np.argsort() for sort indices in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does np.argsort() do in NumPy?

np.argsort() returns the indices that would sort an array. It tells you the order to arrange elements to get a sorted array.

beginner

How can you use np.argsort() to sort an array?

Use the indices from np.argsort() to reorder the original array. For example, arr[np.argsort(arr)] gives the sorted array.

beginner

What is the difference between np.sort() and np.argsort()?

np.sort() returns the sorted array itself, while np.argsort() returns the indices that would sort the array.

intermediate

Can np.argsort() be used on multi-dimensional arrays?

Yes, by specifying the axis parameter, np.argsort() returns indices that sort along that axis.

intermediate

Why might you want to use np.argsort() instead of sorting the array directly?

Using np.argsort() lets you keep track of the original positions of elements after sorting, useful for reordering related data or preserving index relationships.

What does np.argsort([3, 1, 2]) return?

A [0, 1, 2]

B [1, 2, 0]

C [2, 1, 0]

D [1, 0, 2]

The sorted array is [1, 2, 3]. The indices to get this order from the original array are [1, 2, 0].

Which function returns the sorted array itself?

A np.order()

B np.argsort()

C np.indexsort()

D np.sort()

np.sort() returns the sorted array. np.argsort() returns indices.

How do you get a sorted array from np.argsort() indices?

A Multiply the array by the indices

B Add the indices to the array

C Use the indices to reorder the original array

D Sort the indices again

Use the indices to reorder the original array: arr[np.argsort(arr)].

What parameter lets you sort along a specific axis in np.argsort()?

A axis

B order

C direction

D sort_axis

The axis parameter specifies which axis to sort along in multi-dimensional arrays.

Why is np.argsort() useful when working with related data arrays?

A It gives indices to reorder related arrays consistently

B It removes duplicates

C It changes the data type

D It sorts all arrays automatically

np.argsort() provides indices to reorder related arrays in the same sorted order.

Explain how np.argsort() works and how you can use it to sort an array.

Think about how indices tell you the order of elements.

Describe a situation where using np.argsort() is better than directly sorting the array.

Imagine you have two lists that must stay aligned after sorting.

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