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How to rename DataFrame columns in Pandas

Easily change Pandas column names
By Saturn Cloud | | Data Science & ML | Updated:

Whether youā€™re changing the name of one or several columns or completely reassigning your DataFrameā€™s header, renaming columns in pandas is very simple.

Renaming specific columns can be easily accomplished with the built-in rename() method. This method takes a dictionary of columns to be renamed and their new names, in the format old: new. Remember to specify that you want to change column names with the axis argument, as this method can also be used to rename rows. Hereā€™s rename() in action:

import pandas as pd

data = pd.DataFrame({'a': [1, 2, 3], 'b': [10, 20, 30], 'c': [100, 200, 300]})

#either axis value here works to specify columns
data_renamed = data.rename({'a': 'a1', 'b': 'b1'}, axis=1)
data_renamed = data.rename({'a': 'a2', 'b': 'b2'}, axis='columns')

data_renamed

If youā€™d rather rename columns in-place instead of assigning the output to a variable, you can instead use:

import pandas as pd

data = pd.DataFrame({'a': [1, 2, 3], 'b': [10, 20, 30], 'c': [100, 200, 300]})

data.rename({'a': 'a1', 'b': 'b2'}, axis=1, inplace=True)

data

You can also reassign all column names from a list by using set_axis():

import pandas as pd

data = pd.DataFrame({'a': [1, 2, 3], 'b': [10, 20, 30], 'c': [100, 200, 300]})

data2 = data.set_axis(['d', 'e', 'f'], axis=1)

data2

In older versions of pandas, set_axis() also takes an inplace argument. In pandas >1.5.0., set_axis() instead uses a copy boolean argument to specify whether or not to create a new DataFrame object. By default, the DataFrame is copied. To assign headers without copying the underlying data, use:

import pandas as pd

data = pd.DataFrame({'a': [1, 2, 3], 'b': [10, 20, 30], 'c': [100, 200, 300]})

data.set_axis(['d', 'e', 'f'], axis=1, copy=False)

data

Itā€™s also possible to reassign headers directly in-place. Be aware this depends on the positions of the columns rather than their names, so is less robust than the methods above.

import pandas as pd

data = pd.DataFrame({'a': [1, 2, 3], 'b': [10, 20, 30], 'c': [100, 200, 300]})

data.columns = ['d', 'e', 'f']

data

So far, weā€™ve only considered examples of small datasets where itā€™s feasible to manually write out old and new column names. If youā€™re looking for a way to apply a text transformation to every column name in a DataFrame, you can also pass a function to rename(). This functionality is especially useful if, for example, you would like to remove extra delimitors from column names:

import pandas as pd

data = pd.DataFrame({'a,': [1, 2, 3], 'b,': [10, 20, 30], 'c,': [100, 200, 300]})

#creating a new DataFrame, but you can also use inplace here
data_renamed = data.rename(columns=lambda x: x.replace(',', ''))

data_renamed

In summary, pandas provides several quick and easy methods for renaming DataFrame columns, whether youā€™re looking to rename a single column, rename all columns using a list, or rename all columns using a function.

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