Create new numpy array by duplicating each item in an array

Question

I would like to create a new numpy array by repeating each item in another array by a given number of times (n). I am currently doing this with a for loop and .extend() with lists but this is not really efficient, especially for very large arrays.

Is there a more efficient way to do this?

def expandArray(array, n):
    new_array = []
    for item in array:
        new_array.extend([item] * n)
    new_array = np.array(new_array)
    return new_array


print(expandArray([1,2,3,4], 3)

[1,1,1,2,2,2,3,3,3,4,4,4]

Answer 1

You can create a duplicated array with the numpy.repeat function.

array = np.array([1,2,3,4])

new_array = np.repeat(array, repeats=3, axis=None)
print(new_array)

array([1,1,1,2,2,2,3,3,3,4,4,4])


new_array = np.repeat(array.reshape(-1,1).transpose(), repeats=3, axis=0).flatten()
print(new_array)

array([1,2,3,4,1,2,3,4,1,2,3,4])

Answer 2

I don't know exactly why, but this code runs faster than np.repeat for me:

def expandArray(array, n):
    return np.concatenate([array for i in range(0,n)])

I ran this little benchmark:

arr1 = np.random.rand(100000)
%timeit expandArray(arr1, 5)

1.07 ms ± 25.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

And np.repeat gives this result:

%timeit np.repeat(arr1,5)

2.45 ms ± 148 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)