Sorting along axes in NumPy - Mini Project: Build & Apply

Sorting Along Axes with NumPy

📖 Scenario: Imagine you work in a small bakery. You have a list of daily sales for different types of bread over a week. You want to organize this data to see which days had the lowest to highest sales for each bread type and also which bread types sold the least to most on each day.

🎯 Goal: You will create a 2D NumPy array representing sales data, then sort the sales along rows and columns to better understand sales patterns.

📋 What You'll Learn

Create a 2D NumPy array called sales with exact values

Create a variable called axis_row set to 1

Create a variable called axis_col set to 0

Sort the sales array along rows using axis_row

Sort the sales array along columns using axis_col

Print both sorted arrays exactly as specified

💡 Why This Matters

🌍 Real World

Sorting sales data helps businesses quickly identify trends, like which products sell best on certain days or which days have the highest sales.

💼 Career

Data scientists and analysts often sort data along different axes to prepare it for reports, visualizations, or further analysis.

1

Create the sales data array

Create a 2D NumPy array called sales with these exact values: [[20, 35, 30], [25, 32, 34], [22, 30, 35]]

NumPy

import numpy as np
# Create the 2D array called sales with the given values
# Your code here

Need a hint?

Use np.array() to create the 2D array with the exact nested list.

2

Set axis variables for sorting

Create a variable called axis_row and set it to 1. Then create a variable called axis_col and set it to 0.

NumPy

import numpy as np
sales = np.array([[20, 35, 30], [25, 32, 34], [22, 30, 35]])
# Create axis_row and axis_col variables
# Your code here

Need a hint?

Remember, axis=1 means sorting along rows, and axis=0 means sorting along columns.

3

Sort the sales array along rows and columns

Use np.sort() to sort the sales array along rows using axis_row and store it in sorted_rows. Then sort the sales array along columns using axis_col and store it in sorted_cols.

NumPy

import numpy as np
sales = np.array([[20, 35, 30], [25, 32, 34], [22, 30, 35]])
axis_row = 1
axis_col = 0
# Sort sales along rows and columns
# Your code here

Need a hint?

Use np.sort() with the axis parameter set to the axis variables.

4

Print the sorted arrays

Print the sorted_rows array first, then print the sorted_cols array.

NumPy

import numpy as np
sales = np.array([[20, 35, 30], [25, 32, 34], [22, 30, 35]])
axis_row = 1
axis_col = 0
sorted_rows = np.sort(sales, axis=axis_row)
sorted_cols = np.sort(sales, axis=axis_col)
# Print sorted_rows and sorted_cols
# Your code here

Need a hint?

Use two print() statements, one for each sorted array.