{"id":334,"date":"2023-12-09T23:35:54","date_gmt":"2023-10-23T19:59:50","guid":{"rendered":"https:\/\/www.silicloud.com\/ja\/blog\/index.php\/2023\/11\/30\/%e3%83%91%e3%83%b3%e3%83%80%e3%81%ae%e5%88%97%e3%81%a8%e3%82%a4%e3%83%b3%e3%83%87%e3%83%83%e3%82%af%e3%82%b9%e3%82%92%e5%90%8d%e5%89%8d%e5%a4%89%e6%9b%b4\/"},"modified":"2025-07-31T22:45:18","modified_gmt":"2025-07-31T13:45:18","slug":"%e3%83%91%e3%83%b3%e3%83%80%e3%81%ae%e5%88%97%e3%81%a8%e3%82%a4%e3%83%b3%e3%83%87%e3%83%83%e3%82%af%e3%82%b9%e3%82%92%e5%90%8d%e5%89%8d%e5%a4%89%e6%9b%b4","status":"publish","type":"post","link":"https:\/\/www.silicloud.com\/ja\/blog\/%e3%83%91%e3%83%b3%e3%83%80%e3%81%ae%e5%88%97%e3%81%a8%e3%82%a4%e3%83%b3%e3%83%87%e3%83%83%e3%82%af%e3%82%b9%e3%82%92%e5%90%8d%e5%89%8d%e5%a4%89%e6%9b%b4\/","title":{"rendered":"\u30d1\u30f3\u30c0\u306e\u5217\u3068\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u3092\u540d\u524d\u5909\u66f4"},"content":{"rendered":"<p>\u6642\u3005\u3001Pandas\u306eDataFrame\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3067\u5217\u3084\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u306e\u540d\u524d\u3092\u5909\u66f4\u3057\u305f\u3044\u5834\u5408\u304c\u3042\u308a\u307e\u3059\u3002\u5217\u3084\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u306e\u540d\u524d\u3092\u5909\u66f4\u3059\u308b\u305f\u3081\u306b\u306f\u3001pandas DataFrame\u306erename\uff08\uff09\u95a2\u6570\u3092\u4f7f\u7528\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u3053\u306e\u95a2\u6570\u306f\u4ee5\u4e0b\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u30b5\u30dd\u30fc\u30c8\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n<ul class=\"post-ul\">\n<li>mapper: dictionary or a function to apply on the columns and indexes. The \u2018axis\u2019 parameter determines the target axis &#8211; columns or indexes.<\/li>\n<li>index: must be a dictionary or function to change the index names.<\/li>\n<li>columns: must be a dictionary or function to change the column names.<\/li>\n<li>axis: can be int or string. It\u2019s used with \u2018mapper\u2019 parameter to define the target axis. The allowed values are (\u2018index\u2019, \u2018columns\u2019) or number (0, 1). The default value is \u2018index\u2019.<\/li>\n<li>inplace: if True, the DataFrame is changed. Otherwise, a new DataFrame is returned and the current DataFrame remains unchanged. The default value is \u2018False\u2019.<\/li>\n<li>level: can be int or level name. It\u2019s used in case of a MultiIndex, only rename labels in the specified level.<\/li>\n<li>errors: possible values are (\u2018ignore\u2019, \u2018raise\u2019), default is \u2018ignore\u2019. If specified as \u2018raise\u2019 then KeyError is raised when a dict-like \u2018mapper\u2019, \u2018index\u2019, or \u2018columns\u2019 contains labels that are not present in the Index being transformed. If \u2018ignore\u2019, existing keys will be renamed and extra keys will be ignored.