pandas\u306emerge\u95a2\u6570\u306f\u3001\u30c7\u30fc\u30bf\u5206\u6790\u3084\u6a5f\u68b0\u5b66\u7fd2\u306e\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u3067\u6b20\u304b\u305b\u306a\u3044\u30c4\u30fc\u30eb\u3067\u3059\u3002\u3053\u306e\u8a18\u4e8b\u3067\u306f\u3001merge\u95a2\u6570\u306e\u57fa\u672c\u304b\u3089\u5b9f\u8df5\u7684\u306a\u4f7f\u3044\u65b9\u307e\u3067\u30017\u3064\u306e\u30e6\u30fc\u30b9\u30b1\u30fc\u30b9\u3092\u901a\u3058\u3066\u5fb9\u5e95\u7684\u306b\u89e3\u8aac\u3057\u307e\u3059\u3002\u521d\u5fc3\u8005\u304b\u3089\u30c7\u30fc\u30bf\u5206\u6790\u306e\u30d7\u30ed\u307e\u3067\u3001\u5fc5\u898b\u306e\u5185\u5bb9\u3068\u306a\u3063\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
\u76ee\u6b21 <\/p>\n
\u30c7\u30fc\u30bf\u5206\u6790\u3084\u6a5f\u68b0\u5b66\u7fd2\u306e\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u3067\u306f\u3001\u7570\u306a\u308b\u30c7\u30fc\u30bf\u30bd\u30fc\u30b9\u304b\u3089\u53d6\u5f97\u3057\u305f\u30c7\u30fc\u30bf\u3092\u7d44\u307f\u5408\u308f\u305b\u308b\u5fc5\u8981\u304c\u3042\u308b\u3053\u3068\u304c\u5c11\u306a\u304f\u3042\u308a\u307e\u305b\u3093\u3002pandas\u306emerge\u95a2\u6570\u306f\u3001\u305d\u306e\u3088\u3046\u306a\u5834\u9762\u3067\u5f37\u529b\u306a\u30c4\u30fc\u30eb\u3068\u306a\u308a\u307e\u3059\u3002merge\u95a2\u6570\u3092\u4f7f\u3048\u3070\u3001\u8907\u6570\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u7c21\u5358\u306b\u7d50\u5408\u3067\u304d\u308b\u306e\u3067\u3059\u3002<\/p>\n\n\n\n
merge\u95a2\u6570\u306f\u3001SQL\u306eJOIN\u53e5\u306b\u4f3c\u305f\u6a5f\u80fd\u3092\u63d0\u4f9b\u3057\u3066\u3044\u307e\u3059\u3002\u7570\u306a\u308b\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306b\u5171\u901a\u306e\u30ab\u30e9\u30e0\u304c\u3042\u308b\u5834\u5408\u3001\u305d\u306e\u30ab\u30e9\u30e0\u3092\u57fa\u6e96\u306b\u30c7\u30fc\u30bf\u3092\u7d50\u5408\u3067\u304d\u307e\u3059\u3002\u307e\u305f\u3001\u5185\u90e8\u7d50\u5408\u3084\u5916\u90e8\u7d50\u5408\u306a\u3069\u3001\u69d8\u3005\u306a\u7d50\u5408\u65b9\u6cd5\u3092\u9078\u629e\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/p>\n\n\n\n
\u4f8b\u3048\u3070\u3001\u30e6\u30fc\u30b6\u30fc\u60c5\u5831\u3068\u30a2\u30af\u30bb\u30b9\u30ed\u30b0\u304c\u5225\u3005\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306b\u683c\u7d0d\u3055\u308c\u3066\u3044\u308b\u5834\u5408\u3092\u8003\u3048\u3066\u307f\u307e\u3057\u3087\u3046\u3002merge\u95a2\u6570\u3092\u4f7f\u3048\u3070\u3001\u30e6\u30fc\u30b6\u30fcID\u3092\u57fa\u6e96\u306b2\u3064\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u7d50\u5408\u3057\u3001\u30e6\u30fc\u30b6\u30fc\u5c5e\u6027\u3068\u30a2\u30af\u30bb\u30b9\u5c65\u6b74\u30921\u3064\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306b\u307e\u3068\u3081\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n\n\n\n
import pandas as pd\n\n# \u30e6\u30fc\u30b6\u30fc\u60c5\u5831\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\nusers = pd.DataFrame({'user_id': [1, 2, 3], 'name': ['Alice', 'Bob', 'Carol'], 'age': [25, 30, 35]})\n\n# \u30a2\u30af\u30bb\u30b9\u30ed\u30b0\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\nlogs = pd.DataFrame({'user_id': [1, 1, 2, 3], 'date': ['2023-01-01', '2023-01-02', '2023-01-01', '2023-01-03'], 'page': ['home', 'settings', 'home', 'about']})\n\n# user_id\u3092\u57fa\u6e96\u306b\u5185\u90e8\u7d50\u5408\nmerged_df = pd.merge(users, logs, on='user_id')\nprint(merged_df)<\/pre>\n\n\n\n\u51fa\u529b\u7d50\u679c:<\/p>\n\n\n\n
user_id name age date page\n0 1 Alice 25 2023-01-01 home\n1 1 Alice 25 2023-01-02 settings\n2 2 Bob 30 2023-01-01 home\n3 3 Carol 35 2023-01-03 about<\/pre>\n\n\n\n\u3053\u306e\u3088\u3046\u306b\u3001merge\u95a2\u6570\u3092\u4f7f\u3046\u3053\u3068\u3067\u3001\u30c7\u30fc\u30bf\u306e\u524d\u51e6\u7406\u3084\u30af\u30ea\u30fc\u30cb\u30f3\u30b0\u304c\u5bb9\u6613\u306b\u306a\u308a\u307e\u3059\u3002\u7279\u306b\u3001SQL\u306eJOIN\u64cd\u4f5c\u306b\u6163\u308c\u3066\u3044\u308b\u4eba\u306b\u3068\u3063\u3066\u306f\u3001\u76f4\u611f\u7684\u306b\u4f7f\u3044\u3084\u3059\u3044\u95a2\u6570\u3060\u3068\u8a00\u3048\u308b\u3067\u3057\u3087\u3046\u3002<\/p>\n\n\n\n
\u6b21\u306e\u898b\u51fa\u3057\u3067\u306f\u3001merge\u95a2\u6570\u306e\u57fa\u672c\u7684\u306a\u4f7f\u3044\u65b9\u306b\u3064\u3044\u3066\u3001\u8a73\u3057\u304f\u89e3\u8aac\u3057\u3066\u3044\u304d\u307e\u3059\u3002<\/p>\n\n\n\n
merge\u95a2\u6570\u306e\u57fa\u672c\u7684\u306a\u4f7f\u3044\u65b9<\/h2>\n\n\n\n
pandas\u306emerge\u95a2\u6570\u306f\u3001\u8907\u6570\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u7d50\u5408\u3059\u308b\u305f\u3081\u306e\u5f37\u529b\u306a\u30c4\u30fc\u30eb\u3067\u3059\u3002\u57fa\u672c\u7684\u306a\u4f7f\u3044\u65b9\u3092\u7406\u89e3\u3059\u308b\u3053\u3068\u3067\u3001\u30c7\u30fc\u30bf\u5206\u6790\u3084\u6a5f\u68b0\u5b66\u7fd2\u306e\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u3067\u52b9\u679c\u7684\u306b\u6d3b\u7528\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u308b\u3067\u3057\u3087\u3046\u3002<\/p>\n\n\n\n
