NumPy: numpy.array_split() function

numpy.array_split() function

The numpy.array_split() function split an given array into multiple sub-arrays.
The only difference between these functions is that array_split allows indices_or_sections to be an integer that does not equally divide the axis. For an array of length l that should be split into n sections, it returns l % n sub-arrays of size l//n + 1 and the rest of size l//n.

One common application of numpy.array_split() is when you have a large dataset that you want to split into smaller, more manageable pieces for processing or analysis.

Syntax:

numpy.array_split(ary, indices_or_sections, axis=0)

Parameters:

Return value:

Example: Splitting a NumPy array into equal-sized subarrays using numpy.array_split()

>>> import numpy as np
>>> a = np.arange(9.0)
>>> np.array_split(a, 4)
[array([ 0.,  1.,  2.]), array([ 3.,  4.]), array([ 5.,  6.]), array([ 7.,  8.])]

In the above code, the array 'a' is created using np.arange() function and it contains 9 elements. The np.array_split() function is then called with the array a and the number of subarrays to be created as parameters. Here, the number of subarrays is 4. The resulting subarrays are returned as a list of NumPy arrays.

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Example: Splitting a numpy array into multiple sub-arrays using numpy.array_split()

>>> import numpy as np
>>> a = np.arange(7.0)
>>> np.array_split(a, 3)
[array([ 0.,  1.,  2.]), array([ 3.,  4.]), array([ 5.,  6.])]

In the above code numpy.array_split() function splits a one-dimensional numpy array a into multiple sub-arrays of equal or nearly-equal size. In this case, the array a is created using np.arange(7.0), which generates a sequence of numbers from 0 to 6.

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