np.unique() for unique values in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of np.unique() with return_counts

What is the output of this code snippet?

NumPy

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

A

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

B

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

C

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

D

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

💡 Hint

np.unique() sorts the unique values by default.

np.unique() returns sorted unique values. return_counts=True returns how many times each unique value appears in the original array.

❓ data_output

intermediate

Number of unique elements in a 2D array

How many unique elements are in this 2D numpy array?

NumPy

import numpy as np
arr = np.array([[5, 2, 5], [3, 2, 1]])
unique_elements = np.unique(arr)
print(len(unique_elements))

A 3

B 5

C 6

D 4

💡 Hint

Count all distinct numbers in the array.

The unique elements are [1, 2, 3, 5]. There are 4 unique elements.

❓ Predict Output

advanced

Output of np.unique() with return_index and return_inverse

What will be printed by this code?

NumPy

import numpy as np
arr = np.array([7, 2, 7, 3, 2])
unique_vals, indices, inverse = np.unique(arr, return_index=True, return_inverse=True)
print(unique_vals)
print(indices)
print(inverse)

A

[2 3 7]
[1 3 0]
[2 0 2 1 0]

B

[2 3 7]
[1 3 0]
[0 2 0 1 2]

C

[2 3 7]
[1 3 0]
[0 1 0 2 1]

D

[7 2 3]
[0 1 3]
[0 1 0 2 1]

💡 Hint

return_index gives the first index of each unique value in the original array. return_inverse maps original elements to unique indices.

unique_vals are sorted: [2, 3, 7]. Their first indices in arr are [1, 3, 0]. inverse maps each element in arr to its unique value index: arr[0]=7 -> 2, arr[1]=2 -> 0, etc.

❓ visualization

advanced

Visualizing unique values and their counts

Which option shows the correct bar chart for unique values and their counts from this array?

NumPy

import numpy as np
import matplotlib.pyplot as plt
arr = np.array([1, 2, 2, 3, 3, 3, 4])
unique_vals, counts = np.unique(arr, return_counts=True)
plt.bar(unique_vals, counts)
plt.xlabel('Unique Values')
plt.ylabel('Counts')
plt.title('Counts of Unique Values')
plt.show()

A Bar chart with bars at 1,2,3,4 with heights 2,2,2,1

B Bar chart with bars at 1,2,3,4 with heights 1,2,3,1

C Bar chart with bars at 1,2,3,4 with heights 1,3,2,1

D Bar chart with bars at 1,2,3,4 with heights 1,1,3,2

💡 Hint

Count how many times each number appears in the array.

The array has one '1', two '2's, three '3's, and one '4'. The bar heights match these counts.

🧠 Conceptual

expert

Effect of np.unique() on structured arrays

Given a structured numpy array with fields 'name' and 'age', what does np.unique() return by default?

NumPy

import numpy as np
arr = np.array([('Alice', 25), ('Bob', 30), ('Alice', 25)], dtype=[('name', 'U10'), ('age', 'i4')])
unique_arr = np.unique(arr)
print(unique_arr)

A Array with unique names only: ['Alice', 'Bob']

B Array with unique ages only: [25, 30]

C Array with unique rows: [('Alice', 25), ('Bob', 30)]

D Raises a TypeError because structured arrays are not supported

💡 Hint

np.unique() compares entire rows for structured arrays.

np.unique() treats each row as a whole and returns unique rows. It does not extract individual fields unless specified.

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