merge\u95a2\u6570\u306e\u66f8\u5f0f\u3068\u5f15\u6570\u306e\u8aac\u660e<\/h3>\n\n\n\n
merge\u95a2\u6570\u306e\u57fa\u672c\u7684\u306a\u66f8\u5f0f\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\u3002<\/p>\n\n\n\n
pd.merge(left, right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False, suffixes=('_x', '_y'), copy=True, indicator=False, validate=None)<\/pre>\n\n\n\n\u4e3b\u8981\u306a\u5f15\u6570\u306e\u610f\u5473\u306f\u6b21\u306e\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n\n\n\n
\u4ee5\u4e0b\u306f\u3001\u5b66\u751f\u306e\u60c5\u5831\u3068\u6210\u7e3e\u3092\u7d50\u5408\u3059\u308b\u7c21\u5358\u306a\u4f8b\u3067\u3059\u3002<\/p>\n\n\n\n
import pandas as pd\n\n# \u5b66\u751f\u60c5\u5831\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\nstudents = pd.DataFrame({'id': [1, 2, 3], 'name': ['Alice', 'Bob', 'Carol']})\n\n# \u6210\u7e3e\u60c5\u5831\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\ngrades = pd.DataFrame({'id': [1, 2, 4], 'math': [80, 90, 75], 'science': [85, 95, 80]})\n\n# id\u3092\u57fa\u6e96\u306b\u5185\u90e8\u7d50\u5408\nmerged_df = pd.merge(students, grades, on='id', how='inner')\nprint(merged_df)<\/pre>\n\n\n\n\u51fa\u529b\u7d50\u679c:<\/p>\n\n\n\n
id name math science\n0 1 Alice 80 85\n1 2 Bob 90 95<\/pre>\n\n\n\n\u3053\u306e\u4f8b\u3067\u306f\u3001students\u3068grades\u306e2\u3064\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u3001\u5171\u901a\u306e\u30ab\u30e9\u30e0\u2019id\u2019\u3092\u57fa\u6e96\u306b\u5185\u90e8\u7d50\u5408\u3057\u3066\u3044\u307e\u3059\u3002\u7d50\u5408\u5f8c\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0merged_df\u306b\u306f\u3001id\u304c\u4e00\u81f4\u3059\u308b\u884c\u3060\u3051\u304c\u542b\u307e\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
merge\u95a2\u6570\u306e\u4f7f\u3044\u65b9\u3092\u7406\u89e3\u3059\u308b\u3053\u3068\u3067\u3001\u69d8\u3005\u306a\u30c7\u30fc\u30bf\u7d50\u5408\u306e\u30cb\u30fc\u30ba\u306b\u5bfe\u5fdc\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002\u6b21\u306e\u898b\u51fa\u3057\u3067\u306f\u3001\u5177\u4f53\u7684\u306a\u30e6\u30fc\u30b9\u30b1\u30fc\u30b9\u30927\u3064\u7d39\u4ecb\u3057\u3001merge\u95a2\u6570\u306e\u5b9f\u8df5\u7684\u306a\u6d3b\u7528\u65b9\u6cd5\u3092\u63a2\u3063\u3066\u3044\u304d\u307e\u3059\u3002<\/p>\n\n\n\n
merge\u306e\u30e6\u30fc\u30b9\u30b1\u30fc\u30b97\u9078<\/h2>\n\n\n\n
pandas\u306emerge\u95a2\u6570\u306f\u3001\u69d8\u3005\u306a\u30c7\u30fc\u30bf\u7d50\u5408\u306e\u30cb\u30fc\u30ba\u306b\u5bfe\u5fdc\u3067\u304d\u308b\u5f37\u529b\u306a\u30c4\u30fc\u30eb\u3067\u3059\u3002\u3053\u3053\u3067\u306f\u3001\u30c7\u30fc\u30bf\u5206\u6790\u3084\u6a5f\u68b0\u5b66\u7fd2\u306e\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u3067\u3088\u304f\u906d\u9047\u3059\u308b7\u3064\u306e\u30e6\u30fc\u30b9\u30b1\u30fc\u30b9\u3092\u7d39\u4ecb\u3057\u3001\u5b9f\u8df5\u7684\u306amerge\u95a2\u6570\u306e\u4f7f\u3044\u65b9\u3092\u89e3\u8aac\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
1. \u5171\u901a\u306e\u30ab\u30e9\u30e0\u3092\u6307\u5b9a\u3057\u3066\u7d50\u5408\u3059\u308b<\/h3>\n\n\n\n
\u6700\u3082\u57fa\u672c\u7684\u306a\u30e6\u30fc\u30b9\u30b1\u30fc\u30b9\u306f\u3001\u5171\u901a\u306e\u30ab\u30e9\u30e0\u3092\u6307\u5b9a\u3057\u30662\u3064\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u7d50\u5408\u3059\u308b\u3053\u3068\u3067\u3059\u3002\u4f8b\u3048\u3070\u3001\u9867\u5ba2\u60c5\u5831\u3068\u8cfc\u5165\u5c65\u6b74\u3092\u9867\u5ba2ID\u3067\u7d50\u5408\u3057\u3001\u9867\u5ba2\u3054\u3068\u306e\u8cfc\u5165\u5c65\u6b74\u3092\u5206\u6790\u3059\u308b\u5834\u5408\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
import pandas as pd\n\n# \u9867\u5ba2\u60c5\u5831\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\ncustomers = pd.DataFrame({'customer_id': [1, 2, 3], 'name': ['Alice', 'Bob', 'Carol'], 'age': [25, 30, 35]})\n\n# \u8cfc\u5165\u5c65\u6b74\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\npurchases = pd.DataFrame({'customer_id': [1, 1, 2, 3], 'product': ['A', 'B', 'C', 'D'], 'price': [100, 200, 300, 400]})\n\n# customer_id\u3092\u57fa\u6e96\u306b\u5185\u90e8\u7d50\u5408\nmerged_df = pd.merge(customers, purchases, on='customer_id', how='inner')\nprint(merged_df)<\/pre>\n\n\n\n\u51fa\u529b\u7d50\u679c:<\/p>\n\n\n\n
