# Python NumPy numpy.sort() Function

Python NumPy `numpy.sort()` function sorts an N-dimensional array of any data type. The function sorts the array in ascending order by default.

## Syntax of `numpy.sort()`

``````numpy.sort(a,
axis= -1,
kind= None,
order= None)
``````

### Parameters

`a` It is an array-like structure. It is the input array to sort.
`axis` It is an integer. It represents the axis along which the function will sort the array. Its default value is -1 which means that the function will sort the array along the last axis i.e in ascending order. If it is `None`, the function will convert the multi-dimensional array to one-dimensional before sorting. If it is 0, the function will sort the array along the first axis i.e in descending order.
`kind` It is a string. It represents the name of the sorting algorithm. The sorting algorithm names accepted by this function are `quicksort`, `mergesort`, `heapsort`, and `stable`. To read more about the time complexities of these sorting algorithms, click here.
`order` It is a string or a list of strings. When the fields of an array are defined, this parameter is used to specify the field to compare first.

### Return

It returns a sorted array of the same type and shape as the input array.

## Example Codes: `numpy.sort()`

The parameter `a` is mandatory. If we execute this function on a one-dimensional array, it generates the following output.

``````import numpy as np

a = np.array([89, 34, 56, 87, 90, 23, 45, 12, 65, 78, 9, 34, 12, 11, 2, 65, 78, 82, 28, 78])

sorted_array = np.sort(a)
print('The sorted array is:')
print(sorted_array)
``````

Output:

``````The sorted array is:
[2 9 11 12 12 23 28 34 34 45 56 65 65 78 78 78 82 87 89 90]
``````

It has returned an array sorted in ascending order.

## Example Codes: `numpy.sort()` to Sort a Multi-Dimensional Array

We will pass a multi-dimensional array now.

``````import numpy as np

a = np.array([[11, 12, 5], [15, 6,10], [10, 8, 12], [12,15,8], [34, 78, 90]])

sorted_array = np.sort(a)
print('The sorted array is:')
print(sorted_array)
``````

Output:

``````The sorted array is:
[[ 5 11 12]
[ 6 10 15]
[ 8 10 12]
[ 8 12 15]
[34 78 90]]
``````

The function has sorted the array in an ascending order i.e along the last axis as the default value for the `axis = -1`.

## Example Codes: `numpy.sort()` to Sort a Multi-Dimensional Array Along a Specified Axis

We will set the value of the `axis` parameter to `None`.

``````import numpy as np

a = np.array([[11, 12, 5], [15, 6,10], [10, 8, 12], [12,15,8], [34, 78, 90]])

sorted_array = np.sort(a, axis= None)
print('The sorted array is:')
print(sorted_array)
``````

Output:

``````The sorted array is:
[5 6 8 8 10 10 11 12 12 12 15 15 34 78 90]
``````

Note that the function has converted the array to a one-dimensional array first and then it has sorted it.

Now, we will sort our array along the first axis.

``````import numpy as np

a = np.array([[11, 12, 5], [15, 6,10], [10, 8, 12], [12,15,8]])

sorted_array = np.sort(a, axis= 0)
print('The sorted array is:')
print(sorted_array)
``````

Output:

``````The sorted array is:
[[10  6  5]
[11  8  8]
[12 12 10]
[15 15 12]]
``````

The function has sorted the array along the first axis i.e in descending order.

## Example Codes: `numpy.sort()` to Sort Different Types of Arrays

We can use this function to sort arrays of different data types like an array of strings, a boolean array, etc.

``````import numpy as np

a = np.array([['z', 'x'], ['b', 'a'], ['g', 'l'], ['k', 'd']])

sorted_array = np.sort(a)
print('The sorted array is:')
print(sorted_array)
``````

Output:

``````The sorted array is:
[['x' 'z']
['a' 'b']
['g' 'l']
['d' 'k']]
``````

Note that it has sorted the array in increasing alphabetical order. Now, we will pass an array of boolean values.

``````import numpy as np

a = np.array([[True, False, True], [False, False, True], [False, True, True]])

sorted_array = np.sort(a)
print('The sorted array is:')
print(sorted_array)
``````

Output:

``````The sorted array is:
[[False  True  True]
[False False  True]
[False  True  True]]
``````
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