</td>\n <td>201705</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>2969</th>\n <td>2019-02-01 03:48:49</td>\n <td>\u5546\u54c1O</td>\n <td>1500.0</td>\n <td>\u6cb3\u5185\u3055\u3068\u307f</td>\n <td>\u304b\u308f\u3046\u3061 \u3055\u3068\u307f</td>\n <td>E\u5e02</td>\n <td>kawauchi_satomi@example.com</td>\n <td>2017-01-21</td>\n <td>201701</td>\n </tr>\n <tr>\n <th>2970</th>\n <td>2019-03-15 11:17:20</td>\n <td>\u5546\u54c1L</td>\n <td>1200.0</td>\n <td>\u677e\u5ddd\u7dbe\u5973</td>\n <td>\u307e\u3064\u304b\u308f \u3042\u3084\u3081</td>\n <td>E\u5e02</td>\n <td>matsukawa_ayame@example.com</td>\n <td>2018-07-23</td>\n <td>201807</td>\n </tr>\n <tr>\n <th>2971</th>\n <td>2019-02-02 04:52:12</td>\n <td>\u5546\u54c1S</td>\n <td>1900.0</td>\n <td>\u4e95\u4e0a\u6843\u5b50</td>\n <td>\u3044\u306e\u3046\u3048 \u3082\u3082\u3053</td>\n <td>F\u5e02</td>\n <td>inoue_momoko@example.com</td>\n <td>2018-06-18</td>\n <td>201806</td>\n </tr>\n <tr>\n <th>2972</th>\n <td>2019-04-30 19:47:44</td>\n <td>\u5546\u54c1R</td>\n <td>1800.0</td>\n <td>\u6749\u4e0b\u609f\u5fd7</td>\n <td>\u3059\u304e\u3057\u305f \u3055\u3068\u3057</td>\n <td>E\u5e02</td>\n <td>sugishita_satoshi@example.com</td>\n <td>2018-02-17</td>\n <td>201802</td>\n </tr>\n <tr>\n <th>2973</th>\n <td>2019-07-13 21:38:11</td>\n <td>\u5546\u54c1A</td>\n <td>100.0</td>\n <td>\u5927\u5730\u793c\u5b50</td>\n <td>\u304a\u304a\u3061 \u308c\u3044\u3053</td>\n <td>E\u5e02</td>\n <td>oochi_reiko@example.com</td>\n <td>2017-05-09</td>\n <td>201705</td>\n </tr>\n <tr>\n <th>2974</th>\n <td>2019-02-04 17:08:40</td>\n <td>\u5546\u54c1P</td>\n <td>1600.0</td>\n <td>\u5ca1\u6176\u592a</td>\n <td>\u304a\u304b \u3051\u3044\u305f</td>\n <td>C\u5e02</td>\n <td>oka_keita@example.com</td>\n <td>2018-07-22</td>\n <td>201807</td>\n </tr>\n <tr>\n <th>2975</th>\n <td>2019-07-21 10:36:03</td>\n <td>\u5546\u54c1Y</td>\n <td>2500.0</td>\n <td>\u77e2\u90e8\u7f8e\u5e78</td>\n <td>\u3084\u3079 \u307f\u3086\u304d</td>\n <td>F\u5e02</td>\n <td>yabe_miyuki@example.com</td>\n <td>2017-05-27</td>\n <td>201705</td>\n </tr>\n <tr>\n <th>2976</th>\n <td>2019-07-11 00:11:36</td>\n <td>\u5546\u54c1L</td>\n <td>1200.0</td>\n <td>\u4e95\u672c\u30de\u30b5\u30ab\u30ba</td>\n <td>\u3044\u3082\u3068 \u307e\u3055\u304b\u305a</td>\n <td>C\u5e02</td>\n <td>imoto_masakazu@example.com</td>\n <td>2017-04-06</td>\n <td>201704</td>\n </tr>\n <tr>\n <th>2977</th>\n <td>2019-03-02 20:55:58</td>\n <td>\u5546\u54c1X</td>\n <td>2400.0</td>\n <td>\u5408\u7530\u5149</td>\n <td>\u3042\u3044\u3060 \u3072\u304b\u308b</td>\n <td>D\u5e02</td>\n <td>aida_hikaru@example.com</td>\n <td>2018-05-22</td>\n <td>201805</td>\n </tr>\n <tr>\n <th>2978</th>\n <td>2019-04-06 21:20:36</td>\n <td>\u5546\u54c1N</td>\n <td>1400.0</td>\n <td>\u6d45\u7530\u8ce2\u4e8c</td>\n <td>\u3042\u3055\u3060 \u3051\u3093\u3058</td>\n <td>C\u5e02</td>\n <td>asada_kenji@example.com</td>\n <td>2018-04-07</td>\n <td>201804</td>\n </tr>\n <tr>\n <th>2979</th>\n <td>2019-03-09 17:26:00</td>\n <td>\u5546\u54c1N</td>\n <td>1400.0</td>\n <td>\u6df1\u7530\u4fe1\u8f14</td>\n <td>\u3075\u304b\u3060 \u3057\u3093\u3059\u3051</td>\n <td>G\u5e02</td>\n <td>fukada_shinsuke@example.com</td>\n <td>2017-07-07</td>\n <td>201707</td>\n </tr>\n <tr>\n <th>2980</th>\n <td>2019-07-01 02:15:47</td>\n <td>\u5546\u54c1P</td>\n <td>1600.0</td>\n <td>\u77e2\u90e8\u7f8e\u5e78</td>\n <td>\u3084\u3079 \u307f\u3086\u304d</td>\n <td>F\u5e02</td>\n <td>yabe_miyuki@example.com</td>\n <td>2017-05-27</td>\n <td>201705</td>\n </tr>\n <tr>\n <th>2981</th>\n <td>2019-04-26 22:17:47</td>\n <td>\u5546\u54c1R</td>\n <td>1800.0</td>\n <td>\u5965\u5149\u6d0b</td>\n <td>\u304a\u304f \u307f\u3064\u3072\u308d</td>\n <td>A\u5e02</td>\n <td>oku_mitsuhiro@example.com</td>\n <td>2018-01-08</td>\n <td>201801</td>\n </tr>\n <tr>\n <th>2982</th>\n <td>2019-02-18 16:53:38</td>\n <td>\u5546\u54c1L</td>\n <td>1200.0</td>\n <td>\u7be0\u5c71\u96c5\u529f</td>\n <td>\u3057\u306e\u3084\u307e \u307e\u3055\u3068\u3057</td>\n <td>B\u5e02</td>\n <td>shinoyama_masatoshi@example.com</td>\n <td>2018-07-02</td>\n <td>201807</td>\n </tr>\n <tr>\n <th>2983</th>\n <td>2019-05-09 00:44:58</td>\n <td>\u5546\u54c1H</td>\n <td>800.0</td>\n <td>\u3055\u3060\u5343\u4f73\u5b50</td>\n <td>\u3055\u3060 \u3061\u304b\u3053</td>\n <td>H\u5e02</td>\n <td>sada_chikako@example.com</td>\n <td>2017-07-01</td>\n <td>201707</td>\n </tr>\n <tr>\n <th>2984</th>\n <td>2019-03-06 06:16:05</td>\n <td>\u5546\u54c1G</td>\n <td>700.0</td>\n <td>\u7b39\u5ddd\u7167\u751f</td>\n <td>\u3055\u3055\u304c\u308f \u3066\u308b\u304a</td>\n <td>F\u5e02</td>\n <td>sasagawa_teruo@example.com</td>\n <td>2017-04-08</td>\n <td>201704</td>\n </tr>\n <tr>\n <th>2985</th>\n <td>2019-03-06 22:30:55</td>\n <td>\u5546\u54c1X</td>\n <td>2400.0</td>\n <td>\u5ca9\u6ca2\u90a3\u5948</td>\n <td>\u3044\u308f\u3055\u308f \u306a\u306a</td>\n <td>H\u5e02</td>\n <td>iwasawa_nana@example.com</td>\n <td>2017-07-13</td>\n <td>201707</td>\n </tr>\n <tr>\n <th>2986</th>\n <td>2019-03-12 19:34:15</td>\n <td>\u5546\u54c1C</td>\n <td>300.0</td>\n <td>\u718a\u5009\u660e\u65e5</td>\n <td>\u304f\u307e\u304f\u3089 \u3081\u3044\u3073</td>\n <td>G\u5e02</td>\n <td>kumakura_meibi@example.com</td>\n <td>2018-05-07</td>\n <td>201805</td>\n </tr>\n <tr>\n <th>2987</th>\n <td>2019-01-04 13:05:29</td>\n <td>\u5546\u54c1K</td>\n <td>1100.0</td>\n <td>\u677e\u8c37\u611b\u5b50</td>\n <td>\u307e\u3064\u305f\u306b \u3042\u3044\u3053</td>\n <td>D\u5e02</td>\n <td>matsutani_aiko@example.com</td>\n <td>2018-07-30</td>\n <td>201807</td>\n </tr>\n <tr>\n <th>2988</th>\n <td>2019-01-23 07:08:04</td>\n <td>\u5546\u54c1R</td>\n <td>1800.0</td>\n <td>\u77e2\u90e8\u60c7</td>\n <td>\u3084\u3079 \u3058\u3085\u3093</td>\n <td>A\u5e02</td>\n <td>yabe_jun@example.com</td>\n <td>2018-05-20</td>\n <td>201805</td>\n </tr>\n <tr>\n <th>2989</th>\n <td>2019-05-19 03:20:24</td>\n <td>\u5546\u54c1M</td>\n <td>1300.0</td>\n <td>\u690d\u6751\u9065</td>\n <td>\u3046\u3048\u3080\u3089 \u306f\u308b\u304b</td>\n <td>A\u5e02</td>\n <td>uemura_haruka@example.com</td>\n <td>2018-01-06</td>\n <td>201801</td>\n </tr>\n <tr>\n <th>2990</th>\n <td>2019-07-16 11:34:22</td>\n <td>\u5546\u54c1O</td>\n <td>1500.0</td>\n <td>\u698e\u672c\u85ab</td>\n <td>\u3048\u306e\u3082\u3068 \u304b\u304a\u308b</td>\n <td>C\u5e02</td>\n <td>enomoto_kaoru@example.com</td>\n <td>2017-06-17</td>\n <td>201706</td>\n </tr>\n <tr>\n <th>2991</th>\n <td>2019-02-18 14:36:49</td>\n <td>\u5546\u54c1W</td>\n <td>2300.0</td>\n <td>\u5c3e\u4e0a\u52dd\u4e45</td>\n <td>\u304a\u304c\u307f \u304b\u3064\u3072\u3055</td>\n <td>D\u5e02</td>\n <td>ogami_katsuhisa@example.com</td>\n <td>2017-07-20</td>\n <td>201707</td>\n </tr>\n <tr>\n <th>2992</th>\n <td>2019-07-27 10:13:13</td>\n <td>\u5546\u54c1C</td>\n <td>300.0</td>\n <td>\u690d\u6728\u6c99\u77e5\u7d75</td>\n <td>\u3046\u3048\u304d \u3055\u3061\u3048</td>\n <td>F\u5e02</td>\n <td>ueki_sachie@example.com</td>\n <td>2018-03-15</td>\n <td>201803</td>\n </tr>\n <tr>\n <th>2993</th>\n <td>2019-01-25 03:57:54</td>\n <td>\u5546\u54c1C</td>\n <td>300.0</td>\n <td>\u767d\u9ce5\u308a\u3048</td>\n <td>\u3057\u3089\u3068\u308a \u308a\u3048</td>\n <td>F\u5e02</td>\n <td>shiratori_rie@example.com</td>\n <td>2017-04-29</td>\n <td>201704</td>\n </tr>\n <tr>\n <th>2994</th>\n <td>2019-02-15 02:56:39</td>\n <td>\u5546\u54c1Y</td>\n <td>2500.0</td>\n <td>\u798f\u5cf6\u53cb\u4e5f</td>\n <td>\u3075\u304f\u3057\u307e \u3068\u3082\u3084</td>\n <td>B\u5e02</td>\n <td>fukushima_tomoya@example.com</td>\n <td>2017-07-01</td>\n <td>201707</td>\n </tr>\n <tr>\n <th>2995</th>\n <td>2019-06-22 04:03:43</td>\n <td>\u5546\u54c1M</td>\n <td>1300.0</td>\n <td>\u5927\u5009\u6643\u53f8</td>\n <td>\u304a\u304a\u304f\u3089 \u3053\u3046\u3058</td>\n <td>E\u5e02</td>\n <td>ookura_kouji@example.com</td>\n <td>2018-03-31</td>\n <td>201803</td>\n </tr>\n <tr>\n <th>2996</th>\n <td>2019-03-29 11:14:05</td>\n <td>\u5546\u54c1Q</td>\n <td>1700.0</td>\n <td>\u5c3e\u5f62\u5c0f\u96c1</td>\n <td>\u304a\u304c\u305f \u3053\u304c\u3093</td>\n <td>B\u5e02</td>\n <td>ogata_kogan@example.com</td>\n <td>2017-03-15</td>\n <td>201703</td>\n </tr>\n <tr>\n <th>2997</th>\n <td>2019-07-14 12:56:49</td>\n <td>\u5546\u54c1H</td>\n <td>800.0</td>\n <td>\u82a6\u7530\u535a\u4e4b</td>\n <td>\u3042\u3057\u3060 \u3072\u308d\u3086\u304d</td>\n <td>E\u5e02</td>\n <td>ashida_hiroyuki@example.com</td>\n <td>2018-07-15</td>\n <td>201807</td>\n </tr>\n <tr>\n <th>2998</th>\n <td>2019-07-21 00:31:36</td>\n <td>\u5546\u54c1D</td>\n <td>400.0</td>\n <td>\u77f3\u7530\u90c1\u6075</td>\n <td>\u3044\u3057\u3060 \u3044\u304f\u3048</td>\n <td>B\u5e02</td>\n <td>ishida_ikue@example.com</td>\n <td>2017-02-05</td>\n <td>201702</td>\n </tr>\n </tbody>\n</table>\n<p>2999 rows \u00d7 9 columns</p>\n</div>",
"text/plain": " purchase_date item_name item_price \u9867\u5ba2\u540d \u304b\u306a \u5730\u57df \\\n0 2019-06-13 18:02:34 \u5546\u54c1A 100.0 \u6df1\u4e95\u83dc\u3005\u7f8e \u3075\u304b\u3044 \u306a\u306a\u307f C\u5e02 \n1 2019-07-13 13:05:29 \u5546\u54c1S 1900.0 \u6d45\u7530\u8ce2\u4e8c \u3042\u3055\u3060 \u3051\u3093\u3058 C\u5e02 \n2 2019-05-11 19:42:07 \u5546\u54c1A 100.0 \u5357\u90e8\u6176\u4e8c \u306a\u3093\u3076 \u3051\u3044\u3058 A\u5e02 \n3 2019-02-12 23:40:45 \u5546\u54c1Z 2600.0 \u9ebb\u751f\u8389\u7dd2 \u3042\u305d\u3046 \u308a\u304a D\u5e02 \n4 2019-04-22 03:09:35 \u5546\u54c1A 100.0 \u5e73\u7530\u9244\u4e8c \u3072\u3089\u305f \u3066\u3064\u3058 D\u5e02 \n5 2019-03-20 19:16:01 \u5546\u54c1S 1900.0 \u5800\u6c5f\u4f51 \u307b\u308a\u3048 \u305f\u3059\u304f H\u5e02 \n6 2019-05-18 19:16:53 \u5546\u54c1A 100.0 \u6df1\u4e95\u7167\u751f \u3075\u304b\u3044 \u3066\u308b\u304a A\u5e02 \n7 2019-04-18 00:14:21 \u5546\u54c1V 2200.0 \u7267\u7530\u73b2\u90a3 \u307e\u304d\u305f \u308c\u306a A\u5e02 \n8 2019-01-10 15:51:01 \u5546\u54c1O 1500.0 \u5800\u5317\u96c5\u5f66 \u307b\u308a\u304d\u305f \u307e\u3055\u3072\u3053 H\u5e02 \n9 2019-01-28 10:47:03 \u5546\u54c1A 100.0 \u5927\u5730\u793c\u5b50 \u304a\u304a\u3061 \u308c\u3044\u3053 E\u5e02 \n10 2019-06-21 01:54:35 \u5546\u54c1U 2100.0 \u77e2\u90e8\u60c7 \u3084\u3079 \u3058\u3085\u3093 A\u5e02 \n11 2019-06-08 11:32:25 \u5546\u54c1L 1200.0 \u5ca1\u7530\u654f\u4e5f \u304a\u304b\u3060 \u3068\u3057\u3084 E\u5e02 \n12 2019-04-08 02:00:44 \u5546\u54c1V 2200.0 \u6d45\u898b\u5e83\u53f8 \u3042\u3055\u307f \u3053\u3046\u3058 D\u5e02 \n13 2019-06-19 09:50:52 \u5546\u54c1O 1500.0 \u718a\u4e95\u61b2\u53f2 \u304f\u307e\u3044 \u306e\u308a\u3072\u3068 A\u5e02 \n14 2019-06-11 12:57:24 \u5546\u54c1A 100.0 \u9ec4\u5ddd\u7530\u535a\u4e4b \u304d\u304b\u308f\u3060 \u3072\u308d\u3086\u304d C\u5e02 \n15 2019-04-21 00:11:43 \u5546\u54c1C 300.0 \u5c3e\u5f62\u5c0f\u96c1 \u304a\u304c\u305f \u3053\u304c\u3093 B\u5e02 \n16 2019-03-28 23:24:46 \u5546\u54c1V 2200.0 \u795e\u539f\u7f8e\u5609 \u304b\u3093\u3070\u3089 \u307f\u304b D\u5e02 \n17 2019-04-06 12:00:53 \u5546\u54c1I 900.0 \u82e5\u6749\u5fb9 \u308f\u304b\u3059\u304e \u3068\u304a\u308b G\u5e02 \n18 2019-07-16 05:55:57 \u5546\u54c1R 1800.0 \u77f3\u6e21\u5c0f\u96c1 \u3044\u3057\u308f\u305f\u308a \u3053\u304c\u3093 D\u5e02 \n19 2019-07-03 07:04:05 \u5546\u54c1X 2400.0 \u6771\u5149\u535a \u3072\u304c\u3057 \u307f\u3064\u3072\u308d A\u5e02 \n20 2019-07-05 10:49:13 \u5546\u54c1O 1500.0 \u65e5\u91ce\u590f\u5e0c \u3072\u306e \u306a\u3064\u304d A\u5e02 \n21 2019-06-10 19:07:23 \u5546\u54c1G 700.0 \u9ed2\u8c37\u9577\u5229 \u304f\u308d\u305f\u306b \u306a\u304c\u3068\u3057 A\u5e02 \n22 2019-07-10 20:28:12 \u5546\u54c1X 2400.0 \u7530\u8fba\u5149\u6d0b \u305f\u306a\u3079 \u307f\u3064\u3072\u308d A\u5e02 \n23 2019-07-10 12:44:10 \u5546\u54c1R 1800.0 \u6238\u585a\u7f8e\u5e78 \u3068\u3065\u304b \u307f\u3086\u304d H\u5e02 \n24 2019-02-14 01:30:09 \u5546\u54c1P 1600.0 \u698a\u539f\u3057\u307c\u308a \u3055\u304b\u304d\u3070\u3089 \u3057\u307c\u308a D\u5e02 \n25 2019-04-18 01:50:16 \u5546\u54c1Q 