In this article, we are going to see how to fix it: Can only compare identically-labeled series objects in Python.
Reason for Error
Can only compare identically-labeled series objects: It is Value Error, occurred when we compare 2 different DataFrames (Pandas 2-D Data Structure). If we compare DataFrames which are having different labels or indexes then this error can be thrown.
How to Reproduce the Error
Python3
# import necessary packagesimport pandas as pd# create 2 dataframes with different indexeshostelCandidates1 = pd.DataFrame({'Height in CMs': [150, 170, 160], 'Weight in KGs': [70, 55, 60]}, index=[1, 2, 3])hostelCandidates2 = pd.DataFrame({'Height in CMs': [150, 170, 160], 'Weight in KGs': [70, 55, 60]}, index=['A', 'B', 'C'])# displaying 2 dataframesprint(hostelCandidates1)print(hostelCandidates2)# compare 2 dataframeshostelCandidates1 == hostelCandidates2 |
Output:
Even though the data in the 2 DataFrames are the same but the indexes of these are different. So in order to compare the data of 2 DataFrames are the same or not, we need to follow the below approaches/solutions
Method 1: With consideration of indexes
Here we compare data along with index labels between DataFrames to specify whether they are the same or not. So instead of ‘==’ use equals method while the comparison.
Python3
# import necessary packagesimport pandas as pd# create 2 dataframes with different indexeshostelCandidates1 = pd.DataFrame({'Height in CMs': [150, 170, 160], 'Weight in KGs': [70, 55, 60]}, index=[1, 2, 3])hostelCandidates2 = pd.DataFrame({'Height in CMs': [150, 170, 160], 'Weight in KGs': [70, 55, 60]}, index=['A', 'B', 'C'])# displaying 2 dataframesprint(hostelCandidates1)print(hostelCandidates2)# compare 2 dataframeshostelCandidates1.equals(hostelCandidates2) |
Output:
As the data is the same but the index labels of these 2 data frames are different so it returns false instead of an error.
Method 2: Without consideration of indexes
To drop indexes of DataFrame use reset_index method. By dropping the indexes, makes an easy task such that interpreters just check the data irrespective of index values.
Syntax: dataframeName.reset_index(drop=True)
There are 2 ways of comparing data:
- Whole DataFrame
- Row by Row
Example 1: Entire DataFrame Comparison
Python3
# import necessary packagesimport pandas as pd# create 2 dataframes with different indexeshostelCandidates1 = pd.DataFrame({'Height in CMs': [150, 170, 160], 'Weight in KGs': [70, 55, 60]}, index=[1, 2, 3])hostelCandidates2 = pd.DataFrame({'Height in CMs': [150, 170, 160], 'Weight in KGs': [70, 55, 60]}, index=['A', 'B', 'C'])# displaying 2 dataframesprint(hostelCandidates1)print(hostelCandidates2)# compare 2 dataframeshostelCandidates1.reset_index(drop=True).equals( hostelCandidates2.reset_index(drop=True)) |
Output:
Here the data is the same and even though the indexes are different we are comparing the DataFrames by eliminating the index labels so it returns true.
Example 2: Row by Row Comparison
Python3
# import necessary packagesimport pandas as pd# create 2 dataframes with different indexeshostelCandidates1 = pd.DataFrame({'Height in CMs': [150, 170, 160], 'Weight in KGs': [70, 55, 60]}, index=[1, 2, 3])hostelCandidates2 = pd.DataFrame({'Height in CMs': [150, 170, 160], 'Weight in KGs': [70, 55, 60]}, index=['A', 'B', 'C'])# displaying 2 dataframesprint(hostelCandidates1)print(hostelCandidates2)# compare 2 dataframeshostelCandidates1.reset_index( drop=True) == hostelCandidates2.reset_index(drop=True) |
Output:
This approach helps us to identify where there are differences between 2 DataFrames and do not compare its index labels as their index labels are dropped while comparison.
