blocks|key|903636|text|使用两个括号对而不是一个。这将创建一个可以转置的二维数组，这与使用一个括号对时创建的一维数组不同。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|903637|import+numpy+as+np++++
a+=+np.array([[5,+4]])
a.T|code-block|syntax|javascript|903638|更详细的示例：|903639|>>>+a+=+[3,6,9]
>>>+b+=+np.array(a)
>>>+b.T
array([3,+6,+9])+++++++++#Here+it+didn't+transpose+because+'a'+is+1+dimensional
>>>+b+=+np.array([a])
>>>+b.T
array([[3],++++++++++++++#Here+it+did+transpose+because+a+is+2+dimensional
+++++++[6],
+++++++[9]])|903640|使用numpy的shape方法查看这里发生了什么：|offset|length|style|CODE|903641|>>>+b+=+np.array([10,20,30])
>>>+b.shape
(3,)
>>>+b+=+np.array([[10,20,30]])
>>>+b.shape
(1,+3)|903642|entityMap^0|0|0|0|0|8|5|0|0^^$0|@$1|2|3|4|5|6|7|U|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|V|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|W|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|X|8|@]|9|@]|A|$E|F]]|$1|K|3|L|5|6|7|Y|8|@$M|Z|N|10|O|P]]|9|@]|A|$]]|$1|Q|3|R|5|D|7|11|8|@]|9|@]|A|$E|F]]|$1|S|3|-4|5|6|7|12|8|@]|9|@]|A|$]]]|T|$]]

Use two bracket pairs instead of one. This creates a 2D array, which can be transposed, unlike the 1D array you create if you use one bracket pair.

<pre><code>import numpy as np 
a = np.array([[5, 4]])
a.T
</code></pre>

More thorough example:

<pre><code>&gt;&gt;&gt; a = [3,6,9]
&gt;&gt;&gt; b = np.array(a)
&gt;&gt;&gt; b.T
array([3, 6, 9]) #Here it didn't transpose because 'a' is 1 dimensional
&gt;&gt;&gt; b = np.array([a])
&gt;&gt;&gt; b.T
array([[3], #Here it did transpose because a is 2 dimensional
 [6],
 [9]])
</code></pre>

Use numpy's <code>shape</code> method to see what is going on here:

<pre><code>&gt;&gt;&gt; b = np.array([10,20,30])
&gt;&gt;&gt; b.shape
(3,)
&gt;&gt;&gt; b = np.array([[10,20,30]])
&gt;&gt;&gt; b.shape
(1, 3)
</code></pre>

blocks|key|687208|text|您可以将现有的向量转换为矩阵，方法是将其包装在一组额外的方括号中...|type|unstyled|depth|inlineStyleRanges|entityRanges|data|687209|from+numpy+import+*
v=array([5,4])+##+create+a+numpy+vector
array([v]).T+##+transpose+a+vector+into+a+matrix|code-block|syntax|javascript|687210|array+vs.+matrix也有一个matrix类(参见numpy+)……|offset|length|style|CODE|687211|matrix(v).T+##+transpose+a+vector+into+a+matrix|687212|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/questions/4151128/what-are-the-differences-between-numpy-arrays-and-matrices-which-one-should-i-u|1|http://docs.scipy.org/doc/numpy/reference/generated/numpy.matrix.html^0|0|0|K|6|0|G|0|K|6|1|0|0^^$0|@$1|2|3|4|5|6|7|Y|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|Z|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|10|8|@$I|11|J|12|K|L]]|9|@$I|13|J|14|1|15]|$I|16|J|17|1|18]]|A|$]]|$1|M|3|N|5|D|7|19|8|@]|9|@]|A|$E|F]]|$1|O|3|-4|5|6|7|1A|8|@]|9|@]|A|$]]]|P|$Q|$5|R|S|T|A|$U|V]]|W|$5|R|S|T|A|$U|X]]]]

You can convert an existing vector into a matrix by wrapping it in an extra set of square brackets...

<pre><code>from numpy import *
v=array([5,4]) ## create a numpy vector
array([v]).T ## transpose a vector into a matrix
</code></pre>

numpy also has a <a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.matrix.html" rel="noreferrer"><code>matrix</code></a> class (see <a href="https://stackoverflow.com/questions/4151128/what-are-the-differences-between-numpy-arrays-and-matrices-which-one-should-i-u">array vs. matrix</a>)...

