Create a sample numpy array with randomly placed NaN

Question

For testing purposes, I would like to create an M by N numpy array with c randomly placed NaNs

 import numpy as np M = 10; N = 5; c = 15; A = np.random.randn(M,N) A[mask] = np.nan 

Am I having problems creating a mask with c true elements, or maybe this can be done using indexes directly?

Answer 1

You can use np.random.choice with the replace=False option for random selection without replacement and use those that have a flattened version of A (done using .ravel() ), also -

 A.ravel()[np.random.choice(A.size, c, replace=False)] = np.nan 

Run Example -

 In [100]: A Out[100]: array([[-0.35365726, 0.26754527, -0.44985524, -1.29520237, 2.01505444], [ 0.01319146, 0.65150356, -2.32054478, 0.40924753, 0.24761671], [ 0.3014714 , -0.80688589, -2.61431163, 0.07787956, 1.23381951], [-1.70725777, 0.07856845, -1.04354202, -0.68904925, 1.07161002], [-1.08061614, 1.17728247, -1.5913516 , -1.87601976, 1.14655867], [ 1.12542853, -0.26290025, -1.0371326 , 0.53019033, -1.20766258], [ 1.00692277, 0.171661 , -0.89646634, 1.87619114, -1.04900026], [ 0.22238353, -0.6523747 , -0.38951426, 0.78449948, -1.14698869], [ 0.58023183, 1.99987331, -0.85938155, 1.4211672 , -0.43369898], [-2.15682219, -0.6872121 , -1.28073816, -0.97523148, -2.27967001]]) In [101]: A.ravel()[np.random.choice(A.size, c, replace=False)] = np.nan In [102]: A Out[102]: array([[ nan, 0.26754527, -0.44985524, nan, 2.01505444], [ 0.01319146, 0.65150356, -2.32054478, nan, 0.24761671], [ nan, -0.80688589, nan, nan, 1.23381951], [ nan, nan, -1.04354202, -0.68904925, 1.07161002], [-1.08061614, 1.17728247, -1.5913516 , nan, 1.14655867], [ 1.12542853, nan, -1.0371326 , 0.53019033, -1.20766258], [ nan, 0.171661 , -0.89646634, nan, nan], [ 0.22238353, -0.6523747 , -0.38951426, 0.78449948, -1.14698869], [ 0.58023183, 1.99987331, -0.85938155, nan, -0.43369898], [-2.15682219, -0.6872121 , -1.28073816, -0.97523148, nan]]) 

Answer 2

You can use np.random.shuffle in the new array to create your mask:

 import numpy as np M = 10; N = 5; c = 15; A = np.random.randn(M,N) mask=np.zeros(M*N,dtype=bool) mask[:c] = True np.random.shuffle(mask) mask=mask.reshape(M,N) A[mask] = np.nan 

What gives:

 [[ 0.98244168 0.72121195 0.99291217 0.17035834 0.46987918] [ 0.76919975 0.53102064 nan 0.78776918 nan] [ 0.50931304 0.91826809 0.52717345 nan nan] [ 0.35445471 0.28048106 0.91922292 0.76091783 0.43256409] [ 0.69981284 0.0620876 0.92502572 nan nan] [ nan nan nan 0.24466688 0.70259211] [ 0.4916004 nan nan 0.94945378 0.73983538] [ 0.89057404 0.4542628 nan 0.95547377 nan] [ 0.4071912 0.36066797 0.73169132 0.48217226 0.62607888] [ 0.30341337 nan 0.75608859 0.31497997 nan]]