blocks|key|554973|text|来自：https://github.com/numpy/numpy/issues/5173|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|554974|def+disarrange(a,+axis=-1):
++++"""
++++Shuffle+`a`+in-place+along+the+given+axis.

++++Apply+numpy.random.shuffle+to+the+given+axis+of+`a`.
++++Each+one-dimensional+slice+is+shuffled+independently.
++++"""
++++b+=+a.swapaxes(axis,+-1)
++++#+Shuffle+`b`+in-place+along+the+last+axis.++`b`+is+a+view+of+`a`,
++++#+so+`a`+is+shuffled+in+place,+too.
++++shp+=+b.shape[:-1]
++++for+ndx+in+np.ndindex(shp):
++++++++np.random.shuffle(b[ndx])
++++return|code-block|syntax|javascript|554975|entityMap|0|LINK|mutability|MUTABLE|url|https://github.com/numpy/numpy/issues/5173^0|3|16|0|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@$A|R|B|S|1|T]]|C|$]]|$1|D|3|E|5|F|7|U|8|@]|9|@]|C|$G|H]]|$1|I|3|-4|5|6|7|V|8|@]|9|@]|C|$]]]|J|$K|$5|L|M|N|C|$O|P]]]]

From: <a href="https://github.com/numpy/numpy/issues/5173" rel="nofollow noreferrer">https://github.com/numpy/numpy/issues/5173</a>

<pre><code>def disarrange(a, axis=-1):
 """
 Shuffle `a` in-place along the given axis.

 Apply numpy.random.shuffle to the given axis of `a`.
 Each one-dimensional slice is shuffled independently.
 """
 b = a.swapaxes(axis, -1)
 # Shuffle `b` in-place along the last axis. `b` is a view of `a`,
 # so `a` is shuffled in place, too.
 shp = b.shape[:-1]
 for ndx in np.ndindex(shp):
 np.random.shuffle(b[ndx])
 return
</code></pre>

blocks|key|546724|text|因为您只想对列进行混洗，所以可以只对转置后的矩阵执行：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|546725|In+[86]:+np.random.shuffle(Xtrain.T)

In+[87]:+Xtrain
Out[87]:+
array([[2,+3,+1],
+++++++[5,+6,+4],
+++++++[7,+3,+1]])|code-block|syntax|javascript|546726|请注意，2D数组上的random.suffle()会对行进行混洗，而不是对每行中的项进行混洗。即改变行的位置。因此，如果你改变了转置矩阵行的位置，你实际上就是在打乱原始数组的列。|offset|length|546727|如果你仍然想要一个完全独立的混洗，你可以为每一行创建随机索引，然后用一个简单的索引创建最终的数组：|546728|In+[172]:+def+crazyshuffle(arr):
+++++...:+++++x,+y+=+arr.shape
+++++...:+++++rows+=+np.indices((x,y))[0]
+++++...:+++++cols+=+[np.random.permutation(y)+for+_+in+range(x)]
+++++...:+++++return+arr[rows,+cols]
+++++...:+|546729|演示：|546730|In+[173]:+crazyshuffle(Xtrain)
Out[173]:+
array([[1,+3,+2],
+++++++[6,+5,+4],
+++++++[7,+3,+1]])

In+[174]:+crazyshuffle(Xtrain)
Out[174]:+
array([[2,+3,+1],
+++++++[4,+6,+5],
+++++++[1,+3,+7]])|546731|entityMap|0|LINK|mutability|MUTABLE|url|https://docs.scipy.org/doc/numpy-1.10.0/reference/generated/numpy.random.shuffle.html#numpy.random.shuffle^0|0|0|A|F|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|10|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|11|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|12|8|@]|9|@$I|13|J|14|1|15]]|A|$]]|$1|K|3|L|5|6|7|16|8|@]|9|@]|A|$]]|$1|M|3|N|5|D|7|17|8|@]|9|@]|A|$E|F]]|$1|O|3|P|5|6|7|18|8|@]|9|@]|A|$]]|$1|Q|3|R|5|D|7|19|8|@]|9|@]|A|$E|F]]|$1|S|3|-4|5|6|7|1A|8|@]|9|@]|A|$]]]|T|$U|$5|V|W|X|A|$Y|Z]]]]

Since you want to only shuffle the columns you can just perform the <a href="https://docs.scipy.org/doc/numpy-1.10.0/reference/generated/numpy.random.shuffle.html#numpy.random.shuffle" rel="nofollow noreferrer">shuffling</a> on transposed of your matrix:

<pre><code>In [86]: np.random.shuffle(Xtrain.T)

In [87]: Xtrain
Out[87]: 
array([[2, 3, 1],
 [5, 6, 4],
 [7, 3, 1]])
</code></pre>

Note that <a href="https://docs.scipy.org/doc/numpy-1.10.0/reference/generated/numpy.random.shuffle.html#numpy.random.shuffle" rel="nofollow noreferrer">random.suffle()</a> on a 2D array shuffles the rows not items in each rows. i.e. changes the position of the rows. Therefor if your change the position of the transposed matrix rows you're actually shuffling the columns of your original array.

