如何创建全为真或全为假的numpy数组?

问题:如何创建全为真或全为假的numpy数组?

在Python中,如何创建由全True或全False填充的任意形状的numpy数组?

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


回答 0

numpy已经可以非常容易地创建全1或全0的数组:

例如numpy.ones((2, 2))numpy.zeros((2, 2))

由于TrueFalsePython中被表示为10,分别,我们只有指定这个数组应该是布尔使用可选dtype参数,我们正在这样做。

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

返回:

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

更新:2013年10月30日

从numpy 版本1.8开始,我们可以使用full语法更清楚地表明我们意图的语法来达到相同的结果(如fmonegaglia指出):

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

更新:2017年1月16日

因为至少numpy的1.12版full自动转换结果到了dtype第二个参数,所以我们可以这样写:

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

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)


回答 1

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

回答 2

ones和和zeros分别创建一个全为1和0的数组,它们带有一个可选dtype参数:

>>> 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)

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)

回答 3

如果它不是可写的,则可以使用以下方法创建这样的数组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)

如果需要可写,也可以fill自己创建一个空数组:

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

这些方法只是替代建议。通常,您应该坚持np.fullnp.zeros或者np.ones像其他答案所建议的那样。

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.


回答 4

快速运行一个timeit,以查看np.full和之间是否有任何差异np.ones版本。

答:不可以

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")

结果:

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


重要

关于帖子np.empty(由于声誉太低,我无法评论):

不要那样做。请勿使用np.empty初始化全True数组

由于数组为空,因此不会写入内存,也无法保证您的值是多少,例如

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

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]]

回答 5

>>> 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(大小,标量值,类型)。也可以传递其他参数,有关文档,请检查https://docs.scipy.org/doc/numpy/reference/produced/numpy.full.html

>>> 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