In this short tutorial, let’s check out some examples and options to change column names in pandas. It’s very common to have to change to a different column names or replace the headers of your dataset in pandas.
RENAME SPECIFIC COLUMNS
Use the df.rename() function and refer the columns to be renamed. Not all the columns have to be renamed:
df = df.rename(columns={'oldName1': 'newName1', 'oldName2': 'newName2'})
# Or rename the existing DataFrame (rather than creating a copy)
df.rename(columns={'oldName1': 'newName1', 'oldName2': 'newName2'}, inplace=True)
Minimal Code Example
df = pd.DataFrame('x', index=range(3), columns=list('abcde'))
df
a b c d e
0 x x x x x
1 x x x x x
2 x x x x x
The following methods all work and produce the same output:
df2 = df.rename({'a': 'X', 'b': 'Y'}, axis=1) # new method
df2 = df.rename({'a': 'X', 'b': 'Y'}, axis='columns')
df2 = df.rename(columns={'a': 'X', 'b': 'Y'}) # old method
df2
X Y c d e
0 x x x x x
1 x x x x x
2 x x x x x
Remember to assign the result back, as the modification is not-inplace. Alternatively, specify inplace=True:
df.rename({'a': 'X', 'b': 'Y'}, axis=1, inplace=True)
df
X Y c d e
0 x x x x x
1 x x x x x
2 x x x x x
From v0.25, you can also specify errors='raise' to raise errors if an invalid column-to-rename is specified. See v0.25 rename() docs.
REASSIGN COLUMN HEADERS
Use df.set_axis() with axis=1 and inplace=False (to return a copy).
df2 = df.set_axis(['V', 'W', 'X', 'Y', 'Z'], axis=1, inplace=False)
df2
V W X Y Z
0 x x x x x
1 x x x x x
2 x x x x x
This returns a copy, but you can modify the DataFrame in-place by setting inplace=True (this is the default behaviour for versions <=0.24 but is likely to change in the future).
You can also assign headers directly:
df.columns = ['V', 'W', 'X', 'Y', 'Z']
df
V W X Y Z
0 x x x x x
1 x x x x x
2 x x x x x
Take a look at the below resources to know more about Pandas and Data visualization:
Applied Data Science with Python Specialization
Data Analysis with Pandas and Python
References:
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