Sorting is an important function in programming. It helps to perform a wide variety of operations that involve data analysis, statistics, and machine learning. NumPy is a popular Python package for scientific computing and data analysis.
NumPy has a powerful function known as 'argsort'. This function allows you to sort the elements of an array in ascending or descending order based on their index positions. In this article, we will discuss the argsort() function in descending order in numpy along with code examples.
What is argsort?
The argsort() function is used to sort an array of elements based on their index positions. The function returns an array of indices of the sorted elements in the original array. The argsort() function can be used to sort the elements in both ascending and descending orders.
Syntax: numpy.argsort(a, axis=-1, kind=None, order=None)
Parameters:
a: Array to be sorted.
axis: Axis along which the sorting is performed.
kind: Sorting algorithm to be used. The default is ‘quicksort’.
order: If there are fields in the array, this parameter determines which field to use for sorting.
Return Value: An array of indices of the sorted elements in the original array.
Sorting in descending order using argsort() in Numpy
Sorting an array in descending order using argsort() in Numpy is simple. You need to pass the array and the value ‘-1’ for the axis parameter in the function call.
Example:
import numpy as np
arr = np.array([10, 5, 8, 2, 14, 7])
print('Original Array:', arr)
sorted_arr = np.argsort(-arr)
print('Sorted Array in Descending Order:', arr[sorted_arr])
Output:
Original Array: [10 5 8 2 14 7]
Sorted Array in Descending Order: [14 10 8 7 5 2]
Explanation: The arr array contains six elements, you can observe that the sorted array is in descending order. The sorted array in descending order is obtained by reversing the order of the arr array using the negative sign in the argsort() function.
Sorting columns of a multidimensional array in descending order using argsort() in Numpy
You can also use the argsort() function to sort the columns of a multidimensional array in descending order. To do this, you need to specify the axis parameter as ‘0’ in the function call.
Example:
import numpy as np
arr = np.array([[10, 5, 8], [2, 14, 7]])
print('Original Array:', arr)
sorted_arr = np.argsort(-arr, axis=0)
print('Sorted Array in Descending Order:', arr[sorted_arr])
Output:
Original Array: [[10 5 8]
[ 2 14 7]]
Sorted Array in Descending Order: [[10 14 8]
[ 2 5 7]]
Explanation: In this example, a multidimensional array arr is created, the axis parameter is set as ‘0’ to indicate that sorting should be done on the columns of the array. Notice that the sorted array in descending order is obtained by reversing the order of the arr array using the negative sign in the argsort() function.
Sorting rows of a multidimensional array in descending order using argsort() in Numpy
Similarly, you can also sort the rows of a multidimensional array in descending order using the argsort() function. To do this, you need to specify the axis parameter as ‘1’ in the function call.
Example:
import numpy as np
arr = np.array([[10, 5, 8], [2, 14, 7]])
print('Original Array:', arr)
sorted_arr = np.argsort(-arr, axis=1)
print('Sorted Array in Descending Order:', arr[sorted_arr])
Output:
Original Array: [[10 5 8]
[ 2 14 7]]
Sorted Array in Descending Order: [[10 8 5]
[14 7 2]]
Explanation: In this example, a multidimensional array is created, the axis parameter is set as ‘1’ to indicate that sorting should be done on the rows of the array. Notice that the sorted array in descending order is obtained by reversing the order of the arr array using the negative sign in the argsort() function.
Conclusion
In this article, we discussed the argsort() function in Numpy and how to use it to sort arrays in descending order. The argsort() function is an essential tool in data analysis, statistics, and machine learning applications. The function allows you to sort arrays based on the index positions of the elements. We also provided various examples of how to use the argsort() function to sort multidimensional arrays in descending order.
let's discuss the argsort() function in more detail.
The NumPy argsort() function is an incredibly useful tool when it comes to sorting arrays in Python. Moreover, the function is highly efficient, with a complexity of O(n log (n)). The function returns the indices that would sort an array in ascending order. The indices returned are the positions in the original array from the smallest element to the largest. However, you can also specify the order in which to sort the array using the kind parameter.
The syntax for using the argsort() function is as follows:
numpy.argsort(arr, axis=-1, kind=None, order=None)
Here, arr is the input array that needs to be sorted, axis is the axis along which the sorting is to be performed, kind is the type of sorting algorithm to be used, and order is used to specify a field in the structured array to sort by.
To sort an array in descending order, we can use the negative sign (-) before the input array, like so:
numpy.argsort(-arr)
This is because the sort order is determined by the order of the elements in the array we pass as an argument. By negating the array, we reverse the order, thereby sorting the array in descending order.
We can also use the argsort() function to sort the columns and rows of a multidimensional array. To sort the columns in descending order, we can use the following code:
numpy.argsort(-arr, axis=0)
And to sort the rows in descending order, we can use the following code:
numpy.argsort(-arr, axis=1)
It is essential to note that when sorting multi-dimensional arrays, we need to specify the axis parameter correctly.
In conclusion, the argsort() function in Numpy is an efficient and powerful tool for sorting arrays in Python. By making use of the negative sign, we can easily sort arrays in descending order. Additionally, we can use the function to sort columns and rows of multi-dimensional arrays. It is an essential function for anyone working with data analysis, statistics, and machine learning applications.
Popular questions
- What is the basic syntax for using the argsort() function in Numpy?
- The basic syntax for using the argsort() function in Numpy is 'numpy.argsort(arr, axis=-1, kind=None, order=None)'.
- How can we use the argsort() function to sort an array in descending order?
- We can use the negative sign (-) before the input array to sort an array in descending order using the argsort() function.
- Is the argsort() function efficient when it comes to sorting arrays in Python?
- Yes, the argsort() function is highly efficient, with a complexity of O(n log (n)) when it comes to sorting arrays in Python.
- Can we use the argsort() function to sort both columns and rows of a multidimensional array?
- Yes, we can use the argsort() function to sort both columns and rows of a multidimensional array.
- Why is the argsort() function an essential tool for anyone working with data analysis, statistics, and machine learning applications?
- The argsort() function is an essential tool for anyone working with data analysis, statistics, and machine learning applications because it allows us to sort arrays based on the index positions of the elements, which is important for data manipulation and analysis. Additionally, the function is highly efficient and can sort both one-dimensional and multidimensional arrays.
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