blocks|key|168735|text|分别创建包含1和0的数组的ones和zeros接受一个可选的dtype参数：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|168736|>>>+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)|code-block|syntax|javascript|168737|entityMap^0|D|4|I|5|U|5|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@$9|N|A|O|B|C]|$9|P|A|Q|B|C]|$9|R|A|S|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|T|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|U|8|@]|D|@]|E|$]]]|L|$]]

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

<pre><code>&gt;&gt;&gt; numpy.ones((2, 2), dtype=bool)
array([[ True, True],
 [ True, True]], dtype=bool)
&gt;&gt;&gt; numpy.zeros((2, 2), dtype=bool)
array([[False, False],
 [False, False]], dtype=bool)
</code></pre>

blocks|key|4674596|text|numpy.full((2,2),+True,+dtype=bool)|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|4674597|unstyled|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$B|C]]|$1|D|3|-4|5|E|7|H|8|@]|9|@]|A|$]]]|F|$]]

<pre><code>numpy.full((2,2), True, dtype=bool)
</code></pre>

blocks|key|168787|text|>>>+a+=+numpy.full((2,4),+True,+dtype=bool)
>>>+a[1][3]
True
>>>+a
array([[+True,++True,++True,++True],
+++++++[+True,++True,++True,++True]],+dtype=bool)|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|168788|Numpy.full(大小，标量值，类型)。还可以传递其他参数，有关该参数的文档，请查看https://docs.scipy.org/doc/numpy/reference/generated/numpy.full.html|unstyled|offset|length|168789|entityMap|0|LINK|mutability|MUTABLE|url|https://docs.scipy.org/doc/numpy/reference/generated/numpy.full.html^0|0|18|1W|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@]|A|$B|C]]|$1|D|3|E|5|F|7|R|8|@]|9|@$G|S|H|T|1|U]]|A|$]]|$1|I|3|-4|5|F|7|V|8|@]|9|@]|A|$]]]|J|$K|$5|L|M|N|A|$O|P]]]]

<pre><code>&gt;&gt;&gt; a = numpy.full((2,4), True, dtype=bool)
&gt;&gt;&gt; a[1][3]
True
&gt;&gt;&gt; a
array([[ True, True, True, True],
 [ True, True, True, True]], dtype=bool)
</code></pre>

numpy.full(Size, Scalar Value, Type). There is other arguments as well that can be passed, for documentation on that, check <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.full.html" rel="nofollow noreferrer">https://docs.scipy.org/doc/numpy/reference/generated/numpy.full.html</a>

blocks|key|168844|text|如果它不一定是可写的，您可以使用np.broadcast_to创建这样的数组|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|168845|>>>+import+numpy+as+np
>>>+np.broadcast_to(True,+(2,+5))
array([[+True,++True,++True,++True,++True],
+++++++[+True,++True,++True,++True,++True]],+dtype=bool)|code-block|syntax|javascript|168846|如果你需要它是可写的，你也可以创建一个空数组并自己fill它：|168847|>>>+arr+=+np.empty((2,+5),+dtype=bool)
>>>+arr.fill(1)
>>>+arr
array([[+True,++True,++True,++True,++True],
+++++++[+True,++True,++True,++True,++True]],+dtype=bool)|168848|这些方法只是另类建议。一般来说，你应该像其他答案所建议的那样，坚持使用np.full、np.zeros或np.ones。|168849|entityMap|0|LINK|mutability|MUTABLE|url|https://docs.scipy.org/doc/numpy/reference/generated/numpy.broadcast_to.html#numpy-broadcast-to|1|https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.fill.html^0|G|F|G|F|0|0|0|P|4|P|4|1|0|0|Z|7|17|8|1G|7|0^^$0|@$1|2|3|4|5|6|7|10|8|@$9|11|A|12|B|C]]|D|@$9|13|A|14|1|15]]|E|$]]|$1|F|3|G|5|H|7|16|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|17|8|@$9|18|A|19|B|C]]|D|@$9|1A|A|1B|1|1C]]|E|$]]|$1|M|3|N|5|H|7|1D|8|@]|D|@]|E|$I|J]]|$1|O|3|P|5|6|7|1E|8|@$9|1F|A|1G|B|C]|$9|1H|A|1I|B|C]|$9|1J|A|1K|B|C]]|D|@]|E|$]]|$1|Q|3|-4|5|6|7|1L|8|@]|D|@]|E|$]]]|R|$S|$5|T|U|V|E|$W|X]]|Y|$5|T|U|V|E|$W|Z]]]]

If it doesn't have to be writeable you can create such an array with <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.broadcast_to.html#numpy-broadcast-to" rel="noreferrer"><code>np.broadcast_to</code></a>:

<pre><code>&gt;&gt;&gt; import numpy as np
&gt;&gt;&gt; np.broadcast_to(True, (2, 5))
array([[ True, True, True, True, True],
 [ True, True, True, True, True]], dtype=bool)
</code></pre>

If you need it writable you can also create an empty array and <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.fill.html" rel="noreferrer"><code>fill</code></a> it yourself:

<pre><code>&gt;&gt;&gt; arr = np.empty((2, 5), dtype=bool)
&gt;&gt;&gt; arr.fill(1)
&gt;&gt;&gt; arr
array([[ True, True, True, True, True],
 [ True, True, True, True, True]], dtype=bool)
</code></pre>

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

blocks|key|4674732|text|迈克尔·柯里答案的基准|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4674733|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()|code-block|syntax|javascript|4674734|​|4674735|📷|atomic|offset|length|4674736|4674737|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/4ca918d77db89d4b16f4e61587a5e6cc.png|imageAlt^0|0|0|0|0|1|0|0|0^^$0|@$1|2|3|4|5|6|7|X|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|Y|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Z|8|@]|9|@]|A|$]]|$1|I|3|J|5|K|7|10|8|@]|9|@$L|11|M|12|1|13]]|A|$]]|$1|N|3|H|5|6|7|14|8|@]|9|@]|A|$]]|$1|O|3|-4|5|6|7|15|8|@]|9|@]|A|$]]]|P|$Q|$5|R|S|T|A|$U|V|W|-4]]]]

benchmark for Michael Currie's answer
<pre><code>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()
</code></pre>
<a href="https://i.stack.imgur.com/MVG0n.png" rel="noreferrer"><img src="https://i.stack.imgur.com/MVG0n.png" alt="enter image description here" /></a>

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

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

Python

在Python中，如何创建一个填充了所有True或所有False的任意形状的numpy数组？

问如何创建全真或全假的numpy数组？
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