[Python] Efficiently draw random samples without replacement from an array in python

I need to draw random samples without replacement from a 1D NumPy array. However, performance is critical since this operation will be repeated many times.

Here’s the code I’m currently using:

import numpy as np

# Example array
array = np.array([10, 20, 30, 40, 50])

# Number of samples to draw
num_samples = 3

# Draw samples without replacement
samples = np.random.choice(array, size=num_samples, replace=False)

print("Samples:", samples)


While this works for one sample, it requires a loop to generate multiple samples, and I believe there could be a way to optimize or vectorize this operation to improve performance when sampling multiple times.

• Is there a way to vectorize or otherwise optimize this operation?
• Would another library (e.g., TensorFlow, PyTorch) provide better performance for this task?
• Are there specific techniques for bulk sampling that avoid looping in Python?

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