np.unique() for unique elements in NumPy

Introduction

We use np.unique() to find all the different values in a list or array. It helps us see what unique items are there without repeats.

You want to know all the different colors used in a photo's pixel data.

You have survey answers and want to list all distinct responses.

You want to remove duplicate numbers from a list of measurements.

You need to count how many unique products were sold in a store.

You want to find unique words in a text converted to an array.

Syntax

NumPy

np.unique(array, return_index=False, return_inverse=False, return_counts=False, axis=None)

array is the input list or array you want to check.

Optional arguments let you get extra info like where each unique item first appears (return_index) or how many times each appears (return_counts).

Examples

This finds unique numbers in the array and prints them sorted.

NumPy

import numpy as np
arr = np.array([1, 2, 2, 3, 4, 4, 4])
unique_values = np.unique(arr)
print(unique_values)

This also counts how many times each unique number appears.

NumPy

unique_vals, counts = np.unique(arr, return_counts=True)
print(unique_vals)
print(counts)

This finds unique rows in a 2D array.

NumPy

arr2d = np.array([[1, 2], [2, 3]])
unique_rows = np.unique(arr2d, axis=0)
print(unique_rows)

Sample Program

This program shows how to find unique numbers in a list and count how many times each appears.

NumPy

import numpy as np

# Create an array with repeated numbers
numbers = np.array([5, 3, 5, 2, 3, 8, 9, 2, 8])

# Find unique numbers
unique_numbers = np.unique(numbers)
print('Unique numbers:', unique_numbers)

# Find unique numbers and their counts
unique_vals, counts = np.unique(numbers, return_counts=True)
print('Counts of each unique number:')
for val, count in zip(unique_vals, counts):
    print(f'{val} appears {count} times')

Important Notes

np.unique() returns sorted unique values by default.

Use return_counts=True to see how often each unique value appears.

For 2D arrays, use axis=0 or axis=1 to find unique rows or columns.

Summary

np.unique() helps find all different values in data.

It can also tell you how many times each unique value appears.

Works with 1D and 2D arrays, and sorts the results automatically.