np.unique() for unique values 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.

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

When you have survey answers and want to see all the unique responses.

When cleaning data to find and remove duplicate entries.

When counting how many different categories appear in a dataset.

Syntax

NumPy

numpy.unique(ar, return_index=False, return_inverse=False, return_counts=False, axis=None)

ar is the input array or list.

You can ask for extra info like where the unique values came from or how many times they appear by setting return_index, return_inverse, or return_counts to True.

Examples

This finds unique values in a simple list and prints them sorted.

NumPy

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

This finds unique values and how many times each appears.

NumPy

arr = np.array([5, 3, 5, 2, 3])
unique_vals, counts = np.unique(arr, return_counts=True)
print(unique_vals)
print(counts)

This finds unique rows in a 2D array.

NumPy

arr = np.array([[1, 2], [2, 3]])
unique_axis0 = np.unique(arr, axis=0)
print(unique_axis0)

Sample Program

This program shows how to get unique values from an array and also count how many times each unique value appears.

NumPy

import numpy as np

# Sample data with repeated values
data = np.array([10, 20, 20, 30, 10, 40, 50, 50, 50])

# Find unique values
unique_vals = np.unique(data)
print("Unique values:", unique_vals)

# Find unique values and their counts
unique_vals, counts = np.unique(data, return_counts=True)
print("Counts of each unique value:", counts)

Important Notes

np.unique() always returns sorted unique values.

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

For multi-dimensional arrays, use the axis parameter to find unique rows or columns.

Summary

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

You can get extra info like counts or original positions.

It works on lists and arrays, including multi-dimensional ones.

Practice

1. What does the np.unique() function do in NumPy?

easy

A. Calculates the sum of all elements

B. Sorts the array in descending order

C. Reverses the order of elements

D. Finds all unique values in an array

Solution

1. Step 1: Understand the purpose of np.unique()

   This function is designed to find all different (unique) values in a NumPy array or list.

2. Step 2: Compare with other options

   Sorting, summing, or reversing are different operations and not what np.unique() does.

3. Final Answer:

   Finds all unique values in an array -> Option D

4. Quick Check:

   np.unique() = unique values [OK]

Hint: Remember: unique means different values only [OK]

Common Mistakes:

• Confusing unique with sorting
• Thinking it sums values
• Assuming it reverses array

2. Which of the following is the correct syntax to get unique values from a NumPy array arr?

easy

A. np.arr.unique()

B. np.unique(arr)

C. unique(arr)

D. arr.unique()

Solution

1. Step 1: Recall the correct function call

   The function unique is part of the NumPy module and is called as np.unique().

2. Step 2: Check other options for errors

   arr.unique() is not a NumPy array method, unique(arr) misses the module prefix, and np.arr.unique() is invalid syntax.

3. Final Answer:

   np.unique(arr) -> Option B

4. Quick Check:

   Correct syntax = np.unique(arr) [OK]

Hint: Use np.unique(array) always [OK]

Common Mistakes:

• Calling unique as method on array
• Missing np prefix
• Wrong module usage

3. What is the output of this code?

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

medium

A. [1 2 3 4]

B. [3 1 2 4]

C. [4 3 2 1]

D. [1 1 2 2 3 3 4]

Solution

1. Step 1: Identify unique values in the array

   The array contains values 3, 1, 2, 3, 2, 1, 4. Unique values are 1, 2, 3, and 4.

2. Step 2: Understand np.unique() output order

   np.unique() returns sorted unique values, so output is [1 2 3 4].

3. Final Answer:

   [1 2 3 4] -> Option A

4. Quick Check:

   Unique sorted values = [1 2 3 4] [OK]

Hint: Unique values are sorted by default [OK]

Common Mistakes:

• Ignoring sorting order
• Listing duplicates
• Wrong output format

4. The following code throws an error. What is the problem?

import numpy as np
arr = [1, 2, 2, 3]
print(np.unique(arr, return_counts=True))

medium

A. Incorrect argument name, should be count_return=True

B. List input causes error, must convert to np.array first

C. No error, code runs fine

D. np.unique() does not support return_counts argument

Solution

1. Step 1: Check input type compatibility

   np.unique() accepts lists or arrays as input without error.

2. Step 2: Verify return_counts argument

   The argument return_counts=True is valid and returns counts of unique values.

3. Final Answer:

   No error, code runs fine -> Option C

4. Quick Check:

   List input + return_counts works [OK]

Hint: np.unique accepts lists and arrays [OK]

Common Mistakes:

• Thinking list input causes error
• Wrong argument name
• Assuming return_counts unsupported

5. You have a 2D NumPy array:

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

How do you get all unique values from this 2D array as a sorted 1D array?

hard

A. np.unique(arr)

B. np.unique(arr, axis=1)

C. np.unique(arr, axis=0)

D. arr.unique()

Solution

1. Step 1: Understand np.unique() on 2D arrays

   Calling np.unique() without axis flattens the array and returns unique sorted values.

2. Step 2: Check axis arguments

   Using axis=0 or axis=1 returns unique rows or columns, not unique elements overall.

3. Final Answer:

   np.unique(arr) -> Option A

4. Quick Check:

   Flatten and unique = np.unique(arr) [OK]

Hint: No axis means flatten and find unique [OK]

Common Mistakes:

• Using axis to get unique elements
• Calling unique as method
• Expecting 2D unique output