Split 1d array to 2d array python

Overview

In Python, an array is a data structure used to store multiple items of the same type. Arrays are useful when dealing with many values of the same data type. An array needs to explicitly import the array module for declaration.

A 2D array is simply an array of arrays. The numpy.array_split() method in Python is used to split a 2D array into multiple sub-arrays of equal size.

Parameters

array_split() takes the following parameters:

• array (required): Represents the input array.
• indices_or_section (required): Represents the number of splits to be returned.
• axis (optional): The axis along which the values are appended.

To split a 2D array, pass in the array and specify the number of splits you want.

Example

Now, let’s split a 2D array into three sections or indices.

Code

import numpy as np
array1 = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]])
# splitting the array into three indexes
new_array1 = np.array_split(array1, 3)
print('The arrays splitted into 3 sections are:', new_array1)

Example

Explanation

• Line 1: We import the numpy module.
• Line 3: We create an array array1.
• Line 5: We use the numpy.array_split() method to split the given array into three sections, and then assign the result to a variable named new_array1.
• Line 6: We print the split 2D array, new_array1.

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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.

Example

Split the array in 3 parts:

import numpy as np

arr = np.array([1, 2, 3, 4, 5, 6])

newarr = np.array_split(arr, 3)

print(newarr)

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Note: The return value is an array containing three arrays.

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

Example

Split the array in 4 parts:

import numpy as np

arr = np.array([1, 2, 3, 4, 5, 6])

newarr = np.array_split(arr, 4)

print(newarr)

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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 an array 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:

Example

Access the splitted arrays:

import numpy as np

arr = np.array([1, 2, 3, 4, 5, 6])

newarr = np.array_split(arr, 3)

print(newarr[0])
print(newarr[1])
print(newarr[2])

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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.

Example

Split the 2-D array into three 2-D arrays.

import numpy as np

arr = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]])

newarr = np.array_split(arr, 3)

print(newarr)

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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.

Example

Split the 2-D array into three 2-D arrays.

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.array_split(arr, 3)

print(newarr)

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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 row (axis=1).

Example

Split the 2-D array into three 2-D arrays along rows.

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.array_split(arr, 3, axis=1)

print(newarr)

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

Example

Use the hsplit() method to split the 2-D array into three 2-D arrays along rows.

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)

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

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