Renaming column names 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  e0  x  x  x  x  x1  x  x  x  x  x2  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 methoddf2 = df.rename({'a': 'X', 'b': 'Y'}, axis='columns')df2 = df.rename(columns={'a': 'X', 'b': 'Y'})  # old method  df2   X  Y  c  d  e0  x  x  x  x  x1  x  x  x  x  x2  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  e0  x  x  x  x  x1  x  x  x  x  x2  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  Z0  x  x  x  x  x1  x  x  x  x  x2  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  Z0  x  x  x  x  x1  x  x  x  x  x2  x  x  x  x  x

Just assign it to the .columns attribute:

>>> df = pd.DataFrame({'$a':[1,2], '$b': [10,20]})>>> df   $a  $b0   1  101   2  20>>> df.columns = ['a', 'b']>>> df   a   b0  1  101  2  20

The rename method can take a function, for example:

In [11]: df.columnsOut[11]: Index([u'$a', u'$b', u'$c', u'$d', u'$e'], dtype=object)In [12]: df.rename(columns=lambda x: x[1:], inplace=True)In [13]: df.columnsOut[13]: Index([u'a', u'b', u'c', u'd', u'e'], dtype=object)