In Python, how do I create a numpy array of arbitrary shape filled with all True or all False?
This question is related to
python
arrays
numpy
boolean
numpy-ndarray
numpy.full((2,2), True, dtype=bool)
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)
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 https://docs.scipy.org/doc/numpy/reference/generated/numpy.full.html
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.
Source: Stackoverflow.com