How to count the frequency of unique values in NumPy array?
Let’s see How to count the frequency of unique values in the NumPy array. Python’s Numpy library provides a numpy.unique() function to find the unique elements and their corresponding frequency in a NumPy array.
numpy.unique() Syntax
Syntax: numpy.unique(arr, return_counts=False)
Return: Sorted unique elements of an array with their corresponding frequency counts NumPy array.
Get Unique Items and Counts in Numpy Array
There are various ways to get unique items and counts in the Numpy array here we explain some generally used methods for getting unique items and counts in the Numpy array those are following.
- Using the np.unique() Function
- Using NumPy Unique Frequency
- Using NumPy in Transpose Form
- Using
numpy.bincount() - Using the collections.Counter() Function
Using the np.unique() Function
In this example code uses NumPy to create an array (`ini_array`) and then utilizes `np.unique()` to obtain unique values and their frequencies. The unique values are printed with “Unique Values:”, and their corresponding frequencies are printed with “Frequency Values:”.
- Python3
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]
Using NumPy Unique Frequency
In this example code, utilizing NumPy, generates a 1D array (`ini_array`) and then employs `np.unique()` to obtain unique values and their frequencies. The results are combined into a single NumPy array (`count`) and printed, displaying the values and their frequencies.
- Python3
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]]
Using NumPy in Transporse Form
In this example code uses NumPy to create a 1D array (`ini_array`) and employs `np.unique()` to obtain unique values and their frequencies. The results are combined into a NumPy array (`count`), which is then transposed for a display of values and frequencies in a transposed form.
- Python3
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]]
Using numpy.bincount() Function
In this example, a 1D NumPy array `arr` is created with integer values. Using `numpy.unique()`, unique values and their counts are obtained. The results are displayed, showcasing the unique values and their corresponding counts using the `numpy.bincount()` method, ensuring proper alignment with the unique values.
- Python3
Python3
import numpy as np# Create a 1D arrayarr = np.array([10, 20, 5, 10, 8, 20, 8, 9])# Get unique values and their counts using bincountunique_values = np.unique(arr)counts = np.bincount(arr)# Display the resultsprint("\nMethod 2:")print("Unique Values:", unique_values)print("Counts:", counts[unique_values]) |
Output:
Unique Values: [ 5 8 9 10 20]
Counts: [1 2 1 2 2]
Using the collections.Counter() Function
In this example The code employs `Counter` from the `collections` module to count occurrences of elements in the list `my_list`. It then prints the unique values and their respective counts, offering a concise summary of the element frequencies in the list.
- Python3
Python3
from collections import Counter# Create a listmy_list = [10, 20, 5, 10, 8, 20, 8, 9]# Use Counter to count occurrencescounts = Counter(my_list)# Display the resultsprint("Unique Values:", list(counts.keys()))print("Counts:", list(counts.values())) |
Output:
Unique Values: [10, 20, 5, 8, 9]
Counts: [2, 2, 1, 2, 1]
