np.argsort() for sort indices in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of np.argsort() on a 1D array

What is the output of the following code?

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

NumPy

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

A [0 1 2 3]

B [3 2 1 0]

C [1 3 2 0]

D [1 2 3 0]

💡 Hint

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

The smallest element is 10 at index 1, then 20 at index 3, then 30 at index 2, and finally 40 at index 0. So the sorted indices are [1, 3, 2, 0].

❓ data_output

intermediate

Number of items in argsort result for 2D array

Given this 2D array, how many elements does np.argsort(arr, axis=1) return?

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

NumPy

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

A (2, 3)

B (3, 2)

C (6,)

D (2,)

💡 Hint

np.argsort with axis=1 sorts each row and returns indices with the same shape as input.

The input array has shape (2, 3). Sorting along axis=1 returns indices for each row, so the output shape is also (2, 3).

🔧 Debug

advanced

Identify the error in argsort usage

What error does this code raise?

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

NumPy

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

A ValueError: operands could not be broadcast together

B TypeError: argsort() got an unexpected keyword argument 'axis'

C No error, prints [0 1 2]

D AxisError: axis 1 is out of bounds for array of dimension 1

💡 Hint

Check the dimension of the array and the axis parameter.

The array is 1D, so axis=1 is invalid. This causes an AxisError.

🚀 Application

advanced

Using np.argsort() to sort one array by another

You have two arrays:

import numpy as np
names = np.array(['Bob', 'Alice', 'Eve'])
scores = np.array([85, 95, 90])

How do you get the names sorted by their scores in ascending order?

NumPy

import numpy as np
names = np.array(['Bob', 'Alice', 'Eve'])
scores = np.array([85, 95, 90])
sorted_names = names[np.argsort(scores)]
print(sorted_names)

A ['Bob' 'Eve' 'Alice']

B ['Bob' 'Alice' 'Eve']

C ['Eve' 'Bob' 'Alice']

D ['Alice' 'Eve' 'Bob']

💡 Hint

Use np.argsort on scores to get indices that sort scores, then index names with those indices.

np.argsort(scores) returns indices [0, 2, 1] which sorts scores ascending. Indexing names with this gives ['Bob', 'Eve', 'Alice'].

🧠 Conceptual

expert

Understanding np.argsort() with kind parameter

Which statement about the 'kind' parameter in np.argsort() is correct?

A 'kind' is used to specify the data type of the output indices.

B 'kind' specifies the sorting algorithm and can affect stability of the sort.

C 'kind' controls the axis along which sorting is performed.

D 'kind' determines whether the output is sorted ascending or descending.

💡 Hint

Think about sorting algorithms and their properties.

The 'kind' parameter lets you choose the sorting algorithm (e.g., 'quicksort', 'mergesort'). Some algorithms are stable, others are not.

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