NumPy: Create an array that represents the rank of each item of a given array
Rank Items in Array
Write a NumPy program to create an array that represents the rank of each item in a given array.
Sample Solution:
Python Code:
# Importing the NumPy library and aliasing it as 'np'
import numpy as np
# Creating a NumPy array 'array' containing integers
array = np.array([24, 27, 30, 29, 18, 14])
# Displaying a message indicating the original array will be printed
print("Original array:")
# Printing the original array
print(array)
# Getting the indices that would sort the 'array' in ascending order
argsort_array = array.argsort()
# Creating an empty array 'ranks_array' with the same shape as 'argsort_array'
ranks_array = np.empty_like(argsort_array)
# Assigning ranks to elements based on their sorted indices
ranks_array[argsort_array] = np.arange(len(array))
# Displaying a message indicating the ranks of each item in the array
print("\nRank of each item of the said array:")
# Printing the ranks of each item in the 'array'
print(ranks_array)
Sample Output:
Original array: [24 27 30 29 18 14] Rank of each item of the said array: [2 3 5 4 1 0]
Explanation:
In the above exercise -
array = numpy.array([24, 27, 30, 29, 18, 14]): It creates a 1-dimensional NumPy array array with the given elements.
argsort_array = array.argsort(): It applies the argsort() function to the array, which returns the indices that would sort the array in ascending order.
ranks_array = numpy.empty_like(argsort_array): It creates a new NumPy array ranks_array with the same shape as argsort_array and uninitialized elements.
ranks_array[argsort_array] = numpy.arange(len(array)): It assigns the rank (position) of each element in the sorted array to the corresponding index in ranks_array. The numpy.arange(len(array)) creates an array of indices [0, 1, 2, 3, 4, 5], which represents the ranks of the sorted elements
print(ranks_array): Finally print() function prints the ranks_array, which shows the rank of each element in the original array.
Pictorial Presentation:
For more Practice: Solve these Related Problems:
- Write a NumPy program to compute the ranking of each element in a 1D array using argsort.
- Create a function that returns the rank of each element in an array, with lower values having lower ranks.
- Test the ranking function on an array with duplicate elements and verify that ties are handled appropriately.
- Implement an alternative solution using np.argsort twice to determine the rank order and compare results.
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