NumPy Value Counts in Python

This tutorial will introduce the method to determine each unique element’s number of occurrences inside a NumPy array in Python.

NumPy Value Counts With the numpy.unique() Function in Python

If we have an array and want to determine each unique element’s number of occurrences inside the array, we can use the numpy.unique() function in Python. The numpy.unique() function takes the array as an input argument and returns all the unique elements inside the array in ascending order. We can specify the return_counts=True parameter of the numpy.unique() function to also get the number of times each element is repeated inside the array. The following code example shows us how to get the number of occurrences of each unique element inside a NumPy array with the numpy.unique() function in Python.

import numpy as np

array = np.array([1,1,1,2,3,4,4,4])

unique, counts = np.unique(array, return_counts=True)

result = np.column_stack((unique, counts)) 
print (result)

Output:

[[1 3]
 [2 1]
 [3 1]
 [4 3]]

In the above code, we determined the number of occurrences of each unique element inside the NumPy array array with the np.unique() function in Python. We first created a NumPy array with the np.array() function. We then stored all the unique elements of the array inside the unique array and their respective number of occurrences inside the counts array with the np.unique() function. We then zipped the two 1-dimensional arrays unique and counts inside a single 2-dimensional array result with the np.column_stack() function. In the end, we printed the elements of the result array.

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