<\/li>\n<\/ul>\n<p>rename()\u95a2\u6570\u306b\u3064\u3044\u3066\u306e\u3044\u304f\u3064\u304b\u306e\u91cd\u8981\u306a\u30dd\u30a4\u30f3\u30c8\u3002<\/p>\n<ol>\n<li style=\"list-style-type: none;\">\n<ol>\u610f\u56f3\u3092\u660e\u78ba\u306b\u3059\u308b\u305f\u3081\u306b\u3001\u30ad\u30fc\u30ef\u30fc\u30c9\u5f15\u6570\u3092\u4f7f\u7528\u3059\u308b\u3053\u3068\u304c\u304a\u3059\u3059\u3081\u3067\u3059\u3002<\/ol>\n<\/li>\n<\/ol>\n<p>&nbsp;<\/p>\n<ol>\u3053\u306e\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u8f9e\u66f8\u5185\u306e\u5024\u306b\u3088\u3063\u3066\u3001\u5358\u4e00\u306e\u5217\u307e\u305f\u306f\u8907\u6570\u306e\u5217\u306e\u540d\u524d\u3092\u5909\u66f4\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/ol>\n<p>Pandas\u306erename()\u95a2\u6570\u306e\u4f7f\u7528\u4f8b\u3092\u3044\u304f\u3064\u304b\u898b\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n<h2>1. \u30d1\u30f3\u30c0\u306e\u5217\u540d\u3092\u5909\u66f4\u3059\u308b\u3002<\/h2>\n<pre class=\"post-pre\"><code>import pandas as pd\r\n\r\nd1 = {'Name': ['Pankaj', 'Lisa', 'David'], 'ID': [1, 2, 3], 'Role': ['CEO', 'Editor', 'Author']}\r\n\r\ndf = pd.DataFrame(d1)\r\n\r\nprint('Source DataFrame:\\n', df)\r\n\r\n# rename columns\r\ndf1 = df.rename(columns={'Name': 'EmpName', 'ID': 'EmpID', 'Role': 'EmpRole'})\r\nprint('Result DataFrame:\\n', df1)\r\n<\/code><\/pre>\n<p>\u51fa\u529b:<\/p>\n<pre class=\"post-pre\"><code>Source DataFrame:\r\n      Name  ID    Role\r\n0  Pankaj   1     CEO\r\n1    Lisa   2  Editor\r\n2   David   3  Author\r\nResult DataFrame:\r\n   EmpName  EmpID EmpRole\r\n0  Pankaj      1     CEO\r\n1    Lisa      2  Editor\r\n2   David      3  Author\r\n<\/code><\/pre>\n<p>\u4e0a\u8a18\u306erename\uff08\uff09\u95a2\u6570\u306e\u547c\u3073\u51fa\u3057\u306f\u3001\u6b21\u306e\u3088\u3046\u306b\u3082\u66f8\u304f\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n<pre class=\"post-pre\"><code>df1 = df.rename(mapper={'Name': 'EmpName', 'ID': 'EmpID', 'Role': 'EmpRole'},\r\n                axis='columns')  # axis=1 corresponds to columns\r\n<\/code><\/pre>\n<p>\u30ad\u30fc\u30ef\u30fc\u30c9\u5f15\u6570\u3092\u4f7f\u7528\u3059\u308b\u65b9\u304c\u3001\u30de\u30c3\u30d1\u30fc\u3084\u8ef8\u5f15\u6570\u3092\u4f7f\u7528\u3059\u308b\u3088\u308a\u3082\u660e\u78ba\u3067\u3059\u3002<\/p>\n<h2>2. \u30d1\u30f3\u30c0\u306e1\u3064\u306e\u30ab\u30e9\u30e0\u306e\u540d\u524d\u3092\u5909\u66f4\u3059\u308b\u3002<\/h2>\n<p>\u5358\u4e00\u306e\u5217\u306e\u540d\u524d\u3092\u5909\u66f4\u3057\u305f\u3044\u5834\u5408\u306f\u3001columns\u8f9e\u66f8\u30d1\u30e9\u30e1\u30fc\u30bf\u306b\u5358\u4e00\u306e\u30ad\u30fc\u3068\u5024\u306e\u30da\u30a2\u3092\u6e21\u3059\u3060\u3051\u3067\u3059\u3002<\/p>\n<pre class=\"post-pre\"><code>df1 = df.rename(columns={'Name': 'EmpName'})\r\nprint(df1)\r\n<\/code><\/pre>\n<p>\u51fa\u529b:<\/p>\n<pre class=\"post-pre\"><code>  EmpName  ID    Role\r\n0  Pankaj   1     CEO\r\n1    Lisa   2  Editor\r\n2   David   3  Author\r\n<\/code><\/pre>\n<p>\u3082\u3057\u5217\u306e\u8f9e\u66f8\u306b\u4e00\u81f4\u3057\u306a\u3044\u30de\u30c3\u30d4\u30f3\u30b0\u304c\u3042\u308b\u5834\u5408\u3001\u7d50\u679c\u306f\u540c\u3058\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n<pre