1700.0 \u6d45\u7530\u8ce2\u4e8c \u3042\u3055\u3060 \u3051\u3093\u3058 C\u5e02 \n26 2019-05-16 04:45:21 \u5546\u54c1Y 2500.0 \u660e\u77f3\u5bb6\u660e \u3042\u304b\u3057\u3084 \u3042\u304d\u3089 B\u5e02 \n27 2019-05-26 10:58:00 \u5546\u54c1P 1600.0 \u624b\u585a\u9032 \u3066\u3065\u304b \u3059\u3059\u3080 A\u5e02 \n28 2019-01-04 12:05:04 \u5546\u54c1P 1600.0 \u5800\u6c5f\u4f51 \u307b\u308a\u3048 \u305f\u3059\u304f H\u5e02 \n29 2019-02-11 18:32:05 \u5546\u54c1R 1800.0 \u837b\u91ce\u611b \u304a\u304e\u306e \u3042\u3044 F\u5e02 \n... ... ... ... ... ... .. \n2969 2019-02-01 03:48:49 \u5546\u54c1O 1500.0 \u6cb3\u5185\u3055\u3068\u307f \u304b\u308f\u3046\u3061 \u3055\u3068\u307f E\u5e02 \n2970 2019-03-15 11:17:20 \u5546\u54c1L 1200.0 \u677e\u5ddd\u7dbe\u5973 \u307e\u3064\u304b\u308f \u3042\u3084\u3081 E\u5e02 \n2971 2019-02-02 04:52:12 \u5546\u54c1S 1900.0 \u4e95\u4e0a\u6843\u5b50 \u3044\u306e\u3046\u3048 \u3082\u3082\u3053 F\u5e02 \n2972 2019-04-30 19:47:44 \u5546\u54c1R 1800.0 \u6749\u4e0b\u609f\u5fd7 \u3059\u304e\u3057\u305f \u3055\u3068\u3057 E\u5e02 \n2973 2019-07-13 21:38:11 \u5546\u54c1A 100.0 \u5927\u5730\u793c\u5b50 \u304a\u304a\u3061 \u308c\u3044\u3053 E\u5e02 \n2974 2019-02-04 17:08:40 \u5546\u54c1P 1600.0 \u5ca1\u6176\u592a \u304a\u304b \u3051\u3044\u305f C\u5e02 \n2975 2019-07-21 10:36:03 \u5546\u54c1Y 2500.0 \u77e2\u90e8\u7f8e\u5e78 \u3084\u3079 \u307f\u3086\u304d F\u5e02 \n2976 2019-07-11 00:11:36 \u5546\u54c1L 1200.0 \u4e95\u672c\u30de\u30b5\u30ab\u30ba \u3044\u3082\u3068 \u307e\u3055\u304b\u305a C\u5e02 \n2977 2019-03-02 20:55:58 \u5546\u54c1X 2400.0 \u5408\u7530\u5149 \u3042\u3044\u3060 \u3072\u304b\u308b D\u5e02 \n2978 2019-04-06 21:20:36 \u5546\u54c1N 1400.0 \u6d45\u7530\u8ce2\u4e8c \u3042\u3055\u3060 \u3051\u3093\u3058 C\u5e02 \n2979 2019-03-09 17:26:00 \u5546\u54c1N 1400.0 \u6df1\u7530\u4fe1\u8f14 \u3075\u304b\u3060 \u3057\u3093\u3059\u3051 G\u5e02 \n2980 2019-07-01 02:15:47 \u5546\u54c1P 1600.0 \u77e2\u90e8\u7f8e\u5e78 \u3084\u3079 \u307f\u3086\u304d F\u5e02 \n2981 2019-04-26 22:17:47 \u5546\u54c1R 1800.0 \u5965\u5149\u6d0b \u304a\u304f \u307f\u3064\u3072\u308d A\u5e02 \n2982 2019-02-18 16:53:38 \u5546\u54c1L 1200.0 \u7be0\u5c71\u96c5\u529f \u3057\u306e\u3084\u307e \u307e\u3055\u3068\u3057 B\u5e02 \n2983 2019-05-09 00:44:58 \u5546\u54c1H 800.0 \u3055\u3060\u5343\u4f73\u5b50 \u3055\u3060 \u3061\u304b\u3053 H\u5e02 \n2984 2019-03-06 06:16:05 \u5546\u54c1G 700.0 \u7b39\u5ddd\u7167\u751f \u3055\u3055\u304c\u308f \u3066\u308b\u304a F\u5e02 \n2985 2019-03-06 22:30:55 \u5546\u54c1X 2400.0 \u5ca9\u6ca2\u90a3\u5948 \u3044\u308f\u3055\u308f \u306a\u306a H\u5e02 \n2986 2019-03-12 19:34:15 \u5546\u54c1C 300.0 \u718a\u5009\u660e\u65e5 \u304f\u307e\u304f\u3089 \u3081\u3044\u3073 G\u5e02 \n2987 2019-01-04 13:05:29 \u5546\u54c1K 1100.0 \u677e\u8c37\u611b\u5b50 \u307e\u3064\u305f\u306b \u3042\u3044\u3053 D\u5e02 \n2988 2019-01-23 07:08:04 \u5546\u54c1R 1800.0 \u77e2\u90e8\u60c7 \u3084\u3079 \u3058\u3085\u3093 A\u5e02 \n2989 2019-05-19 03:20:24 \u5546\u54c1M 1300.0 \u690d\u6751\u9065 \u3046\u3048\u3080\u3089 \u306f\u308b\u304b A\u5e02 \n2990 2019-07-16 11:34:22 \u5546\u54c1O 1500.0 \u698e\u672c\u85ab \u3048\u306e\u3082\u3068 \u304b\u304a\u308b C\u5e02 \n2991 2019-02-18 14:36:49 \u5546\u54c1W 2300.0 \u5c3e\u4e0a\u52dd\u4e45 \u304a\u304c\u307f \u304b\u3064\u3072\u3055 D\u5e02 \n2992 2019-07-27 10:13:13 \u5546\u54c1C 300.0 \u690d\u6728\u6c99\u77e5\u7d75 \u3046\u3048\u304d \u3055\u3061\u3048 F\u5e02 \n2993 2019-01-25 03:57:54 \u5546\u54c1C 300.0 \u767d\u9ce5\u308a\u3048 \u3057\u3089\u3068\u308a \u308a\u3048 F\u5e02 \n2994 2019-02-15 02:56:39 \u5546\u54c1Y 2500.0 \u798f\u5cf6\u53cb\u4e5f \u3075\u304f\u3057\u307e \u3068\u3082\u3084 B\u5e02 \n2995 2019-06-22 04:03:43 \u5546\u54c1M 1300.0 \u5927\u5009\u6643\u53f8 \u304a\u304a\u304f\u3089 \u3053\u3046\u3058 E\u5e02 \n2996 2019-03-29 11:14:05 \u5546\u54c1Q 1700.0 \u5c3e\u5f62\u5c0f\u96c1 \u304a\u304c\u305f \u3053\u304c\u3093 B\u5e02 \n2997 2019-07-14 12:56:49 \u5546\u54c1H 800.0 \u82a6\u7530\u535a\u4e4b \u3042\u3057\u3060 \u3072\u308d\u3086\u304d E\u5e02 \n2998 2019-07-21 00:31:36 \u5546\u54c1D 400.0 \u77f3\u7530\u90c1\u6075 \u3044\u3057\u3060 \u3044\u304f\u3048 B\u5e02 \n\n \u30e1\u30fc\u30eb\u30a2\u30c9\u30ec\u30b9 \u767b\u9332\u65e5 \u767b\u9332\u5e74\u6708 \n0 fukai_nanami@example.com 2017-01-26 201701 \n1 asada_kenji@example.com 2018-04-07 201804 \n2 nannbu_keiji@example.com 2018-06-19 201806 \n3 asou_rio@example.com 2018-07-22 201807 \n4 hirata_tetsuji@example.com 2017-06-07 201706 \n5 horie_tasuku@example.com 2018-05-14 201805 \n6 fukai_teruo@example.com 2018-02-21 201802 \n7 makita_rena@example.com 2017-05-13 201705 \n8 horikita_masahiko@example.com 2017-05-05 201705 \n9 oochi_reiko@example.com 2017-05-09 201705 \n10 yabe_jun@example.com 2018-05-20 201805 \n11 okada_toshiya@example.com 2017-02-18 201702 \n12 asami_kouji@example.com 2018-06-05 201806 \n13 kumai_norihito@example.com 2017-03-29 201703 \n14 kikawada_hiroyuki@example.com 2018-02-14 201802 \n15 ogata_kogan@example.com 2017-03-15 201703 \n16 kannbara_mika@example.com 2017-03-23 201703 \n17 wakasugi_tohru@example.com 2017-03-26 201703 \n18 ishiwatari_kogan@example.com 2017-07-28 201707 \n19 higashi_mitsuhiro@example.com 2018-02-06 201802 \n20 hino_natsuki@example.com 2017-05-23 201705 \n21 kurotani_nagatoshi@example.com 2017-04-27 201704 \n22 tanabe_mitsuhiro@example.com 2018-07-04 201807 \n23 toduka_miyuki@example.com 2017-01-30 201701 \n24 sakakibara_shibori@example.com 2017-03-13 201703 \n25 asada_kenji@example.com 2018-04-07 201804 \n26 akashiya_akira@example.com 2018-02-13 201802 \n27 teduka_susumu@example.com 2018-05-01 201805 \n28 horie_tasuku@example.com 2018-05-14 201805 \n29 ogino_ai@example.com 2017-05-19 201705 \n... ... ... ... \n2969 kawauchi_satomi@example.com 2017-01-21 201701 \n2970 matsukawa_ayame@example.com 2018-07-23 201807 \n2971 inoue_momoko@example.com 2018-06-18 201806 \n2972 sugishita_satoshi@example.com 2018-02-17 201802 \n2973 oochi_reiko@example.com 2017-05-09 201705 \n2974 oka_keita@example.com 2018-07-22 201807 \n2975 yabe_miyuki@example.com 2017-05-27 201705 \n2976 imoto_masakazu@example.com 2017-04-06 201704 \n2977 aida_hikaru@example.com 2018-05-22 201805 \n2978 asada_kenji@example.com 2018-04-07 201804 \n2979 fukada_shinsuke@example.com 2017-07-07 201707 \n2980 yabe_miyuki@example.com 2017-05-27 201705 \n2981 oku_mitsuhiro@example.com 2018-01-08 201801 \n2982 shinoyama_masatoshi@example.com 2018-07-02 201807 \n2983 sada_chikako@example.com 2017-07-01 201707 \n2984 sasagawa_teruo@example.com 2017-04-08 201704 \n2985 iwasawa_nana@example.com 2017-07-13 201707 \n2986 kumakura_meibi@example.com 2018-05-07 201805 \n2987 matsutani_aiko@example.com 2018-07-30 201807 \n2988 yabe_jun@example.com 2018-05-20 201805 \n2989 uemura_haruka@example.com 2018-01-06 201801 \n2990 enomoto_kaoru@example.com 2017-06-17 201706 \n2991 ogami_katsuhisa@example.com 2017-07-20 201707 \n2992 ueki_sachie@example.com 2018-03-15 201803 \n2993 shiratori_rie@example.com 2017-04-29 201704 \n2994 fukushima_tomoya@example.com 2017-07-01 201707 \n2995 ookura_kouji@example.com 2018-03-31 201803 \n2996 ogata_kogan@example.com 2017-03-15 201703 \n2997 ashida_hiroyuki@example.com 2018-07-15 201807 \n2998 ishida_ikue@example.com 2017-02-05 201702 \n\n[2999 rows x 9 columns]"
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "join_data = pd.merge(uriage_data, kokyaku_data, left_on=\"customer_name\", right_on=\"\u9867\u5ba2\u540d\",how=\"left\")\njoin_data = join_data.drop(\"customer_name\", axis=1)\njoin_data"
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": "join_data[\"purchase_date\"] = pd.to_datetime(join_data[\"purchase_date\"])\njoin_data[\"purchase_month\"] = join_data[\"purchase_date\"].dt.strftime(\"%Y%m\")"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "\u30c7\u30fc\u30bf\u306e\u30c0\u30f3\u30d7"
},
{
"cell_type": "code",
"execution_count": 61,
"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>purchase_date</th>\n <th>purchase_month</th>\n <th>item_name</th>\n <th>item_price</th>\n <th>\u9867\u5ba2\u540d</th>\n <th>\u304b\u306a</th>\n <th>\u5730\u57df</th>\n <th>\u30e1\u30fc\u30eb\u30a2\u30c9\u30ec\u30b9</th>\n <th>\u767b\u9332\u65e5</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2019-06-13 18:02:34</td>\n <td>201906</td>\n <td>\u5546\u54c1A</td>\n <td>100.0</td>\n <td>\u6df1\u4e95\u83dc\u3005\u7f8e</td>\n <td>\u3075\u304b\u3044 \u306a\u306a\u307f</td>\n <td>C\u5e02</td>\n <td>fukai_nanami@example.com</td>\n <td>2017-01-26</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2019-07-13 13:05:29</td>\n <td>201907</td>\n <td>\u5546\u54c1S</td>\n <td>1900.0</td>\n <td>\u6d45\u7530\u8ce2\u4e8c</td>\n <td>\u3042\u3055\u3060 \u3051\u3093\u3058</td>\n <td>C\u5e02</td>\n <td>asada_kenji@example.com</td>\n <td>2018-04-07</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2019-05-11 19:42:07</td>\n <td>201905</td>\n <td>\u5546\u54c1A</td>\n <td>100.0</td>\n <td>\u5357\u90e8\u6176\u4e8c</td>\n <td>\u306a\u3093\u3076 \u3051\u3044\u3058</td>\n <td>A\u5e02</td>\n <td>nannbu_keiji@example.com</td>\n <td>2018-06-19</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2019-02-12 23:40:45</td>\n <td>201902</td>\n <td>\u5546\u54c1Z</td>\n <td>2600.0</td>\n <td>\u9ebb\u751f\u8389\u7dd2</td>\n <td>\u3042\u305d\u3046 \u308a\u304a</td>\n <td>D\u5e02</td>\n <td>asou_rio@example.com</td>\n <td>2018-07-22</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2019-04-22 03:09:35</td>\n <td>201904</td>\n <td>\u5546\u54c1A</td>\n <td>100.0</td>\n <td>\u5e73\u7530\u9244\u4e8c</td>\n <td>\u3072\u3089\u305f \u3066\u3064\u3058</td>\n <td>D\u5e02</td>\n <td>hirata_tetsuji@example.com</td>\n <td>2017-06-07</td>\n </tr>\n <tr>\n <th>5</th>\n <td>2019-03-20 19:16:01</td>\n <td>201903</td>\n <td>\u5546\u54c1S</td>\n <td>1900.0</td>\n <td>\u5800\u6c5f\u4f51</td>\n <td>\u307b\u308a\u3048 \u305f\u3059\u304f</td>\n <td>H\u5e02</td>\n <td>horie_tasuku@example.com</td>\n <td>2018-05-14</td>\n </tr>\n <tr>\n <th>6</th>\n <td>2019-05-18 19:16:53</td>\n <td>201905</td>\n <td>\u5546\u54c1A</td>\n <td>100.0</td>\n <td>\u6df1\u4e95\u7167\u751f</td>\n <td>\u3075\u304b\u3044 \u3066\u308b\u304a</td>\n <td>A\u5e02</td>\n <td>fukai_teruo@example.com</td>\n <td>2018-02-21</td>\n </tr>\n <tr>\n <th>7</th>\n <td>2019-04-18 00:14:21</td>\n <td>201904</td>\n <td>\u5546\u54c1V</td>\n <td>2200.0</td>\n <td>\u7267\u7530\u73b2\u90a3</td>\n <td>\u307e\u304d\u305f \u308c\u306a</td>\n <td>A\u5e02</td>\n <td>makita_rena@example.com</td>\n <td>2017-05-13</td>\n </tr>\n <tr>\n <th>8</th>\n <td>2019-01-10 15:51:01</td>\n <td>201901</td>\n <td>\u5546\u54c1O</td>\n <td>1500.0</td>\n <td>\u5800\u5317\u96c5\u5f66</td>\n <td>\u307b\u308a\u304d\u305f \u307e\u3055\u3072\u3053</td>\n <td>H\u5e02</td>\n <td>horikita_masahiko@example.com</td>\n <td>2017-05-05</td>\n </tr>\n <tr>\n <th>9</th>\n <td>2019-01-28 10:47:03</td>\n <td>201901</td>\n <td>\u5546\u54c1A</td>\n <td>100.0</td>\n <td>\u5927\u5730\u793c\u5b50</td>\n <td>\u304a\u304a\u3061 \u308c\u3044\u3053</td>\n <td>E\u5e02</td>\n <td>oochi_reiko@example.com</td>\n <td>2017-05-09</td>\n </tr>\n <tr>\n <th>10</th>\n <td>2019-06-21 01:54:35</td>\n <td>201906</td>\n <td>\u5546\u54c1U</td>\n <td>2100.0</td>\n <td>\u77e2\u90e8\u60c7</td>\n <td>\u3084\u3079 \u3058\u3085\u3093</td>\n <td>A\u5e02</td>\n <td>yabe_jun@example.com</td>\n <td>2018-05-20</td>\n </tr>\n <tr>\n <th>11</th>\n <td>2019-06-08 11:32:25</td>\n <td>201906</td>\n <td>\u5546\u54c1L</td>\n <td>1200.0</td>\n <td>\u5ca1\u7530\u654f\u4e5f</td>\n <td>\u304a\u304b\u3060 \u3068\u3057\u3084</td>\n <td>E\u5e02</td>\n <td>okada_toshiya@example.com</td>\n <td>2017-02-18</td>\n </tr>\n <tr>\n <th>12</th>\n <td>2019-04-08 02:00:44</td>\n <td>201904</td>\n <td>\u5546\u54c1V</td>\n <td>2200.0</td>\n <td>\u6d45\u898b\u5e83\u53f8</td>\n <td>\u3042\u3055\u307f \u3053\u3046\u3058</td>\n <td>D\u5e02</td>\n <td>asami_kouji@example.com</td>\n <td>2018-06-05</td>\n </tr>\n <tr>\n <th>13</th>\n <td>2019-06-19 09:50:52</td>\n <td>201906</td>\n <td>\u5546\u54c1O</td>\n <td>1500.0</td>\n <td>\u718a\u4e95\u61b2\u53f2</td>\n <td>\u304f\u307e\u3044 \u306e\u308a\u3072\u3068</td>\n <td>A\u5e02</td>\n <td>kumai_norihito@example.com</td>\n <td>2017-03-29</td>\n </tr>\n <tr>\n <th>14</th>\n <td>2019-06-11 12:57:24</td>\n <td>201906</td>\n <td>\u5546\u54c1A</td>\n <td>100.0</td>\n <td>\u9ec4\u5ddd\u7530\u535a\u4e4b</td>\n <td>\u304d\u304b\u308f\u3060 \u3072\u308d\u3086\u304d</td>\n <td>C\u5e02</td>\n <td>kikawada_hiroyuki@example.com</td>\n <td>2018-02-14</td>\n </tr>\n <tr>\n <th>15</th>\n <td>2019-04-21 00:11:43</td>\n <td>201904</td>\n <td>\u5546\u54c1C</td>\n <td>300.0</td>\n <td>\u5c3e\u5f62\u5c0f\u96c1</td>\n <td>\u304a\u304c\u305f \u3053\u304c\u3093</td>\n <td>B\u5e02</td>\n <td>ogata_kogan@example.com</td>\n <td>2017-03-15</td>\n </tr>\n <tr>\n <th>16</th>\n <td>2019-03-28 23:24:46</td>\n <td>201903</td>\n <td>\u5546\u54c1V</td>\n <td>2200.0</td>\n <td>\u795e\u539f\u7f8e\u5609</td>\n <td>\u304b\u3093\u3070\u3089 \u307f\u304b</td>\n <td>D\u5e02</td>\n <td>kannbara_mika@example.com</td>\n <td>2017-03-23</td>\n </tr>\n <tr>\n <th>17</th>\n <td>2019-04-06 12:00:53</td>\n <td>201904</td>\n <td>\u5546\u54c1I</td>\n <td>900.0</td>\n <td>\u82e5\u6749\u5fb9</td>\n <td>\u308f\u304b\u3059\u304e \u3068\u304a\u308b</td>\n <td>G\u5e02</td>\n <td>wakasugi_tohru@example.com</td>\n <td>2017-03-26</td>\n </tr>\n <tr>\n <th>18</th>\n <td>2019-07-16 05:55:57</td>\n <td>201907</td>\n <td>\u5546\u54c1R</td>\n <td>1800.0</td>\n <td>\u77f3\u6e21\u5c0f\u96c1</td>\n <td>\u3044\u3057\u308f\u305f\u308a \u3053\u304c\u3093</td>\n <td>D\u5e02</td>\n <td>ishiwatari_kogan@example.com</td>\n <td>2017-07-28</td>\n </tr>\n <tr>\n <th>19</th>\n <td>2019-07-03 07:04:05</td>\n <td>201907</td>\n <td>\u5546\u54c1X</td>\n <td>2400.0</td>\n <td>\u6771\u5149\u535a</td>\n <td>\u3072\u304c\u3057 \u307f\u3064\u3072\u308d</td>\n <td>A\u5e02</td>\n <td>higashi_mitsuhiro@example.com</td>\n <td>2018-02-06</td>\n </tr>\n <tr>\n <th>20</th>\n <td>2019-07-05 10:49:13</td>\n <td>201907</td>\n <td>\u5546\u54c1O</td>\n <td>1500.0</td>\n <td>\u65e5\u91ce\u590f\u5e0c</td>\n <td>\u3072\u306e \u306a\u3064\u304d</td>\n <td>A\u5e02</td>\n <td>hino_natsuki@example.com</td>\n <td>2017-05-23</td>\n </tr>\n <tr>\n <th>21</th>\n <td>2019-06-10 19:07:23</td>\n <td>201906</td>\n <td>\u5546\u54c1G</td>\n <td>700.0</td>\n <td>\u9ed2\u8c37\u9577\u5229</td>\n <td>\u304f\u308d\u305f\u306b \u306a\u304c\u3068\u3057</td>\n <td>A\u5e02</td>\n <td>kurotani_nagatoshi@example.com</td>\n <td>2017-04-27</td>\n </tr>\n <tr>\n <th>22</th>\n <td>2019-07-10 20:28:12</td>\n <td>201907</td>\n <td>\u5546\u54c1X</td>\n <td>2400.0</td>\n <td>\u7530\u8fba\u5149\u6d0b</td>\n <td>\u305f\u306a\u3079 \u307f\u3064\u3072\u308d</td>\n <td>A\u5e02</td>\n <td>tanabe_mitsuhiro@example.com</td>\n <td>2018-07-04</td>\n </tr>\n <tr>\n <th>23</th>\n <td>2019-07-10 12:44:10</td>\n <td>201907</td>\n <td>\u5546\u54c1R</td>\n <td>1800.0</td>\n <td>\u6238\u585a\u7f8e\u5e78</td>\n <td>\u3068\u3065\u304b \u307f\u3086\u304d</td>\n <td>H\u5e02</td>\n <td>toduka_miyuki@example.com</td>\n <td>2017-01-30</td>\n </tr>\n <tr>\n <th>24</th>\n <td>2019-02-14 01:30:09</td>\n <td>201902</td>\n <td>\u5546\u54c1P</td>\n <td>1600.0</td>\n <td>\u698a\u539f\u3057\u307c\u308a</td>\n <td>\u3055\u304b\u304d\u3070\u3089 \u3057\u307c\u308a</td>\n <td>D\u5e02</td>\n <td>sakakibara_shibori@example.com</td>\n <td>2017-03-13</td>\n </tr>\n <tr>\n <th>25</th>\n <td>2019-04-18 01:50:16</td>\n <td>201904</td>\n <td>\u5546\u54c1Q</td>\n <td>1700.0</td>\n <td>\u6d45\u7530\u8ce2\u4e8c</td>\n <td>\u3042\u3055\u3060 \u3051\u3093\u3058</td>\n <td>C\u5e02</td>\n <td>asada_kenji@example.com</td>\n <td>2018-04-07</td>\n </tr>\n <tr>\n <th>26</th>\n <td>2019-05-16 04:45:21</td>\n <td>201905</td>\n <td>\u5546\u54c1Y</td>\n <td>2500.0</td>\n <td>\u660e\u77f3\u5bb6\u660e</td>\n <td>\u3042\u304b\u3057\u3084 \u3042\u304d\u3089</td>\n <td>B\u5e02</td>\n <td>akashiya_akira@example.com</td>\n <td>2018-02-13</td>\n </tr>\n <tr>\n <th>27</th>\n <td>2019-05-26 10:58:00</td>\n <td>201905</td>\n <td>\u5546\u54c1P</td>\n <td>1600.0</td>\n <td>\u624b\u585a\u9032</td>\n <td>\u3066\u3065\u304b \u3059\u3059\u3080</td>\n <td>A\u5e02</td>\n <td>teduka_susumu@example.com</td>\n <td>2018-05-01</td>\n </tr>\n <tr>\n <th>28</th>\n <td>2019-01-04 12:05:04</td>\n <td>201901</td>\n <td>\u5546\u54c1P</td>\n <td>1600.0</td>\n <td>\u5800\u6c5f\u4f51</td>\n <td>\u307b\u308a\u3048 \u305f\u3059\u304f</td>\n <td>H\u5e02</td>\n <td>horie_tasuku@example.com</td>\n <td>2018-05-14</td>\n </tr>\n <tr>\n <th>29</th>\n <td>2019-02-11 18:32:05</td>\n <td>201902</td>\n <td>\u5546\u54c1R</td>\n <td>1800.0</td>\n <td>\u837b\u91ce\u611b</td>\n <td>\u304a\u304e\u306e \u3042\u3044</td>\n <td>F\u5e02</td>\n <td>ogino_ai@example.com</td>\n <td>2017-05-19</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>2969</th>\n <td>2019-02-01 03:48:49</td>\n <td>201902</td>\n <td>\u5546\u54c1O</td>\n <td>1500.0</td>\n <td>\u6cb3\u5185\u3055\u3068\u307f</td>\n <td>\u304b\u308f\u3046\u3061 \u3055\u3068\u307f</td>\n <td>E\u5e02</td>\n <td>kawauchi_satomi@example.com</td>\n <td>2017-01-21</td>\n </tr>\n <tr>\n <th>2970</th>\n <td>2019-03-15 11:17:20</td>\n <td>201903</td>\n <td>\u5546\u54c1L</td>\n <td>1200.0</td>\n <td>\u677e\u5ddd\u7dbe\u5973</td>\n <td>\u307e\u3064\u304b\u308f \u3042\u3084\u3081</td>\n <td>E\u5e02</td>\n <td>matsukawa_ayame@example.com</td>\n <td>2018-07-23</td>\n </tr>\n <tr>\n <th>2971</th>\n <td>2019-02-02 04:52:12</td>\n <td>201902</td>\n <td>\u5546\u54c1S</td>\n <td>1900.0</td>\n <td>\u4e95\u4e0a\u6843\u5b50</td>\n <td>\u3044\u306e\u3046\u3048 \u3082\u3082\u3053</td>\n <td>F\u5e02</td>\n <td>inoue_momoko@example.com</td>\n <td>2018-06-18</td>\n </tr>\n <tr>\n <th>2972</th>\n <td>2019-04-30 19:47:44</td>\n <td>201904</td>\n <td>\u5546\u54c1R</td>\n <td>1800.0</td>\n <td>\u6749\u4e0b\u609f\u5fd7</td>\n <td>\u3059\u304e\u3057\u305f \u3055\u3068\u3057</td>\n <td>E\u5e02</td>\n <td>sugishita_satoshi@example.com</td>\n <td>2018-02-17</td>\n </tr>\n <tr>\n <th>2973</th>\n <td>2019-07-13 21:38:11</td>\n <td>201907</td>\n <td>\u5546\u54c1A</td>\n <td>100.0</td>\n <td>\u5927\u5730\u793c\u5b50</td>\n <td>\u304a\u304a\u3061 \u308c\u3044\u3053</td>\n <td>E\u5e02</td>\n <td>oochi_reiko@example.com</td>\n <td>2017-05-09</td>\n </tr>\n <tr>\n <th>2974</th>\n <td>2019-02-04 17:08:40</td>\n <td>201902</td>\n <td>\u5546\u54c1P</td>\n <td>1600.0</td>\n <td>\u5ca1\u6176\u592a</td>\n <td>\u304a\u304b \u3051\u3044\u305f</td>\n <td>C\u5e02</td>\n <td>oka_keita@example.com</td>\n <td>2018-07-22</td>\n </tr>\n <tr>\n <th>2975</th>\n <td>2019-07-21 10:36:03</td>\n <td>201907</td>\n <td>\u5546\u54c1Y</td>\n <td>2500.0</td>\n <td>\u77e2\u90e8\u7f8e\u5e78</td>\n <td>\u3084\u3079 \u307f\u3086\u304d</td>\n <td>F\u5e02</td>\n <td>yabe_miyuki@example.com</td>\n <td>2017-05-27</td>\n </tr>\n <tr>\n <th>2976</th>\n <td>2019-07-11 00:11:36</td>\n <td>201907</td>\n <td>\u5546\u54c1L</td>\n <td>1200.0</td>\n <td>\u4e95\u672c\u30de\u30b5\u30ab\u30ba</td>\n <td>\u3044\u3082\u3068 \u307e\u3055\u304b\u305a</td>\n <td>C\u5e02</td>\n <td>imoto_masakazu@example.com</td>\n <td>2017-04-06</td>\n </tr>\n <tr>\n <th>2977</th>\n <td>2019-03-02 20:55:58</td>\n <td>201903</td>\n <td>\u5546\u54c1X</td>\n <td>2400.0</td>\n <td>\u5408\u7530\u5149</td>\n <td>\u3042\u3044\u3060 \u3072\u304b\u308b</td>\n <td>D\u5e02</td>\n <td>aida_hikaru@example.com</td>\n <td>2018-05-22</td>\n </tr>\n <tr>\n <th>2978</th>\n <td>2019-04-06 21:20:36</td>\n <td>201904</td>\n <td>\u5546\u54c1N</td>\n <td>1400.0</td>\n <td>\u6d45\u7530\u8ce2\u4e8c</td>\n <td>\u3042\u3055\u3060 \u3051\u3093\u3058</td>\n <td>C\u5e02</td>\n <td>asada_kenji@example.com</td>\n <td>2018-04-07</td>\n </tr>\n <tr>\n <th>2979</th>\n <td>2019-03-09 17:26:00</td>\n <td>201903</td>\n <td>\u5546\u54c1N</td>\n <td>1400.0</td>\n <td>\u6df1\u7530\u4fe1\u8f14</td>\n <td>\u3075\u304b\u3060 \u3057\u3093\u3059\u3051</td>\n <td>G\u5e02</td>\n <td>fukada_shinsuke@example.com</td>\n <td>2017-07-07</td>\n </tr>\n <tr>\n <th>2980</th>\n <td>2019-07-01 02:15:47</td>\n <td>201907</td>\n <td>\u5546\u54c1P</td>\n <td>1600.0</td>\n <td>\u77e2\u90e8\u7f8e\u5e78</td>\n <td>\u3084\u3079 \u307f\u3086\u304d</td>\n <td>F\u5e02</td>\n <td>yabe_miyuki@example.com</td>\n <td>2017-05-27</td>\n </tr>\n <tr>\n <th>2981</th>\n <td>2019-04-26 22:17:47</td>\n <td>201904</td>\n <td>\u5546\u54c1R</td>\n <td>1800.0</td>\n <td>\u5965\u5149\u6d0b</td>\n <td>\u304a\u304f \u307f\u3064\u3072\u308d</td>\n <td>A\u5e02</td>\n <td>oku_mitsuhiro@example.com</td>\n <td>2018-01-08</td>\n </tr>\n <tr>\n <th>2982</th>\n <td>2019-02-18 16:53:38</td>\n <td>201902</td>\n <td>\u5546\u54c1L</td>\n <td>1200.0</td>\n <td>\u7be0\u5c71\u96c5\u529f</td>\n <td>\u3057\u306e\u3084\u307e \u307e\u3055\u3068\u3057</td>\n <td>B\u5e02</td>\n <td>shinoyama_masatoshi@example.com</td>\n <td>2018-07-02</td>\n </tr>\n <tr>\n <th>2983</th>\n <td>2019-05-09 00:44:58</td>\n <td>201905</td>\n <td>\u5546\u54c1H</td>\n <td>800.0</td>\n <td>\u3055\u3060\u5343\u4f73\u5b50</td>\n <td>\u3055\u3060 \u3061\u304b\u3053</td>\n <td>H\u5e02</td>\n <td>sada_chikako@example.com</td>\n <td>2017-07-01</td>\n </tr>\n <tr>\n <th>2984</th>\n <td>2019-03-06 06:16:05</td>\n <td>201903</td>\n <td>\u5546\u54c1G</td>\n <td>700.0</td>\n <td>\u7b39\u5ddd\u7167\u751f</td>\n <td>\u3055\u3055\u304c\u308f \u3066\u308b\u304a</td>\n <td>F\u5e02</td>\n <td>sasagawa_teruo@example.com</td>\n <td>2017-04-08</td>\n </tr>\n <tr>\n <th>2985</th>\n <td>2019-03-06 22:30:55</td>\n <td>201903</td>\n <td>\u5546\u54c1X</td>\n <td>2400.0</td>\n <td>\u5ca9\u6ca2\u90a3\u5948</td>\n <td>\u3044\u308f\u3055\u308f \u306a\u306a</td>\n <td>H\u5e02</td>\n <td>iwasawa_nana@example.com</td>\n <td>2017-07-13</td>\n </tr>\n <tr>\n <th>2986</th>\n <td>2019-03-12 19:34:15</td>\n <td>201903</td>\n <td>\u5546\u54c1C</td>\n <td>300.0</td>\n <td>\u718a\u5009\u660e\u65e5</td>\n <td>\u304f\u307e\u304f\u3089 \u3081\u3044\u3073</td>\n <td>G\u5e02</td>\n <td>kumakura_meibi@example.com</td>\n <td>2018-05-07</td>\n </tr>\n <tr>\n <th>2987</th>\n <td>2019-01-04 13:05:29</td>\n <td>201901</td>\n <td>\u5546\u54c1K</td>\n <td>1100.0</td>\n <td>\u677e\u8c37\u611b\u5b50</td>\n <td>\u307e\u3064\u305f\u306b \u3042\u3044\u3053</td>\n <td>D\u5e02</td>\n <td>matsutani_aiko@example.com</td>\n <td>2018-07-30</td>\n </tr>\n <tr>\n <th>2988</th>\n <td>2019-01-23 07:08:04</td>\n <td>201901</td>\n <td>\u5546\u54c1R</td>\n <td>1800.0</td>\n <td>\u77e2\u90e8\u60c7</td>\n <td>\u3084\u3079 \u3058\u3085\u3093</td>\n <td>A\u5e02</td>\n <td>yabe_jun@example.com</td>\n <td>2018-05-20</td>\n </tr>\n <tr>\n <th>2989</th>\n <td>2019-05-19 03:20:24</td>\n <td>201905</td>\n <td>\u5546\u54c1M</td>\n <td>1300.0</td>\n <td>\u690d\u6751\u9065</td>\n <td>\u3046\u3048\u3080\u3089 \u306f\u308b\u304b</td>\n <td>A\u5e02</td>\n <td>uemura_haruka@example.com</td>\n <td>2018-01-06</td>\n </tr>\n <tr>\n <th>2990</th>\n <td>2019-07-16 11:34:22</td>\n <td>201907</td>\n <td>\u5546\u54c1O</td>\n <td>1500.0</td>\n <td>\u698e\u672c\u85ab</td>\n <td>\u3048\u306e\u3082\u3068 \u304b\u304a\u308b</td>\n <td>C\u5e02</td>\n <td>enomoto_kaoru@example.com</td>\n <td>2017-06-17</td>\n </tr>\n <tr>\n <th>2991</th>\n <td>2019-02-18 14:36:49</td>\n <td>201902</td>\n <td>\u5546\u54c1W</td>\n <td>2300.0</td>\n <td>\u5c3e\u4e0a\u52dd\u4e45</td>\n <td>\u304a\u304c\u307f \u304b\u3064\u3072\u3055</td>\n <td>D\u5e02</td>\n <td>ogami_katsuhisa@example.com</td>\n <td>2017-07-20</td>\n </tr>\n <tr>\n <th>2992</th>\n <td>2019-07-27 10:13:13</td>\n <td>201907</td>\n <td>\u5546\u54c1C</td>\n <td>300.0</td>\n <td>\u690d\u6728\u6c99\u77e5\u7d75</td>\n <td>\u3046\u3048\u304d \u3055\u3061\u3048</td>\n <td>F\u5e02</td>\n <td>ueki_sachie@example.com</td>\n <td>2018-03-15</td>\n </tr>\n <tr>\n <th>2993</th>\n <td>2019-01-25 03:57:54</td>\n <td>201901</td>\n <td>\u5546\u54c1C</td>\n <td>300.0</td>\n <td>\u767d\u9ce5\u308a\u3048</td>\n <td>\u3057\u3089\u3068\u308a \u308a\u3048</td>\n <td>F\u5e02</td>\n <td>shiratori_rie@example.com</td>\n <td>2017-04-29</td>\n </tr>\n <tr>\n <th>2994</th>\n <td>2019-02-15 02:56:39</td>\n <td>201902</td>\n <td>\u5546\u54c1Y</td>\n <td>2500.0</td>\n <td>\u798f\u5cf6\u53cb\u4e5f</td>\n <td>\u3075\u304f\u3057\u307e \u3068\u3082\u3084</td>\n <td>B\u5e02</td>\n <td>fukushima_tomoya@example.com</td>\n <td>2017-07-01</td>\n </tr>\n <tr>\n <th>2995</th>\n <td>2019-06-22 04:03:43</td>\n <td>201906</td>\n <td>\u5546\u54c1M</td>\n <td>1300.0</td>\n <td>\u5927\u5009\u6643\u53f8</td>\n <td>\u304a\u304a\u304f\u3089 \u3053\u3046\u3058</td>\n <td>E\u5e02</td>\n <td>ookura_kouji@example.com</td>\n <td>2018-03-31</td>\n </tr>\n <tr>\n <th>2996</th>\n <td>2019-03-29 11:14:05</td>\n <td>201903</td>\n <td>\u5546\u54c1Q</td>\n <td>1700.0</td>\n <td>\u5c3e\u5f62\u5c0f\u96c1</td>\n <td>\u304a\u304c\u305f \u3053\u304c\u3093</td>\n <td>B\u5e02</td>\n <td>ogata_kogan@example.com</td>\n <td>2017-03-15</td>\n </tr>\n <tr>\n <th>2997</th>\n <td>2019-07-14 12:56:49</td>\n <td>201907</td>\n <td>\u5546\u54c1H</td>\n <td>800.0</td>\n <td>\u82a6\u7530\u535a\u4e4b</td>\n <td>\u3042\u3057\u3060 \u3072\u308d\u3086\u304d</td>\n <td>E\u5e02</td>\n <td>ashida_hiroyuki@example.com</td>\n <td>2018-07-15</td>\n </tr>\n <tr>\n <th>2998</th>\n <td>2019-07-21 00:31:36</td>\n <td>201907</td>\n <td>\u5546\u54c1D</td>\n <td>400.0</td>\n <td>\u77f3\u7530\u90c1\u6075</td>\n <td>\u3044\u3057\u3060 \u3044\u304f\u3048</td>\n <td>B\u5e02</td>\n <td>ishida_ikue@example.com</td>\n <td>2017-02-05</td>\n </tr>\n </tbody>\n</table>\n<p>2999 rows \u00d7 9 columns</p>\n</div>",
"text/plain": " purchase_date purchase_month item_name item_price \u9867\u5ba2\u540d \\\n0 2019-06-13 18:02:34 201906 \u5546\u54c1A 100.0 \u6df1\u4e95\u83dc\u3005\u7f8e \n1 2019-07-13 13:05:29 201907 \u5546\u54c1S 1900.0 \u6d45\u7530\u8ce2\u4e8c \n2 2019-05-11 19:42:07 201905 \u5546\u54c1A 100.0 \u5357\u90e8\u6176\u4e8c \n3 2019-02-12 23:40:45 201902 \u5546\u54c1Z 2600.0 \u9ebb\u751f\u8389\u7dd2 \n4 2019-04-22 03:09:35 201904 \u5546\u54c1A 100.0 \u5e73\u7530\u9244\u4e8c \n5 2019-03-20 19:16:01 201903 \u5546\u54c1S 1900.0 \u5800\u6c5f\u4f51 \n6 2019-05-18 19:16:53 201905 \u5546\u54c1A 100.0 \u6df1\u4e95\u7167\u751f \n7 2019-04-18 00:14:21 201904 \u5546\u54c1V 2200.0 \u7267\u7530\u73b2\u90a3 \n8 2019-01-10 15:51:01 201901 \u5546\u54c1O 1500.0 \u5800\u5317\u96c5\u5f66 \n9 2019-01-28 10:47:03 201901 \u5546\u54c1A 100.0 \u5927\u5730\u793c\u5b50 \n10 2019-06-21 01:54:35 201906 \u5546\u54c1U 2100.0 \u77e2\u90e8\u60c7 \n11 2019-06-08 11:32:25 201906 \u5546\u54c1L 1200.0 \u5ca1\u7530\u654f\u4e5f \n12 2019-04-08 02:00:44 201904 \u5546\u54c1V 2200.0 \u6d45\u898b\u5e83\u53f8 \n13 2019-06-19 09:50:52 201906 \u5546\u54c1O 1500.0 \u718a\u4e95\u61b2\u53f2 \n14 2019-06-11 12:57:24 201906 \u5546\u54c1A 100.0 \u9ec4\u5ddd\u7530\u535a\u4e4b \n15 2019-04-21 00:11:43 201904 \u5546\u54c1C 300.0 \u5c3e\u5f62\u5c0f\u96c1 \n16 2019-03-28 23:24:46 201903 \u5546\u54c1V 2200.0 \u795e\u539f\u7f8e\u5609 \n17 2019-04-06 12:00:53 201904 \u5546\u54c1I 900.0 \u82e5\u6749\u5fb9 \n18 2019-07-16 05:55:57 201907 \u5546\u54c1R 1800.0 \u77f3\u6e21\u5c0f\u96c1 \n19 2019-07-03 07:04:05 201907 \u5546\u54c1X 2400.0 \u6771\u5149\u535a \n20 2019-07-05 10:49:13 201907 \u5546\u54c1O 1500.0 \u65e5\u91ce\u590f\u5e0c \n21 2019-06-10 19:07:23 201906 \u5546\u54c1G 700.0 \u9ed2\u8c37\u9577\u5229 \n22 2019-07-10 20:28:12 201907 \u5546\u54c1X 2400.0 \u7530\u8fba\u5149\u6d0b \n23 2019-07-10 12:44:10 201907 \u5546\u54c1R 1800.0 \u6238\u585a\u7f8e\u5e78 \n24 2019-02-14 01:30:09 201902 \u5546\u54c1P 1600.0 \u698a\u539f\u3057\u307c\u308a \n25 2019-04-18 01:50:16 201904 \u5546\u54c1Q 1700.0 \u6d45\u7530\u8ce2\u4e8c \n26 2019-05-16 04:45:21 201905 \u5546\u54c1Y 2500.0 \u660e\u77f3\u5bb6\u660e \n27 2019-05-26 10:58:00 201905 \u5546\u54c1P 1600.0 \u624b\u585a\u9032 \n28 2019-01-04 12:05:04 201901 \u5546\u54c1P 1600.0 \u5800\u6c5f\u4f51 \n29 2019-02-11 18:32:05 201902 \u5546\u54c1R 1800.0 \u837b\u91ce\u611b \n... ... ... ... ... ... \n2969 2019-02-01 03:48:49 201902 \u5546\u54c1O 1500.0 \u6cb3\u5185\u3055\u3068\u307f \n2970 2019-03-15 11:17:20 201903 \u5546\u54c1L 1200.0 \u677e\u5ddd\u7dbe\u5973 \n2971 2019-02-02 04:52:12 201902 \u5546\u54c1S 1900.0 \u4e95\u4e0a\u6843\u5b50 \n2972 2019-04-30 19:47:44 201904 \u5546\u54c1R 1800.0 \u6749\u4e0b\u609f\u5fd7 \n2973 2019-07-13 21:38:11 201907 \u5546\u54c1A 100.0 \u5927\u5730\u793c\u5b50 \n2974 2019-02-04 17:08:40 201902 \u5546\u54c1P 1600.0 \u5ca1\u6176\u592a \n2975 2019-07-21 10:36:03 201907 \u5546\u54c1Y 2500.0 \u77e2\u90e8\u7f8e\u5e78 \n2976 2019-07-11 00:11:36 201907 \u5546\u54c1L 1200.0 \u4e95\u672c\u30de\u30b5\u30ab\u30ba \n2977 2019-03-02 20:55:58 201903 \u5546\u54c1X 2400.0 \u5408\u7530\u5149 \n2978 2019-04-06 21:20:36 201904 \u5546\u54c1N 1400.0 \u6d45\u7530\u8ce2\u4e8c \n2979 2019-03-09 17:26:00 201903 \u5546\u54c1N 1400.0 \u6df1\u7530\u4fe1\u8f14 \n2980 2019-07-01 02:15:47 201907 \u5546\u54c1P 1600.0 \u77e2\u90e8\u7f8e\u5e78 \n2981 2019-04-26 22:17:47 201904 \u5546\u54c1R 1800.0 \u5965\u5149\u6d0b \n2982 2019-02-18 16:53:38 201902 \u5546\u54c1L 1200.0 \u7be0\u5c71\u96c5\u529f \n2983 2019-05-09 00:44:58 201905 \u5546\u54c1H 800.0 \u3055\u3060\u5343\u4f73\u5b50 \n2984 2019-03-06 06:16:05 201903 \u5546\u54c1G 700.0 \u7b39\u5ddd\u7167\u751f \n2985 2019-03-06 22:30:55 201903 \u5546\u54c1X 2400.0 \u5ca9\u6ca2\u90a3\u5948 \n2986 2019-03-12 19:34:15 201903 \u5546\u54c1C 300.0 \u718a\u5009\u660e\u65e5 \n2987 2019-01-04 13:05:29 201901 \u5546\u54c1K 1100.0 \u677e\u8c37\u611b\u5b50 \n2988 2019-01-23 07:08:04 201901 \u5546\u54c1R 1800.0 \u77e2\u90e8\u60c7 \n2989 2019-05-19 03:20:24 201905 \u5546\u54c1M 1300.0 \u690d\u6751\u9065 \n2990 2019-07-16 11:34:22 201907 \u5546\u54c1O 1500.0 \u698e\u672c\u85ab \n2991 2019-02-18 14:36:49 201902 \u5546\u54c1W 2300.0 \u5c3e\u4e0a\u52dd\u4e45 \n2992 2019-07-27 10:13:13 201907 \u5546\u54c1C 300.0 \u690d\u6728\u6c99\u77e5\u7d75 \n2993 2019-01-25 03:57:54 201901 \u5546\u54c1C 300.0 \u767d\u9ce5\u308a\u3048 \n2994 2019-02-15 02:56:39 201902 \u5546\u54c1Y 2500.0 \u798f\u5cf6\u53cb\u4e5f \n2995 2019-06-22 04:03:43 201906 \u5546\u54c1M 1300.0 \u5927\u5009\u6643\u53f8 \n2996 2019-03-29 11:14:05 201903 \u5546\u54c1Q 1700.0 \u5c3e\u5f62\u5c0f\u96c1 \n2997 2019-07-14 12:56:49 201907 \u5546\u54c1H 800.0 \u82a6\u7530\u535a\u4e4b \n2998 2019-07-21 00:31:36 201907 \u5546\u54c1D 400.0 \u77f3\u7530\u90c1\u6075 \n\n \u304b\u306a \u5730\u57df \u30e1\u30fc\u30eb\u30a2\u30c9\u30ec\u30b9 \u767b\u9332\u65e5 \n0 \u3075\u304b\u3044 \u306a\u306a\u307f C\u5e02 fukai_nanami@example.com 2017-01-26 \n1 \u3042\u3055\u3060 \u3051\u3093\u3058 C\u5e02 asada_kenji@example.com 2018-04-07 \n2 \u306a\u3093\u3076 \u3051\u3044\u3058 A\u5e02 nannbu_keiji@example.com 2018-06-19 \n3 \u3042\u305d\u3046 \u308a\u304a D\u5e02 asou_rio@example.com 2018-07-22 \n4 \u3072\u3089\u305f \u3066\u3064\u3058 D\u5e02 hirata_tetsuji@example.com 2017-06-07 \n5 \u307b\u308a\u3048 \u305f\u3059\u304f H\u5e02 horie_tasuku@example.com 2018-05-14 \n6 \u3075\u304b\u3044 \u3066\u308b\u304a A\u5e02 fukai_teruo@example.com 2018-02-21 \n7 \u307e\u304d\u305f \u308c\u306a A\u5e02 makita_rena@example.com 2017-05-13 \n8 \u307b\u308a\u304d\u305f \u307e\u3055\u3072\u3053 H\u5e02 horikita_masahiko@example.com 2017-05-05 \n9 \u304a\u304a\u3061 \u308c\u3044\u3053 E\u5e02 oochi_reiko@example.com 2017-05-09 \n10 \u3084\u3079 \u3058\u3085\u3093 A\u5e02 yabe_jun@example.com 2018-05-20 \n11 \u304a\u304b\u3060 \u3068\u3057\u3084 E\u5e02 okada_toshiya@example.com 2017-02-18 \n12 \u3042\u3055\u307f \u3053\u3046\u3058 D\u5e02 asami_kouji@example.com 2018-06-05 \n13 \u304f\u307e\u3044 \u306e\u308a\u3072\u3068 A\u5e02 kumai_norihito@example.com 2017-03-29 \n14 \u304d\u304b\u308f\u3060 \u3072\u308d\u3086\u304d C\u5e02 kikawada_hiroyuki@example.com 2018-02-14 \n15 \u304a\u304c\u305f \u3053\u304c\u3093 B\u5e02 ogata_kogan@example.com 2017-03-15 \n16 \u304b\u3093\u3070\u3089 \u307f\u304b D\u5e02 kannbara_mika@example.com 2017-03-23 \n17 \u308f\u304b\u3059\u304e \u3068\u304a\u308b G\u5e02 wakasugi_tohru@example.com 2017-03-26 \n18 \u3044\u3057\u308f\u305f\u308a \u3053\u304c\u3093 D\u5e02 ishiwatari_kogan@example.com 2017-07-28 \n19 \u3072\u304c\u3057 \u307f\u3064\u3072\u308d A\u5e02 higashi_mitsuhiro@example.com 2018-02-06 \n20 \u3072\u306e \u306a\u3064\u304d A\u5e02 hino_natsuki@example.com 2017-05-23 \n21 \u304f\u308d\u305f\u306b \u306a\u304c\u3068\u3057 A\u5e02 kurotani_nagatoshi@example.com 2017-04-27 \n22 \u305f\u306a\u3079 \u307f\u3064\u3072\u308d A\u5e02 tanabe_mitsuhiro@example.com 2018-07-04 \n23 \u3068\u3065\u304b \u307f\u3086\u304d H\u5e02 toduka_miyuki@example.com 2017-01-30 \n24 \u3055\u304b\u304d\u3070\u3089 \u3057\u307c\u308a D\u5e02 sakakibara_shibori@example.com 2017-03-13 \n25 \u3042\u3055\u3060 \u3051\u3093\u3058 C\u5e02 asada_kenji@example.com 2018-04-07 \n26 \u3042\u304b\u3057\u3084 \u3042\u304d\u3089 B\u5e02 akashiya_akira@example.com 2018-02-13 \n27 \u3066\u3065\u304b \u3059\u3059\u3080 A\u5e02 teduka_susumu@example.com 2018-05-01 \n28 \u307b\u308a\u3048 \u305f\u3059\u304f H\u5e02 horie_tasuku@example.com 2018-05-14 \n29 \u304a\u304e\u306e \u3042\u3044 F\u5e02 ogino_ai@example.com 2017-05-19 \n... ... .. ... ... \n2969 \u304b\u308f\u3046\u3061 \u3055\u3068\u307f E\u5e02 kawauchi_satomi@example.com 2017-01-21 \n2970 \u307e\u3064\u304b\u308f \u3042\u3084\u3081 E\u5e02 matsukawa_ayame@example.com 2018-07-23 \n2971 \u3044\u306e\u3046\u3048 \u3082\u3082\u3053 F\u5e02 inoue_momoko@example.com 2018-06-18 \n2972 \u3059\u304e\u3057\u305f \u3055\u3068\u3057 E\u5e02 sugishita_satoshi@example.com 2018-02-17 \n2973 \u304a\u304a\u3061 \u308c\u3044\u3053 E\u5e02 oochi_reiko@example.com 2017-05-09 \n2974 \u304a\u304b \u3051\u3044\u305f C\u5e02 oka_keita@example.com 2018-07-22 \n2975 \u3084\u3079 \u307f\u3086\u304d F\u5e02 yabe_miyuki@example.com 2017-05-27 \n2976 \u3044\u3082\u3068 \u307e\u3055\u304b\u305a C\u5e02 imoto_masakazu@example.com 2017-04-06 \n2977 \u3042\u3044\u3060 \u3072\u304b\u308b D\u5e02 aida_hikaru@example.com 2018-05-22 \n2978 \u3042\u3055\u3060 \u3051\u3093\u3058 C\u5e02 asada_kenji@example.com 2018-04-07 \n2979 \u3075\u304b\u3060 \u3057\u3093\u3059\u3051 G\u5e02 fukada_shinsuke@example.com 2017-07-07 \n2980 \u3084\u3079 \u307f\u3086\u304d F\u5e02 yabe_miyuki@example.com 2017-05-27 \n2981 \u304a\u304f \u307f\u3064\u3072\u308d A\u5e02 oku_mitsuhiro@example.com 2018-01-08 \n2982 \u3057\u306e\u3084\u307e \u307e\u3055\u3068\u3057 B\u5e02 shinoyama_masatoshi@example.com 2018-07-02 \n2983 \u3055\u3060 \u3061\u304b\u3053 H\u5e02 sada_chikako@example.com 2017-07-01 \n2984 \u3055\u3055\u304c\u308f \u3066\u308b\u304a F\u5e02 sasagawa_teruo@example.com 2017-04-08 \n2985 \u3044\u308f\u3055\u308f \u306a\u306a H\u5e02 iwasawa_nana@example.com 2017-07-13 \n2986 \u304f\u307e\u304f\u3089 \u3081\u3044\u3073 G\u5e02 kumakura_meibi@example.com 2018-05-07 \n2987 \u307e\u3064\u305f\u306b \u3042\u3044\u3053 D\u5e02 matsutani_aiko@example.com 2018-07-30 \n2988 \u3084\u3079 \u3058\u3085\u3093 A\u5e02 yabe_jun@example.com 2018-05-20 \n2989 \u3046\u3048\u3080\u3089 \u306f\u308b\u304b A\u5e02 uemura_haruka@example.com 2018-01-06 \n2990 \u3048\u306e\u3082\u3068 \u304b\u304a\u308b C\u5e02 enomoto_kaoru@example.com 2017-06-17 \n2991 \u304a\u304c\u307f \u304b\u3064\u3072\u3055 D\u5e02 ogami_katsuhisa@example.com 2017-07-20 \n2992 \u3046\u3048\u304d \u3055\u3061\u3048 F\u5e02 ueki_sachie@example.com 2018-03-15 \n2993 \u3057\u3089\u3068\u308a \u308a\u3048 F\u5e02 shiratori_rie@example.com 2017-04-29 \n2994 \u3075\u304f\u3057\u307e \u3068\u3082\u3084 B\u5e02 fukushima_tomoya@example.com 2017-07-01 \n2995 \u304a\u304a\u304f\u3089 \u3053\u3046\u3058 E\u5e02 ookura_kouji@example.com 2018-03-31 \n2996 \u304a\u304c\u305f \u3053\u304c\u3093 B\u5e02 ogata_kogan@example.com 2017-03-15 \n2997 \u3042\u3057\u3060 \u3072\u308d\u3086\u304d E\u5e02 ashida_hiroyuki@example.com 2018-07-15 \n2998 \u3044\u3057\u3060 \u3044\u304f\u3048 B\u5e02 ishida_ikue@example.com 2017-02-05 \n\n[2999 rows x 9 columns]"