<pre><code>matrix(v).T ## transpose a vector into a matrix
</code></pre>

blocks|key|687256|text|的转置|type|unstyled|depth|inlineStyleRanges|entityRanges|data|687257|x+=+[[0+1],
+++++[2+3]]|code-block|syntax|javascript|687258|是|687259|xT+=+[[0+2],
++++++[1+3]]|687260|代码是这样的：|687261|import+numpy+as+np
a+=+[[0,+1],[2,+3]]
x+=+np.array(a);
np.transpose(x)+++++++|687262|或者简单的方式：|687263|x.T+++++|687264|这是更多信息的链接：http://docs.scipy.org/doc/numpy/reference/generated/numpy.transpose.html|offset|length|687265|**转置一维数组将返回原始数组的未更改视图。对1D数组试试这个：|687266|b+=+np.array([a])|687267|entityMap|0|LINK|mutability|MUTABLE|url|http://docs.scipy.org/doc/numpy/reference/generated/numpy.transpose.html^0|0|0|0|0|0|0|0|0|A|20|0|0|0|0^^$0|@$1|2|3|4|5|6|7|18|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|19|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|1A|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|1B|8|@]|9|@]|A|$E|F]]|$1|K|3|L|5|6|7|1C|8|@]|9|@]|A|$]]|$1|M|3|N|5|D|7|1D|8|@]|9|@]|A|$E|F]]|$1|O|3|P|5|6|7|1E|8|@]|9|@]|A|$]]|$1|Q|3|R|5|D|7|1F|8|@]|9|@]|A|$E|F]]|$1|S|3|T|5|6|7|1G|8|@]|9|@$U|1H|V|1I|1|1J]]|A|$]]|$1|W|3|X|5|6|7|1K|8|@]|9|@]|A|$]]|$1|Y|3|Z|5|D|7|1L|8|@]|9|@]|A|$E|F]]|$1|10|3|-4|5|6|7|1M|8|@]|9|@]|A|$]]]|11|$12|$5|13|14|15|A|$16|17]]]]

The transpose of
<pre><code>x = [[0 1],
 [2 3]]
</code></pre>
is
<pre><code>xT = [[0 2],
 [1 3]]
</code></pre>
well the code is:
<pre><code>import numpy as np
a = [[0, 1],[2, 3]]
x = np.array(a);
np.transpose(x) 
</code></pre>
Or the simple way:
<pre><code>x.T 
</code></pre>
this a link for more information: <a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.transpose.html" rel="nofollow noreferrer">http://docs.scipy.org/doc/numpy/reference/generated/numpy.transpose.html</a>
** Transposing a 1-D array returns an unchanged view of the original array. Try this for 1D array:
<pre><code>b = np.array([a])
</code></pre>

blocks|key|687310|text|numpy一维数组-->列/行矩阵：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|687311|>>>+a=np.array([1,2,4])
>>>+a[:,+None]++++#+col
array([[1],
+++++++[2],
+++++++[4]])
>>>+a[None,+:]++++#+row,+or+faster+`a[None]`
array([[1,+2,+4]])|code-block|syntax|javascript|687312|正如@joe-kington所说，为了提高可读性，你可以用np.newaxis代替None。|offset|length|style|CODE|687313|entityMap^0|0|0|T|A|15|4|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Q|8|@$I|R|J|S|K|L]|$I|T|J|U|K|L]]|9|@]|A|$]]|$1|M|3|-4|5|6|7|V|8|@]|9|@]|A|$]]]|N|$]]

numpy 1D array --> column/row matrix:

<pre><code>&gt;&gt;&gt; a=np.array([1,2,4])
&gt;&gt;&gt; a[:, None] # col
array([[1],
 [2],
 [4]])
&gt;&gt;&gt; a[None, :] # row, or faster `a[None]`
array([[1, 2, 4]])
</code></pre>

And as @joe-kington said, you can replace <code>None</code> with <code>np.newaxis</code> for readability.

blocks|key|903844|text|只能转置二维数组。可以使用numpy.matrix创建二维阵列。这已经晚了三年，但我只是添加了一组可能的解决方案：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|903845|import+numpy+as+np
m+=+np.matrix([2,+3])
m.T|code-block|syntax|javascript|903846|entityMap^0|D|C|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@$9|N|A|O|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|P|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|Q|8|@]|D|@]|E|$]]]|L|$]]

You can only transpose a 2D array. You can use <code>numpy.matrix</code> to create a 2D array. This is three years late, but I am just adding to the possible set of solutions:

<pre><code>import numpy as np
m = np.matrix([2, 3])
m.T
</code></pre>

blocks|key|687419|text|正如上面提到的一些注释，一维数组的转置是一维数组，因此转置一维数组的一种方法是将数组转换为矩阵，如下所示：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|687420|np.transpose(a.reshape(len(a),+1))|code-block|syntax|javascript|687421|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|$]]

As some of the comments above mentioned, the transpose of 1D arrays are 1D arrays, so one way to transpose a 1D array would be to convert the array to a matrix like so:

<pre><code>np.transpose(a.reshape(len(a), 1))
</code></pre>

blocks|key|687473|text|numpy中函数的名称是column_stack。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|687474|>>>a=np.array([5,4])
>>>np.column_stack(a)
array([[5,+4]])|code-block|syntax|javascript|687475|entityMap|0|LINK|mutability|MUTABLE|url|https://docs.scipy.org/doc/numpy-1.10.1/reference/generated/numpy.column_stack.html^0|0|5|C|C|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]]]]

The name of the function in <code>numpy</code> is <a href="https://docs.scipy.org/doc/numpy-1.10.1/reference/generated/numpy.column_stack.html" rel="nofollow noreferrer">column_stack</a>. 

