# numpy.hsplit() function | Python

`numpy.hsplit() ` function split an array into multiple sub-arrays horizontally (column-wise). hsplit is equivalent to split with axis=1, the array is always split along the second axis regardless of the array dimension.
Syntax : numpy.hsplit(arr, indices_or_sections) Parameters : arr : [ndarray] Array to be divided into sub-arrays. indices_or_sections : [int or 1-D array] If indices_or_sections is an integer, N, the array will be divided into N equal arrays along axis. If indices_or_sections is a 1-D array of sorted integers, the entries indicate where along axis the array is split Return : [ndarray] A list of sub-arrays.
Code #1 :
 `# Python program explaining ``# numpy.hsplit() function `` ` `# importing numpy as geek  ``import` `numpy as geek `` ` `arr ``=` `geek.arange(``16.0``).reshape(``4``, ``4``) `` ` `gfg ``=` `geek.hsplit(arr, ``2``) `` ` `print` `(gfg) `

Output :
```[array([[  0.,   1.],
[  4.,   5.],
[  8.,   9.],
[ 12.,  13.]]), array([[  2.,   3.],
[  6.,   7.],
[ 10.,  11.],
[ 14.,  15.]])]
```
Code #2 :
 `# Python program explaining ``# numpy.hsplit() function `` ` `# importing numpy as geek  ``import` `numpy as geek `` ` `arr ``=` `geek.arange(``27.0``).reshape(``3``, ``3``, ``3``) `` ` `gfg ``=` `geek.hsplit(arr, ``1``) `` ` `print` `(gfg) `

Output :
```[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.],
[ 24.,  25.,  26.]]])]
```

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