CODEWITHSUNDEEP
pythonpandas
user1486457
user1486457

Reputation: 59

rename index of a pandas dataframe

I have a pandas dataframe whose indices look like:

df.index
['a_1', 'b_2', 'c_3', ... ]

I want to rename these indices to:

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

How do I do this without specifying a dictionary with explicit keys for each index value?
I tried:

df.rename( index = lambda x: x.split( '_' )[0] )

but this throws up an error:

AssertionError: New axis must be unique to rename

Upvotes: 5

Views: 9403

Answers (2)

unutbu
unutbu

Reputation: 880987

Perhaps you could get the best of both worlds by using a MultiIndex:

import numpy as np
import pandas as pd
df = pd.DataFrame(np.arange(8).reshape(4,2), index=['a_1', 'b_2', 'c_3', 'c_4'])
print(df)
#      0  1
# a_1  0  1
# b_2  2  3
# c_3  4  5
# c_4  6  7

index = pd.MultiIndex.from_tuples([item.split('_') for item in df.index])
df.index = index
print(df)
#      0  1
# a 1  0  1
# b 2  2  3
# c 3  4  5
#   4  6  7

This way, you can access things according to first level of the index:

In [30]: df.ix['c']
Out[30]: 
   0  1
3  4  5
4  6  7

or according to both levels of the index:

In [31]: df.ix[('c','3')]
Out[31]: 
0    4
1    5
Name: (c, 3)

Moreover, all the DataFrame methods are built to work with DataFrames with MultiIndices, so you lose nothing.

However, if you really want to drop the second level of the index, you could do this:

df.reset_index(level=1, drop=True, inplace=True)
print(df)
#    0  1
# a  0  1
# b  2  3
# c  4  5
# c  6  7

Upvotes: 5

DSM
DSM

Reputation: 353604

That's the error you'd get if your function produced duplicate index values:

>>> df = pd.DataFrame(np.random.random((4,3)),index="a_1 b_2 c_3 c_4".split())
>>> df
            0         1         2
a_1  0.854839  0.830317  0.046283
b_2  0.433805  0.629118  0.702179
c_3  0.390390  0.374232  0.040998
c_4  0.667013  0.368870  0.637276
>>> df.rename(index=lambda x: x.split("_")[0])
[...]
AssertionError: New axis must be unique to rename

If you really want that, I'd use a list comp:

>>> df.index = [x.split("_")[0] for x in df.index]
>>> df
          0         1         2
a  0.854839  0.830317  0.046283
b  0.433805  0.629118  0.702179
c  0.390390  0.374232  0.040998
c  0.667013  0.368870  0.637276

but I'd think about whether that's really the right direction.

Upvotes: 4

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