I have a 2-d numpy array that I would like to shuffle. Is the best way to reshape it to 1-d, shuffle and reshape again to 2-d or is it possible to shuffle without reshaping?
just using the random.shuffle doesn't yield expected results and numpy.random.shuffle shuffles only rows:
import random
import numpy as np
a=np.arange(9).reshape((3,3))
random.shuffle(a)
print a
[[0 1 2]
[3 4 5]
[3 4 5]]
a=np.arange(9).reshape((3,3))
np.random.shuffle(a)
print a
[[6 7 8]
[3 4 5]
[0 1 2]]
You can tell np.random.shuffle to act on the flattened version:
>>> a = np.arange(9).reshape((3,3))
>>> a
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
>>> np.random.shuffle(a.flat)
>>> a
array([[3, 5, 8],
[7, 6, 2],
[1, 4, 0]])
You could shuffle a.flat:
>>> np.random.shuffle(a.flat)
>>> a
array([[6, 1, 2],
[3, 5, 0],
[7, 8, 4]])
I think this is very important to note.
You can use random.shuffle(a)
if a is 1-D numpy array.
If it is N-D (where N > 2) than
random.shuffle(a)
will spoil your data and return some random thing. As you can see here:
import random
import numpy as np
a=np.arange(9).reshape((3,3))
random.shuffle(a)
print a
[[0 1 2]
[3 4 5]
[3 4 5]]
This is a known bug (or feature?) of numpy.
So, use only numpy.random.shuffle(a) for numpy arrays.
