{"id":405,"date":"2022-10-10T07:53:12","date_gmt":"2023-08-20T20:16:02","guid":{"rendered":"https:\/\/www.silicloud.com\/ja\/blog\/index.php\/2023\/11\/30\/pandas%e3%81%aemerge-2%e3%81%a4%e3%81%aedataframe%e3%82%aa%e3%83%96%e3%82%b8%e3%82%a7%e3%82%af%e3%83%88%e3%82%92%e7%b5%90%e5%90%88\/"},"modified":"2025-07-31T23:20:26","modified_gmt":"2025-07-31T14:20:26","slug":"pandas%e3%81%aemerge-2%e3%81%a4%e3%81%aedataframe%e3%82%aa%e3%83%96%e3%82%b8%e3%82%a7%e3%82%af%e3%83%88%e3%82%92%e7%b5%90%e5%90%88","status":"publish","type":"post","link":"https:\/\/www.silicloud.com\/ja\/blog\/pandas%e3%81%aemerge-2%e3%81%a4%e3%81%aedataframe%e3%82%aa%e3%83%96%e3%82%b8%e3%82%a7%e3%82%af%e3%83%88%e3%82%92%e7%b5%90%e5%90%88\/","title":{"rendered":"Pandas\u306emerge() – 2\u3064\u306eDataFrame\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3092\u7d50\u5408"},"content":{"rendered":"

\u30d1\u30f3\u30c0\u306eDataFrame\u306emerge()\u95a2\u6570\u306f\u3001\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u30b9\u30bf\u30a4\u30eb\u306e\u7d50\u5408\u64cd\u4f5c\u3092\u7528\u3044\u30662\u3064\u306eDataFrame\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3092\u7d50\u5408\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002\u7d50\u5408\u306f\u5217\u307e\u305f\u306f\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u3067\u884c\u308f\u308c\u307e\u3059\u3002\u3082\u3057\u7d50\u5408\u304c\u5217\u3067\u884c\u308f\u308c\u308b\u5834\u5408\u3001\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u306f\u7121\u8996\u3055\u308c\u307e\u3059\u3002\u3053\u306e\u95a2\u6570\u306f\u65b0\u3057\u3044DataFrame\u3092\u8fd4\u3057\u3001\u5143\u306eDataFrame\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u306f\u5909\u66f4\u3055\u308c\u307e\u305b\u3093\u3002<\/p>\n

\u30d1\u30f3\u30c0\u306eDataFrame\u306emerge()\u95a2\u6570\u306e\u69cb\u6587<\/h2>\n

merge()\u95a2\u6570\u306e\u69cb\u6587\u306f\u6b21\u306e\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n

def merge(\r\n    self,\r\n    right,\r\n    how=\"inner\",\r\n    on=None,\r\n    left_on=None,\r\n    right_on=None,\r\n    left_index=False,\r\n    right_index=False,\r\n    sort=False,\r\n    suffixes=(\"_x\", \"_y\"),\r\n    copy=True,\r\n    indicator=False,\r\n    validate=None,\r\n)\r\n<\/code><\/pre>\n
    \n
  • right: The other DataFrame to merge with the source DataFrame.<\/li>\n
  • how: {\u2018left\u2019, \u2018right\u2019, \u2018outer\u2019, \u2018inner\u2019}, default \u2018inner\u2019. This is the most important parameter to define the merge operation type. These are similar to SQL left outer join, right outer join, full outer join, and inner join.<\/li>\n
  • on: Column or index level names to join on. These columns must be present in both the DataFrames. If not provided, the intersection of the columns in both DataFrames are used.<\/li>\n
  • left_on: Column or index level names to join on in the left DataFrame.<\/li>\n
  • right_on: Column or index level names to join on in the right DataFrame.<\/li>\n
  • left_index: Use the index from the left DataFrame as the join key(s).<\/li>\n
  • right_index: Use the index from the right DataFrame as the join key.<\/li>\n
  • sort: Sort the join keys lexicographically in the result DataFrame.<\/li>\n
  • suffixes: Suffix to apply to overlapping column names in the left and right side, respectively.<\/li>\n
