blocks|key|1010033|text|您可以vstack它：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1010034|>>>+a+=+np.array([4,6,6,1])
>>>+np.vstack((a,)*3)
array([[4,+6,+6,+1],
+++++++[4,+6,+6,+1],
+++++++[4,+6,+6,+1]])|code-block|syntax|javascript|1010035|注意你经常不需要这样做..。你可以用numpy的广播做很多巧妙的技巧.：|1010036|>>>+a+=+np.array([4,6,6,1])
>>>+ones+=+np.ones((4,+4))
>>>+ones+*+a
array([[+4.,++6.,++6.,++1.],
+++++++[+4.,++6.,++6.,++1.],
+++++++[+4.,++6.,++6.,++1.],
+++++++[+4.,++6.,++6.,++1.]])|1010037|在某些情况下，您也可以使用np.newaxis和...来完成整洁的工作。要熟悉这些选项，可能值得查看numpy索引文档。|1010038|entityMap|0|LINK|mutability|MUTABLE|url|https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html^0|3|6|0|0|0|0|D|A|O|3|1E|7|0|0^^$0|@$1|2|3|4|5|6|7|Y|8|@$9|Z|A|10|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|11|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|12|8|@]|D|@]|E|$]]|$1|M|3|N|5|H|7|13|8|@]|D|@]|E|$I|J]]|$1|O|3|P|5|6|7|14|8|@$9|15|A|16|B|C]|$9|17|A|18|B|C]]|D|@$9|19|A|1A|1|1B]]|E|$]]|$1|Q|3|-4|5|6|7|1C|8|@]|D|@]|E|$]]]|R|$S|$5|T|U|V|E|$W|X]]]]

You can <code>vstack</code> it:

<pre><code>&gt;&gt;&gt; a = np.array([4,6,6,1])
&gt;&gt;&gt; np.vstack((a,)*3)
array([[4, 6, 6, 1],
 [4, 6, 6, 1],
 [4, 6, 6, 1]])
</code></pre>

Note that you frequently don't need to do this... You can do a lot of neat tricks with numpy's broadcasting...:

<pre><code>&gt;&gt;&gt; a = np.array([4,6,6,1])
&gt;&gt;&gt; ones = np.ones((4, 4))
&gt;&gt;&gt; ones * a
array([[ 4., 6., 6., 1.],
 [ 4., 6., 6., 1.],
 [ 4., 6., 6., 1.],
 [ 4., 6., 6., 1.]])
</code></pre>

In some cases, you can also use <code>np.newaxis</code> and <code>...</code> to do neat things as well. It's probably worth looking at <a href="https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html" rel="nofollow noreferrer">numpy's indexing</a> documentation to get familiar with the options.

blocks|key|1209011|text|当您想以各种方式重复数组时，tile和repeat是方便的函数：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1209012|In+[233]:+np.tile(np.array([4,6,6,1]),(3,1))
Out[233]:+
array([[4,+6,+6,+1],
+++++++[4,+6,+6,+1],
+++++++[4,+6,+6,+1]])|code-block|syntax|javascript|1209013|在失败时，请注意fill的文档|1209014|a.fill(value)

