If you liked this post then please share it with others and also subscribe to our blog below to learn more about pandas. # df.columns with list comprehensionÄf.columns = levelint or level name, default None In case of a MultiIndex, only rename labels in the specified level. inplacebool, default False Whether to return a new DataFrame. copybool, default True Also copy underlying data. And you also have to make sure that the new column names are in the right position as in the dataframe otherwise it will rename incorrectly.Īnother way to do the same thing is with list comprehension. Can be either the axis name (âindexâ, âcolumnsâ) or number (0, 1). 'Method_of_Payment', 'Gender', 'Marital_Status','Age']Ä«ut when you use this method, you have to make sure that the length of the cols matches with the number of columns in your dataframe otherwise pandas will throw an error. # new column namesĬols = ['Customer', 'Type_of_Customer', 'Items', 'Net_Sales', Now to change the column names all we have to do is assign the new column names in a list to df.columns. df.columns returns the names of the columns df.columns # rename column names with lambda functionÄf.rename(columns = lambda x : x.replace(' ', '_').lower())Īnother way to rename column names is using df.columns attribute. We can also do method chaining to lowercase all the column names along with replacing whitespace with underscore in one call. # rename column names with lambda functionÄf.rename(columns = lambda x : x.replace(' ', '_')) To change the column names we can use the lambda function with the rename function. The column names contains white spaces between words which means you can not use the dot notation for column selection. Or you can use a mapping function: df.rename (columnsstr.upper, inplaceTrue) df.columns Expected result Index ( 'DATE', 'REGION', 'SALES', dtype'object') Here we pass the classmethod. Here, we have some data for clothing store sales. This is the easiest way to rename a single column, but you can rename multiple columns by adding more : pairs to your dictionary.This is useful when you want to rename all of the columns in a particular way like replacing spaces with underscore. You can also pass a lambda function to the columns and index parameters of the rename function. (2) Using df.rename() with lambda function â # import pandasĬorporations = Äf = pd.DataFrame(Äf.rename(cols, axis='columns', inplace=True) Letâs create a pandas dataframe to work with. There could be multiple reasons why do you want to do that, so letâs take a look. You have a dataframe and you want to rename the column names because either the names have extra useless characters or you want to change the names to all lowercase or you want to replace the spaces with underscores so that you can use the dot notation for column selection.
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