Let’s see How to count the frequency of unique values in NumPy array. Python’s numpy library provides a numpy.unique() function to find the unique elements and it’s corresponding frequency in a numpy array.
Syntax: numpy.unique(arr, return_counts=False)
Return: Sorted unique elements of an array with their corresponding frequency counts NumPy array.
Now, Let’s see examples:
Example 1:
Python3
# import libraryimport numpy as npini_array = np.array([10, 20, 5, 10, 8, 20, 8, 9])# Get a tuple of unique values # and their frequency in# numpy arrayunique, frequency = np.unique(ini_array, return_counts = True)# print unique values arrayprint("Unique Values:", unique)# print frequency arrayprint("Frequency Values:", frequency) |
Output:
Unique Values: [ 5 8 9 10 20] Frequency Values: [1 2 1 2 2]
Example 2:
Python3
# import libraryimport numpy as np# create a 1d-arrayini_array = np.array([10, 20, 5, 10, 8, 20, 8, 9])# Get a tuple of unique values # and their frequency # in numpy arrayunique, frequency = np.unique(ini_array, return_counts = True) # convert both into one numpy arraycount = np.asarray((unique, frequency ))print("The values and their frequency are:\n", count) |
Output:
The values and their frequency are: [[ 5 8 9 10 20] [ 1 2 1 2 2]]
Example 3:
Python3
# import libraryimport numpy as np# create a 1d-arrayini_array = np.array([10, 20, 5, 10, 8, 20, 8, 9])# Get a tuple of unique values # and their frequency in# numpy arrayunique, frequency = np.unique(ini_array, return_counts = True) # convert both into one numpy array # and then transpose itcount = np.asarray((unique,frequency )).Tprint("The values and their frequency are in transpose form:\n", count) |
Output:
The values and their frequency are in transpose form: [[ 5 1] [ 8 2] [ 9 1] [10 2] [20 2]]
