Injecting random numbers in random places in an 1D numpy array

You can use np.insert together with np.random.choice:

n = 10
np.insert(a, np.random.choice(len(a), size=n), np.random.normal(size=n))

Here's one based on masking -

def addrand(a, N):
    n = len(a)
    m = np.concatenate((np.ones(n, dtype=bool), np.zeros(N, dtype=bool)))
    np.random.shuffle(m)
    out = np.empty(len(a)+N, dtype=a.dtype)
    out[m] = a
    out[~m] = np.random.uniform(N)
    return out

Sample run -

In [22]: a = 10+np.random.rand(20)

In [23]: a
Out[23]: 
array([10.65458302, 10.18034826, 10.08652451, 10.03342622, 10.63930492,
       10.48439184, 10.2859206 , 10.91419282, 10.56905636, 10.01595702,
       10.21063965, 10.23080433, 10.90546147, 10.02823502, 10.67987108,
       10.00583747, 10.24664158, 10.78030108, 10.33638157, 10.32471524])

In [24]: addrand(a, N=3) # adding 3 rand numbers
Out[24]: 
array([10.65458302, 10.18034826, 10.08652451, 10.03342622,  0.79989563,
       10.63930492, 10.48439184, 10.2859206 , 10.91419282, 10.56905636,
       10.01595702,  0.23873077, 10.21063965, 10.23080433, 10.90546147,
       10.02823502,  0.66857723, 10.67987108, 10.00583747, 10.24664158,
       10.78030108, 10.33638157, 10.32471524])

Timings :

In [71]: a = np.random.rand(1000)

In [72]: %timeit addrand(a, N=10)
37.3 µs ± 273 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)

# @a_guest's soln
In [73]: %timeit np.insert(a, np.random.choice(len(a), size=10), np.random.normal(size=10))
63.3 µs ± 2.18 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

Note: If you are working with bigger arrays, it seems np.insert one is doing better.

Not sure if this is the most efficient way, but it works, at least.

A = np.arange(1000)
for i in np.random.randint(low = 0, high = 1000, size = 10):
    A = np.concatenate((A[:i], [np.random.normal(),], A[i:]))

Edit, checking performance:

def insert_random(A):
    for i in np.random.randint(low = 0, high = len(A), size = 10):
        A = np.concatenate((A[:i], [np.random.normal(),], A[i:]))
    return A

A = np.arange(1000)
%timeit test(A)

83.2 µs ± 2.47 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

So definitely not the most efficient. np.insert seems to be the way to go.

You could use numpy.insert(arr, obj, values, axis=None).

import numpy as np

a = np.arange(1000)

a = np.insert(a, np.random.randint(low = 1, high = 999, size=10), np.random.normal(loc=0.0, scale=1.0, size=10))

Keep in mind that insert doesn't automatically change your original array, but it returns a modified copy.