ValueError: Can only compare identically-labeled Series objects python

Question

df = df1.loc[df1['CUST_ACCT_KEY'] != df2['CUST_ACCT_KEY']]

When I execute the above command, I get the following error:

ValueError: Can only compare identically-labeled Series objects

What am I doing wrong?*

The dtypes of both the column are int64.

The indexed of df1 and df1 are both resetted before performing the action — Sumukh
– Sumukh, Commented Jun 27, 2018 at 16:25

Dadep · Accepted Answer · 2018-09-24 06:41:54Z

8

Pandas does almost all of its operations with intrinsic data alignment, meaning it uses indexes to compare, and perform operations.

You could avoid this error by converting one of the series to a numpy array using .values:

df = df1.loc[df1['CUST_ACCT_KEY'] != df2['CUST_ACCT_KEY']].values

However, you are comparing row to row with no index alignment.

MCVE:

df1 = pd.DataFrame(np.arange(1,10), index=np.arange(1,10),columns=['A'])

df2 = pd.DataFrame(np.arange(11,20), index=np.arange(11,20),columns=['B'])

df1['A'] != df2['B']

Output:

ValueError: Can only compare identically-labeled Series objects

Change to numpy array:

df1['A'] != df2['B'].values

Output:

1    True
2    True
3    True
4    True
5    True
6    True
7    True
8    True
9    True
Name: A, dtype: bool

edited Sep 24, 2018 at 6:41

Dadep

2,7805 gold badges30 silver badges40 bronze badges

answered Jun 27, 2018 at 16:32

Scott Boston

154k15 gold badges160 silver badges207 bronze badges

Sign up to request clarification or add additional context in comments.

3 Comments

Sumukh Over a year ago

The indexes of both df1 and df2 were resetted before I perfomed the task. I also tried numpy.where, but yielded the same result

Scott Boston Over a year ago

Can you post some data along with code in the question.

Sumukh Over a year ago

I made use of my unique indexes to filter the data. Didn't need to reset the data - df.drop(df_subset.index)

Collectives™ on Stack Overflow

ValueError: Can only compare identically-labeled Series objects python

1 Answer 1

3 Comments

Your Answer

Linked

Hot Network Questions

Collectives™ on Stack Overflow

1 Answer 1

3 Comments

Your Answer

Sign up or log in

Post as a guest

Linked

Related