class=\"post-pre\"><code>df1 = df.rename(columns={'Name': 'EmpName', 'X': 'Y'})  # same result since there is no X column\r\n<\/code><\/pre>\n<h2>3. \u30d1\u30f3\u30c0\u306e\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u540d\u3092\u5909\u66f4\u3059\u308b\u3002<\/h2>\n<p>\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u306e\u540d\u524d\u3092\u5909\u66f4\u3057\u305f\u3044\u5834\u5408\u306f\u3001&#8217;index&#8217; \u30d1\u30e9\u30e1\u30fc\u30bf\u30fc\u306b\u8f9e\u66f8\u3092\u6e21\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<pre class=\"post-pre\"><code>df2 = df.rename(index={0: '#0', 1: '#1', 2: '#2'})\r\nprint('Renamed Indexes:\\n', df2)\r\n<\/code><\/pre>\n<p>\u51fa\u529b\u3059\u308b\u3002<\/p>\n<pre class=\"post-pre\"><code>Renamed Indexes:\r\n       Name  ID    Role\r\n#0  Pankaj   1     CEO\r\n#1    Lisa   2  Editor\r\n#2   David   3  Author\r\n<\/code><\/pre>\n<p>\u79c1\u305f\u3061\u306f\u3001\u30de\u30c3\u30d1\u30fc\u3084\u8ef8\u306e\u5f15\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u306e\u540d\u524d\u3092\u5909\u66f4\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/p>\n<pre class=\"post-pre\"><code>df2 = df.rename({0: '#0', 1: '#1', 2: '#2'}, axis=0)  \r\n# axis='index' will work, first argument is assigned to 'mapper'\r\n<\/code><\/pre>\n<h2>4. \u30d1\u30f3\u30c0\u306e\u5358\u4e00\u306e\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u306e\u540d\u524d\u3092\u5909\u66f4\u3059\u308b\u3002 (Panda no tan&#8217;itsu no indekkusu no namae o henk\u014d suru)<\/h2>\n<pre class=\"post-pre\"><code>df2 = df.rename(index={1: '#1'})\r\nprint(df2)\r\n<\/code><\/pre>\n<p>\u51fa\u529b\uff1a<\/p>\n<pre class=\"post-pre\"><code>      Name  ID    Role\r\n0   Pankaj   1     CEO\r\n#1    Lisa   2  Editor\r\n2    David   3  Author\r\n<\/code><\/pre>\n<h2>5. \u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u30a4\u30f3\u30d7\u30ec\u30fc\u30b9\u3067\u5909\u66f4\u3059\u308b<\/h2>\n<p>\u3082\u3057\u30bd\u30fc\u30b9\u306eDataFrame\u81ea\u4f53\u3092\u5909\u66f4\u3057\u305f\u3044\u5834\u5408\u306f\u3001inplace\u5f15\u6570\u306bTrue\u3092\u6e21\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<pre class=\"post-pre\"><code>import pandas as pd\r\n\r\nd1 = {'Name': ['Pankaj', 'Lisa', 'David'], 'ID': [1, 2, 3], 'Role': ['CEO', 'Editor', 'Author']}\r\n\r\ndf = pd.DataFrame(d1)\r\n\r\nprint('Source DataFrame:\\n', df)\r\n\r\ndf.rename(index={0: '#0', 1: '#1', 2: '#2'}, columns={'Name': 'EmpName', 'ID': 'EmpID', 'Role': 'EmpRole'}, inplace=True)\r\n\r\nprint('Source DataFrame:\\n', df)\r\n<\/code><\/pre>\n<p>\u51fa\u529b:<\/p>\n<pre class=\"post-pre\"><code>Source DataFrame:\r\n      Name  ID    Role\r\n0  Pankaj   1     CEO\r\n1    Lisa   2  Editor\r\n2   David   3  Author\r\n\r\nSource DataFrame:\r\n    EmpName  EmpID EmpRole\r\n#0  Pankaj      1     CEO\r\n#1    Lisa      2  Editor\r\n#2   David      3  