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

User · Answer

At the time when this question was asked there wasn't another function in Pandas to test equality, but it has been added a while ago: pandas.equals

You use it like this:

df1.equals(df2)

Some differenes to == are:

You don't get the error described in the question
It returns a simple boolean.
NaN values in the same location are considered equal
2 DataFrames need to have the same dtype to be considered equal, see this stackoverflow question

User · Answer

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

User · Answer

When you compare two DataFrames  you must ensure that the number of records in the first DataFrame matches with the number of records in the second DataFrame  In our example  each of the two DataFrames had 4 records  with 4 products and 4 prices  If  for example  one of the DataFrames had 5 products  while the other DataFrame had 4 products  and you tried to run the comparison  you would get the following error  ValueError  Can only compare identically-labeled Series objects this should work import pandas as pd import numpy as np  firstProductSet     Product1     Computer   Phone   Printer   Desk                        Price1    1200 800 200 350                       df1   pd DataFrame firstProductSet columns    Product1    Price1      secondProductSet     Product2     Computer   Phone   Printer   Desk                         Price2    900 800 300 350                        df2   pd DataFrame secondProductSet columns    Product2    Price2      df1  Price2     df2  Price2    add the Price2 column from df2 to df1  df1  pricesMatch      np where df1  Price1      df2  Price2     True    False     create new column in df1 to check if prices match df1  priceDiff      np where df1  Price1      df2  Price2    0  df1  Price1   - df2  Price2     create new column in df1 for price diff  print  df1   example from https   datatofish com compare-values-dataframes

User · Answer

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

[python] Pandas "Can only compare identically-labeled DataFrame objects" error

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