Python NumPy numpy.sort() Function
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Syntax of
numpy.sort() -
Example Codes:
numpy.sort() -
Example Codes:
numpy.sort()to Sort a Multi-Dimensional Array -
Example Codes:
numpy.sort()to Sort a Multi-Dimensional Array Along a Specified Axis -
Example Codes:
numpy.sort()to Sort Different Types of Arrays
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]]