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home / developersection / interviews / how to use numpy splitting array in python?
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Jk Malhotra
Jk Malhotra

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1 Answers

Jk Malhotra
Jk Malhotra 16 Nov 2025
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NumPy provides several ways to split an array into multiple sub-arrays, depending on how you want to divide it. The most commonly used functions are:

1. np.split()

Splits an array into equal or specified sections along an axis.

Example 1: Split 1D array into equal parts

import numpy as np

arr = np.array([1, 2, 3, 4, 5, 6])
result = np.split(arr, 3)

print(result)

Output:

[array([1, 2]), array([3, 4]), array([5, 6])]

Example 2: Split at specific indices

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

result = np.split(arr, [1, 3])
print(result)

Output:

[array([10]), array([20, 30]), array([40, 50])]

2. np.array_split()

Same as np.split(), but allows unequal splits, which is useful when the array size isn’t divisible.

arr = np.array([1, 2, 3, 4, 5])

result = np.array_split(arr, 3)
print(result)

Output:

[array([1, 2]), array([3, 4]), array([5])]

3. np.hsplit() — Split Horizontally (column-wise)

For 2D arrays, splits by columns.

arr = np.arange(12).reshape(3, 4)
print(arr)

# Split into 2 equal parts horizontally
result = np.hsplit(arr, 2)
print(result)

4. np.vsplit() — Split Vertically (row-wise)

Splits along rows.

arr = np.arange(12).reshape(3, 4)

result = np.vsplit(arr, 3)
print(result)

5. np.dsplit() — Split along depth (for 3D arrays)

arr = np.arange(27).reshape(3, 3, 3)

result = np.dsplit(arr, 3)
print(result)

When to use which?

Function Used For
np.split Equal splits OR specific indices
np.array_split Unequal splits allowed
np.hsplit Split by columns (2D)
np.vsplit Split by rows (2D)
np.dsplit Split 3D arrays along depth
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