{"id":21084,"date":"2020-08-12T11:13:03","date_gmt":"2020-08-12T18:13:03","guid":{"rendered":"https:\/\/careerkarma.com\/blog\/?p=21084"},"modified":"2023-12-01T03:57:46","modified_gmt":"2023-12-01T11:57:46","slug":"pandas-rename-column","status":"publish","type":"post","link":"https:\/\/careerkarma.com\/blog\/pandas-rename-column\/","title":{"rendered":"Rename Column in Pandas: A Beginner\u2019s Guide"},"content":{"rendered":"\n

So you want to rename a column in a Python<\/a> Pandas dataframe. Is that possible? Yes, it is. You use the rename()<\/code> method to rename an individual column or the \u201ccolumns\u201d attribute to assign a new set of column headers to a dataframe.
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In this guide, we cover how to rename an individual column and multiple columns in a Pandas dataframe. We walk through two examples to help you get started with these techniques.<\/p>\n\n\n\n

Rename a Single Column in Pandas<\/h2>\n\n\n\n

A Pandas dataframe is a grid that stores data<\/a>. Data is stored in a table using rows and columns. Each axis in a dataframe has its own label.
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You rename a single column using the rename()<\/code> function. This method is useful because it lets you modify a column heading without having to create a new column.
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Take a look at a Pandas dataframe object:<\/p>\n\n\n\n

import pandas as pd\n\nbooks = {\n\t"name": ["The Great Gatsby", "To Kill a Mockingbird", "The Count of Monte Cristo"],\n\t"author": ["F. Scott Fitzgerald", "Harper Lee", "Alexandre Dumas"],\n\t"sold": [42, 53, 39]\n}\n\nbooks_frame = pd.DataFrame(books)<\/pre><\/div>\n\n\n\n
\u201cname\u201d, \u201cauthor\u201d, and \u201csold\u201d are our column headings. This dataframe has three columns and three rows. We see our dataframe by printing it out to the console<\/a>:
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print(books_frame)<\/pre><\/div>\n\n\n\n
Our dataframe appears as expected:
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<\/td>		name<\/td>author<\/td>sold<\/td><\/tr>
0<\/td>The Great Gatsby<\/td>F. Scott Fitzgerald<\/td>42<\/td><\/tr>
1<\/td>To Kill a Mockingbird<\/td>Harper Lee<\/td>53<\/td><\/tr>
2<\/td>The Count of Monte Cristo<\/td>Alexandre Dumas<\/td>39<\/td><\/tr><\/tbody><\/table>\n\n\n\n
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Next we rename the column \u201csold\u201d to say \u201ccopies sold\u201d. We can do this using the rename()<\/code> method:
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books_frame.rename(columns = {"sold": "copies sold"}, inplace=True)<\/pre><\/div>\n\n\n\n
We specify one parameter with the rename()<\/code> method: columns. This parameter accepts a list of columns to rename.
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We also specify a dictionary<\/a> as the value of the columns parameter. \u201csold\u201d refers to the name of the column we want to rename. \u201ccopies sold\u201d refers to the name with which we want to replace the old name. 
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Let\u2019s run our code and print out our list of columns to the console:<\/p>\n\n\n\n
books_frame.rename(columns = {"copies": "copies sold"}, inplace=True)\nprint(books_frame.columns)<\/pre><\/div>\n\n\n\n
Our code returns:<\/p>\n\n\n\n
Index(['name', 'author', 'copies sold'], dtype='object')<\/pre><\/div>\n\n\n\n
The \u201csold\u201d column has been renamed to \u201ccopies sold\u201d.<\/p>\n\n\n\n
Rename Multiple Columns in Pandas<\/h2>\n\n\n\n
You use the rename()<\/code> method to rename multiple columns. You do this by specifying multiple column values in the dictionary assigned to the \u201ccolumns\u201d parameter.
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Let\u2019s change the \u201cname\u201d header to say \u201cBook Name\u201d and \u201csold\u201d to say \u201cCopies Sold\u201d:<\/p>\n\n\n\n
books_frame.rename(columns=\n{\n"sold": "Book Name",\n"name": "Book Name"\n}, inplace=True)\nprint(books_frame.columns)<\/pre><\/div>\n\n\n\n
The rename()<\/code> method renames our columns. Our code returns:
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Index(['Book Name', 'author', 'Book Name'], dtype='object')\n\n"name" and "sold" are renamed. "author" remains the same.<\/pre><\/div>\n\n\n\n
Rename All Columns in Pandas<\/h2>\n\n\n\n
You rename all the columns in a Pandas dataframe by assigning the \u201ccolumns\u201d attribute a list of new column headings. This approach only works if you want to rename every column in a table; you cannot exclude columns whose names should stay the same.
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We overhaul our column headings from the last example:
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