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Estimating Pi with Python

π is one of the fundamental constants in mathematics. It is an irrational number and it's value has been calculated up to an extremely large number of digits. In most cases a value of 3.14 will suffice as an approximation but there are many ways in which it can be approximated to a higher degree of accuracy. A particularly fun and simple example can be completed with Python in approximately 15 lines of code.

For this little project I am going to use numpy and I begin by importing this Python module as np,

import numpy as np

If we considering a quarter circle with unit radius we can use N uniform randomly generated points between 0 and 1 to estimate it's area. We can generate our circle for plotting later using the following code,

x = np.linspace(0, 1, 100)
y = np.sqrt(1 - x**2)

Our N randomly generated points are produced like so,

r_x = []
r_y = []
for i in range(N):
    r_x.append(np.random.uniform(0, 1))
    r_y.append(np.random.uniform(0, 1))

Two examples of this are shown below for N = 100 and N = 10000 randomly generated points. The orange line shows the parameter of  our quarter circle.


By summing up the number of points inside the quarter circle we can approximate it's area and it is clear that increasing the value of N improves the accuracy of our estimate. We perform this sum by the following,

counts_inside = 0
counts_outside = 0
for i in range(len(r_x)):
    distance = r_x[i]**2 + r_y[i]**2
    if distance <= 1:
        counts_inside += 1
    else:
        counts_outside +=1

Where distance delineates the radius of a circle drawn through each random point centered on the origin. If this distance exceeds 1 then the point is outside our quarter circle.

Now we note that the area of our quarter circle is given by π/4 since our radius is 1. Consequently we can evaluate π using the following formula,

$\frac{A_{quarter~~circle}}{A_{unit~~square}} = \frac{counts\_inside}{counts\_inside + counts\_outside}$

which becomes,

$\pi = 4 \frac{counts\_inside}{counts\_inside + counts\_outside}$

and in Python this translates too,

pi_estimate = 4*(counts_inside/(counts_outside+counts_inside))

As N increases the estimate of the area of both the unit square and the quarter circle become more accurate and consequently so does the estimate of $\pi$. This is shown in the left hand figure below where the blue line tracks the estimate of $\pi$ as N increases. The black line is numpy's stored value of $\pi$ which is commonly used. The right hand figure shows the ratio of the numpy value and the estimate calculated using the quarter circle which tends to 1 with increasing N. For $N = 10^7$ this method approximates $\pi$ to be 3.1414856 which to 4 decimal places matches the accepted value!
The full code including that used to produce the figures can be found at https://github.com/htjb/Blog/tree/master/Estimating_Pi_with_Python.
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