Pandas "Can only compare identically-labeled DataFrame objects" error

Question

I'm using Pandas to compare the outputs of two files loaded into two data frames (uat, prod): ...

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 

For the third print, I get an error: Can only compare identically-labeled DataFrame objects. If the first two compared fine, what's wrong with the 3rd?

Thanks

Answer 1

Here's a small example to demonstrate this (which only applied to DataFrames, not Series, until Pandas 0.19 where it applies to both):

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 

One solution is to sort the index first (Note: some functions require sorted indexes):

In [4]: df2.sort_index(inplace=True)  In [5]: df1 == df2 Out[5]:        0     1 0  True  True 1  True  True 

Note: == is also sensitive to the order of columns, so you may have to use sort_index(axis=1):

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 

Note: This can still raise (if the index/columns aren't identically labelled after sorting).

Answer 2

You can also try dropping the index column if it is not needed to compare:

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

I have used this same technique in a unit test like so:

from pandas.util.testing import assert_frame_equal  assert_frame_equal(actual.reset_index(drop=True), expected.reset_index(drop=True))