np.argsort() for sort indices in NumPy - Step-by-Step Execution

Concept Flow - np.argsort() for sort indices

Input Array

↓

Compute Indices that Sort

↓

Return Sorted Indices Array

↓

Use Indices to Access Sorted Elements

np.argsort() takes an array and returns the indices that would sort the array. These indices can then be used to reorder the original array.

Execution Sample

NumPy

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

This code finds the indices that sort the array and then uses them to get the sorted array.

Execution Table

Step	Action	Array State	Indices Computed	Result
1	Start with array	[30, 10, 20]	N/A	N/A
2	Call np.argsort(arr)	[30, 10, 20]	[1, 2, 0]	Indices that sort array
3	Use indices to reorder array	[30, 10, 20]	[1, 2, 0]	[10, 20, 30]
4	End	[30, 10, 20]	[1, 2, 0]	Sorted array obtained

💡 All steps completed; indices correctly represent sorting order

Variable Tracker

Variable	Start	After np.argsort	After indexing	Final
arr	[30, 10, 20]	[30, 10, 20]	[30, 10, 20]	[30, 10, 20]
indices	N/A	[1, 2, 0]	[1, 2, 0]	[1, 2, 0]
sorted_arr	N/A	N/A	[10, 20, 30]	[10, 20, 30]

Key Moments - 3 Insights

Why does np.argsort return indices instead of the sorted array?

np.argsort returns indices to show the order to rearrange the original array without changing it. This is shown in execution_table step 2, where indices [1, 2, 0] tell us how to reorder arr.

How do we get the sorted array from the indices?

By using the indices to index the original array, as in execution_table step 3, arr[indices] gives the sorted array [10, 20, 30].

What if the array has duplicate values?

np.argsort will return indices that sort duplicates in their original order (stable sort). This behavior is consistent with the indices shown in the example.

Visual Quiz - 3 Questions

Look at the execution_table at step 2, what does the indices array [1, 2, 0] represent?

A Positions to rearrange arr to sorted order

B Sorted values of arr

C Original array reversed

D Indices of maximum values

💡 Hint

Check the 'Indices Computed' column at step 2 in execution_table

At which step in the execution_table do we get the sorted array values?

A Step 2

B Step 3

C Step 1

D Step 4

💡 Hint

Look at the 'Result' column for the sorted array values

If the original array was [5, 5, 1], what would np.argsort(arr) return?

A [2, 1, 0]

B [0, 1, 2]

C [2, 0, 1]

D [1, 0, 2]

💡 Hint

Indices should sort the array: smallest value index first, duplicates keep original order

Concept Snapshot

np.argsort(array) returns indices that sort the array.
Use these indices to reorder the original array.
It does not sort the array itself.
Useful for sorting related arrays or stable sorting.
Example: indices = np.argsort(arr); sorted_arr = arr[indices]

Full Transcript

np.argsort() is a function in numpy that returns the indices which would sort an array. Instead of returning the sorted array directly, it gives you the order to rearrange the original array. For example, if you have an array [30, 10, 20], np.argsort returns [1, 2, 0] because the element at index 1 (10) is the smallest, then index 2 (20), then index 0 (30). You can then use these indices to get the sorted array by indexing the original array with them. This method is helpful when you want to sort multiple related arrays in the same order or keep track of the original positions. The execution table shows each step: starting with the array, computing indices, using them to reorder, and obtaining the sorted array. Key points include understanding that argsort returns indices, not sorted values, and how to use those indices to get sorted data.

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