Why sorting matters in NumPy - Challenge Your Understanding

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of sorting a NumPy array

What is the output of this code that sorts a NumPy array?

NumPy

import numpy as np
arr = np.array([3, 1, 4, 1, 5])
sorted_arr = np.sort(arr)
print(sorted_arr)

A [5 4 3 1 1]

B [3 1 4 1 5]

C [1 1 3 4 5]

D [1 3 4 5]

💡 Hint

Think about what sorting does to the order of elements.

np.sort returns a new array with elements in ascending order. The original array stays the same.

❓ data_output

intermediate

Number of elements after sorting with unique

What is the number of unique elements after sorting this NumPy array?

NumPy

import numpy as np
arr = np.array([2, 3, 2, 5, 3, 7])
unique_sorted = np.unique(arr)
print(len(unique_sorted))

A 4

B 5

C 6

D 3

💡 Hint

Unique elements are counted after sorting and removing duplicates.

np.unique returns sorted unique elements. The array has 4 unique values: 2, 3, 5, 7.

🔧 Debug

advanced

Identify the error in sorting a 2D array

What error does this code raise when trying to sort a 2D NumPy array incorrectly?

NumPy

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

A ValueError: could not broadcast input array

B IndexError: axis 2 is out of bounds for array of dimension 2

C TypeError: sort() missing required argument 'axis'

D No error, prints sorted array

💡 Hint

Check the dimensions of the array and the axis parameter.

The array has 2 dimensions (axes 0 and 1). Axis 2 does not exist, causing IndexError.

🚀 Application

advanced

Sorting to find median in NumPy

Which code correctly finds the median of a NumPy array by sorting?

A median = np.mean(np.sort(arr))

B median = np.median(arr)

C median = np.sort(arr)[len(arr)//2]

D

sorted_arr = np.sort(arr)
median = sorted_arr[len(arr)//2]

💡 Hint

Median is the middle value after sorting.

Option D sorts the array and picks the middle element correctly. Option D uses np.median but does not show sorting. Option D is similar but misses storing sorted array. Option D calculates mean, not median.

🧠 Conceptual

expert

Why sorting is important in data science

Why is sorting data important before applying many data science algorithms?

A Sorting organizes data to enable efficient searching and grouping.

B Sorting changes data values to improve model accuracy.

C Sorting removes duplicates automatically from data.

D Sorting encrypts data to protect privacy.

💡 Hint

Think about how sorted data helps algorithms work faster or better.

Sorting arranges data in order, which helps algorithms like binary search, grouping, and visualization work efficiently. It does not change values, remove duplicates, or encrypt data.