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

Question

numpy already 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)

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)

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()