Python 如何创建一个全部为 True 或全部为 False 的 numpy 数组？
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How to create a numpy array of all True or all False?
提问by Michael Currie
In Python, how do I create a numpy array of arbitrary shape filled with all True or all False?
在 Python 中，如何创建一个填充所有 True 或全部 False 的任意形状的 numpy 数组？
采纳答案by Michael Currie
numpy already allows the creation of arrays of all ones or all zeros very easily:
numpy 已经允许非常轻松地创建全 1 或全 0 的数组：
e.g. numpy.ones((2, 2))or numpy.zeros((2, 2))
例如numpy.ones((2, 2))或numpy.zeros((2, 2))
Since Trueand Falseare represented in Python as 1and 0, respectively, we have only to specify this array should be boolean using the optional dtypeparameter and we are done.
由于TrueandFalse在 Python 中分别表示为1and 0，我们只需要使用可选dtype参数指定这个数组应该是布尔值，我们就完成了。
numpy.ones((2, 2), dtype=bool)
numpy.ones((2, 2), dtype=bool)
returns:
返回：
array([[ True, True],
[ True, True]], dtype=bool)
UPDATE: 30 October 2013
更新：2013 年 10 月 30 日
Since numpy version 1.8, we can use fullto achieve the same result with syntax that more clearly shows our intent (as fmonegaglia points out):
从 numpy版本 1.8 开始，我们可以使用full更清楚地显示我们意图的语法来实现相同的结果（正如 fmonegaglia 指出的那样）：
numpy.full((2, 2), True, dtype=bool)
numpy.full((2, 2), True, dtype=bool)
UPDATE: 16 January 2017
更新：2017 年 1 月 16 日
Since at least numpy version 1.12, fullautomatically casts results to the dtypeof the second parameter, so we can just write:
由于至少 numpy版本 1.12，full自动将结果转换dtype为第二个参数的，所以我们可以只写：
numpy.full((2, 2), True)
numpy.full((2, 2), True)
回答by user2357112 supports Monica
onesand zeros, which create arrays full of ones and zeros respectively, take an optional dtypeparameter:
ones和zeros，分别创建充满 1 和 0 的数组，采用一个可选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)
回答by fmonegaglia
numpy.full((2,2), True, dtype=bool)
回答by nikithashr
>>> 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
numpy.full（大小，标量值，类型）。还有其他参数可以传递，有关文档，请查看https://docs.scipy.org/doc/numpy/reference/generated/numpy.full.html
回答by MSeifert
If it doesn't have to be writeable you can create such an array with np.broadcast_to:
如果它不必是可写的，您可以使用以下命令创建这样一个数组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)
If you need it writable you can also create an empty array and fillit yourself:
如果您需要它可写，您还fill可以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)
These approaches are only alternative suggestions. In general you should stick with np.full, np.zerosor np.oneslike the other answers suggest.
这些方法只是替代建议。一般来说，您应该坚持使用np.full，np.zeros或者np.ones像其他答案所建议的那样。
回答by Joschua
Quickly ran a timeit to see, if there are any differences between the np.fulland np.onesversion.
赶紧跑个timeit看看，np.full和np.ones版本有没有区别。
Answer: No
答案：没有
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:
结果：
np.ones: 0.38416870904620737s
np.full: 0.38430388597771525s
IMPORTANT
重要的
Regarding the post about np.empty(and I cannot comment, as my reputation is too low):
关于帖子np.empty（我无法发表评论，因为我的声誉太低）：
DON'T DO THAT. DON'T USE np.emptyto initialize an all-Truearray
不要那样做。不要np.empty用于初始化全True数组
As the array is empty, the memory is not written and there is no guarantee, what your values will be, e.g.
由于数组为空，因此不会写入内存并且无法保证您的值是什么，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]]
