> For the complete documentation index, see [llms.txt](https://kks32-courses.gitbook.io/data-analytics/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://kks32-courses.gitbook.io/data-analytics/spark/rdd/exercise-compute-pi.md).

# Exercise: Compute Pi

Spark can be used for compute-intensive tasks. This code estimates π by "throwing darts" at a circle. We pick random points in the unit square ((0, 0) to (1,1)) and see how many fall in the unit circle. The fraction should be π / 4, so we use this to get our estimate.

* Write a function to generate random coordinates and return `True` if the point is within the circle `x^2 + y^2 < 1`.
* Generate `n` samples, and use a filter to `count` the number of points within the circle.
* Pi is `4.0 * count / n`

> [Exercise: Compute Pi Jupyter notebook](https://nbviewer.jupyter.org/urls/raw.githubusercontent.com/kks32-courses/data-analytics/master/spark/rdd/pi-exercise.ipynb)
>
> [Solution for Pi Jupyter notebooks](https://nbviewer.jupyter.org/urls/raw.githubusercontent.com/kks32-courses/data-analytics/master/spark/rdd/pi.ipynb)