If you still want a completely independent shuffle you can create random indexes for each row and then create the final array with a simple indexing:

<pre><code>In [172]: def crazyshuffle(arr):
 ...: x, y = arr.shape
 ...: rows = np.indices((x,y))[0]
 ...: cols = [np.random.permutation(y) for _ in range(x)]
 ...: return arr[rows, cols]
 ...: 
</code></pre>

Demo:

<pre><code>In [173]: crazyshuffle(Xtrain)
Out[173]: 
array([[1, 3, 2],
 [6, 5, 4],
 [7, 3, 1]])

In [174]: crazyshuffle(Xtrain)
Out[174]: 
array([[2, 3, 1],
 [4, 6, 5],
 [1, 3, 7]])
</code></pre>

blocks|key|5801409|text|我们可以创建一个随机的二维矩阵，按行排序，然后使用argsort提供的索引矩阵对目标矩阵进行重新排序。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|5801410|target+=+np.random.randint(10,+size=(5,+5))
#+[[7+4+0+2+5]
#+[5+6+4+8+7]
#+[6+4+7+9+5]
#+[8+6+6+2+8]
#+[8+1+6+7+3]]

shuffle_helper+=+np.argsort(np.random.rand(5,5),+axis=1)
#+[[0+4+3+2+1]
#+[4+2+1+3+0]
#+[1+2+3+4+0]
#+[1+2+4+3+0]
#+[1+2+3+0+4]]

target[np.arange(shuffle_helper.shape[0])[:,+None],+shuffle_helper]
#+array([[7,+5,+2,+0,+4],
#+++++++[7,+4,+6,+8,+5],
#+++++++[4,+7,+9,+5,+6],
#+++++++[6,+6,+8,+2,+8],
#+++++++[1,+6,+7,+8,+3]])|code-block|syntax|javascript|5801411|说明|BOLD|5801412|5801413|我们使用np.random.rand和argsort来模拟混洗的效果。|unordered-list-item|5801414|random.rand提供了randomness.|5801415|Then，，我们使用argsort和axis=1来帮助对每一行进行排序。这将创建可用于重新排序的索引。|5801416|5801417|entityMap^0|P|7|0|0|0|2|0|0|4|E|J|7|0|0|B|0|A|7|I|6|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|@$9|13|A|14|B|M]]|D|@]|E|$]]|$1|N|3|-4|5|6|7|15|8|@]|D|@]|E|$]]|$1|O|3|P|5|Q|7|16|8|@$9|17|A|18|B|C]|$9|19|A|1A|B|C]]|D|@]|E|$]]|$1|R|3|S|5|Q|7|1B|8|@$9|1C|A|1D|B|C]]|D|@]|E|$]]|$1|T|3|U|5|Q|7|1E|8|@$9|1F|A|1G|B|C]|$9|1H|A|1I|B|C]]|D|@]|E|$]]|$1|V|3|-4|5|6|7|1J|8|@]|D|@]|E|$]]|$1|W|3|-4|5|6|7|1K|8|@]|D|@]|E|$]]]|X|$]]

We can create a random 2-dimensional matrix, sort it by each row, and then use the index matrix given by <code>argsort</code> to reorder the target matrix. 

<pre><code>target = np.random.randint(10, size=(5, 5))
# [[7 4 0 2 5]
# [5 6 4 8 7]
# [6 4 7 9 5]
# [8 6 6 2 8]
# [8 1 6 7 3]]

shuffle_helper = np.argsort(np.random.rand(5,5), axis=1)
# [[0 4 3 2 1]
# [4 2 1 3 0]
# [1 2 3 4 0]
# [1 2 4 3 0]
# [1 2 3 0 4]]

target[np.arange(shuffle_helper.shape[0])[:, None], shuffle_helper]
# array([[7, 5, 2, 0, 4],
# [7, 4, 6, 8, 5],
# [4, 7, 9, 5, 6],
# [6, 6, 8, 2, 8],
# [1, 6, 7, 8, 3]])
</code></pre>

Explanation

<ul>
<li>We use <code>np.random.rand</code> and <code>argsort</code> to mimic the effect from shuffling. </li>
<li><code>random.rand</code> gives randomness.</li>
<li>Then, we use <code>argsort</code> with <code>axis=1</code> to help rank each row. This creates the index that can be used for reordering.</li>
</ul>

blocks|key|546748|text|假设你有一个形状为100000+x+1000的数组a。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|546749|b+=+np.random.choice(100000+*+1000,+(100000,+1000),+replace=False)
ind+=+np.argsort(b,+axis=1)
a_shuffled+=+a[np.arange(100000)[:,np.newaxis],+ind]|code-block|syntax|javascript|546750|我不知道这是否比循环更快，因为它需要排序，但使用这种解决方案可能会发明出更好的东西，例如使用np.argpartition而不是np.argsort|546751|entityMap^0|P|1|0|0|1A|F|1S|A|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|$]]

Lets say you have array <code>a</code> with shape 100000 x 1000. 