customer_id name age product price\n0 1 Alice 25 A 100\n1 1 Alice 25 B 200\n2 2 Bob 30 C 300\n3 3 Carol 35 D 400<\/pre>\n\n\n\n\u3053\u306e\u4f8b\u3067\u306f\u3001customers\u3068purchases\u306e2\u3064\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u3001\u5171\u901a\u306e\u30ab\u30e9\u30e0\u2019customer_id\u2019\u3092\u57fa\u6e96\u306b\u5185\u90e8\u7d50\u5408\u3057\u3066\u3044\u307e\u3059\u3002\u7d50\u5408\u5f8c\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0merged_df\u306b\u306f\u3001\u9867\u5ba2\u60c5\u5831\u3068\u8cfc\u5165\u5c65\u6b74\u304c\u7d50\u5408\u3055\u308c\u305f\u72b6\u614b\u3067\u4fdd\u5b58\u3055\u308c\u307e\u3059\u3002<\/p>\n\n\n\n
2. \u7d50\u5408\u3059\u308b\u30ab\u30e9\u30e0\u540d\u304c\u7570\u306a\u308b\u5834\u5408\u306e\u5bfe\u51e6\u6cd5<\/h3>\n\n\n\n
\u7d50\u5408\u3059\u308b\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306e\u30ab\u30e9\u30e0\u540d\u304c\u7570\u306a\u308b\u5834\u5408\u306f\u3001left_on\u3068right_on\u3092\u4f7f\u3063\u3066\u5bfe\u5fdc\u3059\u308b\u30ab\u30e9\u30e0\u3092\u6307\u5b9a\u3057\u307e\u3059\u3002\u4f8b\u3048\u3070\u3001\u793e\u54e1\u60c5\u5831\u3068\u7d66\u4e0e\u60c5\u5831\u3092\u7d50\u5408\u3059\u308b\u969b\u3001\u793e\u54e1\u60c5\u5831\u3067\u306f’employee_id\u2019\u3001\u7d66\u4e0e\u60c5\u5831\u3067\u306f\u2019id\u2019\u3068\u3044\u3046\u30ab\u30e9\u30e0\u540d\u3092\u4f7f\u7528\u3057\u3066\u3044\u308b\u5834\u5408\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
import pandas as pd\n\n# \u793e\u54e1\u60c5\u5831\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\nemployees = pd.DataFrame({'employee_id': [1, 2, 3], 'name': ['Alice', 'Bob', 'Carol'], 'department': ['Sales', 'Marketing', 'Engineering']})\n\n# \u7d66\u4e0e\u60c5\u5831\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\nsalaries = pd.DataFrame({'id': [1, 2, 4], 'salary': [5000, 6000, 7000]})\n\n# employee_id\u3068id\u3092\u57fa\u6e96\u306b\u5185\u90e8\u7d50\u5408\nmerged_df = pd.merge(employees, salaries, left_on='employee_id', right_on='id', how='inner')\nprint(merged_df)<\/pre>\n\n\n\n\u51fa\u529b\u7d50\u679c:<\/p>\n\n\n\n
employee_id name department id salary\n0 1 Alice Sales 1 5000\n1 2 Bob Marketing 2 6000<\/pre>\n\n\n\n\u3053\u306e\u4f8b\u3067\u306f\u3001left_on=’employee_id\u2019\u3068right_on=\u2019id\u2019\u3092\u6307\u5b9a\u3059\u308b\u3053\u3068\u3067\u3001\u30ab\u30e9\u30e0\u540d\u304c\u7570\u306a\u308b\u5834\u5408\u3067\u3082\u6b63\u3057\u304f\u7d50\u5408\u3067\u304d\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
3. \u7247\u65b9\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306b\u3057\u304b\u306a\u3044\u30c7\u30fc\u30bf\u306e\u6271\u3044\u65b9<\/h3>\n\n\n\n
\u7247\u65b9\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306b\u3057\u304b\u306a\u3044\u30c7\u30fc\u30bf\u3092\u7d50\u5408\u5f8c\u3082\u6b8b\u3057\u305f\u3044\u5834\u5408\u306f\u3001how\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u2019left\u2019\u307e\u305f\u306f\u2019right\u2019\u306b\u8a2d\u5b9a\u3057\u307e\u3059\u3002\u4f8b\u3048\u3070\u3001\u30e6\u30fc\u30b6\u30fc\u60c5\u5831\u3068\u30a2\u30af\u30bb\u30b9\u30ed\u30b0\u3092\u7d50\u5408\u3059\u308b\u969b\u3001\u30a2\u30af\u30bb\u30b9\u30ed\u30b0\u306b\u306f\u306a\u3044\u30e6\u30fc\u30b6\u30fc\u306e\u60c5\u5831\u3092\u6b8b\u3059\u5834\u5408\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
import pandas as pd\n\n# \u30e6\u30fc\u30b6\u30fc\u60c5\u5831\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\nusers = pd.DataFrame({'user_id': [1, 2, 3, 4], 'name': ['Alice', 'Bob', 'Carol', 'David']})\n\n# \u30a2\u30af\u30bb\u30b9\u30ed\u30b0\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\nlogs = pd.DataFrame({'user_id': [1, 1, 2, 3], 'date': ['2023-01-01', '2023-01-02', '2023-01-01', '2023-01-03'], 'page': ['home', 'settings', 'home', 'about']})\n\n# user_id\u3092\u57fa\u6e96\u306b\u5de6\u7d50\u5408\nmerged_df = pd.merge(users, logs, on='user_id', how='left')\nprint(merged_df)<\/pre>\n\n\n\n\u51fa\u529b\u7d50\u679c:<\/p>\n\n\n\n
user_id name date page\n0 1 Alice 2023-01-01 home\n1 1 Alice 2023-01-02 settings\n2 2 Bob 2023-01-01 home\n3 3 Carol 2023-01-03 about\n4 4 David NaN NaN<\/pre>\n\n\n\n\u3053\u306e\u4f8b\u3067\u306f\u3001how=\u2019left\u2019\u3092\u6307\u5b9a\u3059\u308b\u3053\u3068\u3067\u3001\u30e6\u30fc\u30b6\u30fc\u60c5\u5831\u306b\u306f\u3042\u308b\u304c\u30a2\u30af\u30bb\u30b9\u30ed\u30b0\u306b\u306f\u306a\u3044\u30e6\u30fc\u30b6\u30fc\uff08David\uff09\u306e\u60c5\u5831\u3082\u7d50\u5408\u5f8c\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306b\u6b8b\u3063\u3066\u3044\u307e\u3059\u3002\u30a2\u30af\u30bb\u30b9\u30ed\u30b0\u306b\u5bfe\u5fdc\u3059\u308b\u60c5\u5831\u304c\u306a\u3044\u5834\u5408\u306f\u3001NaN\u3067\u57cb\u3081\u3089\u308c\u307e\u3059\u3002<\/p>\n\n\n\n
4. \u91cd\u8907\u30c7\u30fc\u30bf\u306e\u6271\u3044\u65b9<\/h3>\n\n\n\n
