How to Split an Array into N Random Sub-arrays in NumPy

Introduction

Splitting an array into multiple sub-arrays is a common task in data processing and analysis. When working with NumPy, Python’s highly regarded library for numerical computations, there are several ways to divide an array into random chunks. This guide explores various methods to divide an array into ‘N’ random sub-arrays using NumPy, highlighting the use cases, pros and cons, and efficiency of each method.

Solution 1: Using np.array_split

np.array_split is a versatile function in NumPy to split arrays. Unlike np.split, it does not raise an exception if the array cannot be divided into equal parts. Instead, it adjusts the sizes of the sub-arrays accordingly.

1. Determine the size of the original array and decide on the number of sub-arrays ‘N’.
2. Generate a list of indices where the splits should occur.
3. Randomly shuffle these indices for random split points.
4. Use np.array_split to divide the array at the chosen indices.

Example:

import numpy as np

# Original array
arr = np.arange(100)
# Number of sub-arrays 'N'
N = 5

# Generating split indices
indices = list(range(1, arr.size))
np.random.shuffle(indices)
split_indices = sorted(indices[:N-1])

# Split the array
sub_arrays = np.array_split(arr, split_indices)

# Optional: Print the result
for sub_arr in sub_arrays:
    print(sub_arr)

Notes: The performance of np.array_split is efficient for arbitrary splits. It is convenient because it allows for uneven divisions. However, there is an overhead of generating and shuffling indices which could be a factor for very large arrays. It’s versatile but randomization is manual.

Solution 2: Using Random Split and Concatenation

This method involves randomly shuffling the array using np.random.shuffle, splitting the array into roughly equal chunks, and then if necessary, to make the pieces exactly ‘N’, cutting off any leftover elements and appending them to the existing chunks appropriately.

1. Shuffle the original array using np.random.shuffle.
2. Split the array into ‘N’ equal parts using np.array_split.
3. If the array size is not divisible by ‘N’, redistribute the leftover elements.

Example:

import numpy as np

# Original array
arr = np.arange(100)
# Number of sub-arrays 'N'
N = 5

# Shuffle the array
np.random.shuffle(arr)

# Split the array into 'N' parts
sub_arrays = np.array_split(arr, N)

# Redistribute leftovers if needed
leftovers = arr.size % N
if leftovers:
    for i in range(leftovers):
        sub_arrays[i] = np.concatenate((sub_arrays[i], [arr[-(i+1)]]))

# Optional: Print the result
for sub_arr in sub_arrays:
    print(sub_arr)

Notes: This method ensures that the array is shuffled only once, which might be beneficial in terms of performance for large arrays. However, the last step of redistribution could complicate things and might lead to less readability of the code. The sub-arrays resulting from this method will be random and will have nearly equal size.

Final Words

When it comes to splitting an array into ‘N’ random sub-arrays using NumPy, there are several viable methods, each with its own use case. Solution 1 offers versatility with minimal initial setup cost, whereas Solution 2 may offer better performance with equally sized sub-arrays post-shuffling. Your choice between the solutions will largely be guided by the specific requirements of your data set and application, including the need for balance between sub-array sizes and the overhead of randomization process.