Why does pandas use (&, |) instead of the normal, pythonic (and, or)?

Question

I understand the pandas docs explain that this is the convention, but I was wondering why?

For example:

import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(6,4), index=list('abcdef'), columns=list('ABCD'))
print(df[(df.A < .5) | (df.B > .5)])
print(df[(df.A < .5) or (df.B > .5)])   

Returns the following:

          A         B         C         D
a -0.284669 -0.277413  2.524311 -1.386008
b -0.190307  0.325620 -0.367727  0.347600
c -0.763290 -0.108921 -0.467167  1.387327
d -0.241815 -0.869941 -0.756848 -0.335477
e -1.583691 -0.236361 -1.007421  0.298171
f -3.173293  0.521770 -0.326190  1.604712
Traceback (most recent call last):
  File "C:\test.py", line 64, in <module>
    print(df[(df.A < .5) or (df.B > .5)])   
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

Answer 1

Because & and | are overridable (customizable). You can write the code that drives the operators for any class.

The logic operators and and or, on the other hand, have standard behavior that cannot be modified.

See here for the relevant documentation.