<pre><code>&gt;&gt;&gt;a=np.array([5,4])
&gt;&gt;&gt;np.column_stack(a)
array([[5, 4]])
</code></pre>

blocks|key|904016|text|要将一维数组‘转置’为二维数组，可以使用numpy.vstack|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|904017|>>>+numpy.vstack(numpy.array([1,2,3]))
array([[1],
+++++++[2],
+++++++[3]])|code-block|syntax|javascript|904018|它也适用于香草列表：|904019|>>>+numpy.vstack([1,2,3])
array([[1],
+++++++[2],
+++++++[3]])|904020|entityMap|0|LINK|mutability|MUTABLE|url|https://docs.scipy.org/doc/numpy/reference/generated/numpy.vstack.html^0|K|C|K|C|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|W|8|@$9|X|A|Y|B|C]]|D|@$9|Z|A|10|1|11]]|E|$]]|$1|F|3|G|5|H|7|12|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|13|8|@]|D|@]|E|$]]|$1|M|3|N|5|H|7|14|8|@]|D|@]|E|$I|J]]|$1|O|3|-4|5|6|7|15|8|@]|D|@]|E|$]]]|P|$Q|$5|R|S|T|E|$U|V]]]]

To 'transpose' a 1d array to a 2d column, you can use <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.vstack.html" rel="noreferrer"><code>numpy.vstack</code></a>:

<pre><code>&gt;&gt;&gt; numpy.vstack(numpy.array([1,2,3]))
array([[1],
 [2],
 [3]])
</code></pre>

It also works for vanilla lists:

<pre><code>&gt;&gt;&gt; numpy.vstack([1,2,3])
array([[1],
 [2],
 [3]])
</code></pre>

blocks|key|687550|text|而是使用arr[:,None]来创建列向量|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|687551|entityMap^0|4|B|0^^$0|@$1|2|3|4|5|6|7|H|8|@$9|I|A|J|B|C]]|D|@]|E|$]]|$1|F|3|-4|5|6|7|K|8|@]|D|@]|E|$]]]|G|$]]

instead use <code>arr[:,None]</code> to create column vector

blocks|key|687611|text|基本上，转置函数的作用是交换数组的形状和步长：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|687612|>>>+a+=+np.ones((1,2,3))

>>>+a.shape
(1,+2,+3)

>>>+a.T.shape
(3,+2,+1)

>>>+a.strides
(48,+24,+8)

>>>+a.T.strides
(8,+24,+48)|code-block|syntax|javascript|687613|在1Dnumpy数组(秩-1数组)的情况下，形状和步长是1元素元组，并且不能交换，并且这种1D数组的转置返回它不变。相反，您可以将“行向量”(形状为(n,+1)的numpy数组)转换为“列向量”(形状为(1,+n)的numpy数组)。要做到这一点，你必须首先将你的1Dnumpy数组转换成行向量，然后交换形状和步长(转置它)。下面是一个执行此操作的函数：|offset|length|style|CODE|687614|from+numpy.lib.stride_tricks+import+as_strided

def+transpose(a):
++++a+=+np.atleast_2d(a)
++++return+as_strided(a,+shape=a.shape[::-1],+strides=a.strides[::-1])|687615|示例：|687616|>>>+a+=+np.arange(3)
>>>+a
array([0,+1,+2])

>>>+transpose(a)
array([[0],
+++++++[1],
+++++++[2]])

>>>+a+=+np.arange(1,+7).reshape(2,3)
>>>+a+++++
array([[1,+2,+3],
+++++++[4,+5,+6]])