  • indicator: If True, adds a column to output DataFrame called \u201c_merge\u201d with information on the source of each row.<\/li>\n
  • validate: used to validate the merge process. The valid values are {\u201cone_to_one\u201d or \u201c1:1\u201d, \u201cone_to_many\u201d or \u201c1:m\u201d, \u201cmany_to_one\u201d or \u201cm:1\u201d, \u201cmany_to_many\u201d or \u201cm:m\u201d}.<\/li>\n<\/ul>\n
    \u30d1\u30f3\u30c0\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306emerge()\u306e\u4f8b<\/h2>\n
    DataFrame\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3092\u7d50\u5408\u3059\u308b\u3044\u304f\u3064\u304b\u306e\u4f8b\u3092\u898b\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n
    1. \u30c7\u30d5\u30a9\u30eb\u30c8\u7d50\u5408 – \u5185\u90e8\u7d50\u5408<\/h3>\n
    import pandas as pd\r\n\r\nd1 = {'Name': ['Pankaj', 'Meghna', 'Lisa'], 'Country': ['India', 'India', 'USA'], 'Role': ['CEO', 'CTO', 'CTO']}\r\n\r\ndf1 = pd.DataFrame(d1)\r\n\r\nprint('DataFrame 1:\\n', df1)\r\n\r\ndf2 = pd.DataFrame({'ID': [1, 2, 3], 'Name': ['Pankaj', 'Anupam', 'Amit']})\r\nprint('DataFrame 2:\\n', df2)\r\n\r\ndf_merged = df1.merge(df2)\r\nprint('Result:\\n', df_merged)\r\n<\/code><\/pre>\n
    \u51fa\u529b:<\/p>\n
    DataFrame 1:\r\n      Name Country Role\r\n0  Pankaj   India  CEO\r\n1  Meghna   India  CTO\r\n2    Lisa     USA  CTO\r\nDataFrame 2:\r\n    ID    Name\r\n0   1  Pankaj\r\n1   2  Anupam\r\n2   3    Amit\r\nResult:\r\n      Name Country Role  ID\r\n0  Pankaj   India  CEO   1\r\n<\/code><\/pre>\n
    2. \u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u5de6\u7d50\u5408\u3001\u53f3\u7d50\u5408\u3001\u5916\u90e8\u7d50\u5408\u3067\u30de\u30fc\u30b8\u3059\u308b<\/h3>\n
    print('Result Left Join:\\n', df1.merge(df2, how='left'))\r\nprint('Result Right Join:\\n', df1.merge(df2, how='right'))\r\nprint('Result Outer Join:\\n', df1.merge(df2, how='outer'))\r\n<\/code><\/pre>\n
    \u51fa\u529b\uff1a<\/p>\n
    Result Left Join:\r\n      Name Country Role   ID\r\n0  Pankaj   India  CEO  1.0\r\n1  Meghna   India  CTO  NaN\r\n2    Lisa     USA  CTO  NaN\r\nResult Right Join:\r\n      Name Country Role  ID\r\n0  Pankaj   India  CEO   1\r\n1  Anupam     NaN  NaN   2\r\n2    Amit     NaN  NaN   3\r\nResult Outer Join:\r\n      Name Country Role   ID\r\n0  Pankaj   India  CEO  1.0\r\n1  Meghna   India  CTO  NaN\r\n2    Lisa     USA  CTO  NaN\r\n3  Anupam     NaN  NaN  2.0\r\n4    Amit     NaN  NaN  3.0\r\n<\/code><\/pre>\n
    3. \u7279\u5b9a\u306e\u5217\u3092\u57fa\u6e96\u306b\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u7d50\u5408\u3057\u307e\u3059\u3002<\/h3>\n
    import pandas as pd\r\n\r\nd1 = {'Name': ['Pankaj', 'Meghna', 'Lisa'], 'ID': [1, 2, 3], 'Country': ['India', 'India', 'USA'],\r\n      'Role': ['CEO', 'CTO', 'CTO']}\r\ndf1 = pd.DataFrame(d1)\r\n\r\ndf2 = pd.DataFrame({'ID': [1, 2, 3], 'Name': ['Pankaj', 'Anupam', 'Amit']})\r\n\r\nprint(df1.merge(df2, on='ID'))\r\nprint(df1.merge(df2, on='Name'))\r\n<\/code><\/pre>\n
    \u51fa\u529b:<\/p>\n
       Name_x  ID Country Role  Name_y\r\n0  Pankaj   1   India  CEO  Pankaj\r\n1  Meghna   2   India  CTO  Anupam\r\n2    Lisa   3     USA  CTO    Amit\r\n\r\n     Name  ID_x Country Role  ID_y\r\n0  Pankaj     1   India  CEO     1\r\n<\/code><\/pre>\n