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "dump_data = join_data[[\"purchase_date\", \"purchase_month\", \"item_name\", \"item_price\", \"\u9867\u5ba2\u540d\", \"\u304b\u306a\", \"\u5730\u57df\", \"\u30e1\u30fc\u30eb\u30a2\u30c9\u30ec\u30b9\", \"\u767b\u9332\u65e5\"]]\ndump_data"
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": "dump_data.to_csv(\"dump_data.csv\", index=False)"
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "dump_data.csv\r\n"
}
],
"source": "!ls"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "\u30c7\u30fc\u30bf\u96c6\u8a08"
},
{
"cell_type": "code",
"execution_count": 64,
"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>purchase_date</th>\n <th>purchase_month</th>\n <th>item_name</th>\n <th>item_price</th>\n <th>\u9867\u5ba2\u540d</th>\n <th>\u304b\u306a</th>\n <th>\u5730\u57df</th>\n <th>\u30e1\u30fc\u30eb\u30a2\u30c9\u30ec\u30b9</th>\n <th>\u767b\u9332\u65e5</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2019-06-13 18:02:34</td>\n <td>201906</td>\n <td>\u5546\u54c1A</td>\n <td>100.0</td>\n <td>\u6df1\u4e95\u83dc\u3005\u7f8e</td>\n <td>\u3075\u304b\u3044 \u306a\u306a\u307f</td>\n <td>C\u5e02</td>\n <td>fukai_nanami@example.com</td>\n <td>2017-01-26 00:00:00</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2019-07-13 13:05:29</td>\n <td>201907</td>\n <td>\u5546\u54c1S</td>\n <td>1900.0</td>\n <td>\u6d45\u7530\u8ce2\u4e8c</td>\n <td>\u3042\u3055\u3060 \u3051\u3093\u3058</td>\n <td>C\u5e02</td>\n <td>asada_kenji@example.com</td>\n <td>2018-04-07 00:00:00</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2019-05-11 19:42:07</td>\n <td>201905</td>\n <td>\u5546\u54c1A</td>\n <td>100.0</td>\n <td>\u5357\u90e8\u6176\u4e8c</td>\n <td>\u306a\u3093\u3076 \u3051\u3044\u3058</td>\n <td>A\u5e02</td>\n <td>nannbu_keiji@example.com</td>\n <td>2018-06-19 00:00:00</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2019-02-12 23:40:45</td>\n <td>201902</td>\n <td>\u5546\u54c1Z</td>\n <td>2600.0</td>\n <td>\u9ebb\u751f\u8389\u7dd2</td>\n <td>\u3042\u305d\u3046 \u308a\u304a</td>\n <td>D\u5e02</td>\n <td>asou_rio@example.com</td>\n <td>2018-07-22 00:00:00</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2019-04-22 03:09:35</td>\n <td>201904</td>\n <td>\u5546\u54c1A</td>\n <td>100.0</td>\n <td>\u5e73\u7530\u9244\u4e8c</td>\n <td>\u3072\u3089\u305f \u3066\u3064\u3058</td>\n <td>D\u5e02</td>\n <td>hirata_tetsuji@example.com</td>\n <td>2017-06-07 00:00:00</td>\n </tr>\n <tr>\n <th>5</th>\n <td>2019-03-20 19:16:01</td>\n <td>201903</td>\n <td>\u5546\u54c1S</td>\n <td>1900.0</td>\n <td>\u5800\u6c5f\u4f51</td>\n <td>\u307b\u308a\u3048 \u305f\u3059\u304f</td>\n <td>H\u5e02</td>\n <td>horie_tasuku@example.com</td>\n <td>2018-05-14 00:00:00</td>\n </tr>\n <tr>\n <th>6</th>\n <td>2019-05-18 19:16:53</td>\n <td>201905</td>\n <td>\u5546\u54c1A</td>\n <td>100.0</td>\n <td>\u6df1\u4e95\u7167\u751f</td>\n <td>\u3075\u304b\u3044 \u3066\u308b\u304a</td>\n <td>A\u5e02</td>\n <td>fukai_teruo@example.com</td>\n <td>2018-02-21 00:00:00</td>\n </tr>\n <tr>\n <th>7</th>\n <td>2019-04-18 00:14:21</td>\n <td>201904</td>\n <td>\u5546\u54c1V</td>\n <td>2200.0</td>\n <td>\u7267\u7530\u73b2\u90a3</td>\n <td>\u307e\u304d\u305f \u308c\u306a</td>\n <td>A\u5e02</td>\n <td>makita_rena@example.com</td>\n <td>2017-05-13 00:00:00</td>\n </tr>\n <tr>\n <th>8</th>\n <td>2019-01-10 15:51:01</td>\n <td>201901</td>\n <td>\u5546\u54c1O</td>\n <td>1500.0</td>\n <td>\u5800\u5317\u96c5\u5f66</td>\n <td>\u307b\u308a\u304d\u305f \u307e\u3055\u3072\u3053</td>\n <td>H\u5e02</td>\n <td>horikita_masahiko@example.com</td>\n <td>2017-05-05 00:00:00</td>\n </tr>\n <tr>\n <th>9</th>\n <td>2019-01-28 10:47:03</td>\n <td>201901</td>\n <td>\u5546\u54c1A</td>\n <td>100.0</td>\n <td>\u5927\u5730\u793c\u5b50</td>\n <td>\u304a\u304a\u3061 \u308c\u3044\u3053</td>\n <td>E\u5e02</td>\n <td>oochi_reiko@example.com</td>\n <td>2017-05-09 00:00:00</td>\n </tr>\n <tr>\n <th>10</th>\n <td>2019-06-21 01:54:35</td>\n <td>201906</td>\n <td>\u5546\u54c1U</td>\n <td>2100.0</td>\n <td>\u77e2\u90e8\u60c7</td>\n <td>\u3084\u3079 \u3058\u3085\u3093</td>\n <td>A\u5e02</td>\n <td>yabe_jun@example.com</td>\n <td>2018-05-20 00:00:00</td>\n </tr>\n <tr>\n <th>11</th>\n <td>2019-06-08 11:32:25</td>\n <td>201906</td>\n <td>\u5546\u54c1L</td>\n <td>1200.0</td>\n <td>\u5ca1\u7530\u654f\u4e5f</td>\n <td>\u304a\u304b\u3060 \u3068\u3057\u3084</td>\n <td>E\u5e02</td>\n <td>okada_toshiya@example.com</td>\n <td>2017-02-18 00:00:00</td>\n </tr>\n <tr>\n <th>12</th>\n <td>2019-04-08 02:00:44</td>\n <td>201904</td>\n <td>\u5546\u54c1V</td>\n <td>2200.0</td>\n <td>\u6d45\u898b\u5e83\u53f8</td>\n <td>\u3042\u3055\u307f \u3053\u3046\u3058</td>\n <td>D\u5e02</td>\n <td>asami_kouji@example.com</td>\n <td>2018-06-05 00:00:00</td>\n </tr>\n <tr>\n <th>13</th>\n <td>2019-06-19 09:50:52</td>\n <td>201906</td>\n <td>\u5546\u54c1O</td>\n <td>1500.0</td>\n <td>\u718a\u4e95\u61b2\u53f2</td>\n <td>\u304f\u307e\u3044 \u306e\u308a\u3072\u3068</td>\n <td>A\u5e02</td>\n <td>kumai_norihito@example.com</td>\n <td>2017-03-29 00:00:00</td>\n </tr>\n <tr>\n <th>14</th>\n <td>2019-06-11 12:57:24</td>\n <td>201906</td>\n <td>\u5546\u54c1A</td>\n <td>100.0</td>\n <td>\u9ec4\u5ddd\u7530\u535a\u4e4b</td>\n <td>\u304d\u304b\u308f\u3060 \u3072\u308d\u3086\u304d</td>\n <td>C\u5e02</td>\n <td>kikawada_hiroyuki@example.com</td>\n <td>2018-02-14 00:00:00</td>\n </tr>\n <tr>\n <th>15</th>\n <td>2019-04-21 00:11:43</td>\n <td>201904</td>\n <td>\u5546\u54c1C</td>\n <td>300.0</td>\n <td>\u5c3e\u5f62\u5c0f\u96c1</td>\n <td>\u304a\u304c\u305f \u3053\u304c\u3093</td>\n <td>B\u5e02</td>\n <td>ogata_kogan@example.com</td>\n <td>2017-03-15 00:00:00</td>\n </tr>\n <tr>\n <th>16</th>\n <td>2019-03-28 23:24:46</td>\n <td>201903</td>\n <td>\u5546\u54c1V</td>\n <td>2200.0</td>\n <td>\u795e\u539f\u7f8e\u5609</td>\n <td>\u304b\u3093\u3070\u3089 \u307f\u304b</td>\n <td>D\u5e02</td>\n <td>kannbara_mika@example.com</td>\n <td>2017-03-23 00:00:00</td>\n </tr>\n <tr>\n <th>17</th>\n <td>2019-04-06 12:00:53</td>\n <td>201904</td>\n <td>\u5546\u54c1I</td>\n <td>900.0</td>\n <td>\u82e5\u6749\u5fb9</td>\n <td>\u308f\u304b\u3059\u304e \u3068\u304a\u308b</td>\n <td>G\u5e02</td>\n <td>wakasugi_tohru@example.com</td>\n <td>2017-03-26 00:00:00</td>\n </tr>\n <tr>\n <th>18</th>\n <td>2019-07-16 05:55:57</td>\n <td>201907</td>\n <td>\u5546\u54c1R</td>\n <td>1800.0</td>\n <td>\u77f3\u6e21\u5c0f\u96c1</td>\n <td>\u3044\u3057\u308f\u305f\u308a \u3053\u304c\u3093</td>\n <td>D\u5e02</td>\n <td>ishiwatari_kogan@example.com</td>\n <td>2017-07-28 00:00:00</td>\n </tr>\n <tr>\n <th>19</th>\n <td>2019-07-03 07:04:05</td>\n <td>201907</td>\n <td>\u5546\u54c1X</td>\n <td>2400.0</td>\n <td>\u6771\u5149\u535a</td>\n <td>\u3072\u304c\u3057 \u307f\u3064\u3072\u308d</td>\n <td>A\u5e02</td>\n <td>higashi_mitsuhiro@example.com</td>\n <td>2018-02-06 00:00:00</td>\n </tr>\n <tr>\n <th>20</th>\n <td>2019-07-05 10:49:13</td>\n <td>201907</td>\n <td>\u5546\u54c1O</td>\n <td>1500.0</td>\n <td>\u65e5\u91ce\u590f\u5e0c</td>\n <td>\u3072\u306e \u306a\u3064\u304d</td>\n <td>A\u5e02</td>\n <td>hino_natsuki@example.com</td>\n <td>2017-05-23 00:00:00</td>\n </tr>\n <tr>\n <th>21</th>\n <td>2019-06-10 19:07:23</td>\n <td>201906</td>\n <td>\u5546\u54c1G</td>\n <td>700.0</td>\n <td>\u9ed2\u8c37\u9577\u5229</td>\n <td>\u304f\u308d\u305f\u306b \u306a\u304c\u3068\u3057</td>\n <td>A\u5e02</td>\n <td>kurotani_nagatoshi@example.com</td>\n <td>2017-04-27 00:00:00</td>\n </tr>\n <tr>\n <th>22</th>\n <td>2019-07-10 20:28:12</td>\n <td>201907</td>\n <td>\u5546\u54c1X</td>\n <td>2400.0</td>\n <td>\u7530\u8fba\u5149\u6d0b</td>\n <td>\u305f\u306a\u3079 \u307f\u3064\u3072\u308d</td>\n <td>A\u5e02</td>\n <td>tanabe_mitsuhiro@example.com</td>\n <td>2018-07-04 00:00:00</td>\n </tr>\n <tr>\n <th>23</th>\n <td>2019-07-10 12:44:10</td>\n <td>201907</td>\n <td>\u5546\u54c1R</td>\n <td>1800.0</td>\n <td>\u6238\u585a\u7f8e\u5e78</td>\n <td>\u3068\u3065\u304b \u307f\u3086\u304d</td>\n <td>H\u5e02</td>\n <td>toduka_miyuki@example.com</td>\n <td>2017-01-30 00:00:00</td>\n </tr>\n <tr>\n <th>24</th>\n <td>2019-02-14 01:30:09</td>\n <td>201902</td>\n <td>\u5546\u54c1P</td>\n <td>1600.0</td>\n <td>\u698a\u539f\u3057\u307c\u308a</td>\n <td>\u3055\u304b\u304d\u3070\u3089 \u3057\u307c\u308a</td>\n <td>D\u5e02</td>\n <td>sakakibara_shibori@example.com</td>\n <td>2017-03-13 00:00:00</td>\n </tr>\n <tr>\n <th>25</th>\n <td>2019-04-18 01:50:16</td>\n <td>201904</td>\n <td>\u5546\u54c1Q</td>\n <td>1700.0</td>\n <td>\u6d45\u7530\u8ce2\u4e8c</td>\n <td>\u3042\u3055\u3060 \u3051\u3093\u3058</td>\n <td>C\u5e02</td>\n <td>asada_kenji@example.com</td>\n <td>2018-04-07 00:00:00</td>\n </tr>\n <tr>\n <th>26</th>\n <td>2019-05-16 04:45:21</td>\n <td>201905</td>\n <td>\u5546\u54c1Y</td>\n <td>2500.0</td>\n <td>\u660e\u77f3\u5bb6\u660e</td>\n <td>\u3042\u304b\u3057\u3084 \u3042\u304d\u3089</td>\n <td>B\u5e02</td>\n <td>akashiya_akira@example.com</td>\n <td>2018-02-13 00:00:00</td>\n </tr>\n <tr>\n <th>27</th>\n <td>2019-05-26 10:58:00</td>\n <td>201905</td>\n <td>\u5546\u54c1P</td>\n <td>1600.0</td>\n <td>\u624b\u585a\u9032</td>\n <td>\u3066\u3065\u304b \u3059\u3059\u3080</td>\n <td>A\u5e02</td>\n <td>teduka_susumu@example.com</td>\n <td>2018-05-01 00:00:00</td>\n </tr>\n <tr>\n <th>28</th>\n <td>2019-01-04 12:05:04</td>\n <td>201901</td>\n <td>\u5546\u54c1P</td>\n <td>1600.0</td>\n <td>\u5800\u6c5f\u4f51</td>\n <td>\u307b\u308a\u3048 \u305f\u3059\u304f</td>\n <td>H\u5e02</td>\n <td>horie_tasuku@example.com</td>\n <td>2018-05-14 00:00:00</td>\n </tr>\n <tr>\n <th>29</th>\n <td>2019-02-11 18:32:05</td>\n <td>201902</td>\n <td>\u5546\u54c1R</td>\n <td>1800.0</td>\n <td>\u837b\u91ce\u611b</td>\n <td>\u304a\u304e\u306e \u3042\u3044</td>\n <td>F\u5e02</td>\n <td>ogino_ai@example.com</td>\n <td>2017-05-19 00:00:00</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>2969</th>\n <td>2019-02-01 03:48:49</td>\n <td>201902</td>\n <td>\u5546\u54c1O</td>\n <td>1500.0</td>\n <td>\u6cb3\u5185\u3055\u3068\u307f</td>\n <td>\u304b\u308f\u3046\u3061 \u3055\u3068\u307f</td>\n <td>E\u5e02</td>\n <td>kawauchi_satomi@example.com</td>\n <td>2017-01-21 00:00:00</td>\n </tr>\n <tr>\n <th>2970</th>\n <td>2019-03-15 11:17:20</td>\n <td>201903</td>\n <td>\u5546\u54c1L</td>\n <td>1200.0</td>\n <td>\u677e\u5ddd\u7dbe\u5973</td>\n <td>\u307e\u3064\u304b\u308f \u3042\u3084\u3081</td>\n <td>E\u5e02</td>\n <td>matsukawa_ayame@example.com</td>\n <td>2018-07-23 00:00:00</td>\n </tr>\n <tr>\n <th>2971</th>\n <td>2019-02-02 04:52:12</td>\n <td>201902</td>\n <td>\u5546\u54c1S</td>\n <td>1900.0</td>\n <td>\u4e95\u4e0a\u6843\u5b50</td>\n <td>\u3044\u306e\u3046\u3048 \u3082\u3082\u3053</td>\n <td>F\u5e02</td>\n <td>inoue_momoko@example.com</td>\n <td>2018-06-18 00:00:00</td>\n </tr>\n <tr>\n <th>2972</th>\n <td>2019-04-30 19:47:44</td>\n <td>201904</td>\n <td>\u5546\u54c1R</td>\n <td>1800.0</td>\n <td>\u6749\u4e0b\u609f\u5fd7</td>\n <td>\u3059\u304e\u3057\u305f \u3055\u3068\u3057</td>\n <td>E\u5e02</td>\n <td>sugishita_satoshi@example.com</td>\n <td>2018-02-17 00:00:00</td>\n </tr>\n <tr>\n <th>2973</th>\n <td>2019-07-13 21:38:11</td>\n <td>201907</td>\n <td>\u5546\u54c1A</td>\n <td>100.0</td>\n <td>\u5927\u5730\u793c\u5b50</td>\n <td>\u304a\u304a\u3061 \u308c\u3044\u3053</td>\n <td>E\u5e02</td>\n <td>oochi_reiko@example.com</td>\n <td>2017-05-09 00:00:00</td>\n </tr>\n <tr>\n <th>2974</th>\n <td>2019-02-04 17:08:40</td>\n <td>201902</td>\n <td>\u5546\u54c1P</td>\n <td>1600.0</td>\n <td>\u5ca1\u6176\u592a</td>\n <td>\u304a\u304b \u3051\u3044\u305f</td>\n <td>C\u5e02</td>\n <td>oka_keita@example.com</td>\n <td>2018-07-22 00:00:00</td>\n </tr>\n <tr>\n <th>2975</th>\n <td>2019-07-21 10:36:03</td>\n <td>201907</td>\n <td>\u5546\u54c1Y</td>\n <td>2500.0</td>\n <td>\u77e2\u90e8\u7f8e\u5e78</td>\n <td>\u3084\u3079 \u307f\u3086\u304d</td>\n <td>F\u5e02</td>\n <td>yabe_miyuki@example.com</td>\n <td>2017-05-27 00:00:00</td>\n </tr>\n <tr>\n <th>2976</th>\n <td>2019-07-11 00:11:36</td>\n <td>201907</td>\n <td>\u5546\u54c1L</td>\n <td>1200.0</td>\n <td>\u4e95\u672c\u30de\u30b5\u30ab\u30ba</td>\n <td>\u3044\u3082\u3068 \u307e\u3055\u304b\u305a</td>\n <td>C\u5e02</td>\n <td>imoto_masakazu@example.com</td>\n <td>2017-04-06 00:00:00</td>\n </tr>\n <tr>\n <th>2977</th>\n <td>2019-03-02 20:55:58</td>\n <td>201903</td>\n <td>\u5546\u54c1X</td>\n <td>2400.0</td>\n <td>\u5408\u7530\u5149</td>\n <td>\u3042\u3044\u3060 \u3072\u304b\u308b</td>\n <td>D\u5e02</td>\n <td>aida_hikaru@example.com</td>\n <td>2018-05-22 00:00:00</td>\n </tr>\n <tr>\n <th>2978</th>\n <td>2019-04-06 21:20:36</td>\n <td>201904</td>\n <td>\u5546\u54c1N</td>\n <td>1400.0</td>\n <td>\u6d45\u7530\u8ce2\u4e8c</td>\n <td>\u3042\u3055\u3060 \u3051\u3093\u3058</td>\n <td>C\u5e02</td>\n <td>asada_kenji@example.com</td>\n <td>2018-04-07 00:00:00</td>\n </tr>\n <tr>\n <th>2979</th>\n <td>2019-03-09 17:26:00</td>\n <td>201903</td>\n <td>\u5546\u54c1N</td>\n <td>1400.0</td>\n <td>\u6df1\u7530\u4fe1\u8f14</td>\n <td>\u3075\u304b\u3060 \u3057\u3093\u3059\u3051</td>\n <td>G\u5e02</td>\n <td>fukada_shinsuke@example.com</td>\n <td>2017-07-07 00:00:00</td>\n </tr>\n <tr>\n <th>2980</th>\n <td>2019-07-01 02:15:47</td>\n <td>201907</td>\n <td>\u5546\u54c1P</td>\n <td>1600.0</td>\n <td>\u77e2\u90e8\u7f8e\u5e78</td>\n <td>\u3084\u3079 \u307f\u3086\u304d</td>\n <td>F\u5e02</td>\n <td>yabe_miyuki@example.com</td>\n <td>2017-05-27 00:00:00</td>\n </tr>\n <tr>\n <th>2981</th>\n <td>2019-04-26 22:17:47</td>\n <td>201904</td>\n <td>\u5546\u54c1R</td>\n <td>1800.0</td>\n <td>\u5965\u5149\u6d0b</td>\n <td>\u304a\u304f \u307f\u3064\u3072\u308d</td>\n <td>A\u5e02</td>\n <td>oku_mitsuhiro@example.com</td>\n <td>2018-01-08 00:00:00</td>\n </tr>\n <tr>\n <th>2982</th>\n <td>2019-02-18 16:53:38</td>\n <td>201902</td>\n <td>\u5546\u54c1L</td>\n <td>1200.0</td>\n <td>\u7be0\u5c71\u96c5\u529f</td>\n <td>\u3057\u306e\u3084\u307e \u307e\u3055\u3068\u3057</td>\n <td>B\u5e02</td>\n <td>shinoyama_masatoshi@example.com</td>\n <td>2018-07-02 00:00:00</td>\n </tr>\n <tr>\n <th>2983</th>\n <td>2019-05-09 00:44:58</td>\n <td>201905</td>\n <td>\u5546\u54c1H</td>\n <td>800.0</td>\n <td>\u3055\u3060\u5343\u4f73\u5b50</td>\n <td>\u3055\u3060 \u3061\u304b\u3053</td>\n <td>H\u5e02</td>\n <td>sada_chikako@example.com</td>\n <td>2017-07-01 00:00:00</td>\n </tr>\n <tr>\n <th>2984</th>\n <td>2019-03-06 06:16:05</td>\n <td>201903</td>\n <td>\u5546\u54c1G</td>\n <td>700.0</td>\n <td>\u7b39\u5ddd\u7167\u751f</td>\n <td>\u3055\u3055\u304c\u308f \u3066\u308b\u304a</td>\n <td>F\u5e02</td>\n <td>sasagawa_teruo@example.com</td>\n <td>2017-04-08 00:00:00</td>\n </tr>\n <tr>\n <th>2985</th>\n <td>2019-03-06 22:30:55</td>\n <td>201903</td>\n <td>\u5546\u54c1X</td>\n <td>2400.0</td>\n <td>\u5ca9\u6ca2\u90a3\u5948</td>\n <td>\u3044\u308f\u3055\u308f \u306a\u306a</td>\n <td>H\u5e02</td>\n <td>iwasawa_nana@example.com</td>\n <td>2017-07-13 00:00:00</td>\n </tr>\n <tr>\n <th>2986</th>\n <td>2019-03-12 19:34:15</td>\n <td>201903</td>\n <td>\u5546\u54c1C</td>\n <td>300.0</td>\n <td>\u718a\u5009\u660e\u65e5</td>\n <td>\u304f\u307e\u304f\u3089 \u3081\u3044\u3073</td>\n <td>G\u5e02</td>\n <td>kumakura_meibi@example.com</td>\n <td>2018-05-07 00:00:00</td>\n </tr>\n <tr>\n <th>2987</th>\n <td>2019-01-04 13:05:29</td>\n <td>201901</td>\n <td>\u5546\u54c1K</td>\n <td>1100.0</td>\n <td>\u677e\u8c37\u611b\u5b50</td>\n <td>\u307e\u3064\u305f\u306b \u3042\u3044\u3053</td>\n <td>D\u5e02</td>\n <td>matsutani_aiko@example.com</td>\n <td>2018-07-30 00:00:00</td>\n </tr>\n <tr>\n <th>2988</th>\n <td>2019-01-23 07:08:04</td>\n <td>201901</td>\n <td>\u5546\u54c1R</td>\n <td>1800.0</td>\n <td>\u77e2\u90e8\u60c7</td>\n <td>\u3084\u3079 \u3058\u3085\u3093</td>\n <td>A\u5e02</td>\n <td>yabe_jun@example.com</td>\n <td>2018-05-20 00:00:00</td>\n </tr>\n <tr>\n <th>2989</th>\n <td>2019-05-19 03:20:24</td>\n <td>201905</td>\n <td>\u5546\u54c1M</td>\n <td>1300.0</td>\n <td>\u690d\u6751\u9065</td>\n <td>\u3046\u3048\u3080\u3089 \u306f\u308b\u304b</td>\n <td>A\u5e02</td>\n <td>uemura_haruka@example.com</td>\n <td>2018-01-06 00:00:00</td>\n </tr>\n <tr>\n <th>2990</th>\n <td>2019-07-16 11:34:22</td>\n <td>201907</td>\n <td>\u5546\u54c1O</td>\n <td>1500.0</td>\n <td>\u698e\u672c\u85ab</td>\n <td>\u3048\u306e\u3082\u3068 \u304b\u304a\u308b</td>\n <td>C\u5e02</td>\n <td>enomoto_kaoru@example.com</td>\n <td>2017-06-17 00:00:00</td>\n </tr>\n <tr>\n <th>2991</th>\n <td>2019-02-18 14:36:49</td>\n <td>201902</td>\n <td>\u5546\u54c1W</td>\n <td>2300.0</td>\n <td>\u5c3e\u4e0a\u52dd\u4e45</td>\n <td>\u304a\u304c\u307f \u304b\u3064\u3072\u3055</td>\n <td>D\u5e02</td>\n <td>ogami_katsuhisa@example.com</td>\n <td>2017-07-20 00:00:00</td>\n </tr>\n <tr>\n <th>2992</th>\n <td>2019-07-27 10:13:13</td>\n <td>201907</td>\n <td>\u5546\u54c1C</td>\n <td>300.0</td>\n <td>\u690d\u6728\u6c99\u77e5\u7d75</td>\n <td>\u3046\u3048\u304d \u3055\u3061\u3048</td>\n <td>F\u5e02</td>\n <td>ueki_sachie@example.com</td>\n <td>2018-03-15 00:00:00</td>\n </tr>\n <tr>\n <th>2993</th>\n <td>2019-01-25 03:57:54</td>\n <td>201901</td>\n <td>\u5546\u54c1C</td>\n <td>300.0</td>\n <td>\u767d\u9ce5\u308a\u3048</td>\n <td>\u3057\u3089\u3068\u308a \u308a\u3048</td>\n <td>F\u5e02</td>\n <td>shiratori_rie@example.com</td>\n <td>2017-04-29 00:00:00</td>\n </tr>\n <tr>\n <th>2994</th>\n <td>2019-02-15 02:56:39</td>\n <td>201902</td>\n <td>\u5546\u54c1Y</td>\n <td>2500.0</td>\n <td>\u798f\u5cf6\u53cb\u4e5f</td>\n <td>\u3075\u304f\u3057\u307e \u3068\u3082\u3084</td>\n <td>B\u5e02</td>\n <td>fukushima_tomoya@example.com</td>\n <td>2017-07-01 00:00:00</td>\n </tr>\n <tr>\n <th>2995</th>\n <td>2019-06-22 04:03:43</td>\n <td>201906</td>\n <td>\u5546\u54c1M</td>\n <td>1300.0</td>\n <td>\u5927\u5009\u6643\u53f8</td>\n <td>\u304a\u304a\u304f\u3089 \u3053\u3046\u3058</td>\n <td>E\u5e02</td>\n <td>ookura_kouji@example.com</td>\n <td>2018-03-31 00:00:00</td>\n </tr>\n <tr>\n <th>2996</th>\n <td>2019-03-29 11:14:05</td>\n <td>201903</td>\n <td>\u5546\u54c1Q</td>\n <td>1700.0</td>\n <td>\u5c3e\u5f62\u5c0f\u96c1</td>\n <td>\u304a\u304c\u305f \u3053\u304c\u3093</td>\n <td>B\u5e02</td>\n <td>ogata_kogan@example.com</td>\n <td>2017-03-15 00:00:00</td>\n </tr>\n <tr>\n <th>2997</th>\n <td>2019-07-14 12:56:49</td>\n <td>201907</td>\n <td>\u5546\u54c1H</td>\n <td>800.0</td>\n <td>\u82a6\u7530\u535a\u4e4b</td>\n <td>\u3042\u3057\u3060 \u3072\u308d\u3086\u304d</td>\n <td>E\u5e02</td>\n <td>ashida_hiroyuki@example.com</td>\n <td>2018-07-15 00:00:00</td>\n </tr>\n <tr>\n <th>2998</th>\n <td>2019-07-21 00:31:36</td>\n <td>201907</td>\n <td>\u5546\u54c1D</td>\n <td>400.0</td>\n <td>\u77f3\u7530\u90c1\u6075</td>\n <td>\u3044\u3057\u3060 \u3044\u304f\u3048</td>\n <td>B\u5e02</td>\n <td>ishida_ikue@example.com</td>\n <td>2017-02-05 00:00:00</td>\n </tr>\n </tbody>\n</table>\n<p>2999 rows \u00d7 9 columns</p>\n</div>",
"text/plain": " purchase_date purchase_month item_name item_price \u9867\u5ba2\u540d \\\n0 2019-06-13 18:02:34 201906 \u5546\u54c1A 100.0 \u6df1\u4e95\u83dc\u3005\u7f8e \n1 2019-07-13 13:05:29 201907 \u5546\u54c1S 1900.0 \u6d45\u7530\u8ce2\u4e8c \n2 2019-05-11 19:42:07 201905 \u5546\u54c1A 100.0 \u5357\u90e8\u6176\u4e8c \n3 2019-02-12 23:40:45 201902 \u5546\u54c1Z 2600.0 \u9ebb\u751f\u8389\u7dd2 \n4 2019-04-22 03:09:35 201904 \u5546\u54c1A 100.0 \u5e73\u7530\u9244\u4e8c \n5 2019-03-20 19:16:01 201903 \u5546\u54c1S 1900.0 \u5800\u6c5f\u4f51 \n6 2019-05-18 19:16:53 201905 \u5546\u54c1A 100.0 \u6df1\u4e95\u7167\u751f \n7 2019-04-18 00:14:21 201904 \u5546\u54c1V 2200.0 \u7267\u7530\u73b2\u90a3 \n8 2019-01-10 15:51:01 201901 \u5546\u54c1O 1500.0 \u5800\u5317\u96c5\u5f66 \n9 2019-01-28 10:47:03 201901 \u5546\u54c1A 100.0 \u5927\u5730\u793c\u5b50 \n10 2019-06-21 01:54:35 201906 \u5546\u54c1U 2100.0 \u77e2\u90e8\u60c7 \n11 2019-06-08 11:32:25 201906 \u5546\u54c1L 1200.0 \u5ca1\u7530\u654f\u4e5f \n12 2019-04-08 02:00:44 201904 \u5546\u54c1V 2200.0 \u6d45\u898b\u5e83\u53f8 \n13 2019-06-19 09:50:52 201906 \u5546\u54c1O 1500.0 \u718a\u4e95\u61b2\u53f2 \n14 2019-06-11 12:57:24 201906 \u5546\u54c1A 100.0 \u9ec4\u5ddd\u7530\u535a\u4e4b \n15 2019-04-21 00:11:43 201904 \u5546\u54c1C 300.0 \u5c3e\u5f62\u5c0f\u96c1 \n16 2019-03-28 23:24:46 201903 \u5546\u54c1V 2200.0 \u795e\u539f\u7f8e\u5609 \n17 2019-04-06 12:00:53 201904 \u5546\u54c1I 900.0 \u82e5\u6749\u5fb9 \n18 2019-07-16 05:55:57 201907 \u5546\u54c1R 1800.0 \u77f3\u6e21\u5c0f\u96c1 \n19 2019-07-03 07:04:05 201907 \u5546\u54c1X 2400.0 \u6771\u5149\u535a \n20 2019-07-05 10:49:13 201907 \u5546\u54c1O 1500.0 \u65e5\u91ce\u590f\u5e0c \n21 2019-06-10 19:07:23 201906 \u5546\u54c1G 700.0 \u9ed2\u8c37\u9577\u5229 \n22 2019-07-10 20:28:12 201907 \u5546\u54c1X 2400.0 \u7530\u8fba\u5149\u6d0b \n23 2019-07-10 12:44:10 201907 \u5546\u54c1R 1800.0 \u6238\u585a\u7f8e\u5e78 \n24 2019-02-14 01:30:09 201902 \u5546\u54c1P 1600.0 \u698a\u539f\u3057\u307c\u308a \n25 2019-04-18 01:50:16 201904 \u5546\u54c1Q 1700.0 \u6d45\u7530\u8ce2\u4e8c \n26 2019-05-16 04:45:21 201905 \u5546\u54c1Y 2500.0 \u660e\u77f3\u5bb6\u660e \n27 2019-05-26 10:58:00 201905 \u5546\u54c1P 1600.0 \u624b\u585a\u9032 \n28 2019-01-04 12:05:04 201901 \u5546\u54c1P 1600.0 \u5800\u6c5f\u4f51 \n29 2019-02-11 18:32:05 201902 \u5546\u54c1R 1800.0 \u837b\u91ce\u611b \n... ... ... ... ... ... \n2969 2019-02-01 03:48:49 201902 \u5546\u54c1O 1500.0 \u6cb3\u5185\u3055\u3068\u307f \n2970 2019-03-15 11:17:20 201903 \u5546\u54c1L 1200.0 \u677e\u5ddd\u7dbe\u5973 \n2971 2019-02-02 04:52:12 201902 \u5546\u54c1S 1900.0 \u4e95\u4e0a\u6843\u5b50 \n2972 2019-04-30 19:47:44 201904 \u5546\u54c1R 1800.0 \u6749\u4e0b\u609f\u5fd7 \n2973 2019-07-13 21:38:11 201907 \u5546\u54c1A 100.0 \u5927\u5730\u793c\u5b50 \n2974 2019-02-04 17:08:40 201902 \u5546\u54c1P 1600.0 \u5ca1\u6176\u592a \n2975 2019-07-21 10:36:03 201907 \u5546\u54c1Y 2500.0 \u77e2\u90e8\u7f8e\u5e78 \n2976 2019-07-11 00:11:36 201907 \u5546\u54c1L 1200.0 \u4e95\u672c\u30de\u30b5\u30ab\u30ba \n2977 2019-03-02 20:55:58 201903 \u5546\u54c1X 2400.0 \u5408\u7530\u5149 \n2978 2019-04-06 21:20:36 201904 \u5546\u54c1N 1400.0 \u6d45\u7530\u8ce2\u4e8c \n2979 2019-03-09 17:26:00 201903 \u5546\u54c1N 1400.0 \u6df1\u7530\u4fe1\u8f14 \n2980 2019-07-01 02:15:47 201907 \u5546\u54c1P 1600.0 \u77e2\u90e8\u7f8e\u5e78 \n2981 2019-04-26 22:17:47 201904 \u5546\u54c1R 1800.0 \u5965\u5149\u6d0b \n2982 2019-02-18 16:53:38 201902 \u5546\u54c1L 1200.0 \u7be0\u5c71\u96c5\u529f \n2983 2019-05-09 00:44:58 201905 \u5546\u54c1H 800.0 \u3055\u3060\u5343\u4f73\u5b50 \n2984 2019-03-06 06:16:05 201903 \u5546\u54c1G 700.0 \u7b39\u5ddd\u7167\u751f \n2985 2019-03-06 22:30:55 201903 \u5546\u54c1X 2400.0 \u5ca9\u6ca2\u90a3\u5948 \n2986 2019-03-12 19:34:15 201903 \u5546\u54c1C 300.0 \u718a\u5009\u660e\u65e5 \n2987 2019-01-04 13:05:29 201901 \u5546\u54c1K 1100.0 \u677e\u8c37\u611b\u5b50 \n2988 2019-01-23 07:08:04 201901 \u5546\u54c1R 1800.0 \u77e2\u90e8\u60c7 \n2989 2019-05-19 03:20:24 201905 \u5546\u54c1M 1300.0 \u690d\u6751\u9065 \n2990 2019-07-16 11:34:22 201907 \u5546\u54c1O 1500.0 \u698e\u672c\u85ab \n2991 2019-02-18 14:36:49 201902 \u5546\u54c1W 2300.0 \u5c3e\u4e0a\u52dd\u4e45 \n2992 2019-07-27 10:13:13 201907 \u5546\u54c1C 300.0 \u690d\u6728\u6c99\u77e5\u7d75 \n2993 2019-01-25 03:57:54 201901 \u5546\u54c1C 300.0 \u767d\u9ce5\u308a\u3048 \n2994 2019-02-15 02:56:39 201902 \u5546\u54c1Y 2500.0 \u798f\u5cf6\u53cb\u4e5f \n2995 2019-06-22 04:03:43 201906 \u5546\u54c1M 1300.0 \u5927\u5009\u6643\u53f8 \n2996 2019-03-29 11:14:05 201903 \u5546\u54c1Q 1700.0 \u5c3e\u5f62\u5c0f\u96c1 \n2997 2019-07-14 12:56:49 201907 \u5546\u54c1H 800.0 \u82a6\u7530\u535a\u4e4b \n2998 2019-07-21 00:31:36 201907 \u5546\u54c1D 400.0 \u77f3\u7530\u90c1\u6075 \n\n \u304b\u306a \u5730\u57df \u30e1\u30fc\u30eb\u30a2\u30c9\u30ec\u30b9 \u767b\u9332\u65e5 \n0 \u3075\u304b\u3044 \u306a\u306a\u307f C\u5e02 fukai_nanami@example.com 2017-01-26 00:00:00 \n1 \u3042\u3055\u3060 \u3051\u3093\u3058 C\u5e02 asada_kenji@example.com 2018-04-07 00:00:00 \n2 \u306a\u3093\u3076 \u3051\u3044\u3058 A\u5e02 nannbu_keiji@example.com 2018-06-19 00:00:00 \n3 \u3042\u305d\u3046 \u308a\u304a D\u5e02 asou_rio@example.com 2018-07-22 00:00:00 \n4 \u3072\u3089\u305f \u3066\u3064\u3058 D\u5e02 hirata_tetsuji@example.com 2017-06-07 00:00:00 \n5 \u307b\u308a\u3048 \u305f\u3059\u304f H\u5e02 horie_tasuku@example.com 2018-05-14 00:00:00 \n6 \u3075\u304b\u3044 \u3066\u308b\u304a A\u5e02 fukai_teruo@example.com 2018-02-21 00:00:00 \n7 \u307e\u304d\u305f \u308c\u306a A\u5e02 makita_rena@example.com 2017-05-13 00:00:00 \n8 \u307b\u308a\u304d\u305f \u307e\u3055\u3072\u3053 H\u5e02 horikita_masahiko@example.com 2017-05-05 00:00:00 \n9 \u304a\u304a\u3061 \u308c\u3044\u3053 E\u5e02 oochi_reiko@example.com 2017-05-09 00:00:00 \n10 \u3084\u3079 \u3058\u3085\u3093 A\u5e02 yabe_jun@example.com 2018-05-20 00:00:00 \n11 \u304a\u304b\u3060 \u3068\u3057\u3084 E\u5e02 okada_toshiya@example.com 2017-02-18 00:00:00 \n12 \u3042\u3055\u307f \u3053\u3046\u3058 D\u5e02 asami_kouji@example.com 2018-06-05 00:00:00 \n13 \u304f\u307e\u3044 \u306e\u308a\u3072\u3068 A\u5e02 kumai_norihito@example.com 2017-03-29 00:00:00 \n14 \u304d\u304b\u308f\u3060 \u3072\u308d\u3086\u304d C\u5e02 kikawada_hiroyuki@example.com 2018-02-14 00:00:00 \n15 \u304a\u304c\u305f \u3053\u304c\u3093 B\u5e02 ogata_kogan@example.com 2017-03-15 00:00:00 \n16 \u304b\u3093\u3070\u3089 \u307f\u304b D\u5e02 kannbara_mika@example.com 2017-03-23 00:00:00 \n17 \u308f\u304b\u3059\u304e \u3068\u304a\u308b G\u5e02 wakasugi_tohru@example.com 2017-03-26 00:00:00 \n18 \u3044\u3057\u308f\u305f\u308a \u3053\u304c\u3093 D\u5e02 ishiwatari_kogan@example.com 2017-07-28 00:00:00 \n19 \u3072\u304c\u3057 \u307f\u3064\u3072\u308d A\u5e02 higashi_mitsuhiro@example.com 2018-02-06 00:00:00 \n20 \u3072\u306e \u306a\u3064\u304d A\u5e02 hino_natsuki@example.com 2017-05-23 00:00:00 \n21 \u304f\u308d\u305f\u306b \u306a\u304c\u3068\u3057 A\u5e02 kurotani_nagatoshi@example.com 2017-04-27 00:00:00 \n22 \u305f\u306a\u3079 \u307f\u3064\u3072\u308d A\u5e02 tanabe_mitsuhiro@example.com 2018-07-04 00:00:00 \n23 \u3068\u3065\u304b \u307f\u3086\u304d H\u5e02 toduka_miyuki@example.com 2017-01-30 00:00:00 \n24 \u3055\u304b\u304d\u3070\u3089 \u3057\u307c\u308a D\u5e02 sakakibara_shibori@example.com 2017-03-13 00:00:00 \n25 \u3042\u3055\u3060 \u3051\u3093\u3058 C\u5e02 asada_kenji@example.com 2018-04-07 00:00:00 \n26 \u3042\u304b\u3057\u3084 \u3042\u304d\u3089 B\u5e02 akashiya_akira@example.com 2018-02-13 00:00:00 \n27 \u3066\u3065\u304b \u3059\u3059\u3080 A\u5e02 teduka_susumu@example.com 2018-05-01 00:00:00 \n28 \u307b\u308a\u3048 \u305f\u3059\u304f H\u5e02 horie_tasuku@example.com 2018-05-14 00:00:00 \n29 \u304a\u304e\u306e \u3042\u3044 F\u5e02 ogino_ai@example.com 2017-05-19 00:00:00 \n... ... .. ... ... \n2969 \u304b\u308f\u3046\u3061 \u3055\u3068\u307f E\u5e02 kawauchi_satomi@example.com 2017-01-21 00:00:00 \n2970 \u307e\u3064\u304b\u308f \u3042\u3084\u3081 E\u5e02 matsukawa_ayame@example.com 2018-07-23 00:00:00 \n2971 \u3044\u306e\u3046\u3048 \u3082\u3082\u3053 F\u5e02 inoue_momoko@example.com 2018-06-18 00:00:00 \n2972 \u3059\u304e\u3057\u305f \u3055\u3068\u3057 E\u5e02 sugishita_satoshi@example.com 2018-02-17 00:00:00 \n2973 \u304a\u304a\u3061 \u308c\u3044\u3053 E\u5e02 oochi_reiko@example.com 2017-05-09 00:00:00 \n2974 \u304a\u304b \u3051\u3044\u305f C\u5e02 oka_keita@example.com 2018-07-22 00:00:00 \n2975 \u3084\u3079 \u307f\u3086\u304d F\u5e02 yabe_miyuki@example.com 2017-05-27 00:00:00 \n2976 \u3044\u3082\u3068 \u307e\u3055\u304b\u305a C\u5e02 imoto_masakazu@example.com 2017-04-06 00:00:00 \n2977 \u3042\u3044\u3060 \u3072\u304b\u308b D\u5e02 aida_hikaru@example.com 2018-05-22 00:00:00 \n2978 \u3042\u3055\u3060 \u3051\u3093\u3058 C\u5e02 asada_kenji@example.com 2018-04-07 00:00:00 \n2979 \u3075\u304b\u3060 \u3057\u3093\u3059\u3051 G\u5e02 fukada_shinsuke@example.com 2017-07-07 00:00:00 \n2980 \u3084\u3079 \u307f\u3086\u304d F\u5e02 yabe_miyuki@example.com 2017-05-27 00:00:00 \n2981 \u304a\u304f \u307f\u3064\u3072\u308d A\u5e02 oku_mitsuhiro@example.com 2018-01-08 00:00:00 \n2982 \u3057\u306e\u3084\u307e \u307e\u3055\u3068\u3057 B\u5e02 shinoyama_masatoshi@example.com 2018-07-02 00:00:00 \n2983 \u3055\u3060 \u3061\u304b\u3053 H\u5e02 sada_chikako@example.com 2017-07-01 00:00:00 \n2984 \u3055\u3055\u304c\u308f \u3066\u308b\u304a F\u5e02 sasagawa_teruo@example.com 2017-04-08 00:00:00 \n2985 \u3044\u308f\u3055\u308f \u306a\u306a H\u5e02 iwasawa_nana@example.com 2017-07-13 00:00:00 \n2986 \u304f\u307e\u304f\u3089 \u3081\u3044\u3073 G\u5e02 kumakura_meibi@example.com 2018-05-07 00:00:00 \n2987 \u307e\u3064\u305f\u306b \u3042\u3044\u3053 D\u5e02 matsutani_aiko@example.com 2018-07-30 00:00:00 \n2988 \u3084\u3079 \u3058\u3085\u3093 A\u5e02 yabe_jun@example.com 2018-05-20 00:00:00 \n2989 \u3046\u3048\u3080\u3089 \u306f\u308b\u304b A\u5e02 uemura_haruka@example.com 2018-01-06 00:00:00 \n2990 \u3048\u306e\u3082\u3068 \u304b\u304a\u308b C\u5e02 enomoto_kaoru@example.com 2017-06-17 00:00:00 \n2991 \u304a\u304c\u307f \u304b\u3064\u3072\u3055 D\u5e02 ogami_katsuhisa@example.com 2017-07-20 00:00:00 \n2992 \u3046\u3048\u304d \u3055\u3061\u3048 F\u5e02 ueki_sachie@example.com 2018-03-15 00:00:00 \n2993 \u3057\u3089\u3068\u308a \u308a\u3048 F\u5e02 shiratori_rie@example.com 2017-04-29 00:00:00 \n2994 \u3075\u304f\u3057\u307e \u3068\u3082\u3084 B\u5e02 fukushima_tomoya@example.com 2017-07-01 00:00:00 \n2995 \u304a\u304a\u304f\u3089 \u3053\u3046\u3058 E\u5e02 ookura_kouji@example.com 2018-03-31 00:00:00 \n2996 \u304a\u304c\u305f \u3053\u304c\u3093 B\u5e02 ogata_kogan@example.com 2017-03-15 00:00:00 \n2997 \u3042\u3057\u3060 \u3072\u308d\u3086\u304d E\u5e02 ashida_hiroyuki@example.com 2018-07-15 00:00:00 \n2998 \u3044\u3057\u3060 \u3044\u304f\u3048 B\u5e02 ishida_ikue@example.com 2017-02-05 00:00:00 \n\n[2999 rows x 9 columns]"
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "import_data = pd.read_csv(\"dump_data.csv\")\nimport_data"
},
{
"cell_type": "code",
"execution_count": 67,
"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>\u5546\u54c1A</th>\n <th>\u5546\u54c1B</th>\n <th>\u5546\u54c1C</th>\n <th>\u5546\u54c1D</th>\n <th>\u5546\u54c1E</th>\n <th>\u5546\u54c1F</th>\n <th>\u5546\u54c1G</th>\n <th>\u5546\u54c1H</th>\n <th>\u5546\u54c1I</th>\n <th>\u5546\u54c1J</th>\n <th>...</th>\n <th>\u5546\u54c1Q</th>\n <th>\u5546\u54c1R</th>\n <th>\u5546\u54c1S</th>\n <th>\u5546\u54c1T</th>\n <th>\u5546\u54c1U</th>\n <th>\u5546\u54c1V</th>\n <th>\u5546\u54c1W</th>\n <th>\u5546\u54c1X</th>\n <th>\u5546\u54c1Y</th>\n <th>\u5546\u54c1Z</th>\n </tr>\n <tr>\n <th>purchase_month</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 <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>201901</th>\n <td>18</td>\n <td>13</td>\n <td>19</td>\n <td>17</td>\n <td>18</td>\n <td>15</td>\n <td>11</td>\n <td>16</td>\n <td>18</td>\n <td>17</td>\n <td>...</td>\n <td>17</td>\n <td>21</td>\n <td>20</td>\n <td>17</td>\n <td>7</td>\n <td>22</td>\n <td>13</td>\n <td>14</td>\n <td>10</td>\n <td>0</td>\n </tr>\n <tr>\n <th>201902</th>\n <td>19</td>\n <td>14</td>\n <td>26</td>\n <td>21</td>\n <td>16</td>\n <td>14</td>\n <td>14</td>\n <td>17</td>\n <td>12</td>\n <td>14</td>\n <td>...</td>\n <td>22</td>\n <td>22</td>\n <td>22</td>\n <td>23</td>\n <td>19</td>\n <td>22</td>\n <td>24</td>\n <td>16</td>\n <td>11</td>\n <td>1</td>\n </tr>\n <tr>\n <th>201903</th>\n <td>17</td>\n <td>21</td>\n <td>20</td>\n <td>17</td>\n <td>9</td>\n <td>27</td>\n <td>14</td>\n <td>18</td>\n <td>12</td>\n <td>16</td>\n <td>...</td>\n <td>23</td>\n <td>16</td>\n <td>20</td>\n <td>12</td>\n <td>23</td>\n <td>18</td>\n <td>16</td>\n <td>21</td>\n <td>16</td>\n <td>0</td>\n </tr>\n <tr>\n <th>201904</th>\n <td>17</td>\n <td>19</td>\n <td>24</td>\n <td>20</td>\n <td>18</td>\n <td>17</td>\n <td>14</td>\n <td>11</td>\n <td>18</td>\n <td>13</td>\n <td>...</td>\n <td>20</td>\n <td>20</td>\n <td>16</td>\n <td>16</td>\n <td>11</td>\n <td>15</td>\n <td>14</td>\n <td>16</td>\n <td>20</td>\n <td>0</td>\n </tr>\n <tr>\n <th>201905</th>\n <td>24</td>\n <td>14</td>\n <td>16</td>\n <td>14</td>\n <td>19</td>\n <td>18</td>\n <td>23</td>\n <td>15</td>\n <td>16</td>\n <td>11</td>\n <td>...</td>\n <td>13</td>\n <td>22</td>\n <td>18</td>\n <td>16</td>\n <td>16</td>\n <td>9</td>\n <td>21</td>\n <td>16</td>\n <td>20</td>\n <td>0</td>\n </tr>\n <tr>\n <th>201906</th>\n <td>24</td>\n <td>12</td>\n <td>11</td>\n <td>19</td>\n <td>13</td>\n <td>18</td>\n <td>15</td>\n <td>13</td>\n <td>19</td>\n <td>22</td>\n <td>...</td>\n <td>15</td>\n <td>16</td>\n <td>21</td>\n <td>12</td>\n <td>18</td>\n <td>20</td>\n <td>17</td>\n <td>15</td>\n <td>13</td>\n <td>0</td>\n </tr>\n <tr>\n <th>201907</th>\n <td>20</td>\n <td>20</td>\n <td>17</td>\n <td>17</td>\n <td>12</td>\n <td>17</td>\n <td>19</td>\n <td>19</td>\n <td>19</td>\n <td>23</td>\n <td>...</td>\n <td>15</td>\n <td>19</td>\n <td>23</td>\n <td>21</td>\n <td>13</td>\n <td>28</td>\n <td>16</td>\n <td>18</td>\n <td>12</td>\n <td>0</td>\n </tr>\n </tbody>\n</table>\n<p>7 rows \u00d7 26 columns</p>\n</div>",
"text/plain": "item_name \u5546\u54c1A \u5546\u54c1B \u5546\u54c1C \u5546\u54c1D \u5546\u54c1E \u5546\u54c1F \u5546\u54c1G \u5546\u54c1H \u5546\u54c1I \u5546\u54c1J ... \u5546\u54c1Q \\\npurchase_month ... \n201901 18 13 19 17 18 15 11 16 18 17 ... 17 \n201902 19 14 26 21 16 14 14 17 12 14 ... 22 \n201903 17 21 20 17 9 27 14 18 12 16 ... 23 \n201904 17 19 24 20 18 17 14 11 18 13 ... 20 \n201905 24 14 16 14 19 18 23 15 16 11 ... 13 \n201906 24 12 11 19 13 18 15 13 19 22 ... 15 \n201907 20 20 17 17 12 17 19 19 19 23 ... 15 \n\nitem_name \u5546\u54c1R \u5546\u54c1S \u5546\u54c1T \u5546\u54c1U \u5546\u54c1V \u5546\u54c1W \u5546\u54c1X \u5546\u54c1Y \u5546\u54c1Z \npurchase_month \n201901 21 20 17 7 22 13 14 10 0 \n201902 22 22 23 19 22 24 16 11 1 \n201903 16 20 12 23 18 16 21 16 0 \n201904 20 16 16 11 15 14 16 20 0 \n201905 22 18 16 16 9 21 16 20 0 \n201906 16 21 12 18 20 17 15 13 0 \n201907 19 23 21 13 28 16 18 12 0 \n\n[7 rows x 26 columns]"
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "byItem = import_data.pivot_table(index=\"purchase_month\", columns=\"item_name\", aggfunc=\"size\", fill_value=0)\nbyItem"
},
{
"cell_type": "code",
"execution_count": 70,
