Python Pandas Only Compare Identically Labeled DataFrame Objects

Question

I tried all the solutions here: Pandas "Can only compare identically-labeled DataFrame objects" error

Didn\'t work for me. Here\'s what I\'ve got. I hav

Answer 1

Replicated with some fake data to achieve the end goal of removing duplicates. Note this is not the answer to the original question, but what the answer was to what I was attempting to do that caused the question.

b = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'],
                    'B': ['B4', 'B5', 'B6', 'B7'],
                    'C': ['C4', 'C5', 'C6', 'C7'],
                    'D': ['D4', 'D5', 'D6', 'D7']},
                    index=[4, 5, 6, 7])


c = pd.DataFrame({'A': ['A7', 'A8', 'A9', 'A10', 'A11'],
                  'A': ['A7', 'A8', 'A9', 'A10', 'A11'],
                  'B': ['B7', 'B8', 'B9', 'B10', 'B11'],
                  'C': ['C7', 'C8', 'C9', 'C10', 'C11'],
                  'D': ['D7', 'D8', 'D9', 'D10', 'D11']},
                   index=[7, 8, 9, 10, 11])

result = pd.concat([b,c])
idx = np.unique(result["A"], return_index=True)[1]
result.iloc[idx].sort()

Answer 2

If you want to compare 2 Data Frames. Check-out flexible comparison in Pandas, using the methods like .eq(), .nq(), gt() and more... --> equal, not equal and greater then.

Example:

df['new_col'] = df.gt(df_1)

http://pandas.pydata.org/pandas-docs/stable/basics.html#flexible-comparisons

Answer 3

In order to get around this, you want to compare the underlying numpy arrays.

import pandas as pd

df1 = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'], index=['One', 'Two'])
df2 = pd.DataFrame([[1, 2], [3, 4]], columns=['a', 'b'], index=['one', 'two'])


df1.values == df2.values

array([[ True,  True],
       [ True,  True]], dtype=bool)