\u8907\u6570\u306e\u30c7\u30fc\u30bf\u30bd\u30fc\u30b9\u304b\u3089\u53ce\u96c6\u3057\u305f\u60c5\u5831\u3092\u7d50\u5408\u3059\u308b\u969b\u3001\u91cd\u8907\u3059\u308b\u30ec\u30b3\u30fc\u30c9\u304c\u5b58\u5728\u3059\u308b\u53ef\u80fd\u6027\u304c\u3042\u308a\u307e\u3059\u3002\u305d\u306e\u3088\u3046\u306a\u5834\u5408\u306f\u3001\u7d50\u5408\u5f8c\u306bdrop_duplicates()\u30e1\u30bd\u30c3\u30c9\u3092\u4f7f\u3063\u3066\u91cd\u8907\u3092\u524a\u9664\u3067\u304d\u307e\u3059\u3002<\/p>\n\n\n\n
import pandas as pd\n\n# \u5546\u54c1\u60c5\u5831\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e01\nproducts1 = pd.DataFrame({\n 'product_id': [1, 2, 3, 4, 3],\n 'name': ['Product A', 'Product B', 'Product C', 'Product D', 'Product C'],\n 'price': [10.99, 15.99, 7.99, 12.99, 8.99]\n})\n\n# \u5546\u54c1\u60c5\u5831\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e02\nproducts2 = pd.DataFrame({\n 'product_id': [3, 4, 5, 6, 3],\n 'name': ['Product C', 'Product D', 'Product E', 'Product F', 'Product C'],\n 'price': [7.99, 12.99, 9.99, 14.99, 9.49]\n})\n\n# \u5546\u54c1ID\u3092\u57fa\u6e96\u306b\u5916\u90e8\u7d50\u5408\nmerged_products = pd.merge(products1, products2, on='product_id', how='outer', suffixes=('_1', '_2'))\nprint(\"\u7d50\u5408\u5f8c\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0:\")\nprint(merged_products)\n\n# \u91cd\u8907\u3092\u524a\u9664\nmerged_products_unique = merged_products.drop_duplicates(subset=['product_id'])\nprint(\"\\n\u91cd\u8907\u524a\u9664\u5f8c\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0:\")\nprint(merged_products_unique)<\/pre>\n\n\n\n\u51fa\u529b\u7d50\u679c:<\/p>\n\n\n\n
\u7d50\u5408\u5f8c\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0:\n product_id name_1 price_1 name_2 price_2\n0 1 Product A 10.99 NaN NaN\n1 2 Product B 15.99 NaN NaN\n2 3 Product C 7.99 Product C 7.99\n3 3 Product C 7.99 Product C 9.49\n4 3 Product C 8.99 Product C 7.99\n5 3 Product C 8.99 Product C 9.49\n6 4 Product D 12.99 Product D 12.99\n7 5 NaN NaN Product E 9.99\n8 6 NaN NaN Product F 14.99\n\n\u91cd\u8907\u524a\u9664\u5f8c\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0:\n product_id name_1 price_1 name_2 price_2\n0 1 Product A 10.99 NaN NaN\n1 2 Product B 15.99 NaN NaN\n2 3 Product C 7.99 Product C 7.99\n6 4 Product D 12.99 Product D 12.99\n7 5 NaN NaN Product E 9.99\n8 6 NaN NaN Product F 14.99<\/pre>\n\n\n\n\u3053\u306e\u4f8b\u3067\u306f\u3001product_id\u304c\u91cd\u8907\u3057\u3066\u3044\u308b\u5546\u54c1\u60c5\u5831\u30922\u3064\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u304b\u3089\u5916\u90e8\u7d50\u5408\u3057\u3066\u3044\u307e\u3059\u3002\u7d50\u5408\u5f8c\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306b\u306f\u91cd\u8907\u30ec\u30b3\u30fc\u30c9\u304c\u5b58\u5728\u3057\u307e\u3059\u304c\u3001drop_duplicates(subset=[\u2018product_id\u2019])\u3092\u4f7f\u3046\u3053\u3068\u3067\u3001product_id\u3092\u57fa\u6e96\u306b\u91cd\u8907\u3092\u524a\u9664\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
5. \u8907\u6570\u306e\u30ab\u30e9\u30e0\u3092\u6307\u5b9a\u3057\u305f\u7d50\u5408<\/h3>\n\n\n\n
\u8907\u6570\u306e\u30ab\u30e9\u30e0\u3092\u6307\u5b9a\u3057\u3066\u7d50\u5408\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002\u4f8b\u3048\u3070\u3001\u5546\u54c1\u60c5\u5831\u3068\u5728\u5eab\u60c5\u5831\u3092\u7d50\u5408\u3059\u308b\u969b\u3001\u5546\u54c1\u30b3\u30fc\u30c9\u3068\u30b5\u30a4\u30ba\u306e2\u3064\u306e\u30ab\u30e9\u30e0\u3092\u6307\u5b9a\u3059\u308b\u5834\u5408\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
import pandas as pd\n\n# \u5546\u54c1\u60c5\u5831\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\nproducts = pd.DataFrame({'product_code': ['A001', 'A001', 'B001', 'B001'], 'size': ['S', 'M', 'S', 'M'], 'price': [1000, 1200, 2000, 2200]})\n\n# \u5728\u5eab\u60c5\u5831\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\ninventory = pd.DataFrame({'product_code': ['A001', 'A001', 'B001', 'C001'], 'size': ['S', 'M', 'M', 'L'], 'stock': [10, 20, 15, 30]})\n\n# product_code\u3068size\u3092\u57fa\u6e96\u306b\u5185\u90e8\u7d50\u5408\nmerged_df = pd.merge(products, inventory, on=['product_code', 'size'], how='inner')\nprint(merged_df)<\/pre>\n\n\n\n\u51fa\u529b\u7d50\u679c:<\/p>\n\n\n\n
product_code size price stock\n0 A001 S 1000 10\n1 A001 M 1200 20\n2 B001 M 2200 15<\/pre>\n\n\n\n\u3053\u306e\u4f8b\u3067\u306f\u3001on=[\u2018product_code\u2019, \u2018size\u2019]\u3092\u6307\u5b9a\u3059\u308b\u3053\u3068\u3067\u3001\u5546\u54c1\u30b3\u30fc\u30c9\u3068\u30b5\u30a4\u30ba\u306e2\u3064\u306e\u30ab\u30e9\u30e0\u3092\u57fa\u6e96\u306b\u5185\u90e8\u7d50\u5408\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
6. \u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u3092\u4f7f\u3063\u305f\u7d50\u5408<\/h3>\n\n\n\n
\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u306a\u3069\u3067\u306f\u3001\u65e5\u4ed8\u3092\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u306b\u8a2d\u5b9a\u3057\u3066\u7d50\u5408\u3059\u308b\u3053\u3068\u304c\u3042\u308a\u307e\u3059\u3002\u305d\u306e\u5834\u5408\u306f\u3001left_index\u3068right_index\u3092True\u306b\u8a2d\u5b9a\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
