Selecting numpy array elements

Question

I have the task of selecting p% of elements within a given numpy array. For example,

# Initialize 5 x 3 array-
x = np.random.randint(low = -10, high = 10, size = (5, 3))

x
'''
array([[-4, -8,  3],
       [-9, -1,  5],
       [ 9,  1,  1],
       [-1, -1, -5],
       [-1, -4, -1]])
'''

Now, I want to select say p = 30% of the numbers in x, so 30% of numbers in x is 5 (rounded up).

Is there a way to select these 30% of numbers in x? Where p can change and the dimensionality of numpy array x can be 3-D or maybe more.

I am using Python 3.7 and numpy 1.18.1

Thanks

What shape do you expect for the output array?

hilberts_drinking_problem
– hilberts_drinking_problem

2020-03-11 01:02:37 +00:00
Commented Mar 11, 2020 at 1:02 — hilberts_drinking_problem
– hilberts_drinking_problem, Commented Mar 11, 2020 at 1:02

hilberts_drinking_problem · Accepted Answer · 2020-03-11 08:12:04Z

1

You can use np.random.choice to sample without replacement from a 1d numpy array:

p = 0.3
np.random.choice(x.flatten(), int(x.size * p) , replace=False)

For large arrays, the performance of sampling without replacement can be pretty bad, but there are some workarounds.

edited Mar 11, 2020 at 8:12

answered Mar 11, 2020 at 1:08

hilberts_drinking_problem

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Comments

Quang Hoang · Accepted Answer · 2020-03-11 12:33:25Z

0

You can randome choice 0,1 and usenp.nonzero and boolean indexing:

np.random.seed(1)
x[np.nonzero(np.random.choice([1, 0], size=x.shape, p=[0.3,0.7]))]

Output:

array([ 3, -1,  5,  9, -1, -1])

answered Mar 11, 2020 at 12:33

Quang Hoang

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Comments

Arun · Accepted Answer · 2020-03-13 11:48:30Z

0

I found a way of selecting p% of numpy elements:

p = 20

# To select p% of elements-
x_abs[x_abs < np.percentile(x_abs, p)]

# To select p% of elements and set them to a value (in this case, zero)-
x_abs[x_abs < np.percentile(x_abs, p)] = 0

answered Mar 13, 2020 at 11:48

Arun

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