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# Python Data Science NumPy Splitting Array

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 to the array we want to split and the number of splits.

Example 1: Split the array in 3 parts.

Code

import numpy as np

arr = np.array([11, 12, 13, 14, 15, 16])

newarr = np.array_split(arr, 3)

print(newarr)

the output will be

[array([11, 12]), array([13, 14]), array([15, 16])]

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 2: Split the array in 4 parts.

Code

import numpy as np

arr = np.array([11, 12, 13, 14, 15, 16])

newarr = np.array_split(arr, 4)

print(newarr)

the output will be

[array([11, 12]), array([13, 14]), array(), array()]

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 3: Access the splitted arrays.

Code

import numpy as np

arr = np.array([11, 12, 13, 14, 15, 16])

newarr = np.array_split(arr, 3)

print(newarr)
print(newarr)
print(newarr)

the output will be

[11 12]
[13 14]
[15 16]

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

Code

import numpy as np

arr = np.array([[11, 12], [13, 14], [15, 16], [17, 18], [19, 20], [21, 22]])

newarr = np.array_split(arr, 3)

print(newarr)

the output will be

[array([[11, 12],

[13, 14]]), array([[15, 16],

[17, 18]]), array([[19, 20],

[21, 22]])]

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

Code

import numpy as np

arr = np.array([[11, 12, 13], [14, 15, 16], [17, 18, 19], [20, 21, 22], [23, 24, 25], [26, 27, 28]])

newarr = np.array_split(arr, 3)

print(newarr)

the output will be

[array([[11, 12, 13],

[14, 15, 16]]), array([[17, 18, 19],

[20, 21, 22]]), array([[23, 24, 25],

[26, 27, 28]])]

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

Code

import numpy as np

arr = np.array([[11, 12, 13], [14, 15, 16], [17, 18, 19], [20, 21, 22], [23, 24, 25], [26, 27, 28]])

newarr = np.array_split(arr, 3, axis=1)

print(newarr)

the output will be

[array([,

,

,

,

,

]), array([,

,

,

,

,

]), array([,

,

,

,

,

])]

Note: An alternate solution is using hsplit() opposite of hstack().

Example 7: Use the hsplit() method to split the 2-D array into three 2-D arrays along rows.

Code

import numpy as np

arr = np.array([[11, 12, 13], [14, 15, 16], [17, 18, 19], [20, 21, 22], [23, 24, 25], [26, 27, 28]])

newarr = np.hsplit(arr, 3)

print(newarr)

the output will be

[array([,

,

,

,

,

]), array([,

,

,

,

,

]), array([,

,

,

,

,

])]

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