Fill+the+array+with+a+scalar+value.|1209015|np.array([4,6,6,1])不是标量值。a被初始化为一个3元素float数组。|1209016|如果形状正确，则可以为数组的元素赋值：|1209017|In+[241]:+a=np.empty(3)
In+[242]:+a[:]=np.array([1,2,3])++++#+3+numbers+into+3+slots
In+[243]:+a
Out[243]:+array([+1.,++2.,++3.])
In+[244]:+a=np.empty((3,4))
In+[245]:+a[:]=np.array([1,2,3,4])+++#+4+numbers+into+4+columns
In+[246]:+a
Out[246]:+
array([[+1.,++2.,++3.,++4.],
+++++++[+1.,++2.,++3.,++4.],
+++++++[+1.,++2.,++3.,++4.]])|1209018|此fill适用于对象类型数组，但结果完全不同，在使用时应非常谨慎：|1209019|In+[247]:+a=np.empty(3,+object)
In+[248]:+a
Out[248]:+array([None,+None,+None],+dtype=object)
In+[249]:+a.fill(np.array([1,2,3,4]))
In+[250]:+a
Out[250]:+array([array([1,+2,+3,+4]),+array([1,+2,+3,+4]),+array([1,+2,+3,+4])],+dtype=object)|1209020|这个(3，)数组与其他方法生成的(3,4)数组不一样。对象数组的每个元素都是指向同一事物的指针。在a的一个元素中更改一个值会改变所有元素中的值(因为它们是同一个对象)。|1209021|In+[251]:+a[0][3]=5
In+[252]:+a
Out[252]:+array([array([1,+2,+3,+5]),+array([1,+2,+3,+5]),+array([1,+2,+3,+5])],+dtype=object)|1209022|entityMap^0|E|4|J|6|0|0|8|4|0|0|0|J|P|1|10|5|0|0|0|1|4|0|0|1D|1|0|0^^$0|@$1|2|3|4|5|6|7|14|8|@$9|15|A|16|B|C]|$9|17|A|18|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|19|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|1A|8|@$9|1B|A|1C|B|C]]|D|@]|E|$]]|$1|M|3|N|5|H|7|1D|8|@]|D|@]|E|$I|J]]|$1|O|3|P|5|6|7|1E|8|@$9|1F|A|1G|B|C]|$9|1H|A|1I|B|C]|$9|1J|A|1K|B|C]]|D|@]|E|$]]|$1|Q|3|R|5|6|7|1L|8|@]|D|@]|E|$]]|$1|S|3|T|5|H|7|1M|8|@]|D|@]|E|$I|J]]|$1|U|3|V|5|6|7|1N|8|@$9|1O|A|1P|B|C]]|D|@]|E|$]]|$1|W|3|X|5|H|7|1Q|8|@]|D|@]|E|$I|J]]|$1|Y|3|Z|5|6|7|1R|8|@$9|1S|A|1T|B|C]]|D|@]|E|$]]|$1|10|3|11|5|H|7|1U|8|@]|D|@]|E|$I|J]]|$1|12|3|-4|5|6|7|1V|8|@]|D|@]|E|$]]]|13|$]]

<code>tile</code> and <code>repeat</code> are handy functions when you want to repeat an array in various ways:

<pre><code>In [233]: np.tile(np.array([4,6,6,1]),(3,1))
Out[233]: 
array([[4, 6, 6, 1],
 [4, 6, 6, 1],
 [4, 6, 6, 1]])
</code></pre>

On the failure, note the docs for <code>fill</code>:

<pre><code>a.fill(value)

Fill the array with a scalar value.
</code></pre>

<code>np.array([4,6,6,1])</code> is not a scalar value. <code>a</code> was initialized as a 3 element <code>float</code> array. 

It is possible to assign values to elements of an array, provided the shapes are right:

<pre><code>In [241]: a=np.empty(3)
In [242]: a[:]=np.array([1,2,3]) # 3 numbers into 3 slots
In [243]: a
Out[243]: array([ 1., 2., 3.])
In [244]: a=np.empty((3,4))
In [245]: a[:]=np.array([1,2,3,4]) # 4 numbers into 4 columns
In [246]: a
Out[246]: 
array([[ 1., 2., 3., 4.],
 [ 1., 2., 3., 4.],
 [ 1., 2., 3., 4.]])
</code></pre>

This <code>fill</code> works with an object type array, but the result is quite different, and should be used with considerable caution:

<pre><code>In [247]: a=np.empty(3, object)
In [248]: a
Out[248]: array([None, None, None], dtype=object)
In [249]: a.fill(np.array([1,2,3,4]))
In [250]: a
Out[250]: array([array([1, 2, 3, 4]), array([1, 2, 3, 4]), array([1, 2, 3, 4])], dtype=object)
</code></pre>

This (3,) array is not the same as the (3,4) array produced by other methods. Each element of the object array is a pointer to the same thing. Changing a value in one element of <code>a</code> changes that value in all the elements (because they are the same object).

