import numpy as np
from random import shuffle
a = np.array([[1,2,3],[4,5,6]])
print(a)
for i in range(1000):
shuffle(a)
print(a)
Output:
[[1 2 3]
[4 5 6]]
[[1 2 3]
[1 2 3]]
Get two [1, 2, 3], [4, 5, 6] is missing.
import numpy as np
from random import shuffle
a = np.arange(10).reshape(5,2)
print(a)
for i in range(1000):
shuffle(a)
print(a)
Output:
[[0 1]
[2 3]
[4 5]
[6 7]
[8 9]]
[[0 1]
[0 1]
[0 1]
[0 1]
[0 1]]
All items in the array become first item.
numpy.random.shuffle(a) should do what you want. Playing around with random.shuffle on the terminal, it doesn't seem to handle matrices very well.
The basic issue occurs because random.shuffle uses the following (code can be found here) -
x[i], x[j] = x[j], x[i]
If you do this kind of assignment for Numpy array (like in your case) , you get the issue -
In [41]: ll
Out[41]:
array([[7, 8],
[5, 6],
[1, 2],
[3, 4]])
In [42]: ll[0] , ll[1] = ll[1] , ll[0]
In [43]: ll
Out[43]:
array([[5, 6],
[5, 6],
[1, 2],
[3, 4]])
The following example may be able to show why the issue occurs -
In [63]: ll = np.array([[1,2],[3,4],[5,6],[7,8]])
In [64]: ll[0]
Out[64]: array([1, 2])
In [65]: x = ll[0]
In [66]: x
Out[66]: array([1, 2])
In [67]: y = ll[1]
In [68]: y
Out[68]: array([3, 4])
In [69]: ll[1] = x
In [70]: y
Out[70]: array([1, 2])
As you can see when you set ll[1] to a new value, y variable reflected the change as well, this is most probably because numpy might be mutating ll[1] inplace (please note, I am not talking about ll , ll[1] the inner ndarray of ll ) instead of assigning the object referenced by x to ll[1] (like it happens in case of lists) .
As a solution you can use np.random.shuffle() instead of random.shuffle() -
In [71]: ll = np.array([[1,2],[3,4],[5,6],[7,8]])
In [72]: ll
Out[72]:
array([[1, 2],
[3, 4],
[5, 6],
[7, 8]])
In [73]: from numpy.random import shuffle
In [74]: shuffle(ll)
In [75]: ll
Out[75]:
array([[7, 8],
[3, 4],
[1, 2],
[5, 6]])
Please do note, np.random.shuffle() only shuffles elements along the first index of a multi-dimensional array. (Though if random.shuffle() worked , it would have worked like that as well) .
