Fundamentals 3 min read

Using Python to Compute the Shaded Area of a Sixth‑Grade Geometry Problem

The article demonstrates how to solve a sixth‑grade geometry problem by programmatically drawing the figure with Python, randomly sampling points inside a rectangle, and estimating the shaded region's area through Monte‑Carlo simulation, achieving results close to the exact answer.

Python Programming Learning Circle
Python Programming Learning Circle
Python Programming Learning Circle
Using Python to Compute the Shaded Area of a Sixth‑Grade Geometry Problem

The author encountered a sixth‑grade mathematics question that asked for the area of a shaded region, found it challenging to solve analytically, and therefore turned to Python to compute the area.

Idea Introduction : First, the picture is reproduced on a coordinate plane using Python, defining the slanted line and the semicircle as functions. Then, the rectangle is uniformly filled with a large number of points (conceptually "beans"); the proportion of points that fall inside the shaded region is used to estimate its area.

Source Code Design :

1. Code for drawing the figure.

2. Code for the calculation, where the parameter * represents the total number of sampled points, side indicates whether points on the border are counted (theoretically should be True), and t controls the number of iterations – larger t yields longer runtime but higher precision.

Conclusion : With t = 100 the estimated shaded area is 1.268; with t = 1000 it becomes 1.253, which is very close to the correct answer of 1.252. The approach shows that any irregular shape whose boundary can be expressed as a function can be handled similarly. Three solution methods are presented, the third being particularly intricate.

algorithmgeometrymonte carloarea calculation
Python Programming Learning Circle
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Python Programming Learning Circle

A global community of Chinese Python developers offering technical articles, columns, original video tutorials, and problem sets. Topics include web full‑stack development, web scraping, data analysis, natural language processing, image processing, machine learning, automated testing, DevOps automation, and big data.

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