How numpy vsplit works in Python? Best example

How numpy vsplit works in Python? Best example
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When working with NumPy arrays in Python, there are times when you need to split an array vertically into multiple sub-arrays. That’s where numpy.vsplit() comes into play. If you’ve ever wondered how this function works and when to use it, you’re in the right place!

Understanding numpy.vsplit()

The numpy.vsplit() function is used to split an array along the vertical axis (i.e., along rows). It’s essentially a convenience function for numpy.split() with axis=0. This means that the function will divide an input array into sub-arrays by slicing it along the first axis.

Syntax of numpy.vsplit()

Here’s the basic syntax:

numpy.vsplit(ary, indices_or_sections)

Where:

  • ary – The input NumPy array that you want to split.
  • indices_or_sections – Determines how to split the array. If it’s an integer n, the array is divided into n equal parts. If it’s a list, the elements specify where to split.

Best Example: How numpy.vsplit() Works in Python

Let’s dive into an example to see how numpy.vsplit() works in action. Consider a 6×4 NumPy array:

import numpy as np

# Creating a 6x4 ndarray
arr = np.arange(24).reshape(6, 4)
print("Original Array:")
print(arr)

# Splitting the array into 3 equal sub-arrays
split_arrays = np.vsplit(arr, 3)

# Display the split sub-arrays
for i, sub_arr in enumerate(split_arrays):
    print(f"Sub-array {i+1}:")
    print(sub_arr)

Output:

Original Array:
[[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]
 [12 13 14 15]
 [16 17 18 19]
 [20 21 22 23]]

Sub-array 1:
[[ 0  1  2  3]
 [ 4  5  6  7]]

Sub-array 2:
[[ 8  9 10 11]
 [12 13 14 15]]

Sub-array 3:
[[16 17 18 19]
 [20 21 22 23]]

Handling Unequal Splits with numpy.vsplit()

NumPy does not allow unequal splits when using an integer as the second argument. Trying to split an array of shape (7,4) into 3 parts will raise an error.

However, you can use a list of indices to specify custom split points:

import numpy as np

# Creating a 6x4 ndarray
arr = np.arange(24).reshape(6, 4)

# Splitting at row indices 2 and 5
split_arrays = np.vsplit(arr, [2, 5])

for i, sub_arr in enumerate(split_arrays):
    print(f"Sub-array {i+1}:")
    print(sub_arr)

Key Differences Between vsplit, hsplit, and array_split

NumPy provides several splitting functions, each designed for different purposes.

Function Direction of Split Usage Scenario
vsplit() Vertically (row-wise) Split along the first axis (axis=0)
hsplit() Horizontally (column-wise) Split along the second axis (axis=1)
array_split() Flexible Handles uneven splits if needed

Common Errors and How to Avoid Them

There are some common mistakes when using numpy.vsplit():

  1. Trying to split an array into unequal parts using an integer: This will raise a ValueError. Instead, use a list of indices.
  2. Applying vsplit() to a 1D array: Since vsplit works along axis 0, it requires at least a 2D array.
  3. Specifying indices that are out of bounds: Ensure all indices provided in a list are within the correct range.

Final Thoughts on numpy.vsplit()

Understanding how numpy.vsplit() works in Python is essential when dealing with matrix data that needs to be divided into smaller parts. Whether you’re processing image data, splitting datasets, or manipulating tabular data, mastering this function can save you a lot of time.

“` Other interesting article: How numpy hsplit works in Python? Best example