import pandas as pd\n\n# \u58f2\u4e0a\u30c7\u30fc\u30bf\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\nsales = pd.DataFrame({'date': ['2023-01-01', '2023-01-02', '2023-01-03'], 'product': ['A', 'B', 'C'], 'revenue': [100, 200, 300]})\nsales.set_index('date', inplace=True)\n\n# \u7d4c\u8cbb\u30c7\u30fc\u30bf\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\nexpenses = pd.DataFrame({'date': ['2023-01-01', '2023-01-03', '2023-01-05'], 'category': ['Rent', 'Utilities', 'Salaries'], 'cost': [1000, 500, 2000]})\nexpenses.set_index('date', inplace=True)\n\n# \u30a4\u30f3\u30c7\u30c3\u30af\u30b9\uff08date\uff09\u3092\u57fa\u6e96\u306b\u5916\u90e8\u7d50\u5408\nmerged_df = pd.merge(sales, expenses, left_index=True, right_index=True, how='outer')\nprint(merged_df)<\/pre>\n\n\n\n\u51fa\u529b\u7d50\u679c:<\/p>\n\n\n\n
product revenue category cost\ndate \n2023-01-01 A 100.0 Rent 1000.0\n2023-01-02 B 200.0 NaN NaN\n2023-01-03 C 300.0 Utilities 500.0\n2023-01-05 NaN NaN Salaries 2000.0<\/pre>\n\n\n\n\u3053\u306e\u4f8b\u3067\u306f\u3001left_index=True, right_index=True\u3092\u6307\u5b9a\u3059\u308b\u3053\u3068\u3067\u3001\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\uff08date\uff09\u3092\u57fa\u6e96\u306b\u5916\u90e8\u7d50\u5408\u3057\u3066\u3044\u307e\u3059\u3002\u8ca9\u58f2\u30c7\u30fc\u30bf\u3068\u7d4c\u8cbb\u30c7\u30fc\u30bf\u3092\u65e5\u4ed8\u3067\u7d50\u5408\u3057\u3001\u8ca9\u58f2\u3068\u7d4c\u8cbb\u306e\u4e21\u65b9\u304c\u5b58\u5728\u3059\u308b\u65e5\u4ed8\u3001\u8ca9\u58f2\u306e\u307f\u5b58\u5728\u3059\u308b\u65e5\u4ed8\u3001\u7d4c\u8cbb\u306e\u307f\u5b58\u5728\u3059\u308b\u65e5\u4ed8\u304c\u7d50\u5408\u5f8c\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306b\u542b\u307e\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
7. \u7d50\u5408\u5f8c\u306e\u30c7\u30fc\u30bf\u78ba\u8a8d\u3068\u30af\u30ea\u30fc\u30cb\u30f3\u30b0<\/h3>\n\n\n\n
\u7d50\u5408\u5f8c\u306e\u30c7\u30fc\u30bf\u306b\u306f\u3001\u6b20\u640d\u5024\u3084\u4e0d\u6574\u5408\u304c\u5b58\u5728\u3059\u308b\u53ef\u80fd\u6027\u304c\u3042\u308a\u307e\u3059\u3002\u305d\u306e\u305f\u3081\u3001\u7d50\u5408\u5f8c\u306b\u30c7\u30fc\u30bf\u306e\u78ba\u8a8d\u3068\u30af\u30ea\u30fc\u30cb\u30f3\u30b0\u304c\u5fc5\u8981\u3067\u3059\u3002<\/p>\n\n\n\n
import pandas as pd\n\n# \u9867\u5ba2\u60c5\u5831\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\ncustomers = pd.DataFrame({'customer_id': [1, 2, 3, 4], 'name': ['Alice', 'Bob', 'Carol', 'David'], 'age': [25, 30, 35, None]})\n\n# \u8cfc\u5165\u5c65\u6b74\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\npurchases = pd.DataFrame({'customer_id': [1, 2, 2, 3, 5], 'product': ['A', 'B', 'C', 'D', 'E'], 'price': [100, 200, 300, 400, 500]})\n\n# customer_id\u3092\u57fa\u6e96\u306b\u5de6\u7d50\u5408\nmerged_df = pd.merge(customers, purchases, on='customer_id', how='left')\nprint(\"\u7d50\u5408\u5f8c\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0:\")\nprint(merged_df)\n\n# \u6b20\u640d\u5024\u306e\u78ba\u8a8d\u3068\u30af\u30ea\u30fc\u30cb\u30f3\u30b0\nprint(\"\\n\u6b20\u640d\u5024\u306e\u6570:\")\nprint(merged_df.isnull().sum())\n\nmerged_df = merged_df.dropna(subset=['age'])\nprint(\"\\nage\u306e\u6b20\u640d\u5024\u3092\u524a\u9664\u5f8c\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0:\")\nprint(merged_df)<\/pre>\n\n\n\n\u51fa\u529b\u7d50\u679c:<\/p>\n\n\n\n
\u7d50\u5408\u5f8c\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0:\n customer_id name age product price\n0 1 Alice 25.0 A 100.0\n1 2 Bob 30.0 B 200.0\n2 2 Bob 30.0 C 300.0\n3 3 Carol 35.0 D 400.0\n4 4 David NaN NaN NaN\n\n\u6b20\u640d\u5024\u306e\u6570:\ncustomer_id 0\nname 0\nage 1\nproduct 1\nprice 1\ndtype: int64\n\nage\u306e\u6b20\u640d\u5024\u3092\u524a\u9664\u5f8c\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0:\n customer_id name age product price\n0 1 Alice 25.0 A 100.0\n1 2 Bob 30.0 B 200.0\n2 2 Bob 30.0 C 300.0\n3 3 Carol 35.0 D 400.0<\/pre>\n\n\n\n\u3053\u306e\u4f8b\u3067\u306f\u3001\u7d50\u5408\u5f8c\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306b\u306fage\u3068product\u3001price\u306b\u6b20\u640d\u5024\u304c\u5b58\u5728\u3057\u3066\u3044\u307e\u3059\u3002isnull().sum()\u3067\u6b20\u640d\u5024\u306e\u6570\u3092\u78ba\u8a8d\u3057\u305f\u5f8c\u3001dropna(subset=[\u2018age\u2019])\u3092\u4f7f\u3063\u3066age\u306e\u6b20\u640d\u5024\u3092\u542b\u3080\u884c\u3092\u524a\u9664\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
