Shuffle an array with python, randomize array item order with python

import random
random.shuffle(array)

The other answers are the easiest, however it’s a bit annoying that the random.shuffle method doesn’t actually return anything – it just sorts the given list. If you want to chain calls or just be able to declare a shuffled array in one line you can do:

import random
def my_shuffle(array):
    random.shuffle(array)
    return array

Then you can do lines like:

for suit in my_shuffle(['hearts', 'spades', 'clubs', 'diamonds']):

When dealing with regular Python lists, random.shuffle() will do the job just as the previous answers show.

But when it come to ndarray(numpy.array), random.shuffle seems to break the original ndarray. Here is an example:

import random
import numpy as np
import numpy.random

a = np.array([1,2,3,4,5,6])
a.shape = (3,2)
print a
random.shuffle(a) # a will definitely be destroyed
print a

Just use: np.random.shuffle(a)

Like random.shuffle, np.random.shuffle shuffles the array in-place.

In addition to the previous replies, I would like to introduce another function.

numpy.random.shuffle as well as random.shuffle perform in-place shuffling. However, if you want to return a shuffled array numpy.random.permutation is the function to use.

I don’t know I used random.shuffle() but it return ‘None’ to me, so I wrote this, might helpful to someone

def shuffle(arr):
    for n in range(len(arr) - 1):
        rnd = random.randint(0, (len(arr) - 1))
        val1 = arr[rnd]
        val2 = arr[rnd - 1]

        arr[rnd - 1] = val1
        arr[rnd] = val2

    return arr

Just in case you want a new array you can use sample:

import random
new_array = random.sample( array, len(array) )

Alternative way to do this using sklearn

from sklearn.utils import shuffle
X=[1,2,3]
y = ['one', 'two', 'three']
X, y = shuffle(X, y, random_state=0)
print(X)
print(y)

Output:

[2, 1, 3]
['two', 'one', 'three']

Advantage: You can random multiple arrays simultaneously without disrupting the mapping. And ‘random_state’ can control the shuffling for reproducible behavior.

You can sort your array with random key

sorted(array, key = lambda x: random.random())

key only be read once so comparing item during sort still efficient.

but look like random.shuffle(array) will be faster since it written in C

this is O(Nlog(N)) btw

# arr = numpy array to shuffle

def shuffle(arr):
    a = numpy.arange(len(arr))
    b = numpy.empty(1)
    for i in range(len(arr)):
        sel = numpy.random.random_integers(0, high=len(a)-1, size=1)
        b = numpy.append(b, a[sel])
        a = numpy.delete(a, sel)
    b = b[1:].astype(int)
    return arr[b]

Be aware that random.shuffle() should not be used on multi-dimensional arrays as it causes repetitions.

Imagine you want to shuffle an array along its first dimension, we can create the following test example,

import numpy as np
x = np.zeros((10, 2, 3))

for i in range(10):
   x[i, ...] = i*np.ones((2,3))

so that along the first axis, the i-th element corresponds to a 2×3 matrix where all the elements are equal to i.

If we use the correct shuffle function for multi-dimensional arrays, i.e. np.random.shuffle(x), the array will be shuffled along the first axis as desired. However, using random.shuffle(x) will cause repetitions. You can check this by running len(np.unique(x)) after shuffling which gives you 10 (as expected) with np.random.shuffle() but only around 5 when using random.shuffle().