Home/ Questions/The complete guide to the valueerror: can only compare identically-labeled series objects issue.
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Amani Lam
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Asked: 2022-05-18T20:33:16+00:00 In:

The complete guide to the valueerror: can only compare identically-labeled series objects issue.

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I get an error:

Can only compare identically-labeled DataFrame objects

when I try to run the following code:

uat = uat[['Customer Number','Product']]
 prod = prod[['Customer Number','Product']]
 print uat['Customer Number'] == prod['Customer Number']
 print uat['Product'] == prod['Product']
 print uat == prod
 
 The first two match exactly:
 74357 True
 74356 True
 Name: Customer Number, dtype: bool
 74357 True
 74356 True
 Name: Product, dtype: bool

How to fix the valueerror: can only compare identically-labeled series objects. Please give me some good ideas.

can only compare identically-labeled series objects
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2 Answers

  1. Best Answer
    Expert

    The cause:

    This error happens because you are comparing DataFrames with different indexes or labels. Below is an example to illustrate it (this example only applys for DataFrames, not for Series, up to the time of Pandas 0.19 it can apply for both of them).

    In [1]: df1 = pd.DataFrame([[1, 2], [3, 4]])

In [2]: df2 = pd.DataFrame([[3, 4], [1, 2]], index=[1, 0])

In [3]: df1 == df2
Exception: Can only compare identically-labeled DataFrame objects

    Solution:

    To solve this error, first you need to classify the index (Remember: some functions ask classified indexes):

    In [4]: df2.sort_index(inplace=True)

In [5]: df1 == df2
Out[5]:
0 1
0 True True
1 True True

    Note that you have to use sort_index(axis=1) because == is  easily affected by the order of columns:

    In [11]: df1.sort_index().sort_index(axis=1) == df2.sort_index().sort_index(axis=1)
Out[11]:
0 1
0 True True
1 True True

    However, this error can be still raised (if the indexes or columns aren’t labelled identically after classifying or sorting).

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  2. If you don’t need to compare, you can also drop the index column. 

    print(df1.reset_index(drop=True) == df2.reset_index(drop=True))

    This is how I used the same technique to test units. 

    from pandas.util.testing import assert_frame_equal

assert_frame_equal(actual.reset_index(drop=True), expected.reset_index(drop=True))
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