\u4ee5\u4e0a\u306e7\u3064\u306e\u30e6\u30fc\u30b9\u30b1\u30fc\u30b9\u306f\u3001merge\u95a2\u6570\u3092\u4f7f\u3063\u305f\u30c7\u30fc\u30bf\u7d50\u5408\u306e\u4ee3\u8868\u7684\u306a\u4f8b\u3067\u3059\u3002\u3053\u308c\u3089\u306e\u30e6\u30fc\u30b9\u30b1\u30fc\u30b9\u3092\u7406\u89e3\u3057\u3001\u9069\u5207\u306b\u6d3b\u7528\u3059\u308b\u3053\u3068\u3067\u3001\u30c7\u30fc\u30bf\u5206\u6790\u3084\u6a5f\u68b0\u5b66\u7fd2\u306e\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u306b\u304a\u3044\u3066\u3001\u52b9\u679c\u7684\u306b\u30c7\u30fc\u30bf\u3092\u7d50\u5408\u3057\u3001\u5206\u6790\u3084\u30e2\u30c7\u30eb\u69cb\u7bc9\u306b\u5fc5\u8981\u306a\u60c5\u5831\u3092\u5f97\u308b\u3053\u3068\u304c\u3067\u304d\u308b\u3067\u3057\u3087\u3046\u3002<\/p>\n\n\n\n
merge\u95a2\u6570\u306f\u3001SQL\u306eJOIN\u3068\u540c\u69d8\u306e\u6a5f\u80fd\u3092\u63d0\u4f9b\u3057\u3066\u304a\u308a\u3001\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u77e5\u8b58\u304c\u3042\u308c\u3070\u7406\u89e3\u3057\u3084\u3059\u3044\u30c4\u30fc\u30eb\u3067\u3059\u3002\u4e00\u65b9\u3067\u3001pandas\u306a\u3089\u3067\u306f\u306e\u67d4\u8edf\u6027\u3068\u4f7f\u3044\u3084\u3059\u3055\u304c\u3042\u308a\u3001Python\u3067\u306e\u30c7\u30fc\u30bf\u51e6\u7406\u306b\u6b20\u304b\u305b\u306a\u3044\u6a5f\u80fd\u3068\u306a\u3063\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
\u3053\u308c\u3089\u306e\u30e6\u30fc\u30b9\u30b1\u30fc\u30b9\u3092\u8e0f\u307e\u3048\u3066\u3001\u6b21\u306e\u898b\u51fa\u3057\u3067\u306f\u3001merge\u95a2\u6570\u3092\u4f7f\u3046\u4e0a\u3067\u306e\u6ce8\u610f\u70b9\u306b\u3064\u3044\u3066\u8aac\u660e\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
merge\u95a2\u6570\u306e\u6ce8\u610f\u70b9<\/h2>\n\n\n\n
merge\u95a2\u6570\u306f\u975e\u5e38\u306b\u4fbf\u5229\u306a\u30c4\u30fc\u30eb\u3067\u3059\u304c\u3001\u4f7f\u3044\u65b9\u306b\u3088\u3063\u3066\u306f\u30a8\u30e9\u30fc\u304c\u767a\u751f\u3057\u305f\u308a\u3001\u4e88\u671f\u3057\u306a\u3044\u7d50\u679c\u306b\u306a\u3063\u305f\u308a\u3059\u308b\u3053\u3068\u304c\u3042\u308a\u307e\u3059\u3002\u3053\u3053\u3067\u306f\u3001merge\u95a2\u6570\u3092\u4f7f\u3046\u4e0a\u3067\u306e\u6ce8\u610f\u70b9\u306b\u3064\u3044\u3066\u8aac\u660e\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
\u30a8\u30e9\u30fc\u304c\u8d77\u3053\u308b\u30b1\u30fc\u30b9\u3068\u5bfe\u51e6\u6cd5<\/h3>\n\n\n\n\n- \u7d50\u5408\u3059\u308b\u30ab\u30e9\u30e0\u540d\u306e\u6307\u5b9a\u30df\u30b9
on\u3001left_on\u3001right_on\u3092\u4f7f\u3063\u3066\u30ab\u30e9\u30e0\u540d\u3092\u6307\u5b9a\u3059\u308b\u969b\u3001\u5b58\u5728\u3057\u306a\u3044\u30ab\u30e9\u30e0\u540d\u3092\u6307\u5b9a\u3059\u308b\u3068\u30a8\u30e9\u30fc\u304c\u767a\u751f\u3057\u307e\u3059\u3002<\/li>\n<\/ul>\n\n\n\nimport pandas as pd\n\ndf1 = pd.DataFrame({'id': [1, 2, 3], 'value': ['A', 'B', 'C']})\ndf2 = pd.DataFrame({'id_new': [1, 2, 3], 'value': [10, 20, 30]})\n\nmerged_df = pd.merge(df1, df2, on='id')<\/pre>\n\n\n\n\u30a8\u30e9\u30fc\u30e1\u30c3\u30bb\u30fc\u30b8:<\/p>\n\n\n\n
KeyError: 'id'<\/pre>\n\n\n\n\u5bfe\u51e6\u6cd5\u306f\u3001\u6b63\u3057\u3044\u30ab\u30e9\u30e0\u540d\u3092\u6307\u5b9a\u3059\u308b\u3053\u3068\u3067\u3059\u3002\u3053\u306e\u4f8b\u3067\u306f\u3001df2\u306e\u30ab\u30e9\u30e0\u540d\u306fid_new\u306a\u306e\u3067\u3001left_on\u3068right_on\u3092\u4f7f\u3063\u3066\u6b63\u3057\u3044\u30ab\u30e9\u30e0\u540d\u3092\u6307\u5b9a\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
merged_df = pd.merge(df1, df2, left_on='id', right_on='id_new')\nprint(merged_df)<\/pre>\n\n\n\n\u51fa\u529b\u7d50\u679c:<\/p>\n\n\n\n
id value_x id_new value_y\n0 1 A 1 10\n1 2 B 2 20\n2 3 C 3 30<\/pre>\n\n\n\n
\n- \u4e88\u671f\u3057\u306a\u3044\u7d50\u5408\u7d50\u679c
merge\u95a2\u6570\u3067\u306f\u3001\u30c7\u30d5\u30a9\u30eb\u30c8\u3067\u306f\u5185\u90e8\u7d50\u5408\uff08inner join\uff09\u304c\u4f7f\u308f\u308c\u307e\u3059\u3002\u5916\u90e8\u7d50\u5408\uff08outer join\uff09\u3092\u4f7f\u3044\u305f\u3044\u5834\u5408\u306f\u3001how\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u660e\u793a\u7684\u306b\u6307\u5b9a\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/li>\n<\/ul>\n\n\n\nimport pandas as pd\n\ndf1 = pd.DataFrame({'id': [1, 2, 3, 4], 'value': ['A', 'B', 'C', 'D']})\ndf2 = pd.DataFrame({'id': [1, 2, 5], 'value': [10, 20, 50]})\n\nmerged_df_inner = pd.merge(df1, df2, on='id')\nprint(\"\u5185\u90e8\u7d50\u5408\u306e\u7d50\u679c:\")\nprint(merged_df_inner)\n\nmerged_df_outer = pd.merge(df1, df2, on='id', how='outer')\nprint(\"\\n\u5916\u90e8\u7d50\u5408\u306e\u7d50\u679c:\")\nprint(merged_df_outer)<\/pre>\n\n\n\n\u51fa\u529b\u7d50\u679c:<\/p>\n\n\n\n
