Renaming columns in pandas


Question

I have a DataFrame using pandas and column labels that I need to edit to replace the original column labels.

I'd like to change the column names in a DataFrame A where the original column names are:

['$a', '$b', '$c', '$d', '$e'] 

to

['a', 'b', 'c', 'd', 'e'].

I have the edited column names stored it in a list, but I don't know how to replace the column names.

1
1505
12/12/2017 6:55:20 PM

Accepted Answer

Just assign it to the .columns attribute:

>>> df = pd.DataFrame({'$a':[1,2], '$b': [10,20]})
>>> df.columns = ['a', 'b']
>>> df
   a   b
0  1  10
1  2  20
1543
7/5/2012 2:23:27 PM

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

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