How to perform element wise boolean operations on numpy arrays [duplicate]

Question

This question already has answers here:

Logical operators for boolean indexing in Pandas (3 answers)

Closed 2 years ago.

For example I would like to create a mask that masks elements with value between 40 and 60:

foo = np.asanyarray(range(100))
mask = (foo < 40).__or__(foo > 60)

Which just looks ugly, I can't write:

(foo < 40) or (foo > 60)

because I end up with:

  ValueError Traceback (most recent call last)
  ...
  ----> 1 (foo < 40) or (foo > 60)
  ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

Is there a canonical way of doing element wise boolean operations on numpy arrays that with good looking code?

Answer 1

Have you tried this?

mask = (foo < 40) | (foo > 60)

Note: the __or__ method in an object overloads the bitwise or operator (|), not the boolean or operator.

Answer 2

If you have comparisons within only booleans, as in your example, you can use the bitwise OR operator | as suggested by Jcollado. But beware, this can give you strange results if you ever use non-booleans, such as mask = (foo < 40) | override. Only as long as override guaranteed to be either False, True, 1, or 0, are you fine.

More general is the use of numpy's comparison set operators, np.any and np.all. This snippet returns all values between 35 and 45 which are less than 40 or not a multiple of 3:

import numpy as np
foo = np.arange(35, 46)
mask = np.any([(foo < 40), (foo % 3)], axis=0)
print foo[mask]
OUTPUT: array([35, 36, 37, 38, 39, 40, 41, 43, 44])

Not as nice as with |, but nicer than the code in your question.

Answer 3

You can use the numpy logical operations. In your example:

np.logical_or(foo < 40, foo > 60)

Answer 4

Note that you can use ~ for elementwise negation.

arr = np.array([False, True])
~arr

OUTPUT: array([ True, False], dtype=bool)

Also & does elementwise and

arr_1 = np.array([False, False, True, True])
arr_2 = np.array([False, True, False, True])

arr_1 & arr_2

OUTPUT:   array([False, False, False,  True], dtype=bool)

These also work with Pandas Series

ser_1 = pd.Series([False, False, True, True])
ser_2 = pd.Series([False, True, False, True])

ser_1 & ser_2

OUTPUT:
0    False
1    False
2    False
3     True
dtype: bool