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.