{"id":13279,"date":"2024-03-15T06:44:42","date_gmt":"2024-03-15T06:44:42","guid":{"rendered":"https:\/\/www.silicloud.com\/blog\/what-is-the-usage-of-group-by-in-python\/"},"modified":"2025-08-05T15:30:22","modified_gmt":"2025-08-05T15:30:22","slug":"what-is-the-usage-of-group-by-in-python","status":"publish","type":"post","link":"https:\/\/www.silicloud.com\/blog\/what-is-the-usage-of-group-by-in-python\/","title":{"rendered":"Python Group By: Usage &#038; Pandas Examples"},"content":{"rendered":"<p>In Python, group by is a operation used to group a dataset by a specific column. It is typically used in conjunction with aggregate functions (such as sum, count, avg, etc.) to perform calculations on each group.<\/p>\n<p>To use group by, you can utilize the DataFrame object in the pandas library to manipulate data. Here is an example:<\/p>\n<pre class=\"post-pre\"><code><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\r\n\r\n<span class=\"hljs-comment\"># \u521b\u5efa\u4e00\u4e2a\u793a\u4f8b\u6570\u636e\u96c6<\/span>\r\ndata = {<span class=\"hljs-string\">'Name'<\/span>: [<span class=\"hljs-string\">'John'<\/span>, <span class=\"hljs-string\">'Mike'<\/span>, <span class=\"hljs-string\">'Sarah'<\/span>, <span class=\"hljs-string\">'John'<\/span>, <span class=\"hljs-string\">'Mike'<\/span>],\r\n        <span class=\"hljs-string\">'Age'<\/span>: [<span class=\"hljs-number\">25<\/span>, <span class=\"hljs-number\">30<\/span>, <span class=\"hljs-number\">28<\/span>, <span class=\"hljs-number\">25<\/span>, <span class=\"hljs-number\">30<\/span>],\r\n        <span class=\"hljs-string\">'City'<\/span>: [<span class=\"hljs-string\">'New York'<\/span>, <span class=\"hljs-string\">'Chicago'<\/span>, <span class=\"hljs-string\">'Los Angeles'<\/span>, <span class=\"hljs-string\">'New York'<\/span>, <span class=\"hljs-string\">'Chicago'<\/span>],\r\n        <span class=\"hljs-string\">'Salary'<\/span>: [<span class=\"hljs-number\">50000<\/span>, <span class=\"hljs-number\">60000<\/span>, <span class=\"hljs-number\">55000<\/span>, <span class=\"hljs-number\">50000<\/span>, <span class=\"hljs-number\">55000<\/span>]}\r\n\r\ndf = pd.DataFrame(data)\r\n\r\n<span class=\"hljs-comment\"># \u6309\u7167Name\u5217\u8fdb\u884c\u5206\u7ec4\uff0c\u5e76\u8ba1\u7b97\u6bcf\u4e2a\u7ec4\u7684\u5e73\u5747\u85aa\u8d44<\/span>\r\ngrouped = df.groupby(<span class=\"hljs-string\">'Name'<\/span>)[<span class=\"hljs-string\">'Salary'<\/span>].mean()\r\n\r\n<span class=\"hljs-built_in\">print<\/span>(grouped)\r\n<\/code><\/pre>\n<p>The output result is:<\/p>\n<pre class=\"post-pre\"><code>Name\r\nJohn     50000.0\r\nMike     57500.0\r\nSarah    55000.0\r\nName: Salary, dtype: float64\r\n<\/code><\/pre>\n<p>In the above example, we used group by to group the data by the Name column and calculate the average salary for each group. Each group in the results is indexed by the group name (unique values in the Name column) and displays the corresponding average salary for that group.<\/p>\n<p>You can also group multiple columns, as shown in the following example:<\/p>\n<pre class=\"post-pre\"><code>grouped = df.groupby([<span class=\"hljs-string\">'Name'<\/span>, <span class=\"hljs-string\">'City'<\/span>])[<span class=\"hljs-string\">'Salary'<\/span>].<span class=\"hljs-built_in\">sum<\/span>()\r\n\r\n<span class=\"hljs-built_in\">print<\/span>(grouped)\r\n<\/code><\/pre>\n<p>The output result is:<\/p>\n<pre class=\"post-pre\"><code>Name   City       \r\nJohn   New York      100000\r\nMike   Chicago       115000\r\nSarah  Los Angeles    55000\r\nName: Salary, dtype: int64\r\n<\/code><\/pre>\n<p>In this example, we group by the Name and City columns and calculate the total salary for each group. Each group in the result is indexed by the group name, which is a unique combination of Name and City, and displays the corresponding total salary for that group.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In Python, group by is a operation used to group a dataset by a specific column. It is typically used in conjunction with aggregate functions (such as sum, count, avg, etc.) to perform calculations on each group. To use group by, you can utilize the DataFrame object in the pandas library to manipulate data. Here [&hellip;]<\/p>\n","protected":false},"author":8,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_import_markdown_pro_load_document_selector":0,"_import_markdown_pro_submit_text_textarea":"","footnotes":""},"categories":[1],"tags":[408,337,871,1094,72],"class_list":["post-13279","post","type-post","status-publish","format-standard","hentry","category-uncategorized","tag-aggregate-functions","tag-data-analysis","tag-group-by","tag-pandas","tag-python"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.5 (Yoast SEO v21.5) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Python Group By: Usage &amp; Pandas Examples - Blog - Silicon Cloud<\/title>\n<meta name=\"description\" content=\"Learn to use group by in Python with pandas. 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