<pre><code>In [251]: a[0][3]=5
In [252]: a
Out[252]: array([array([1, 2, 3, 5]), array([1, 2, 3, 5]), array([1, 2, 3, 5])], dtype=object)
</code></pre>

blocks|key|266746|text|使用广播|type|unstyled|depth|inlineStyleRanges|entityRanges|data|266747|V堆栈，瓷砖和重复都很棒，但是广播速度可以快几个数量级.|266748|import+numpy+as+np
from+time+import+time

t+=+time()
for+_+in+xrange(10000):
++++a+=+np.array([4,6,6,1])
++++b+=+np.vstack((a,)*100)
print+time()-t

t+=+time()
for+_+in+xrange(10000):
++++a+=+np.array([4,6,6,1])
++++b+=+np.tile(a,(3,1))
print+time()-t

t+=+time()
for+_+in+xrange(10000):
++++a+=+np.array([4,6,6,1])
++++b+=+np.empty([100,a.shape[0]])
++++b[:]+=+a
print+time()-t|code-block|syntax|javascript|266749|指纹：|266750|2.76399993896
0.140000104904
0.0490000247955|266751|entityMap^0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|P|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|Q|8|@]|9|@]|A|$G|H]]|$1|I|3|J|5|6|7|R|8|@]|9|@]|A|$]]|$1|K|3|L|5|F|7|S|8|@]|9|@]|A|$G|H]]|$1|M|3|-4|5|6|7|T|8|@]|9|@]|A|$]]]|N|$]]

<h2>Use broadcasting</h2>

vstack, tile, and repeat are all great and whatnot, but broadcasting can be several orders of magnitude faster...

<pre><code>import numpy as np
from time import time

t = time()
for _ in xrange(10000):
 a = np.array([4,6,6,1])
 b = np.vstack((a,)*100)
print time()-t

t = time()
for _ in xrange(10000):
 a = np.array([4,6,6,1])
 b = np.tile(a,(3,1))
print time()-t

t = time()
for _ in xrange(10000):
 a = np.array([4,6,6,1])
 b = np.empty([100,a.shape[0]])
 b[:] = a
print time()-t
</code></pre>

prints:

<pre><code>2.76399993896
0.140000104904
0.0490000247955
</code></pre>

blocks|key|266768|text|正如Numpy阵列文档所述，当您执行以下操作时，第一个param是shape+(子)：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|266769|a+=+np.empty(3)|code-block|syntax|javascript|266770|您正在创建一个维度3的数组(仅为一维)。相反，你应该这样做：|266771|a+=+np.empty([3,3])|266772|它创建一个由3个维度子数组组成的数组，每个数组都具有维度3(即矩阵3x3)。|266773|正如Numpy填土文档所述，fill只接受一个数字(标量)作为param，因此不能使用另一个数组作为参数，并且您所做的工作不能正常工作：|266774|a.fill(np.array([4,6,6,1]))|266775|为了实现你想做的事，我会做：|266776|a+=+np.array([[4,6,6,1]]*3)|266777|希望我的评论能帮助你！|266778|entityMap|0|LINK|mutability|MUTABLE|url|https://docs.scipy.org/doc/numpy/reference/generated/numpy.array.html|1|https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.fill.html^0|X|5|2|7|0|0|0|0|0|0|E|4|2|7|1|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|1A|8|@$9|1B|A|1C|B|C]]|D|@$9|1D|A|1E|1|1F]]|E|$]]|$1|F|3|G|5|H|7|1G|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|1H|8|@]|D|@]|E|$]]|$1|M|3|N|5|H|7|1I|8|@]|D|@]|E|$I|J]]|$1|O|3|P|5|6|7|1J|8|@]|D|@]|E|$]]|$1|Q|3|R|5|6|7|1K|8|@$9|1L|A|1M|B|C]]|D|@$9|1N|A|1O|1|1P]]|E|$]]|$1|S|3|T|5|H|7|1Q|8|@]|D|@]|E|$I|J]]|$1|U|3|V|5|6|7|1R|8|@]|D|@]|E|$]]|$1|W|3|X|5|H|7|1S|8|@]|D|@]|E|$I|J]]|$1|Y|3|Z|5|6|7|1T|8|@]|D|@]|E|$]]|$1|10|3|-4|5|6|7|1U|8|@]|D|@]|E|$]]]|11|$12|$5|13|14|15|E|$16|17]]|18|$5|13|14|15|E|$16|19]]]]

As the <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.array.html" rel="nofollow noreferrer">Numpy Array</a> Documentation states, first param is <code>shape</code>, son when you're doing:

<pre><code>a = np.empty(3)
</code></pre>

You're creating an array of dimension 3 (just one dimension).
Instead, you should do:

<pre><code>a = np.empty([3,3])
</code></pre>

That creates an array of 3 subarrays of dimension each with dimension 3 (that is a matrix 3x3).

