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

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 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]

Example 2:

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]]

Example 3:

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]]