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.

Practice

1. What does the np.unique() function do when applied to a NumPy array?

easy

A. Returns the shape of the array

B. Returns the sum of all elements in the array

C. Returns the maximum value in the array

D. Returns all unique elements sorted from the array

Solution

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

   The function np.unique() extracts all different values from the input array and sorts them.

2. Step 2: Compare with other options

   Options A, B, and D describe different functions like shape, sum, and max, which are not what np.unique() does.

3. Final Answer:

   Returns all unique elements sorted from the array -> Option D

4. Quick Check:

   np.unique() = unique sorted elements [OK]

Hint: np.unique() always returns sorted unique values [OK]

Common Mistakes:

• Thinking it returns counts by default
• Confusing with sum or max functions
• Assuming it returns unsorted unique values

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

easy

A. np.unique(arr)

B. arr.unique()

C. unique(arr)

D. np.arr.unique()

Solution

1. Step 1: Recall the correct function call

   The correct way to call the unique function in NumPy is np.unique(arr).

2. Step 2: Identify incorrect syntax

   Options A, B, and C are invalid because either the method does not exist on the array object or the function is not called from the NumPy module correctly.

3. Final Answer:

   np.unique(arr) -> Option A

4. Quick Check:

   Use np.unique(array) syntax [OK]

Hint: Always call unique as np.unique(array) [OK]

Common Mistakes:

• Trying to call unique as a method on array
• Forgetting the np. prefix
• Using a non-existent unique() function without np

3. What is the output of the following code?

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

medium

A. [4 3 2 1]

B. [3 1 2 4]

C. [1 2 3 4]

D. [1 1 2 2 3 3 4]

Solution

1. Step 1: Apply np.unique() to the array

   The function extracts unique values from the array: 1, 2, 3, and 4.

2. Step 2: Note the sorting behavior

   np.unique() returns these unique values sorted in ascending order: [1 2 3 4].

3. Final Answer:

   [1 2 3 4] -> Option C

4. Quick Check:

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

Hint: np.unique() sorts unique values automatically [OK]

Common Mistakes:

• Expecting original order instead of sorted
• Including duplicates in output
• Confusing with counts output

4. The following code is intended to print unique elements of arr but raises an error. What is the error?

import numpy as np
arr = np.array([5, 6, 5, 7])
result = arr.unique()
print(result)

medium

A. 'numpy.ndarray' object has no attribute 'unique'

B. SyntaxError: invalid syntax

C. TypeError: unique() missing 1 required positional argument

D. NameError: name 'unique' is not defined

Solution

1. Step 1: Identify the method call on the array

   The code calls arr.unique(), but NumPy arrays do not have a method named unique().

2. Step 2: Understand the correct usage

   The correct function is np.unique(arr), a function in the NumPy module, not a method of the array object.

3. Final Answer:

   'numpy.ndarray' object has no attribute 'unique' -> Option A

4. Quick Check:

   Arrays have no unique() method [OK]

Hint: np.unique() is a function, not an array method [OK]

Common Mistakes:

• Calling unique() as a method on array
• Forgetting to import numpy as np
• Using wrong function name or syntax

5. Given a 2D NumPy array arr = np.array([[1, 2, 2], [3, 1, 4]]), which code correctly finds all unique elements in the entire array?

hard

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

B. np.unique(arr)

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

D. arr.unique()

Solution

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

   Calling np.unique(arr) flattens the array and returns all unique elements sorted.

2. Step 2: Check axis parameter effects

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

3. Step 3: Identify invalid method call

   arr.unique() is invalid because arrays do not have a unique method.

4. Final Answer:

   np.unique(arr) -> Option B

5. Quick Check:

   np.unique(array) finds all unique elements [OK]

Hint: Use np.unique(arr) to get all unique elements in any array [OK]

Common Mistakes:

• Using axis parameter expecting unique elements, but it returns unique rows/columns
• Calling unique() as a method on array
• Confusing unique elements with unique rows or columns