\u5185\u90e8\u7d50\u5408\u306e\u7d50\u679c:\n id value_x value_y\n0 1 A 10\n1 2 B 20\n\n\u5916\u90e8\u7d50\u5408\u306e\u7d50\u679c:\n id value_x value_y\n0 1 A 10.0\n1 2 B 20.0\n2 3 C NaN\n3 4 D NaN\n4 5 NaN 50.0<\/pre>\n\n\n\n\u5185\u90e8\u7d50\u5408\u3067\u306f\u3001\u4e21\u65b9\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306b\u5b58\u5728\u3059\u308bid\u306e\u307f\u304c\u7d50\u5408\u3055\u308c\u307e\u3059\u304c\u3001\u5916\u90e8\u7d50\u5408\u3067\u306f\u7247\u65b9\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306b\u3057\u304b\u5b58\u5728\u3057\u306a\u3044id\u3082\u7d50\u5408\u5f8c\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306b\u542b\u307e\u308c\u307e\u3059\u3002\u610f\u56f3\u3057\u305f\u7d50\u679c\u3092\u5f97\u308b\u305f\u3081\u306b\u306f\u3001how\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u9069\u5207\u306b\u8a2d\u5b9a\u3059\u308b\u3053\u3068\u304c\u91cd\u8981\u3067\u3059\u3002<\/p>\n\n\n\n
\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u3092\u610f\u8b58\u3057\u305f\u4f7f\u3044\u65b9<\/h3>\n\n\n\n
merge\u95a2\u6570\u3092\u4f7f\u3046\u969b\u306f\u3001\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u306b\u3082\u914d\u616e\u3059\u308b\u3053\u3068\u304c\u5927\u5207\u3067\u3059\u3002\u4ee5\u4e0b\u306f\u3001\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u3092\u6539\u5584\u3059\u308b\u305f\u3081\u306e\u30d2\u30f3\u30c8\u3067\u3059\u3002<\/p>\n\n\n\n
\n- \u5927\u304d\u306a\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u7d50\u5408\u3059\u308b\u5834\u5408\u3001merge\u306e\u524d\u306bdrop_duplicates()\u3092\u4f7f\u3063\u3066\u91cd\u8907\u3092\u524a\u9664\u3057\u3066\u304a\u304f\u3068\u3001\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u304c\u5411\u4e0a\u3059\u308b\u53ef\u80fd\u6027\u304c\u3042\u308a\u307e\u3059\u3002<\/li>\n\n\n\n
- \u7d50\u5408\u3059\u308b\u30ab\u30e9\u30e0\u306bindex\u3092\u8a2d\u5b9a\u3057\u3066\u304a\u304f\u3053\u3068\u3067\u3001merge\u306e\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u304c\u6539\u5584\u3055\u308c\u308b\u5834\u5408\u304c\u3042\u308a\u307e\u3059\u3002<\/li>\n\n\n\n
- \u7d50\u5408\u5f8c\u306b\u5fc5\u8981\u306a\u30ab\u30e9\u30e0\u3060\u3051\u3092\u9078\u629e\u3059\u308b\u3053\u3068\u3067\u3001\u30e1\u30e2\u30ea\u4f7f\u7528\u91cf\u3092\u524a\u6e1b\u3067\u304d\u307e\u3059\u3002<\/li>\n<\/ul>\n\n\n\n
\u4ee5\u4e0a\u306e\u6ce8\u610f\u70b9\u3068\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u6539\u5584\u306e\u30d2\u30f3\u30c8\u3092\u7406\u89e3\u3057\u3001\u9069\u5207\u306b\u6d3b\u7528\u3059\u308b\u3053\u3068\u3067\u3001merge\u95a2\u6570\u3092\u3088\u308a\u52b9\u679c\u7684\u306b\u4f7f\u3044\u3053\u306a\u3059\u3053\u3068\u304c\u3067\u304d\u308b\u3067\u3057\u3087\u3046\u3002<\/p>\n\n\n\n
\u307e\u3068\u3081<\/h2>\n\n\n\n
\u3053\u306e\u8a18\u4e8b\u3067\u306f\u3001pandas\u306emerge\u95a2\u6570\u306b\u3064\u3044\u3066\u3001\u57fa\u672c\u7684\u306a\u4f7f\u3044\u65b9\u304b\u3089\u30e6\u30fc\u30b9\u30b1\u30fc\u30b9\u3001\u6ce8\u610f\u70b9\u307e\u3067\u8a73\u3057\u304f\u89e3\u8aac\u3057\u3066\u304d\u307e\u3057\u305f\u3002merge\u95a2\u6570\u306f\u3001\u8907\u6570\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u7d50\u5408\u3059\u308b\u305f\u3081\u306e\u5f37\u529b\u306a\u30c4\u30fc\u30eb\u3067\u3042\u308a\u3001\u30c7\u30fc\u30bf\u5206\u6790\u3084\u6a5f\u68b0\u5b66\u7fd2\u306e\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u3067\u983b\u7e41\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002<\/p>\n\n\n\n
merge\u95a2\u6570\u306e\u4f7f\u3044\u3053\u306a\u3057\u30dd\u30a4\u30f3\u30c8<\/h3>\n\n\n\n
merge\u95a2\u6570\u3092\u52b9\u679c\u7684\u306b\u4f7f\u3044\u3053\u306a\u3059\u305f\u3081\u306b\u306f\u3001\u4ee5\u4e0b\u306e\u30dd\u30a4\u30f3\u30c8\u3092\u62bc\u3055\u3048\u3066\u304a\u304f\u3053\u3068\u304c\u91cd\u8981\u3067\u3059\u3002<\/p>\n\n\n\n
\n- \u30c7\u30fc\u30bf\u578b\u306e\u4e0d\u4e00\u81f4\u306b\u3088\u308b\u30a8\u30e9\u30fc\u3092\u907f\u3051\u308b\u305f\u3081\u3001\u7d50\u5408\u3059\u308b\u30ab\u30e9\u30e0\u306e\u30c7\u30fc\u30bf\u578b\u3092\u63c3\u3048\u308b\u3002<\/li>\n\n\n\n
- \u7d50\u5408\u3059\u308b\u30ab\u30e9\u30e0\u540d\u3092\u6b63\u3057\u304f\u6307\u5b9a\u3057\u3001\u5b58\u5728\u3057\u306a\u3044\u30ab\u30e9\u30e0\u540d\u3092\u6307\u5b9a\u3057\u306a\u3044\u3088\u3046\u306b\u3059\u308b\u3002<\/li>\n\n\n\n
- \u5185\u90e8\u7d50\u5408\u3068\u5916\u90e8\u7d50\u5408\u306e\u9055\u3044\u3092\u7406\u89e3\u3057\u3001\u76ee\u7684\u306b\u5fdc\u3058\u3066\u9069\u5207\u306a\u7d50\u5408\u65b9\u6cd5\u3092\u9078\u629e\u3059\u308b\u3002<\/li>\n\n\n\n
- \u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u3092\u610f\u8b58\u3057\u3001\u5927\u304d\u306a\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u7d50\u5408\u3059\u308b\u969b\u306f\u91cd\u8907\u306e\u524a\u9664\u3084\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u306e\u8a2d\u5b9a\u3092\u691c\u8a0e\u3059\u308b\u3002<\/li>\n<\/ol>\n\n\n\n
\u3053\u308c\u3089\u306e\u30dd\u30a4\u30f3\u30c8\u3092\u610f\u8b58\u3057\u306a\u304c\u3089merge\u95a2\u6570\u3092\u4f7f\u3046\u3053\u3068\u3067\u3001\u3088\u308a\u52b9\u7387\u7684\u304b\u3064\u6b63\u78ba\u306b\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u7d50\u5408\u3067\u304d\u308b\u3067\u3057\u3087\u3046\u3002<\/p>\n\n\n\n
