How to count the frequency of unique values in NumPy array?

Last Updated : 02 Jan, 2024

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

   # import library
import numpy as np
 
ini_array = np.array([10, 20, 5,
                      10, 8, 20,
                      8, 9])
 
# Get a tuple of unique values 
# and their frequency in
# numpy array
unique, frequency = np.unique(ini_array, 
                              return_counts = True)
# print unique values array
print("Unique Values:", 
      unique)
 
# print frequency array
print("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

   # import library
import numpy as np
 
# create a 1d-array
ini_array = np.array([10, 20, 5,
                    10, 8, 20,
                    8, 9])
 
# Get a tuple of unique values 
# and their frequency 
# in numpy array
unique, frequency = np.unique(ini_array,
                              return_counts = True) 
 
# convert both into one numpy array
count = 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

   # import library
import numpy as np
 
# create a 1d-array
ini_array = np.array([10, 20, 5,
                      10, 8, 20,
                      8, 9])
 
# Get a tuple of unique values 
# and their frequency in
# numpy array
unique, frequency = np.unique(ini_array, 
                              return_counts = True) 
 
# convert both into one numpy array 
# and then transpose it
count = np.asarray((unique,frequency )).T
 
print("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

   import numpy as np
 
# Create a 1D array
arr = np.array([10, 20, 5, 10, 8, 20, 8, 9])
 
# Get unique values and their counts using bincount
unique_values = np.unique(arr)
counts = np.bincount(arr)
 
# Display the results
print("\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

   from collections import Counter
 
# Create a list
my_list = [10, 20, 5, 10, 8, 20, 8, 9]
 
# Use Counter to count occurrences
counts = Counter(my_list)
 
# Display the results
print("Unique Values:", list(counts.keys()))
print("Counts:", list(counts.values()))
 
  

Output:

Unique Values: [10, 20, 5, 8, 9]
Counts: [2, 2, 1, 2, 1]

NumPy| How to get the unique elements of an Array

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Article Tags :

Practice Tags :

python

How to count the frequency of unique values in NumPy array?

numpy.unique() Syntax

Get Unique Items and Counts in Numpy Array

Using the np.unique() Function

Python3

Using NumPy Unique Frequency

Python3

Using NumPy in Transporse Form

Python3

Using numpy.bincount() Function

Python3

Using the collections.Counter() Function

Python3

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Using `numpy.bincount()` Function