Pandas alignment error during elementwise comparison [duplicate]

Question

When checking element-wise equality of multiple columns of a dataframe against a single column, pandas raises a ValueError: Operands are not aligned. Do 'left, right = left.align(right, axis=1, copy=False)' before operating..

import pandas as pd

df1 = pd.DataFrame({
    'A': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
    'B': [11, 12, 13, 14, 15, 16, 17, 18, 19, 20],
    'C': [21, 22, 23, 24, 25, 26, 27, 28, 29, 30]
})

df2 = pd.DataFrame({
    'X': [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000]
})

cond1 = (df1['A'] == df2['X'])
cond2 = (df1[['A','B','C']] == 5)
cond3 = (df1[['A','B','C']] == df2['X']) # raises an alignment error
cond3 = (df1[['A','B','C']] == df1['A']) # raises an alignment error

Why does pandas raise this error? I would have assumed that pandas performs an element-wise comparison without issues, either aligning on the existing index of the columns (which is the same between the dataframes) or on an ad-hoc-assigned new index (from 0 to N-1).

Is there a way to avoid the left.align() suggestion without converting to numpy arrays as shown below?

cond3 = (df1[['A','B','C']].values == df2['X'].values[:,None])

Answer 1

DataFrame == Series will try to align the Series' index with the DataFrame columns.

You should use eq with axis=0:

df1[['A','B','C']].eq(df2['X'], axis=0)

Output:

       A      B      C
0  False  False  False
1  False  False  False
2  False  False  False
3  False  False  False
4  False  False  False
5  False  False  False
6  False  False  False
7  False  False  False
8  False  False  False
9  False  False  False

Example of the default alignment with ==:

pd.DataFrame({'A': [0,1], 'B': [2,3]}) == pd.Series({'A': 0, 'B': 3})

       A      B
0   True  False
1  False   True

Answer 2

You can use DataFrame.eq with axis=0, because default parameter is axis=1:

cond3 = (df1[['A','B','C']].eq(df2['X'], axis=0)) 
cond3 = (df1[['A','B','C']].eq(df1['A'], axis=0))