<pre><code>b = np.random.choice(100000 * 1000, (100000, 1000), replace=False)
ind = np.argsort(b, axis=1)
a_shuffled = a[np.arange(100000)[:,np.newaxis], ind]
</code></pre>

I don't know if this is faster than loop, because it needs sorting, but with this solution maybe you will invent something better, for example with <code>np.argpartition</code> instead of <code>np.argsort</code>

blocks|key|555019|text|这个解决方案无论如何都不是很有效，但我觉得很有趣，所以把它写了下来。基本上，您将整理数组，并创建一个行标签数组和一个索引数组。您可以混洗索引数组，并用它索引原始和行标签数组。然后，将稳定的+argsort应用于行标签，将数据收集到行中。应用该索引、整形和viola，按行独立地混洗数据：|type|unstyled|depth|inlineStyleRanges|offset|length|style|BOLD|entityRanges|data|555020|import+numpy+as+np

r,+c+=+3,+4++#+x.shape

x+=+np.arange(12)+%2B+1++#+Already+raveled+
inds+=+np.arange(x.size)
rows+=+np.repeat(np.arange(r).reshape(-1,+1),+c,+axis=1).ravel()

np.random.shuffle(inds)
x+=+x[inds]
rows+=+rows[inds]

inds+=+np.argsort(rows,+kind='mergesort')
x+=+x[inds].reshape(r,+c)|code-block|syntax|javascript|555021|这是一个IDEOne+Link|555022|entityMap|0|LINK|mutability|MUTABLE|url|https://ideone.com/OL8jnB^0|2J|3|0|0|4|B|0|0^^$0|@$1|2|3|4|5|6|7|U|8|@$9|V|A|W|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|X|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|Y|8|@]|D|@$9|Z|A|10|1|11]]|E|$]]|$1|M|3|-4|5|6|7|12|8|@]|D|@]|E|$]]]|N|$O|$5|P|Q|R|E|$S|T]]]]

This solution is not efficient by any means, but I had fun thinking about it, so wrote it down. Basically, you ravel the array, and create an array of row labels, and an array of indices. You shuffle the index array, and index the original and row label arrays with that. Then you apply a stable argsort to the row labels to gather the data into rows. Apply that index and reshape and viola, data shuffled independently by rows:

<pre><code>import numpy as np

r, c = 3, 4 # x.shape

x = np.arange(12) + 1 # Already raveled 
inds = np.arange(x.size)
rows = np.repeat(np.arange(r).reshape(-1, 1), c, axis=1).ravel()

np.random.shuffle(inds)
x = x[inds]
rows = rows[inds]

inds = np.argsort(rows, kind='mergesort')
x = x[inds].reshape(r, c)
</code></pre>

Here is an <a href="https://ideone.com/OL8jnB" rel="nofollow noreferrer">IDEOne Link</a>

blocks|key|546770|text|您可以使用Pandas|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|546771|df+=+pd.DataFrame(X_train)
_+=+df.apply(lambda+x:+np.random.shuffle(x.values),+axis=1,+raw=False)
df.values|code-block|syntax|javascript|546772|如果要混洗列，请将关键字更改为axis=0。|546773|entityMap^0|5|6|0|0|F|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|@$9|T|A|U|B|C]]|D|@]|E|$]]|$1|M|3|-4|5|6|7|V|8|@]|D|@]|E|$]]]|N|$]]

You may use <code>Pandas</code>:
<pre class="lang-py prettyprint-override"><code>df = pd.DataFrame(X_train)
_ = df.apply(lambda x: np.random.shuffle(x.values), axis=1, raw=False)
df.values
</code></pre>
Change the keyword to <code>axis=0</code> if you want to shuffle columns.

I have a numpy array like the following:

<pre><code>Xtrain = np.array([[1, 2, 3],
 [4, 5, 6],
 [1, 7, 3]])
</code></pre>

I want to shuffle the items of each row separately, but do not want the shuffle to be the same for each row (as in several examples just shuffle column order).

For example, I want an output like the following:

<pre><code>output = np.array([[3, 2, 1],
 [4, 6, 5],
 [7, 3, 1]])
</code></pre>

How can I randomly shuffle each of the rows randomly in an efficient way? My actual np array is over 100000 rows and 1000 columns.

Randomly shuffle items in each row of 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numpy数组，如下所示：Xtrain = np.array([[1, 2, 3],                   [4, 5, 6],                   [1, 7, 3]])我想对每一行的项目分别进行混洗，但不希望每一行的混洗都是相同的(就像在几个示例中一样，只是混洗列的顺序)。例如...

问随机打乱numpy数组的每一行中的项
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问随机打乱numpy数组的每一行中的项EN