Numpy split() Function
The Numpy split() Function divides an array into multiple subarrays along a specified axis. It can split the array into equal-sized subarrays if given an integer or at specified indices if given a list.
This function is particularly useful for breaking down data into smaller chunks for easier manipulation and analysis.
Syntax
The syntax for the Numpy split() function is as follows −
numpy.split(ary, indices_or_sections, axis=0)
Parameters
Below are the parameters of the Numpy split() Function −
- ary(array_like): The input array to be split.
- indices_or_sections(int or 1-D array): If an integer(N), the array will be divided into N equal sections. If an array, the values in the array indicate where to split the array.
- axis(int, optional): The axis along which to split, default is 0 i.e. split along rows.
Return Value
This Function returns a list of sub-arrays resulting from the split.
Example 1
Following is the example of the shows Numpy split() Function which helps in splitting an array of 9 elements into 3 equal sub-arrays −
import numpy as np
# Create an array
arr = np.arange(9)
print("Original array:")
print(arr)
# Split the array into 3 equal parts
result = np.split(arr, 3)
print("\nSplit array into 3 equal parts:")
for i, sub_array in enumerate(result):
print(f"Sub-array {i+1}:")
print(sub_array)
Output
Original array: [0 1 2 3 4 5 6 7 8] Split array into 3 equal parts: Sub-array 1: [0 1 2] Sub-array 2: [3 4 5] Sub-array 3: [6 7 8]
Example 2
Here in this example we show how to split a 2D array into 2 sub-arrays along the columns i.e. axis=1 −
import numpy as np
# Create a 2D array
arr = np.arange(16).reshape(4, 4)
print("Original 2D array:")
print(arr)
# Split the 2D array into 2 sub-arrays along columns
result = np.split(arr, 2, axis=1)
print("\nSplit 2D array into 2 sub-arrays along columns:")
for i, sub_array in enumerate(result):
print(f"Sub-array {i+1}:")
print(sub_array)
Output
Original 2D array: [[ 0 1 2 3] [ 4 5 6 7] [ 8 9 10 11] [12 13 14 15]] Split 2D array into 2 sub-arrays along columns: Sub-array 1: [[ 0 1] [ 4 5] [ 8 9] [12 13]] Sub-array 2: [[ 2 3] [ 6 7] [10 11] [14 15]]
numpy_array_manipulation.htm
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