np.sort() for sorting arrays in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does np.sort() do in NumPy?

np.sort() sorts the elements of an array in ascending order and returns a new sorted array without changing the original array.

intermediate

How can you sort a 2D NumPy array by rows using np.sort()?

Use np.sort(array, axis=1) to sort each row independently in ascending order.

beginner

Does np.sort() modify the original array?

No, np.sort() returns a new sorted array and leaves the original array unchanged.

intermediate

What parameter in np.sort() controls the sorting axis?

The axis parameter controls which axis to sort along. For example, axis=0 sorts each column, axis=1 sorts each row.

intermediate

How do you sort a NumPy array in descending order using np.sort()?

Sort the array in ascending order with np.sort() and then reverse it using slicing: np.sort(array)[::-1].

What does np.sort() return?

A A new sorted array

B The original array sorted in place

C A boolean indicating if the array is sorted

D The sum of array elements

np.sort() returns a new sorted array and does not change the original array.

How do you sort each column of a 2D array using np.sort()?

A <code>np.sort(array, axis=0)</code>

B <code>np.sort(array, axis=1)</code>

C <code>np.sort(array)</code>

D <code>np.sort(array, axis=2)</code>

Setting axis=0 sorts each column independently.

Which of these sorts an array in descending order using np.sort()?

A <code>np.sort(array, axis=-1)</code>

B <code>np.sort(array, descending=True)</code>

C <code>np.sort(array, reverse=True)</code>

D <code>np.sort(array)[::-1]</code>

NumPy's np.sort() does not have a descending parameter, so you sort ascending then reverse with slicing.

If you call np.sort(array) without axis on a 2D array, what happens?

A Each row is sorted independently

B Each column is sorted independently

C The array is flattened and sorted

D An error is raised

Without specifying axis, the array is flattened to 1D and sorted.

Does np.sort() change the original array?

A Yes, it sorts the original array in place

B No, it returns a new sorted array

C Only if you set <code>inplace=True</code>

D Only for 1D arrays

np.sort() always returns a new sorted array and does not modify the original.

Explain how to use np.sort() to sort a 2D array by rows and by columns.

Think about which axis represents rows and which represents columns.

Describe how to sort a NumPy array in descending order using np.sort().

Consider how to reverse a list or array in Python.

Practice

1. What does the np.sort() function do when applied to a NumPy array?

easy

A. It returns a new array with elements sorted in ascending order.

B. It changes the original array to sorted order in place.

C. It reverses the order of elements in the array.

D. It removes duplicate elements from the array.

Solution

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

   The np.sort() function returns a new sorted array and does not modify the original array.

2. Step 2: Identify the sorting order

   By default, np.sort() sorts elements in ascending order.

3. Final Answer:

   It returns a new array with elements sorted in ascending order. -> Option A

4. Quick Check:

   np.sort() returns sorted copy [OK]

Hint: np.sort() returns a new sorted array, original stays same [OK]

Common Mistakes:

• Thinking np.sort() sorts in place
• Confusing sorting with reversing
• Assuming it removes duplicates

2. Which of the following is the correct syntax to sort a 1D NumPy array named arr using np.sort()?

easy

A. np.sort(arr)

B. arr.sort()

C. np.sort(arr, axis=1)

D. arr.sorted()

Solution

1. Step 1: Recall np.sort() syntax

   The correct way to sort an array using the function is np.sort(arr).

2. Step 2: Check other options

   arr.sort() sorts in place but is a method, not np.sort(). np.sort(arr, axis=1) is invalid for 1D arrays. arr.sorted() is not a valid method.

3. Final Answer:

   np.sort(arr) -> Option A

4. Quick Check:

   np.sort(arr) is correct syntax [OK]

Hint: Use np.sort(array) to get sorted copy [OK]

Common Mistakes:

• Using arr.sorted() which does not exist
• Using axis=1 on 1D array
• Confusing np.sort() with arr.sort() method

3. What is the output of the following code?

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

medium

A. [[1 3 2]
[4 6 5]]

B. [[3 1 2]
[6 4 5]]

C. [[1 2 3]
[4 5 6]]

D. [[3 6]
[1 4]
[2 5]]

Solution

1. Step 1: Understand sorting along axis=1

   Sorting with axis=1 sorts each row independently in ascending order.

2. Step 2: Sort each row

   First row [3,1,2] sorted is [1,2,3]. Second row [6,4,5] sorted is [4,5,6].

3. Final Answer:

   [[1 2 3] [4 5 6]] -> Option C

4. Quick Check:

   Row-wise sort = [[1 2 3], [4 5 6]] [OK]

Hint: axis=1 sorts each row separately [OK]

Common Mistakes:

• Sorting columns instead of rows
• Expecting original array unchanged in print
• Confusing axis parameter meaning

4. The following code throws an error. What is the cause?

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

medium

A. np.sort() cannot sort integer arrays.

B. Missing parentheses in np.sort call.

C. The array must be converted to a list first.

D. Axis 1 does not exist for 1D arrays.

Solution

1. Step 1: Check array dimensions

   The array arr is 1D, so it only has axis 0.

2. Step 2: Understand axis parameter

   Using axis=1 on a 1D array causes an error because axis 1 does not exist.

3. Final Answer:

   Axis 1 does not exist for 1D arrays. -> Option D

4. Quick Check:

   1D array has only axis 0 [OK]

Hint: 1D arrays only have axis=0, axis=1 causes error [OK]

Common Mistakes:

• Assuming axis=1 works on 1D arrays
• Thinking np.sort can't handle integers
• Believing array must be list to sort

5. Given a 2D NumPy array data = np.array([[7, 2, 9], [4, 5, 1], [8, 3, 6]]), how can you sort the entire array as if it were a flat list, then reshape it back to the original shape?

hard

A. np.sort(data, axis=0).reshape(data.shape)

B. np.sort(data, axis=None).reshape(data.shape)

C. data.sort(axis=1).reshape(data.shape)

D. np.sort(data).reshape(data.shape)

Solution

1. Step 1: Flatten and sort the entire array

   Using axis=None in np.sort() sorts the array as a flat 1D array.

2. Step 2: Reshape sorted array back to original shape

   Use .reshape(data.shape) to restore the 2D shape after sorting.

3. Final Answer:

   np.sort(data, axis=None).reshape(data.shape) -> Option B

4. Quick Check:

   axis=None sorts flat, reshape restores shape [OK]

Hint: Use axis=None to sort flat, then reshape [OK]

Common Mistakes:

• Sorting only rows or columns instead of flat
• Using data.sort() which sorts in place
• Omitting reshape after sorting flat