Why sorting matters in NumPy - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to sort the array in ascending order.

NumPy

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

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

Asort

Bsorted

Corder

Darrange

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.sort which sorts in place and returns None

Using a non-existent function like np.order

The np.sorted() function returns a sorted copy of the array.

2fill in blank

medium

Complete the code to get the indices that would sort the array.

NumPy

import numpy as np
arr = np.array([10, 2, 8, 6])
indices = np.[1](arr)
print(indices)

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

Asort

Bargsort

Csorted

Dorder

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.sort which returns sorted values, not indices

Using np.sorted which is not a NumPy function

np.argsort() returns the indices that would sort the array.

3fill in blank

hard

Fix the error in the code to sort the array in descending order.

NumPy

import numpy as np
arr = np.array([7, 3, 9, 1])
sorted_arr = np.sort(arr)[1][::-1]
print(sorted_arr)

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

A+

B*

C(no operator)

D-

Attempts:

3 left

💡 Hint

Common Mistakes

Adding an operator like + or * before the slice causes syntax error

Trying to use np.sort(arr)[::-1] but adding extra characters

No operator is needed between np.sort(arr) and [::-1]. The slice directly reverses the sorted array.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 letters.

NumPy

words = ['data', 'is', 'fun', 'and', 'powerful']
lengths = {word: [1] for word in words if len(word) [2] 3}
print(lengths)

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

Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong comparison operator like '<'

Using the word itself as the value instead of its length

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

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase keys and values for words longer than 2 letters.

NumPy

words = ['cat', 'is', 'big', 'and', 'red']
result = [1]: [2] for w in words if len(w) [3] 2
print(result)

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

Aw.upper()

Bw

C>

Dlen(w)

Attempts:

3 left

💡 Hint

Common Mistakes

Using length as value instead of the word

Using wrong comparison operator like '<='

The dictionary comprehension uses uppercase words as keys, original words as values, and filters words longer than 2 letters.

Practice

1. Why is sorting data important in data analysis using numpy?

easy

A. It helps organize data to find trends and top values easily.

B. It deletes duplicate values automatically.

C. It changes the data type of the array elements.

D. It increases the size of the dataset.

Solution

1. Step 1: Understand sorting purpose

   Sorting arranges data in order, making it easier to analyze and find patterns.

2. Step 2: Identify correct effect of sorting

   Sorting does not delete duplicates or change data types; it only orders data.

3. Final Answer:

   It helps organize data to find trends and top values easily. -> Option A

4. Quick Check:

   Sorting = Organizing data for analysis [OK]

Hint: Sorting arranges data to spot patterns fast [OK]

Common Mistakes:

• Thinking sorting removes duplicates
• Believing sorting changes data types
• Assuming sorting increases data size

2. Which of the following is the correct syntax to return a sorted copy of a 1D numpy array named arr?

easy

A. numpy.sort(arr)

B. arr.sort(numpy)

C. sort.numpy(arr)

D. arr.sort()

Solution

1. Step 1: Recall numpy sorting syntax

   The function numpy.sort() is used to sort arrays and takes the array as argument.

2. Step 2: Evaluate the options

   arr.sort() sorts in place and returns None. arr.sort(numpy) and sort.numpy(arr) are invalid syntax. numpy.sort(arr) returns a sorted copy.

3. Final Answer:

   numpy.sort(arr) -> Option A

4. Quick Check:

   Correct syntax = numpy.sort(arr) [OK]

Hint: Use numpy.sort(array) to sort arrays [OK]

Common Mistakes:

• Using arr.sort() which sorts in place
• Using arr.sort(numpy) which is invalid
• Writing sort.numpy(arr) which is invalid

3. What is the output of the following code?

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

medium

A. [5 4 3 1 1]

B. [3 1 4 1 5]

C. [1 1 3 4 5]

D. [1 3 4 5]

Solution

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

   np.sort() returns a sorted copy of the array in ascending order.

2. Step 2: Sort the array values

   Original array is [3, 1, 4, 1, 5]. Sorted ascending is [1, 1, 3, 4, 5].

3. Final Answer:

   [1 1 3 4 5] -> Option C

4. Quick Check:

   np.sort([3,1,4,1,5]) = [1 1 3 4 5] [OK]

Hint: np.sort() returns ascending sorted array [OK]

Common Mistakes:

• Confusing ascending with descending order
• Expecting original array to change
• Missing duplicate values in output

4. The code below is intended to sort a 2D numpy array by rows, but it raises an error. What is the problem?

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

medium

A. np.sort() cannot sort 2D arrays.

B. Axis 2 does not exist for a 2D array.

C. The array must be flattened before sorting.

D. The print statement is incorrect.

Solution

1. Step 1: Check array dimensions

   The array shape is (2, 2), so it has axes 0 and 1 only.

2. Step 2: Understand axis parameter in np.sort()

   Axis=2 is invalid because the array has no third axis, causing an error.

3. Final Answer:

   Axis 2 does not exist for a 2D array. -> Option B

4. Quick Check:

   Axis must be 0 or 1 for 2D arrays [OK]

Hint: Check array shape before choosing axis [OK]

Common Mistakes:

• Using axis value outside array dimensions
• Thinking np.sort can't handle 2D arrays
• Assuming print statement causes error

5. You have a 2D numpy array representing exam scores of students:

import numpy as np
scores = np.array([[88, 92, 79], [95, 85, 91], [70, 78, 88]])

How would sorting each student's scores help in quickly finding their median score?

hard

A. Sorting changes scores to percentages.

B. Sorting removes the lowest and highest scores automatically.

C. Sorting combines all scores into one list.

D. Sorting arranges scores so the middle value is easy to pick as median.

Solution

1. Step 1: Understand median calculation

   The median is the middle value in sorted data.

2. Step 2: Role of sorting in median

   Sorting each student's scores orders them, making it easy to pick the middle score as median.

3. Final Answer:

   Sorting arranges scores so the middle value is easy to pick as median. -> Option D

4. Quick Check:

   Median needs sorted data [OK]

Hint: Sort to find median easily [OK]

Common Mistakes:

• Thinking sorting removes scores
• Confusing sorting with scaling scores
• Assuming sorting merges all data