Python Pandas

Pandas is convenient but sometimes also a bit inconsistent. For example, None==None is in pure Python and Numpy evaluated as True. In Pandas, it is evaluated as False:

import pandas as pd
import numpy as np

df = pd.DataFrame([None])
x = np.array([None])

print(None == None) # Python says True
print(x == x)       # Numpy says True
print(df == df)     # Pandas says False

While we should generally avoid equality comparison for detection of None in the code (and use is, respectively isnull()), when we are actually comparing two variables coming from the outside, we may end up comparing None to None. And if we care about the result of the comparison (we do, otherwise why we would bother with the comparison in the first place?), we have to be careful, whether we are comparing the variables with "Pandas logic", or "the rest of Python world logic".