np.choose() for conditional selection in NumPy - Mini Project: Build & Apply

Using np.choose() for Conditional Selection

📖 Scenario: Imagine you are analyzing sales data for a small store. You want to categorize daily sales numbers into different labels based on their values.

🎯 Goal: You will create a NumPy array of sales numbers, define conditions to categorize these sales, use np.choose() to assign category labels, and finally display the categorized results.

📋 What You'll Learn

Create a NumPy array called sales with exact values: [150, 85, 230, 45, 120]

Create a NumPy array called conditions with three conditions for sales: less than 50, between 50 and 150 (inclusive), and greater than 150

Use np.choose() with the conditions array to assign categories: 'Low', 'Medium', and 'High'

Print the final array of category labels

💡 Why This Matters

🌍 Real World

Categorizing data based on conditions is common in sales analysis, customer segmentation, and many other fields.

💼 Career

Data scientists often use conditional selection to prepare data for analysis and reporting.

1

Create the sales data array

Create a NumPy array called sales with these exact values: [150, 85, 230, 45, 120].

NumPy

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

Need a hint?

Use np.array() to create the array with the exact numbers.

2

Define conditions for sales categories

Create a NumPy array called conditions with three conditions for the sales array: sales less than 50, sales between 50 and 150 inclusive, and sales greater than 150. Use NumPy comparison operators to create boolean arrays and combine them into a single array of integers representing the condition index for each sale.

NumPy

import numpy as np
sales = np.array([150, 85, 230, 45, 120])
# Create the conditions array with values 0, 1, or 2 based on sales ranges
# Your code here

Need a hint?

Use np.select() with a list of conditions and corresponding integer choices.

3

Use np.choose() to assign category labels

Create a list called categories with the strings 'Low', 'Medium', and 'High'. Then use np.choose() with the conditions array to create a new array called sales_categories that assigns the correct category label to each sale.

NumPy

import numpy as np
sales = np.array([150, 85, 230, 45, 120])
conditions = np.select([sales = 50) & (sales  150], [0, 1, 2])
# Create categories list and use np.choose() to assign labels
# Your code here

Need a hint?

Use np.choose() with the conditions array and the categories list.

4

Print the sales categories

Print the sales_categories array to display the category labels for each sale.

NumPy

import numpy as np
sales = np.array([150, 85, 230, 45, 120])
conditions = np.select([sales = 50) & (sales  150], [0, 1, 2])
categories = ['Low', 'Medium', 'High']
sales_categories = np.choose(conditions, categories)
# Print the sales_categories array
# Your code here

Need a hint?

Use print() to display the sales_categories array.