    \uff14\uff0e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u306e\u30de\u30fc\u30b8\u306e\u305f\u3081\u306b\u3001\u5de6\u5074\u3068\u53f3\u5074\u306e\u5217\u3092\u6307\u5b9a\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/h3>\n
    import pandas as pd\r\n\r\nd1 = {'Name': ['Pankaj', 'Meghna', 'Lisa'], 'ID1': [1, 2, 3], 'Country': ['India', 'India', 'USA'],\r\n      'Role': ['CEO', 'CTO', 'CTO']}\r\ndf1 = pd.DataFrame(d1)\r\n\r\ndf2 = pd.DataFrame({'ID2': [1, 2, 3], 'Name': ['Pankaj', 'Anupam', 'Amit']})\r\n\r\nprint(df1.merge(df2))\r\n\r\nprint(df1.merge(df2, left_on='ID1', right_on='ID2'))\r\n<\/code><\/pre>\n
    \u51fa\u529b\uff1b<\/p>\n
         Name  ID1 Country Role  ID2\r\n0  Pankaj    1   India  CEO    1\r\n\r\n   Name_x  ID1 Country Role  ID2  Name_y\r\n0  Pankaj    1   India  CEO    1  Pankaj\r\n1  Meghna    2   India  CTO    2  Anupam\r\n2    Lisa    3     USA  CTO    3    Amit\r\n<\/code><\/pre>\n
    \u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u30de\u30fc\u30b8\u3059\u308b\u305f\u3081\u306e\u7d50\u5408\u30ad\u30fc\u3068\u3057\u3066\u3001\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u3092\u4f7f\u7528\u3059\u308b\u3002<\/h3>\n
    import pandas as pd\r\n\r\nd1 = {'Name': ['Pankaj', 'Meghna', 'Lisa'], 'Country': ['India', 'India', 'USA'], 'Role': ['CEO', 'CTO', 'CTO']}\r\ndf1 = pd.DataFrame(d1)\r\n\r\ndf2 = pd.DataFrame({'ID': [1, 2, 3], 'Name': ['Pankaj', 'Anupam', 'Amit']})\r\n\r\ndf_merged = df1.merge(df2)\r\nprint('Result Default Merge:\\n', df_merged)\r\n\r\ndf_merged = df1.merge(df2, left_index=True, right_index=True)\r\nprint('\\nResult Index Merge:\\n', df_merged)\r\n<\/code><\/pre>\n
    \u51fa\u529b\uff1a<\/p>\n
    Result Default Merge:\r\n      Name Country Role  ID\r\n0  Pankaj   India  CEO   1\r\n\r\nResult Index Merge:\r\n    Name_x Country Role  ID  Name_y\r\n0  Pankaj   India  CEO   1  Pankaj\r\n1  Meghna   India  CTO   2  Anupam\r\n2    Lisa     USA  CTO   3    Amit\r\n<\/code><\/pre>\n
    \u300cReferences\u300d\u306e\u30cd\u30a4\u30c6\u30a3\u30d6\u306a\u65e5\u672c\u8a9e\u3067\u306e\u8868\u73fe\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\uff08\u4e00\u3064\u306e\u30aa\u30d7\u30b7\u30e7\u30f3\u306e\u307f\u63d0\u4f9b\uff09\uff1a\u53c2\u8003\u6587\u732e<\/h2>\n
      \n
    • Python Pandas Module Tutorial<\/li>\n
    • DataFrame merge() API Doc<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"
      \u30d1\u30f3\u30c0\u306eDataFrame\u306emerge()\u95a2\u6570\u306f\u3001\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u30b9\u30bf\u30a4\u30eb\u306e\u7d50\u5408\u64cd\u4f5c\u3092\u7528\u3044\u30662\u3064\u306eDataFrame\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3092\u7d50\u5408\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002\u7d50\u5408\u306f\u5217\u307e\u305f\u306f\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u3067\u884c\u308f\u308c\u307e\u3059\u3002\u3082\u3057\u7d50\u5408\u304c\u5217\u3067\u884c\u308f\u308c\u308b\u5834\u5408 […]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[26,61],"class_list":["post-405","post","type-post","status-publish","format-standard","hentry","category-uncategorized","tag-26","tag-61"],"yoast_head":"\nPandas\u306emerge() - 2\u3064\u306eDataFrame\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3092\u7d50\u5408 - 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