Let’s see the program for how to get the n-largest values of an array using NumPy library. For getting n-largest values from a NumPy array we have to first sort the NumPy array using numpy.argsort() function of NumPy then applying slicing concept with negative indexing.
Syntax: numpy.argsort(arr, axis=-1, kind=’quicksort’, order=None)
Return: [index_array, ndarray] Array of indices that sort arr along the specified axis.If arr is one-dimensional then arr[index_array] returns a sorted arr.
Let’s see an example:
Example 1: Getting the 1st largest value from a NumPy array.
Python3
# import library import numpy as np # create numpy 1d-array arr = np.array([2, 0, 1, 5, 4, 1, 9]) print("Given array:", arr) # sort an array in # ascending order # np.argsort() return # array of indices for # sorted array sorted_index_array = np.argsort(arr) # sorted array sorted_array = arr[sorted_index_array] print("Sorted array:", sorted_array) # we want 1 largest value n = 1 # we are using negative # indexing concept # take n largest value rslt = sorted_array[-n : ] # show the output print("{} largest value:".format(n), rslt[0]) |
Output:
Given array: [2 0 1 5 4 1 9] Sorted array: [0 1 1 2 4 5 9] 1 largest value: 9
Example 2: Getting the 3-largest values from a NumPy array.
Python3
# import library import numpy as np # create numpy 1d-array arr = np.array([2, 0, 1, 5, 4, 1, 9]) print("Given array:", arr) # sort an array in # ascending order # np.argsort() return # array of indices for # sorted array sorted_index_array = np.argsort(arr) # sorted array sorted_array = arr[sorted_index_array] print("Sorted array:", sorted_array) # we want 3 largest value n = 3 # we are using negative # indexing concept # find n largest value rslt = sorted_array[-n : ] # show the output print("{} largest value:".format(n), rslt) |
Output:
Given array: [2 0 1 5 4 1 9] Sorted array: [0 1 1 2 4 5 9] 3 largest value: [4 5 9]
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