CODEWITHSUNDEEP
pythonarraysnumpy
riyansh.legend
riyansh.legend

Reputation: 137

Shrink a 1D numpy array to a specified number of entries while keeping original order

I have a 1D numpy array which contains measured data. I want to shrink this array from e.g. 2543 data points to 2000 in order to compare it with other results. How can I shrink my 1D array while keeping the order. The best idea would be to somehow select every n-th entry but my measured data always produces arrays with different lenghts. I thought of numpy.random.choice but it's not keeping the original order and I would prefer an approach which is not random.

Upvotes: 1

Views: 510

Answers (1)

Szymon Bednorz
Szymon Bednorz

Reputation: 475

I think this code solves your problem:

import numpy as np

# Let's assume that this is your numpy array
a = np.array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100])
# ...and this is the final array size
n = 3

if n < a.size:
    # Randomly select some indices to remove (without repetitions)
    indices_to_remove = np.random.choice(a.size, a.size - n, replace=False)
    # Remove elements with these indices from your array
    new_a = np.delete(a, indices_to_remove)
else:
    # In this case there is nothing to remove because the array is too small
    new_a = a

print(a, new_a)

Example output:

[10  20  30  40  50  60  70  80  90 100] [20 30 90]

Upvotes: 1

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