How to Count Unique Values in NumPy Arrays Easily

Use numpy.unique() with the return_counts=True argument to count unique values in a NumPy array. This function returns the unique values and their counts as arrays.

Syntax

The basic syntax to count unique values in a NumPy array is:

• numpy.unique(array, return_counts=True)

Here, array is your input NumPy array. The return_counts=True option tells NumPy to also return how many times each unique value appears.

python

numpy.unique(array, return_counts=True)

Example

This example shows how to find unique values and count their occurrences in a NumPy array.

python

import numpy as np

arr = np.array([1, 2, 2, 3, 3, 3, 4])
unique_values, counts = np.unique(arr, return_counts=True)
print("Unique values:", unique_values)
print("Counts:", counts)

Output

Unique values: [1 2 3 4]
Counts: [1 2 3 1]

Common Pitfalls

One common mistake is to use numpy.unique() without return_counts=True, which only returns unique values but not their counts. Another is confusing the order of returned values.

Wrong way:

unique = np.unique(arr)
print(unique)  # Only unique values, no counts

Right way:

unique, counts = np.unique(arr, return_counts=True)
print(unique, counts)  # Unique values and their counts

Quick Reference

Summary tips for counting unique values in NumPy:

• Use np.unique(array, return_counts=True) to get counts.
• The function returns two arrays: unique values and counts.
• Works with any NumPy array of numbers or strings.

Key Takeaways

Use np.unique with return_counts=True to count unique values in arrays.

The function returns two arrays: unique values and their counts.

Without return_counts=True, only unique values are returned.

This method works for numeric and string NumPy arrays.

Always unpack the two returned arrays to access counts.