>>> x = np.array([0, 1, 2, 3, 4, 5]) >>> np.hsplit(x, 2) [array([0, 1, 2]), array([3, 4, 5])]

import numpy as np arr = np.arange(12).reshape(3, 4) print("Original Array:") print(arr) split_arrays = np.hsplit(arr, 2) print("\nSplit Arrays:") for a in split_arrays: print(a) Original Array: [[ 0 1 2 3] [ 4 5 6 7] [ 8 9 10 11]] Split Arrays: [[ 0 1] [ 4 5] [ 8 9]] [[ 2 3] [ 6 7] [10 11]] Here in this example we show how numpy hsplit() function handles indices that exceed array dimensions by creating empty arrays −

>>> x = np.array([0, 1, 2, 3, 4, 5]) >>> np.hsplit(x, 2) [array([0, 1, 2]), array([3, 4, 5])]

April 24, 2026 - In the above code, a NumPy array 'a' is created using arange() function, and then reshaped to a 4x4 matrix. The np.hsplit() function is then used to horizontally split the array into 2 parts.

July 12, 2025 - import numpy as np arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8]]) result = np.hsplit(arr, 2) print(result)

>>> x = np.array([0, 1, 2, 3, 4, 5]) >>> np.hsplit(x, 2) [array([0, 1, 2]), array([3, 4, 5])]

April 16, 2025 - The numpy.hsplit() function is a convenient way to split an array along the second axis. This means that when working with a 2D array (which can be thought of as a matrix), it will split that matrix into parts along its columns.

The following example shows the usage of the HSPLIT control: The split area is divided into two cells: a green cell and a red cell. In addition, there is a line at the bottom in which you can provide the split factor.

May 28, 2022 - >>> import numpy as np >>> a = np.arange(16.0).reshape(4,4) >>> np.hsplit(a, 2) [array([[ 0., 1.], [ 4., 5.], [ 8., 9.], [ 12., 13.]]), array([[ 2., 3.], [ 6., 7.], [ 10., 11.], [ 14., 15.]])]

October 14, 2020 - In Python, the syntax numpy.hsplit() is used to split a list or an array into multiple sub-arrays horizontally. Let us see how to do this using a simple example.

>>> x = np.array([0, 1, 2, 3, 4, 5]) >>> np.hsplit(x, 2) [array([0, 1, 2]), array([3, 4, 5])]

November 6, 2024 - In PyTorch, the .hsplit() method splits a tensor into multiple sub-tensors horizontally (column-wise) along the specified dimension (axis).

February 6, 2024 - np.hsplit(): For horizontal splitting · np.dsplit(): For splitting along the depth · np.split() is the fundamental function, with the others provided for convenience for specific purposes. Understanding np.split() makes it easier to grasp how the others work. Contents · Basic usage of np.split() Returns a list of arrays · Specify the number of splits or split positions: indices_or_sections · Specify the axis to split: axis · Examples with arrays of three or more dimensions ·

The following example shows the usage of the HSPLIT control: The split area is divided into two cells: a green cell and a red cell. In addition, there is a line at the bottom in which you can provide the split factor.

>>> x = np.array([0, 1, 2, 3, 4, 5]) >>> np.hsplit(x, 2) [array([0, 1, 2]), array([3, 4, 5])]

Line 6: We split the input array into 5 subarrays using the hsplit() function.

hSplit is a small command-line tool that lets you split and join files bit by bit very quickly. - hubblec4/hSplit

import numpy as np arr = 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) Try it Yourself »

>>> x = jnp.array([[1, 2, 3, 4], ... [5, 6, 7, 8]]) >>> x1, x2 = jnp.hsplit(x, 2) >>> print(x1) [[1 2] [5 6]] >>> print(x2) [[3 4] [7 8]]

June 17, 2022 - # split the array into 3 subarrays horizontally sub_arrays = np.hsplit(arr, 3) # display the sub_arrays print(sub_arrays) ... You can see that the returned list has 3 sub-arrays created from the column-wise split of the input array.