"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>\u5546\u54c1A</th>\n <th>\u5546\u54c1B</th>\n <th>\u5546\u54c1C</th>\n <th>\u5546\u54c1D</th>\n <th>\u5546\u54c1E</th>\n <th>\u5546\u54c1F</th>\n <th>\u5546\u54c1G</th>\n <th>\u5546\u54c1H</th>\n <th>\u5546\u54c1I</th>\n <th>\u5546\u54c1J</th>\n <th>...</th>\n <th>\u5546\u54c1Q</th>\n <th>\u5546\u54c1R</th>\n <th>\u5546\u54c1S</th>\n <th>\u5546\u54c1T</th>\n <th>\u5546\u54c1U</th>\n <th>\u5546\u54c1V</th>\n <th>\u5546\u54c1W</th>\n <th>\u5546\u54c1X</th>\n <th>\u5546\u54c1Y</th>\n <th>\u5546\u54c1Z</th>\n </tr>\n <tr>\n <th>purchase_month</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 <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>201901</th>\n <td>1800</td>\n <td>2600</td>\n <td>5700</td>\n <td>6800</td>\n <td>9000</td>\n <td>9000</td>\n <td>7700</td>\n <td>12800</td>\n <td>16200</td>\n <td>17000</td>\n <td>...</td>\n <td>28900</td>\n <td>37800</td>\n <td>38000</td>\n <td>34000</td>\n <td>14700</td>\n <td>48400</td>\n <td>29900</td>\n <td>33600</td>\n <td>25000</td>\n <td>0</td>\n </tr>\n <tr>\n <th>201902</th>\n <td>1900</td>\n <td>2800</td>\n <td>7800</td>\n <td>8400</td>\n <td>8000</td>\n <td>8400</td>\n <td>9800</td>\n <td>13600</td>\n <td>10800</td>\n <td>14000</td>\n <td>...</td>\n <td>37400</td>\n <td>39600</td>\n <td>41800</td>\n <td>46000</td>\n <td>39900</td>\n <td>48400</td>\n <td>55200</td>\n <td>38400</td>\n <td>27500</td>\n <td>2600</td>\n </tr>\n <tr>\n <th>201903</th>\n <td>1700</td>\n <td>4200</td>\n <td>6000</td>\n <td>6800</td>\n <td>4500</td>\n <td>16200</td>\n <td>9800</td>\n <td>14400</td>\n <td>10800</td>\n <td>16000</td>\n <td>...</td>\n <td>39100</td>\n <td>28800</td>\n <td>38000</td>\n <td>24000</td>\n <td>48300</td>\n <td>39600</td>\n <td>36800</td>\n <td>50400</td>\n <td>40000</td>\n <td>0</td>\n </tr>\n <tr>\n <th>201904</th>\n <td>1700</td>\n <td>3800</td>\n <td>7200</td>\n <td>8000</td>\n <td>9000</td>\n <td>10200</td>\n <td>9800</td>\n <td>8800</td>\n <td>16200</td>\n <td>13000</td>\n <td>...</td>\n <td>34000</td>\n <td>36000</td>\n <td>30400</td>\n <td>32000</td>\n <td>23100</td>\n <td>33000</td>\n <td>32200</td>\n <td>38400</td>\n <td>50000</td>\n <td>0</td>\n </tr>\n <tr>\n <th>201905</th>\n <td>2400</td>\n <td>2800</td>\n <td>4800</td>\n <td>5600</td>\n <td>9500</td>\n <td>10800</td>\n <td>16100</td>\n <td>12000</td>\n <td>14400</td>\n <td>11000</td>\n <td>...</td>\n <td>22100</td>\n <td>39600</td>\n <td>34200</td>\n <td>32000</td>\n <td>33600</td>\n <td>19800</td>\n <td>48300</td>\n <td>38400</td>\n <td>50000</td>\n <td>0</td>\n </tr>\n <tr>\n <th>201906</th>\n <td>2400</td>\n <td>2400</td>\n <td>3300</td>\n <td>7600</td>\n <td>6500</td>\n <td>10800</td>\n <td>10500</td>\n <td>10400</td>\n <td>17100</td>\n <td>22000</td>\n <td>...</td>\n <td>25500</td>\n <td>28800</td>\n <td>39900</td>\n <td>24000</td>\n <td>37800</td>\n <td>44000</td>\n <td>39100</td>\n <td>36000</td>\n <td>32500</td>\n <td>0</td>\n </tr>\n <tr>\n <th>201907</th>\n <td>2000</td>\n <td>4000</td>\n <td>5100</td>\n <td>6800</td>\n <td>6000</td>\n <td>10200</td>\n <td>13300</td>\n <td>15200</td>\n <td>17100</td>\n <td>23000</td>\n <td>...</td>\n <td>25500</td>\n <td>34200</td>\n <td>43700</td>\n <td>42000</td>\n <td>27300</td>\n <td>61600</td>\n <td>36800</td>\n <td>43200</td>\n <td>30000</td>\n <td>0</td>\n </tr>\n </tbody>\n</table>\n<p>7 rows \u00d7 26 columns</p>\n</div>",
"text/plain": "item_name \u5546\u54c1A \u5546\u54c1B \u5546\u54c1C \u5546\u54c1D \u5546\u54c1E \u5546\u54c1F \u5546\u54c1G \u5546\u54c1H \u5546\u54c1I \\\npurchase_month \n201901 1800 2600 5700 6800 9000 9000 7700 12800 16200 \n201902 1900 2800 7800 8400 8000 8400 9800 13600 10800 \n201903 1700 4200 6000 6800 4500 16200 9800 14400 10800 \n201904 1700 3800 7200 8000 9000 10200 9800 8800 16200 \n201905 2400 2800 4800 5600 9500 10800 16100 12000 14400 \n201906 2400 2400 3300 7600 6500 10800 10500 10400 17100 \n201907 2000 4000 5100 6800 6000 10200 13300 15200 17100 \n\nitem_name \u5546\u54c1J ... \u5546\u54c1Q \u5546\u54c1R \u5546\u54c1S \u5546\u54c1T \u5546\u54c1U \u5546\u54c1V \u5546\u54c1W \\\npurchase_month ... \n201901 17000 ... 28900 37800 38000 34000 14700 48400 29900 \n201902 14000 ... 37400 39600 41800 46000 39900 48400 55200 \n201903 16000 ... 39100 28800 38000 24000 48300 39600 36800 \n201904 13000 ... 34000 36000 30400 32000 23100 33000 32200 \n201905 11000 ... 22100 39600 34200 32000 33600 19800 48300 \n201906 22000 ... 25500 28800 39900 24000 37800 44000 39100 \n201907 23000 ... 25500 34200 43700 42000 27300 61600 36800 \n\nitem_name \u5546\u54c1X \u5546\u54c1Y \u5546\u54c1Z \npurchase_month \n201901 33600 25000 0 \n201902 38400 27500 2600 \n201903 50400 40000 0 \n201904 38400 50000 0 \n201905 38400 50000 0 \n201906 36000 32500 0 \n201907 43200 30000 0 \n\n[7 rows x 26 columns]"
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "byPrice = import_data.pivot_table(index=\"purchase_month\", columns=\"item_name\",values=\"item_price\",aggfunc=\"sum\", fill_value=0)\nbyPrice"
},
{
"cell_type": "code",
"execution_count": 71,
"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>\u9867\u5ba2\u540d</th>\n <th>\u3055\u3060\u5343\u4f73\u5b50</th>\n <th>\u4e2d\u4ec1\u6676</th>\n <th>\u4e2d\u7530\u7f8e\u667a\u5b50</th>\n <th>\u4e38\u5c71\u5149\u81e3</th>\n <th>\u4e45\u4fdd\u7530\u502b\u5b50</th>\n <th>\u4e80\u4e95\u4e00\u5fb3</th>\n <th>\u4e94\u5341\u5d50\u6625\u6a39</th>\n <th>\u4e95\u4e0a\u6843\u5b50</th>\n <th>\u4e95\u53e3\u5bdb\u6cbb</th>\n <th>\u4e95\u5ddd\u771f\u60a0\u5b50</th>\n <th>...</th>\n <th>\u9999\u690e\u512a\u4e00</th>\n <th>\u9ad8\u539f\u5145\u5247</th>\n <th>\u9ad8\u68a8\u7d50\u8863</th>\n <th>\u9ad8\u6ca2\u7f8e\u54b2</th>\n <th>\u9ad8\u7530\u3055\u3093\u307e</th>\n <th>\u9ce5\u5c45\u5e83\u53f8</th>\n <th>\u9db4\u5ca1\u85ab</th>\n <th>\u9ebb\u751f\u8389\u7dd2</th>\n <th>\u9ec4\u5ddd\u7530\u535a\u4e4b</th>\n <th>\u9ed2\u8c37\u9577\u5229</th>\n </tr>\n <tr>\n <th>purchase_month</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 <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>201901</th>\n <td>3</td>\n <td>1</td>\n <td>4</td>\n <td>2</td>\n <td>2</td>\n <td>0</td>\n <td>5</td>\n <td>3</td>\n <td>3</td>\n <td>1</td>\n <td>...</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>5</td>\n <td>2</td>\n <td>0</td>\n <td>2</td>\n <td>2</td>\n <td>5</td>\n </tr>\n <tr>\n <th>201902</th>\n <td>9</td>\n <td>1</td>\n <td>2</td>\n <td>2</td>\n <td>1</td>\n <td>4</td>\n <td>2</td>\n <td>1</td>\n <td>0</td>\n <td>4</td>\n <td>...</td>\n <td>4</td>\n <td>0</td>\n <td>3</td>\n <td>2</td>\n <td>0</td>\n <td>1</td>\n <td>2</td>\n <td>4</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>201903</th>\n <td>1</td>\n <td>2</td>\n <td>1</td>\n <td>6</td>\n <td>1</td>\n <td>4</td>\n <td>3</td>\n <td>3</td>\n <td>2</td>\n <td>2</td>\n <td>...</td>\n <td>3</td>\n <td>1</td>\n <td>6</td>\n <td>2</td>\n <td>4</td>\n <td>2</td>\n <td>4</td>\n <td>2</td>\n <td>2</td>\n <td>1</td>\n </tr>\n <tr>\n <th>201904</th>\n <td>0</td>\n <td>3</td>\n <td>1</td>\n <td>2</td>\n <td>0</td>\n <td>2</td>\n <td>2</td>\n <td>0</td>\n <td>3</td>\n <td>2</td>\n <td>...</td>\n <td>2</td>\n <td>4</td>\n <td>2</td>\n <td>3</td>\n <td>4</td>\n <td>3</td>\n <td>2</td>\n <td>1</td>\n <td>2</td>\n <td>0</td>\n </tr>\n <tr>\n <th>201905</th>\n <td>3</td>\n <td>2</td>\n <td>5</td>\n <td>2</td>\n <td>4</td>\n <td>1</td>\n <td>2</td>\n <td>1</td>\n <td>3</td>\n <td>3</td>\n <td>...</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>2</td>\n <td>2</td>\n <td>3</td>\n <td>4</td>\n <td>4</td>\n <td>1</td>\n </tr>\n <tr>\n <th>201906</th>\n <td>1</td>\n <td>3</td>\n <td>0</td>\n <td>4</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>2</td>\n <td>2</td>\n <td>3</td>\n <td>...</td>\n <td>7</td>\n <td>3</td>\n <td>0</td>\n <td>2</td>\n <td>1</td>\n <td>0</td>\n <td>2</td>\n <td>1</td>\n <td>2</td>\n <td>4</td>\n </tr>\n <tr>\n <th>201907</th>\n <td>3</td>\n <td>0</td>\n <td>3</td>\n <td>2</td>\n <td>5</td>\n <td>3</td>\n <td>5</td>\n <td>2</td>\n <td>5</td>\n <td>5</td>\n <td>...</td>\n <td>2</td>\n <td>4</td>\n <td>4</td>\n <td>2</td>\n <td>0</td>\n <td>2</td>\n <td>4</td>\n <td>3</td>\n <td>4</td>\n <td>1</td>\n </tr>\n </tbody>\n</table>\n<p>7 rows \u00d7 199 columns</p>\n</div>",
"text/plain": "\u9867\u5ba2\u540d \u3055\u3060\u5343\u4f73\u5b50 \u4e2d\u4ec1\u6676 \u4e2d\u7530\u7f8e\u667a\u5b50 \u4e38\u5c71\u5149\u81e3 \u4e45\u4fdd\u7530\u502b\u5b50 \u4e80\u4e95\u4e00\u5fb3 \u4e94\u5341\u5d50\u6625\u6a39 \u4e95\u4e0a\u6843\u5b50 \u4e95\u53e3\u5bdb\u6cbb \\\npurchase_month \n201901 3 1 4 2 2 0 5 3 3 \n201902 9 1 2 2 1 4 2 1 0 \n201903 1 2 1 6 1 4 3 3 2 \n201904 0 3 1 2 0 2 2 0 3 \n201905 3 2 5 2 4 1 2 1 3 \n201906 1 3 0 4 1 1 1 2 2 \n201907 3 0 3 2 5 3 5 2 5 \n\n\u9867\u5ba2\u540d \u4e95\u5ddd\u771f\u60a0\u5b50 ... \u9999\u690e\u512a\u4e00 \u9ad8\u539f\u5145\u5247 \u9ad8\u68a8\u7d50\u8863 \u9ad8\u6ca2\u7f8e\u54b2 \u9ad8\u7530\u3055\u3093\u307e \u9ce5\u5c45\u5e83\u53f8 \u9db4\u5ca1\u85ab \u9ebb\u751f\u8389\u7dd2 \\\npurchase_month ... \n201901 1 ... 0 1 1 1 5 2 0 2 \n201902 4 ... 4 0 3 2 0 1 2 4 \n201903 2 ... 3 1 6 2 4 2 4 2 \n201904 2 ... 2 4 2 3 4 3 2 1 \n201905 3 ... 1 1 1 0 2 2 3 4 \n201906 3 ... 7 3 0 2 1 0 2 1 \n201907 5 ... 2 4 4 2 0 2 4 3 \n\n\u9867\u5ba2\u540d \u9ec4\u5ddd\u7530\u535a\u4e4b \u9ed2\u8c37\u9577\u5229 \npurchase_month \n201901 2 5 \n201902 0 1 \n201903 2 1 \n201904 2 0 \n201905 4 1 \n201906 2 4 \n201907 4 1 \n\n[7 rows x 199 columns]"
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "byCustomer = import_data.pivot_table(index=\"purchase_month\", columns=\"\u9867\u5ba2\u540d\", aggfunc=\"size\", fill_value=0)\nbyCustomer"
},
{
"cell_type": "code",
"execution_count": 72,
"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>\u5730\u57df</th>\n <th>A\u5e02</th>\n <th>B\u5e02</th>\n <th>C\u5e02</th>\n <th>D\u5e02</th>\n <th>E\u5e02</th>\n <th>F\u5e02</th>\n <th>G\u5e02</th>\n <th>H\u5e02</th>\n </tr>\n <tr>\n <th>purchase_month</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>201901</th>\n <td>59</td>\n <td>55</td>\n <td>72</td>\n <td>34</td>\n <td>49</td>\n <td>57</td>\n <td>49</td>\n <td>42</td>\n </tr>\n <tr>\n <th>201902</th>\n <td>71</td>\n <td>46</td>\n <td>65</td>\n <td>48</td>\n <td>61</td>\n <td>52</td>\n <td>43</td>\n <td>63</td>\n </tr>\n <tr>\n <th>201903</th>\n <td>64</td>\n <td>52</td>\n <td>57</td>\n <td>43</td>\n <td>52</td>\n <td>59</td>\n <td>51</td>\n <td>59</td>\n </tr>\n <tr>\n <th>201904</th>\n <td>64</td>\n <td>48</td>\n <td>54</td>\n <td>45</td>\n <td>48</td>\n <td>58</td>\n <td>40</td>\n <td>52</td>\n </tr>\n <tr>\n <th>201905</th>\n <td>57</td>\n <td>52</td>\n <td>68</td>\n <td>48</td>\n <td>59</td>\n <td>65</td>\n <td>35</td>\n <td>43</td>\n </tr>\n <tr>\n <th>201906</th>\n <td>53</td>\n <td>47</td>\n <td>61</td>\n <td>30</td>\n <td>51</td>\n <td>51</td>\n <td>58</td>\n <td>58</td>\n </tr>\n <tr>\n <th>201907</th>\n <td>76</td>\n <td>53</td>\n <td>61</td>\n <td>42</td>\n <td>54</td>\n <td>64</td>\n <td>47</td>\n <td>54</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": "\u5730\u57df A\u5e02 B\u5e02 C\u5e02 D\u5e02 E\u5e02 F\u5e02 G\u5e02 H\u5e02\npurchase_month \n201901 59 55 72 34 49 57 49 42\n201902 71 46 65 48 61 52 43 63\n201903 64 52 57 43 52 59 51 59\n201904 64 48 54 45 48 58 40 52\n201905 57 52 68 48 59 65 35 43\n201906 53 47 61 30 51 51 58 58\n201907 76 53 61 42 54 64 47 54"
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "byRegion = import_data.pivot_table(index=\"purchase_month\", columns=\"\u5730\u57df\", aggfunc=\"size\", fill_value=0)\nbyRegion"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "\u8cfc\u5165\u3057\u3066\u3044\u306a\u3044\u30e6\u30fc\u30b6\u30fc"
},
{
"cell_type": "code",
"execution_count": 77,
"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>\u9867\u5ba2\u540d</th>\n <th>\u30e1\u30fc\u30eb\u30a2\u30c9\u30ec\u30b9</th>\n <th>\u767b\u9332\u65e5</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>2999</th>\n <td>\u798f\u4e95\u7f8e\u5e0c</td>\n <td>fukui_miki1@example.com</td>\n <td>2019-04-23</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " \u9867\u5ba2\u540d \u30e1\u30fc\u30eb\u30a2\u30c9\u30ec\u30b9 \u767b\u9332\u65e5\n2999 \u798f\u4e95\u7f8e\u5e0c fukui_miki1@example.com 2019-04-23"
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "away_data = pd.merge(uriage_data, kokyaku_data, left_on=\"customer_name\", right_on=\"\u9867\u5ba2\u540d\", how=\"right\")\naway_data[away_data[\"purchase_date\"].isnull()][[\"\u9867\u5ba2\u540d\", \"\u30e1\u30fc\u30eb\u30a2\u30c9\u30ec\u30b9\", \"\u767b\u9332\u65e5\"]]"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": ""
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.6",
"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": "3.6.9"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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