python pandas: rename single column label in multi-index dataframe

Question

I have a df that looks like this:

df = pd.DataFrame(np.random.random((4,4)))
df.columns = pd.MultiIndex.from_product([['1','2'],['A','B']])
print df
          1                   2          
          A         B         A         B
0  0.030626  0.494912  0.364742  0.320088
1  0.178368  0.857469  0.628677  0.705226
2  0.886296  0.833130  0.495135  0.246427
3  0.391352  0.128498  0.162211  0.011254

How can I rename column '1' and '2' as 'One' and 'Two'?

I thought df.rename() would've helped but it doesn't. Have no idea how to do this?

In newer versions of pandas, df.rename() can be used to rename particular levels. See kadee's answer below. — normanius
– normanius, Commented Nov 25, 2020 at 18:42

joris · Accepted Answer · 2015-03-31 13:24:44Z

25

That is indeed something missing in rename (ideally it should let you specify the level).
Another way is by setting the levels of the columns index, but then you need to know all values for that level:

In [41]: df.columns.levels[0]
Out[41]: Index([u'1', u'2'], dtype='object')

In [43]: df.columns = df.columns.set_levels(['one', 'two'], level=0)

In [44]: df
Out[44]:
        one                 two
          A         B         A         B
0  0.899686  0.466577  0.867268  0.064329
1  0.162480  0.455039  0.736870  0.759595
2  0.620960  0.922119  0.060141  0.669997
3  0.871107  0.043799  0.080080  0.577421

In [45]: df.columns.levels[0]
Out[45]: Index([u'one', u'two'], dtype='object')

answered Mar 31, 2015 at 13:24

joris

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Comments

kadee · Accepted Answer · 2018-05-15 11:31:31Z

10

As of pandas 0.22.0 (and probably much earlier), you can specify the level:

df = df.rename(columns={'1': one, '2': two}, level=0)

or, alternatively (new notation since pandas 0.21.0):

df = df.rename({'1': one, '2': two}, axis='columns', level=0)

But actually, it works even when omitting the level:

df = df.rename(columns={'1': one, '2': two})

In that case, all column levels are checked for occurrences to be renamed.

edited May 15, 2018 at 11:31

answered May 15, 2018 at 11:22

kadee

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Comments

TheBlackCat · Accepted Answer · 2015-03-31 13:24:33Z

5

Use set_levels:

>>> df.columns.set_levels(['one','two'], 0, inplace=True)
>>> print(df)
        one                 two          
          A         B         A         B
0  0.731851  0.489611  0.636441  0.774818
1  0.996034  0.298914  0.377097  0.404644
2  0.217106  0.808459  0.588594  0.009408
3  0.851270  0.799914  0.328863  0.009914

answered Mar 31, 2015 at 13:24

TheBlackCat

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1 Comment

Boosted_d16 Over a year ago

Thanks for the effort!

Cristián Antuña · Accepted Answer · 2015-03-31 13:24:40Z

4

df.columns.set_levels(['one', 'two'], level=0, inplace=True)

answered Mar 31, 2015 at 13:24

Cristián Antuña

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Boosted_d16 Over a year ago

Thanks for the effort!

amn34 · Accepted Answer · 2016-11-03 19:58:29Z

4

df.rename_axis({'1':'one', '2':'two'}, axis='columns', inplace=True)

answered Nov 3, 2016 at 19:58

amn34

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Comments

Ying Zhang · Accepted Answer · 2016-06-14 15:22:18Z

This is a good question. Combining the answer above, you can write a function:

def rename_col( df, columns, level = 0 ):

    def rename_apply ( x, rename_dict ):
        try:
            return rename_dict[x]
        except KeyError:
            return x

    if  isinstance(df.columns, pd.core.index.MultiIndex):
        df.columns = df.columns.set_levels([rename_apply(x, rename_dict = columns ) for x in df.columns.levels[level]], level= level)
    else:
        df.columns =                       [rename_apply(x, rename_dict = columns ) for x in df.columns              ] 

    return df

It worked for me.

Ideally, a functionality like this should be integrated into the "official" "rename" function in the future, so you don't need to write a hack like this.

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