np.sort() for sorting arrays in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to sort the array using numpy.

NumPy

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

Drag options to blanks, or click blank then click option'

Asorted

Bsort

Corder

Darrange

Attempts:

3 left

💡 Hint

Common Mistakes

Using Python's built-in sorted() which returns a list, not a numpy array.

Trying to use non-existing functions like np.order() or np.arrange().

The np.sort() function sorts the array and returns a new sorted array.

2fill in blank

medium

Complete the code to sort the 2D array along rows (axis=1).

NumPy

import numpy as np
arr = np.array([[3, 2, 1], [6, 5, 4]])
sorted_arr = np.sort(arr, axis=[1])
print(sorted_arr)

Drag options to blanks, or click blank then click option'

A1

B0

C-1

D2

Attempts:

3 left

💡 Hint

Common Mistakes

Using axis=0 which sorts columns instead of rows.

Using axis=2 which is invalid for 2D arrays.

Setting axis=1 sorts each row individually.

3fill in blank

hard

Fix the error in the code to sort the array in-place.

NumPy

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

Drag options to blanks, or click blank then click option'

Anp.sort

Bsorted

Csort

Dsort_array

Attempts:

3 left

💡 Hint

Common Mistakes

Using sorted() which returns a list, not sorting in-place.

Trying to call np.sort() as a method on the array.

The sort() method sorts the array in-place, changing the original array.

4fill in blank

hard

Fill both blanks to create a dictionary with words as keys and their lengths as values, only for words longer than 3 letters.

NumPy

words = ['cat', 'elephant', 'dog', 'horse']
lengths = {word: [1] for word in words if [2]
print(lengths)

Drag options to blanks, or click blank then click option'

Alen(word)

Bword

Clen(word) > 3

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length.

Checking if the word string is greater than 3 instead of its length.

The dictionary comprehension uses len(word) as values and filters words with length greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase words as keys, their lengths as values, only for words longer than 3 letters.

NumPy

words = ['cat', 'elephant', 'dog', 'horse']
lengths = [1]: [2] for word in words if [3]
print(lengths)

Drag options to blanks, or click blank then click option'

Aword.upper()

Blen(word)

Clen(word) > 3

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase words as keys instead of uppercase.

Using the word itself as values instead of length.

Incorrect filter condition.

The dictionary comprehension uses uppercase words as keys, their lengths as values, and filters words longer than 3 letters.

Practice

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

easy

A. It returns a new array with elements sorted in ascending order.

B. It changes the original array to sorted order in place.

C. It reverses the order of elements in the array.

D. It removes duplicate elements from the array.

Solution

1. Step 1: Understand np.sort() behavior

   The np.sort() function returns a new sorted array and does not modify the original array.

2. Step 2: Identify the sorting order

   By default, np.sort() sorts elements in ascending order.

3. Final Answer:

   It returns a new array with elements sorted in ascending order. -> Option A

4. Quick Check:

   np.sort() returns sorted copy [OK]

Hint: np.sort() returns a new sorted array, original stays same [OK]

Common Mistakes:

• Thinking np.sort() sorts in place
• Confusing sorting with reversing
• Assuming it removes duplicates

2. Which of the following is the correct syntax to sort a 1D NumPy array named arr using np.sort()?

easy

A. np.sort(arr)

B. arr.sort()

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

D. arr.sorted()

Solution

1. Step 1: Recall np.sort() syntax

   The correct way to sort an array using the function is np.sort(arr).

2. Step 2: Check other options

   arr.sort() sorts in place but is a method, not np.sort(). np.sort(arr, axis=1) is invalid for 1D arrays. arr.sorted() is not a valid method.

3. Final Answer:

   np.sort(arr) -> Option A

4. Quick Check:

   np.sort(arr) is correct syntax [OK]

Hint: Use np.sort(array) to get sorted copy [OK]

Common Mistakes:

• Using arr.sorted() which does not exist
• Using axis=1 on 1D array
• Confusing np.sort() with arr.sort() method

3. What is the output of the following code?

import numpy as np
arr = np.array([[3, 1, 2], [6, 4, 5]])
sorted_arr = np.sort(arr, axis=1)
print(sorted_arr)

medium

A. [[1 3 2]
[4 6 5]]

B. [[3 1 2]
[6 4 5]]

C. [[1 2 3]
[4 5 6]]

D. [[3 6]
[1 4]
[2 5]]

Solution

1. Step 1: Understand sorting along axis=1

   Sorting with axis=1 sorts each row independently in ascending order.

2. Step 2: Sort each row

   First row [3,1,2] sorted is [1,2,3]. Second row [6,4,5] sorted is [4,5,6].

3. Final Answer:

   [[1 2 3] [4 5 6]] -> Option C

4. Quick Check:

   Row-wise sort = [[1 2 3], [4 5 6]] [OK]

Hint: axis=1 sorts each row separately [OK]

Common Mistakes:

• Sorting columns instead of rows
• Expecting original array unchanged in print
• Confusing axis parameter meaning

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

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

medium

A. np.sort() cannot sort integer arrays.

B. Missing parentheses in np.sort call.

C. The array must be converted to a list first.

D. Axis 1 does not exist for 1D arrays.

Solution

1. Step 1: Check array dimensions

   The array arr is 1D, so it only has axis 0.

2. Step 2: Understand axis parameter

   Using axis=1 on a 1D array causes an error because axis 1 does not exist.

3. Final Answer:

   Axis 1 does not exist for 1D arrays. -> Option D

4. Quick Check:

   1D array has only axis 0 [OK]

Hint: 1D arrays only have axis=0, axis=1 causes error [OK]

Common Mistakes:

• Assuming axis=1 works on 1D arrays
• Thinking np.sort can't handle integers
• Believing array must be list to sort

5. Given a 2D NumPy array data = np.array([[7, 2, 9], [4, 5, 1], [8, 3, 6]]), how can you sort the entire array as if it were a flat list, then reshape it back to the original shape?

hard

A. np.sort(data, axis=0).reshape(data.shape)

B. np.sort(data, axis=None).reshape(data.shape)

C. data.sort(axis=1).reshape(data.shape)

D. np.sort(data).reshape(data.shape)

Solution

1. Step 1: Flatten and sort the entire array

   Using axis=None in np.sort() sorts the array as a flat 1D array.

2. Step 2: Reshape sorted array back to original shape

   Use .reshape(data.shape) to restore the 2D shape after sorting.

3. Final Answer:

   np.sort(data, axis=None).reshape(data.shape) -> Option B

4. Quick Check:

   axis=None sorts flat, reshape restores shape [OK]

Hint: Use axis=None to sort flat, then reshape [OK]

Common Mistakes:

• Sorting only rows or columns instead of flat
• Using data.sort() which sorts in place
• Omitting reshape after sorting flat