>>>+transpose(a)
array([[1,+4],
+++++++[2,+5],
+++++++[3,+6]])|687617|当然，你不需要这样做，因为你有一个一维数组，你可以通过a.reshape((-1,+1))或a[:,+None]直接将它重塑成(n,+1)数组。我只是想演示转置数组是如何工作的。|687618|entityMap^0|0|0|22|6|2T|6|0|0|0|0|R|I|1A|A|1R|6|0^^$0|@$1|2|3|4|5|6|7|W|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|X|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Y|8|@$I|Z|J|10|K|L]|$I|11|J|12|K|L]]|9|@]|A|$]]|$1|M|3|N|5|D|7|13|8|@]|9|@]|A|$E|F]]|$1|O|3|P|5|6|7|14|8|@]|9|@]|A|$]]|$1|Q|3|R|5|D|7|15|8|@]|9|@]|A|$E|F]]|$1|S|3|T|5|6|7|16|8|@$I|17|J|18|K|L]|$I|19|J|1A|K|L]|$I|1B|J|1C|K|L]]|9|@]|A|$]]|$1|U|3|-4|5|6|7|1D|8|@]|9|@]|A|$]]]|V|$]]

Basically what the transpose function does is to swap the shape and strides of the array:

<pre><code>&gt;&gt;&gt; a = np.ones((1,2,3))

&gt;&gt;&gt; a.shape
(1, 2, 3)

&gt;&gt;&gt; a.T.shape
(3, 2, 1)

&gt;&gt;&gt; a.strides
(48, 24, 8)

&gt;&gt;&gt; a.T.strides
(8, 24, 48)
</code></pre>

In case of 1D numpy array (rank-1 array) the shape and strides are 1-element tuples and cannot be swapped, and the transpose of such an 1D array returns it unchanged. Instead, you can transpose a "row-vector" (numpy array of shape <code>(1, n)</code>) into a "column-vector" (numpy array of shape <code>(n, 1)</code>). To achieve this you have to first convert your 1D numpy array into row-vector and then swap the shape and strides (transpose it). Below is a function that does it:

<pre><code>from numpy.lib.stride_tricks import as_strided

def transpose(a):
 a = np.atleast_2d(a)
 return as_strided(a, shape=a.shape[::-1], strides=a.strides[::-1])
</code></pre>

Example:

<pre><code>&gt;&gt;&gt; a = np.arange(3)
&gt;&gt;&gt; a
array([0, 1, 2])

&gt;&gt;&gt; transpose(a)
array([[0],
 [1],
 [2]])

&gt;&gt;&gt; a = np.arange(1, 7).reshape(2,3)
&gt;&gt;&gt; a 
array([[1, 2, 3],
 [4, 5, 6]])

&gt;&gt;&gt; transpose(a)
array([[1, 4],
 [2, 5],
 [3, 6]])
</code></pre>

Of course you don't have to do it this way since you have a 1D array and you can directly reshape it into <code>(n, 1)</code> array by <code>a.reshape((-1, 1))</code> or <code>a[:, None]</code>. I just wanted to demonstrate how transposing an array works.

blocks|key|687649|text|要像在示例中那样转置一维数组(平面数组)，可以使用np.expand_dims()函数：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|687650|>>>+a+=+np.expand_dims(np.array([5,+4]),+axis=1)
array([[5],
+++++++[4]])|code-block|syntax|javascript|687651|np.expand_dims()将向所选轴添加尺寸。在本例中，我们使用axis=1，它添加了一个列维，有效地转置了原来的平面数组。|687652|entityMap^0|P|G|0|0|0|G|Z|6|0^^$0|@$1|2|3|4|5|6|7|O|8|@$9|P|A|Q|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|R|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|S|8|@$9|T|A|U|B|C]|$9|V|A|W|B|C]]|D|@]|E|$]]|$1|M|3|-4|5|6|7|X|8|@]|D|@]|E|$]]]|N|$]]

To transpose a 1-D array (flat array) as you have in your example, you can use the <code>np.expand_dims()</code> function:
<pre><code>&gt;&gt;&gt; a = np.expand_dims(np.array([5, 4]), axis=1)
array([[5],
 [4]])
</code></pre>
<code>np.expand_dims()</code> will add a dimension to the chosen axis. In this case, we use <code>axis=1</code>, which adds a column dimension, effectively transposing your original flat array.

I use Python and NumPy and have some problems with "transpose":

<pre><code>import numpy as np
a = np.array([5,4])
print(a)
print(a.T)
</code></pre>

Invoking <code>a.T</code> is not transposing the array. If <code>a</code> is for example <code>[[],[]]</code> then it transposes correctly, but I need the transpose of <code>[...,...,...]</code>.

Transposing a 1D NumPy array

Python

我使用Python和NumPy，在“转置”方面有一些问题：import numpy as npa = np.array([5,4])print(a)print(a.T)调用a.T不会转置数组。例如，如果a是[[],[]]，那么它的转置是正确的，但我需要[...,...,...]的转置。

问转置一维NumPy数组
EN

社区

活动

资源

关于

腾讯云开发者

热门产品

热门推荐

更多推荐

问转置一维NumPy数组EN

社区

活动

资源

关于

腾讯云开发者

热门产品

热门推荐

更多推荐

问转置一维NumPy数组
EN