np.sort() for sorting arrays in NumPy - Mini Project: Build & Apply

Sorting Arrays with np.sort()

📖 Scenario: You work in a small shop that tracks daily sales numbers. You want to organize the sales data from smallest to largest to see trends easily.

🎯 Goal: Learn how to use np.sort() to sort a list of daily sales numbers in ascending order.

📋 What You'll Learn

Create a NumPy array with exact daily sales numbers

Use a variable to hold the sorted array

Use np.sort() to sort the array

Print the sorted array

💡 Why This Matters

🌍 Real World

Sorting sales data helps businesses quickly understand trends and make decisions.

💼 Career

Data scientists often sort data to prepare it for analysis and visualization.

1

Create the sales data array

Create a NumPy array called sales with these exact daily sales numbers: 250, 100, 300, 150, 200.

NumPy

import numpy as np
# Create the sales array with the exact numbers
# Your code here

Hint

Use np.array() and put the numbers inside a list.

2

Prepare a variable for sorted sales

Create a variable called sorted_sales and set it to None for now.

NumPy

import numpy as np
sales = np.array([250, 100, 300, 150, 200])
# Create sorted_sales variable and set it to None
# Your code here

Hint

Just write sorted_sales = None to create the variable.

3

Sort the sales array using np.sort()

Use np.sort() to sort the sales array and assign the result to sorted_sales.

NumPy

import numpy as np
sales = np.array([250, 100, 300, 150, 200])
sorted_sales = None
# Sort sales using np.sort() and assign to sorted_sales
# Your code here

Hint

Call np.sort(sales) and save it in sorted_sales.

4

Print the sorted sales array

Print the sorted_sales array to see the sales numbers sorted from smallest to largest.

NumPy

import numpy as np
sales = np.array([250, 100, 300, 150, 200])
sorted_sales = np.sort(sales)
# Print the sorted_sales array
# Your code here

Hint

Use print(sorted_sales) to display the sorted array.

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