Author\r\n<\/code><\/pre>\n<h2>\uff16\uff0e\u30ab\u30e9\u30e0\u306e\u540d\u524d\u3092\u5909\u66f4\u3059\u308b\u305f\u3081\u306b\u30de\u30c3\u30d1\u30fc\u95a2\u6570\u3092\u4f7f\u7528\u3059\u308b\u3002<\/h2>\n<pre class=\"post-pre\"><code>df = pd.DataFrame({'NAME': ['Pankaj', 'Lisa'], 'ID': [1, 2], 'ROLE': ['CEO', 'Editor']})\r\nprint(df)\r\n\r\ndf.rename(mapper=str.lower, axis=1, inplace=True)\r\nprint(df)\r\n<\/code><\/pre>\n<p>\u51fa\u529b\uff1a<\/p>\n<pre class=\"post-pre\"><code>     NAME  ID    ROLE\r\n0  Pankaj   1     CEO\r\n1    Lisa   2  Editor\r\n\r\n     name  id    role\r\n0  Pankaj   1     CEO\r\n1    Lisa   2  Editor\r\n<\/code><\/pre>\n<h2>7. \u5217\u3084\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u306e\u540d\u524d\u3092\u5909\u66f4\u3059\u308b\u305f\u3081\u306e\u95a2\u6570\u3092\u4f7f\u7528\u3059\u308b<\/h2>\n<pre class=\"post-pre\"><code>import pandas as pd\r\nimport math\r\n\r\ndf = pd.DataFrame({'NAME': ['Pankaj', 'Lisa'], 'ID': [1, 2], 'ROLE': ['CEO', 'Editor']})\r\n\r\ndf.rename(columns=str.lower, index=math.degrees, inplace=True)\r\nprint(df)\r\n<\/code><\/pre>\n<p>\u51fa\u529b\uff1a<\/p>\n<pre class=\"post-pre\"><code>            name  id    role\r\n0.00000   Pankaj   1     CEO\r\n57.29578    Lisa   2  Editor\r\n<\/code><\/pre>\n<h2>8. \u53b3\u5bc6\u306a\u540d\u524d\u306e\u5909\u66f4\u3068KeyError\u306e\u767a\u751f<\/h2>\n<pre class=\"post-pre\"><code>import pandas as pd\r\n\r\ndf = pd.DataFrame({'NAME': ['Pankaj', 'Lisa'], 'ID': [1, 2], 'ROLE': ['CEO', 'Editor']})\r\n\r\ndf1 = df.rename(columns={'Salary': 'EmpSalary'})  # unmatched mappings are ignored\r\n\r\ndf1 = df.rename(columns={'Salary': 'EmpSalary'}, errors='raise')  # unmatched mappings raising KeyError\r\n<\/code><\/pre>\n<p>\u51fa\u529b:<\/p>\n<pre class=\"post-pre\"><code>Traceback (most recent call last):\r\n  File \"\/Users\/scdev\/Documents\/PycharmProjects\/hello-world\/scdev\/pandas\/pandas_rename_column.py\", line 58, in &lt;module&gt;\r\n    df1 = df.rename(columns={'Salary': 'EmpSalary'}, errors='raise')\r\nKeyError: \"['Salary'] not found in axis\"\r\n<\/code><\/pre>\n<h2>9. \u53c2\u8003\u6587\u732e<\/h2>\n<ul class=\"post-ul\">\n<li>pandas DataFrame rename() API Doc<\/li>\n<li>Python Pandas Module Tutorial<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>\u6642\u3005\u3001Pandas\u306eDataFrame\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3067\u5217\u3084\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u306e\u540d\u524d\u3092\u5909\u66f4\u3057\u305f\u3044\u5834\u5408\u304c\u3042\u308a\u307e\u3059\u3002\u5217\u3084\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u306e\u540d\u524d\u3092\u5909\u66f4\u3059\u308b\u305f\u3081\u306b\u306f\u3001pandas DataFrame\u306erename\uff08\uff09\u95a2\u6570\u3092\u4f7f\u7528\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e [&hellip;]<\/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-334","post","type-post","status-publish","format-standard","hentry","category-uncategorized","tag-26","tag-61"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.5 (Yoast SEO v21.5) 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