![]() Note that this method works only for pandas version 1.5.0 and above (as the names parameter was introduced to this function in the version 1.5.0). In the call to the reset_index() function, specify the name you’d like the column with the original index data to have using the names parameter.Now, if you’d like the new column to have a different name, you can use one of the following three methods – This is because the original index in the dataframe didn’t have a name for itself. Notice, that the column with the previous index has the name “index”. You’d get the previous index (we say previous index because the reset_index() function “resets” the index to the default integer index starting from 0) as a separate column. Now, if you were to apply the reset_index() function with default parameters. You can see that the index in this particular dataframe is the name of the respective employee. Here, we created a dataframe with some information about some employees in an office. "Department": ,ĭf = pd.DataFrame(data, index=) Let’s now look at both these methods with the help of some examples.įirst, we will create a dataframe that we’ll use throughout this tutorial. After applying the reset_index() function, apply the pandas dataframe rename() function on the resulting dataframe to change one or more column names as per your requirements.Before using the reset_index() function, give the index axis the name you’d like the resulting column to have using the rename_axis() function and then apply the reset_index() function.Pass a list of names if the dataframe has a multi-index. For pandas version 1.5.0 and above, when calling the reset_index() function, pass the name you’d like the column (with the previous index) to have with the help of the names parameter.You can, however, change the name of this column in the following ways – If the index axis itself doesn’t have a name, the new column is added with the name “index”. Methods to rename the column resulting from reset_index() The pandas dataframe reset_index() function is used to reset the index of a dataframe and by default, it adds the previous index as a separate column in the dataframe (pass drop=True if you do not want to retain the previous index as a separate column). It does not change the data in the DataFrame.In this tutorial, we will look at how to rename the column resulting from the reset_index() function in pandas with the help of some examples. Print(df)*Note that the rename() method only changes the column names of the DataFrame. ![]() # rename the columns of the DataFrame in placeĭf.rename(columns=, inplace=True) You can also use the inplace parameter of the rename() method to modify the DataFrame in place, without assigning the result to a new variable. ![]() The resulting DataFrame will have the new column names. This method allows you to specify a new name for one or more columns by providing a mapping of old column names to new column names.In the example above, the df.rename() method takes a columns parameter that is a dictionary mapping the old column names (the keys of the dictionary) to the new column names (the values of the dictionary). ![]() To rename the columns of a DataFrame in Pandas, you can use the DataFrame.rename() method.
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