How to create a numpy array of all True or all False?

In Python, how do I create a numpy array of arbitrary shape filled with all True or all False?

7 Answers

TLDR; this is the answer:

numpy.full((2, 2), True)

Longer Explanation:

numpy allows the creation of arrays of all ones or all zeros very easily:

e.g. numpy.ones((2, 2)) or numpy.zeros((2, 2))

Since True and False are represented in Python as 1 and 0, respectively, we have only to specify this array should be boolean using the optional dtype parameter and we are done.

numpy.ones((2, 2), dtype=bool)

returns:

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

UPDATE: 30 October 2013

Since numpy version 1.8, we can use full to achieve the same result with syntax that more clearly shows our intent (as fmonegaglia points out):

numpy.full((2, 2), True, dtype=bool)

UPDATE: 16 January 2017

Since at least numpy version 1.12, full automatically casts results to the dtype of the second parameter, so we can just write:

numpy.full((2, 2), True)

3

numpy.full((2,2), True, dtype=bool)

7

ones and zeros, which create arrays full of ones and zeros respectively, take an optional dtype parameter:

>>> numpy.ones((2, 2), dtype=bool)
array([[ True, True], [ True, True]], dtype=bool)
>>> numpy.zeros((2, 2), dtype=bool)
array([[False, False], [False, False]], dtype=bool)

0

If it doesn't have to be writeable you can create such an array with np.broadcast_to:

>>> import numpy as np
>>> np.broadcast_to(True, (2, 5))
array([[ True, True, True, True, True], [ True, True, True, True, True]], dtype=bool)

If you need it writable you can also create an empty array and fill it yourself:

>>> arr = np.empty((2, 5), dtype=bool)
>>> arr.fill(1)
>>> arr
array([[ True, True, True, True, True], [ True, True, True, True, True]], dtype=bool)

These approaches are only alternative suggestions. In general you should stick with np.full, np.zeros or np.ones like the other answers suggest.

benchmark for Michael Currie's answer

import perfplot
bench_x = perfplot.bench( n_range= range(1, 200), setup = lambda n: (n, n), kernels= [ lambda shape: np.ones(shape, dtype= bool), lambda shape: np.full(shape, True) ], labels = ['ones', 'full']
)
bench_x.show()

0

Quickly ran a timeit to see, if there are any differences between the np.full and np.ones version.

Answer: No

import timeit
n_array, n_test = 1000, 10000
setup = f"import numpy as np; n = {n_array};"
print(f"np.ones: {timeit.timeit('np.ones((n, n), dtype=bool)', number=n_test, setup=setup)}s")
print(f"np.full: {timeit.timeit('np.full((n, n), True)', number=n_test, setup=setup)}s")

Result:

np.ones: 0.38416870904620737s
np.full: 0.38430388597771525s

IMPORTANT

Regarding the post about np.empty (and I cannot comment, as my reputation is too low):

DON'T DO THAT. DON'T USE np.empty to initialize an all-True array

As the array is empty, the memory is not written and there is no guarantee, what your values will be, e.g.

>>> print(np.empty((4,4), dtype=bool))
[[ True True True True] [ True True True True] [ True True True True] [ True True False False]]

>>> a = numpy.full((2,4), True, dtype=bool)
>>> a[1][3]
True
>>> a
array([[ True, True, True, True], [ True, True, True, True]], dtype=bool)

numpy.full(Size, Scalar Value, Type). There is other arguments as well that can be passed, for documentation on that, check

1