pandas\u3092\u4f7f\u3063\u305f\u30c7\u30fc\u30bf\u5206\u6790\u306e\u30b9\u30c6\u30c3\u30d7\u30a2\u30c3\u30d7\u306b\u5411\u3051\u3066<\/h3>\n\n\n\n
merge\u95a2\u6570\u3092\u7fd2\u5f97\u3057\u305f\u3089\u3001\u6b21\u306fpandas\u306e\u4ed6\u306e\u6a5f\u80fd\u306b\u3064\u3044\u3066\u3082\u5b66\u7fd2\u3092\u6df1\u3081\u3066\u3044\u304d\u307e\u3057\u3087\u3046\u3002\u4ee5\u4e0b\u306f\u3001\u30c7\u30fc\u30bf\u5206\u6790\u306e\u30b9\u30ad\u30eb\u30a2\u30c3\u30d7\u306b\u5f79\u7acb\u3064\u30c8\u30d4\u30c3\u30af\u306e\u4f8b\u3067\u3059\u3002<\/p>\n\n\n\n
\n- groupby()\u3092\u4f7f\u3063\u305f\u30c7\u30fc\u30bf\u306e\u30b0\u30eb\u30fc\u30d7\u5316\u3068\u96c6\u8a08<\/li>\n\n\n\n
- pivot_table()\u3092\u4f7f\u3063\u305f\u30d4\u30dc\u30c3\u30c8\u30c6\u30fc\u30d6\u30eb\u306e\u4f5c\u6210<\/li>\n\n\n\n
- apply()\u3092\u4f7f\u3063\u305f\u30ab\u30b9\u30bf\u30e0\u95a2\u6570\u306e\u9069\u7528<\/li>\n\n\n\n
- matplotlib\u3001seaborn\u306a\u3069\u306e\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u4f7f\u3063\u305f\u30c7\u30fc\u30bf\u306e\u53ef\u8996\u5316<\/li>\n<\/ul>\n\n\n\n
\u3053\u308c\u3089\u306e\u6a5f\u80fd\u3092\u7d44\u307f\u5408\u308f\u305b\u308b\u3053\u3068\u3067\u3001\u8907\u96d1\u306a\u30c7\u30fc\u30bf\u51e6\u7406\u3084\u5206\u6790\u3092\u52b9\u7387\u7684\u306b\u884c\u3048\u308b\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002\u4ee5\u4e0b\u306f\u3001groupby()\u3092\u4f7f\u3063\u305f\u30c7\u30fc\u30bf\u306e\u96c6\u8a08\u4f8b\u3067\u3059\u3002<\/p>\n\n\n\n
import pandas as pd\n\n# \u30b5\u30f3\u30d7\u30eb\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306e\u4f5c\u6210\ndata = {\n 'category': ['A', 'B', 'C', 'A', 'B', 'C'],\n 'value': [10, 20, 30, 40, 50, 60]\n}\ndf = pd.DataFrame(data)\n\n# groupby()\u3092\u4f7f\u3063\u305f\u30c7\u30fc\u30bf\u306e\u96c6\u8a08\ngrouped_df = df.groupby('category')['value'].sum().reset_index()\nprint(grouped_df)<\/pre>\n\n\n\n\u51fa\u529b\u7d50\u679c:<\/p>\n\n\n\n
category value\n0 A 50\n1 B 70\n2 C 90<\/pre>\n\n\n\n\u3053\u306e\u3088\u3046\u306b\u3001pandas\u306e\u5404\u6a5f\u80fd\u3092\u4f7f\u3044\u3053\u306a\u305b\u308b\u3088\u3046\u306b\u306a\u308b\u3068\u3001\u30c7\u30fc\u30bf\u5206\u6790\u306e\u5e45\u304c\u5927\u304d\u304f\u5e83\u304c\u308a\u307e\u3059\u3002\u4eca\u56de\u5b66\u3093\u3060merge\u95a2\u6570\u3092\u6d3b\u7528\u3057\u3064\u3064\u3001\u4ed6\u306e\u6a5f\u80fd\u306b\u3064\u3044\u3066\u3082\u5b66\u7fd2\u3092\u6df1\u3081\u3066\u3044\u304f\u3053\u3068\u3067\u3001\u30c7\u30fc\u30bf\u5206\u6790\u306e\u30d7\u30ed\u30d5\u30a7\u30c3\u30b7\u30e7\u30ca\u30eb\u3092\u76ee\u6307\u3057\u307e\u3057\u3087\u3046\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
pandas\u306emerge\u95a2\u6570\u306f\u3001\u30c7\u30fc\u30bf\u5206\u6790\u3084\u6a5f\u68b0\u5b66\u7fd2\u306e\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u3067\u6b20\u304b\u305b\u306a\u3044\u30c4\u30fc\u30eb\u3067\u3059\u3002\u3053\u306e\u8a18\u4e8b\u3067\u306f\u3001merge\u95a2\u6570\u306e\u57fa\u672c\u304b\u3089\u5b9f\u8df5\u7684\u306a\u4f7f\u3044\u65b9\u307e\u3067\u30017\u3064\u306e\u30e6\u30fc\u30b9\u30b1\u30fc\u30b9\u3092\u901a\u3058\u3066\u5fb9\u5e95\u7684\u306b\u89e3\u8aac\u3057\u307e\u3059\u3002\u521d\u5fc3\u8005\u304b\u3089\u30c7\u30fc\u30bf\u5206\u6790\u306e\u30d7\u30ed … <\/p>\n","protected":false},"author":1,"featured_media":835,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[],"class_list":{"0":"post-793","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-python"},"_links":{"self":[{"href":"https:\/\/chocottopro.com\/index.php?rest_route=\/wp\/v2\/posts\/793","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/chocottopro.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/chocottopro.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/chocottopro.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/chocottopro.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=793"}],"version-history":[{"count":3,"href":"https:\/\/chocottopro.com\/index.php?rest_route=\/wp\/v2\/posts\/793\/revisions"}],"predecessor-version":[{"id":796,"href":"https:\/\/chocottopro.com\/index.php?rest_route=\/wp\/v2\/posts\/793\/revisions\/796"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/chocottopro.com\/index.php?rest_route=\/wp\/v2\/media\/835"}],"wp:attachment":[{"href":"https:\/\/chocottopro.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=793"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/chocottopro.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=793"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/chocottopro.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=793"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}