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NumPy Array Split

Learn all about NumPy Array Split in this comprehensive tutorial.

5 min read intermediate

NumPy Array Join

NumPy Array Search

•Splitting is reverse operation of Joining.
•The return value of the array_split() method is a list containing each of the split as an array.
•Use the same syntax when splitting 2-D arrays.

Splitting NumPy Arrays

Splitting is reverse operation of Joining.

Joining merges multiple arrays into one and Splitting breaks one array into multiple.

We use array_split() for splitting arrays, we pass it the array we want to split and the number of splits.

python

import numpy as nparr = np.array([1, 2, 3, 4, 5, 6])
  newarr =
  np.array_split(arr, 3)print(newarr)

Note: Note: The return value is a list containing three arrays.

If the array has less elements than required, it will adjust from the end accordingly.

python

import numpy as nparr = np.array([1, 2, 3, 4, 5, 6])
  newarr =
  np.array_split(arr, 4)print(newarr)

Note: Note: We also have the method split() available but it will not adjust the elements when elements are less in source array for splitting like in example above, array_split() worked properly but split() would fail.

Split Into Arrays

The return value of the array_split() method is a list containing each of the split as an array.

If you split an array into 3 arrays, you can access them from the result just like any array element:

python

import numpy as nparr = np.array([1, 2, 3, 4, 5, 6])
  newarr =
  np.array_split(arr, 3)print(newarr[0])print(newarr[1])print(newarr[2])

Splitting 2-D Arrays

Use the same syntax when splitting 2-D arrays.

Use the array_split() method, pass in the array you want to split and the number of splits you want to do.

python

import numpy as nparr = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 
  10], [11, 12]])newarr = np.array_split(arr, 3)print(newarr)

The example above returns three 2-D arrays.

Let's look at another example, this time each element in the 2-D arrays contains 3 elements.

python

import numpy as nparr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 
  11, 12], [13, 14, 15], [16, 17, 18]])newarr = np.array_split(arr, 3)print(newarr)

The example above returns three 2-D arrays.

In addition, you can specify which axis you want to do the split around.

The example below also returns three 2-D arrays, but they are split along the column (axis=1).

python

import numpy as nparr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 
  11, 12], [13, 14, 15], [16, 17, 18]])newarr = np.array_split(arr, 3, axis=1)
  print(newarr)

An alternate solution is using hsplit() opposite of hstack()

python

import numpy as nparr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], 
  [10, 11, 12], [13, 14, 15], [16, 17, 18]])newarr = np.hsplit(arr, 3)
print(newarr)

Note: Note: Similar alternates to vstack() and dstack() are available as vsplit() and dsplit().

Module quiz

2 questions

Which of the following is true about NumPy Array Split?

It is strictly typedIt is evaluated at runtimeIt requires explicit memory managementIt is deprecated

What is the most common pitfall when working with NumPy Array Split?

SyntaxErrorIndentationErrorNameErrorTypeError

Answer all questions to submit.

NumPy Array Join

NumPy Array Search

NumPy Array Split

AI Summary

Splitting NumPy Arrays

Split Into Arrays

Splitting 2-D Arrays

Module quiz