np.argsort() for sort indices in NumPy - Mini Project: Build & Apply

Using np.argsort() to Sort Indices in NumPy

📖 Scenario: You have a list of exam scores from students. You want to find out the order of students from the lowest score to the highest score without changing the original list.

🎯 Goal: Build a program that uses np.argsort() to find the indices that would sort the scores array.

📋 What You'll Learn

Create a NumPy array called scores with exact values

Create a variable called sorted_indices using np.argsort() on scores

Print the sorted_indices to show the order of scores from lowest to highest

💡 Why This Matters

🌍 Real World

Sorting indices is useful when you want to know the order of items without changing the original data, such as ranking students by scores or sorting products by price.

💼 Career

Data scientists often use <code>np.argsort()</code> to efficiently sort data and keep track of original positions, which is important for data analysis and machine learning tasks.

1

Create the scores array

Create a NumPy array called scores with these exact values: 55, 89, 76, 65, 92.

NumPy

import numpy as np
# Create the scores array with values 55, 89, 76, 65, 92
# Your code here

Hint

Use np.array() to create the array with the exact values.

2

Use np.argsort() to get sorted indices

Create a variable called sorted_indices and set it to np.argsort(scores).

NumPy

import numpy as np
scores = np.array([55, 89, 76, 65, 92])
# Use np.argsort() on scores and assign to sorted_indices
# Your code here

Hint

Use np.argsort() to get the indices that would sort the array.

3

Print the sorted indices

Write a print() statement to display the sorted_indices variable.

NumPy

import numpy as np
scores = np.array([55, 89, 76, 65, 92])
sorted_indices = np.argsort(scores)
# Print the sorted_indices
# Your code here

Hint

The output should be the indices that sort the scores from lowest to highest.

4

Use sorted indices to sort the scores

Create a variable called sorted_scores by using scores[sorted_indices]. Then print sorted_scores.

NumPy

import numpy as np
scores = np.array([55, 89, 76, 65, 92])
sorted_indices = np.argsort(scores)
print(sorted_indices)
# Use sorted_indices to get sorted_scores and print it
# Your code here

Hint

Use the sorted indices to reorder the original scores array and print the sorted scores.

Practice

1. What does the np.argsort() function return when applied to a numpy array?

easy

A. The sum of all elements in the array

B. The sorted array itself

C. The maximum value in the array

D. An array of indices that would sort the original array

Solution

1. Step 1: Understand the purpose of np.argsort()

   This function does not sort the array directly but returns the indices that would sort the array.

2. Step 2: Differentiate from sorting functions

   Unlike np.sort() which returns the sorted array, np.argsort() returns the order of indices to achieve that sorting.

3. Final Answer:

   An array of indices that would sort the original array -> Option D

4. Quick Check:

   np.argsort() = indices order [OK]

Hint: Remember: argsort returns indices, not sorted values [OK]

Common Mistakes:

• Confusing argsort with sort and expecting sorted values
• Thinking argsort returns the maximum or minimum value
• Assuming argsort returns a scalar instead of an array

2. Which of the following is the correct syntax to get the indices that would sort the array arr using a NumPy function?

easy

A. arr.sort()

B. np.argsort(arr)

C. np.sort(arr)

D. arr.argsort()

Solution

1. Step 1: Identify the numpy function for argsort

   The function np.argsort() is called with the array as argument: np.argsort(arr).

2. Step 2: Differentiate from other methods

   arr.argsort() is an array method (not the NumPy function), while np.sort(arr) returns sorted values, and arr.sort() sorts in place.

3. Final Answer:

   np.argsort(arr) -> Option B

4. Quick Check:

   Correct function call = np.argsort(arr) [OK]

Hint: Use np.argsort(array), the NumPy function, to get sort indices [OK]

Common Mistakes:

• Using arr.argsort() (array method instead of NumPy function)
• Confusing np.sort() with np.argsort()
• Using arr.sort() which sorts in place and returns None

3. Given the code:

import numpy as np
arr = np.array([40, 10, 30, 20])
indices = np.argsort(arr)
print(indices)

What will be the output?

medium

A. [1 3 2 0]

B. [3 2 1 0]

C. [0 1 2 3]

D. [1 2 3 0]

Solution

1. Step 1: Understand the array and sorting order

   The array is [40, 10, 30, 20]. Sorting it ascending gives [10, 20, 30, 40].

2. Step 2: Find indices that sort the array

   10 is at index 1, 20 at index 3, 30 at index 2, and 40 at index 0. So, indices are [1, 3, 2, 0].

3. Final Answer:

   [1 3 2 0] -> Option A

4. Quick Check:

   Sorted indices = [1 3 2 0] [OK]

Hint: Match sorted values to original indices for argsort output [OK]

Common Mistakes:

• Confusing sorted values with indices
• Reversing the order of indices
• Using sorted array instead of indices

4. What is wrong with this code snippet?

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

medium

A. The code will run correctly and print the sorted indices

B. The method argsort() does not exist for numpy arrays

C. The array must be sorted before calling argsort()

D. The print statement is missing parentheses

Solution

1. Step 1: Check if argsort() is a valid numpy array method

   In numpy, arrays do have an argsort() method, so arr.argsort() is valid.

2. Step 2: Verify code correctness

   The code will run and print the indices that sort the array, which are [1, 2, 0].

3. Final Answer:

   The code will run correctly and print the sorted indices -> Option A

4. Quick Check:

   arr.argsort() is valid and works [OK]

Hint: Remember numpy arrays have argsort() method too [OK]

Common Mistakes:

• Assuming argsort() is only in np module, not array method
• Thinking array must be sorted before argsort()
• Confusing Python 2 print syntax with Python 3

5. You have two related numpy arrays:

names = np.array(['apple', 'banana', 'cherry', 'date'])
prices = np.array([3.5, 2.0, 4.0, 1.5])

You want to list the fruit names sorted by their prices in ascending order. Which code snippet correctly achieves this?

hard

A. sorted_names = np.argsort(names)[prices]

B. sorted_names = np.sort(names)[np.argsort(prices)]

C. sorted_names = names[np.argsort(prices)]

D. sorted_names = names[np.sort(prices)]

Solution

1. Step 1: Use np.argsort(prices) to get indices that sort prices

   This returns indices that sort prices ascending.

2. Step 2: Use these indices to reorder names

   Indexing names with these indices sorts names by price.

3. Final Answer:

   sorted_names = names[np.argsort(prices)] -> Option C

4. Quick Check:

   Index names by argsort(prices) to sort by price [OK]

Hint: Index names by argsort of prices to sort related arrays [OK]

Common Mistakes:

• Trying to sort names directly without using indices
• Using np.sort(names) which sorts names alphabetically
• Indexing with sorted prices instead of indices