As the <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.fill.html" rel="nofollow noreferrer">Numpy fill</a> Documentation states, <code>fill</code> only takes a number(scalar) as param, so you cannot use another array as argument and what you had done isn't properly working:

<pre><code>a.fill(np.array([4,6,6,1]))
</code></pre>

To achieve what you're trying to do, I would do:

<pre><code>a = np.array([[4,6,6,1]]*3)
</code></pre>

Hope my comments help you!

blocks|key|1010202|text|像这样的重复任务常常被简化为矩阵或向量操作。np.outer()比用眼睛矩阵乘法或填充空数组更快：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1010203|>>>a+=+np.array([4,6,6,1])
>>>b+=+np.outer(np.ones(3,+dtype=np.int),+a)
>>>b
array([[4,+6,+6,+1],
+++++++[4,+6,+6,+1],
+++++++[4,+6,+6,+1]])|code-block|syntax|javascript|1010204|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

Repeating tasks like this often get reduced to matrix or vector operations. np.outer() can do it even faster than multiplication with the eye matrix or filling in empty array:

<pre><code>&gt;&gt;&gt;a = np.array([4,6,6,1])
&gt;&gt;&gt;b = np.outer(np.ones(3, dtype=np.int), a)
&gt;&gt;&gt;b
array([[4, 6, 6, 1],
 [4, 6, 6, 1],
 [4, 6, 6, 1]])
</code></pre>

blocks|key|1010206|text|您可以使用np.full()，就像这里描述的那样。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1010207|>>>repetitions+=+3
>>>fill_array+=+np.array([4,6,6,1])
>>>fill_shape+=+np.shape(fill_array)
>>>a+=+np.full([*(repetitions,),*fill_shape],fill_array)
>>>a
array([[4,+6,+6,+1],
+++++++[4,+6,+6,+1],
+++++++[4,+6,+6,+1]])|code-block|syntax|javascript|1010208|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/q/62771314/13883300^0|5|9|H|2|0|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@$9|T|A|U|B|C]]|D|@$9|V|A|W|1|X]]|E|$]]|$1|F|3|G|5|H|7|Y|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|Z|8|@]|D|@]|E|$]]]|L|$M|$5|N|O|P|E|$Q|R]]]]

You could use <code>np.full()</code>, as described <a href="https://stackoverflow.com/q/62771314/13883300">here</a>.
<pre class="lang-py prettyprint-override"><code>&gt;&gt;&gt;repetitions = 3
&gt;&gt;&gt;fill_array = np.array([4,6,6,1])
&gt;&gt;&gt;fill_shape = np.shape(fill_array)
&gt;&gt;&gt;a = np.full([*(repetitions,),*fill_shape],fill_array)
&gt;&gt;&gt;a
array([[4, 6, 6, 1],
 [4, 6, 6, 1],
 [4, 6, 6, 1]])
</code></pre>

I have an empty numpy array, and another one populated with values. I want to fill the empty numpy array with the populated one, x times.
So, when x = 3, the (originally empty array) would look like <code>[[populated_array],[populated_array], [populated_array]]</code>

Where populated_array is the same value/array each time.
I have tried this

<pre><code>a = np.empty(3)
a.fill(np.array([4,6,6,1]))
</code></pre>

but get this

<pre><code>ValueError: Input object to FillWithScalar is not a scalar
</code></pre>

and want this

<pre><code>[[4,6,6,1],[4,6,6,1],[4,6,6,1]]
</code></pre>

cheers for any help.

How to fill numpy array with another numpy array

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我有一个空的numpy数组，另一个填充了值。我想用填充的x次填充空的numpy数组。因此，当x=3时，(最初为空数组)将类似于[[populated_array],[populated_array], [populated_array]]其中populated_array每次都是相同的值/数组。我试过这个a = np....

问如何用另一个numpy数组填充numpy数组
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问如何用另一个numpy数组填充numpy数组EN