# How to use pi in Python?

In this guide, we learn how to use pi in Python. We also look at the pros and cons of all the methods that can be used.

## Table of Contents

- Pi in Python
- Using math.pi()
- Using numpy.pi()
- Closing thoughts

## Pi in Python:

Pi (π) is a mathematical constant that is defined as the ratio of a circle's circumference to its diameter. Given Python is extensively used in the field of mathematics, it extends support to multiple mathematical constants including pi.

There are multiple methods to use the Pi constant in Python. However, most of these methods would involve importing a module. In this tutorial, we look at the most common and well-documented methods.

## Using math.pi

This method belongs to the mathematical module in Python. This module extends support to numerous mathematical constants and functions. A few commonly used constants are math.pi, math.e and math.tau.

Let us look at the syntax of math.pi()```
math.pi
```

This method returns a float value that is equivalent to the pi constant (3.141592653589793).

### Code and Explanation:

The math module needs to be imported in order to use the pi methods.

```
# Importing math Library
import math
# Print pi in Python using the math module
print (math.pi)
```

## Using numpy.pi:

The numpy.pi is a method that is very similar to the previous method. However, rather than use the math modules we are using numpy. Numpy or Numerical Python is a very powerful package in Python that is commonly used by data professionals.

We use the pi methods within this package to use pi in Python. The syntax is quite similar to the earlier method.```
numpy.pi
```

Similarly, this method also returns a float value equivalent to the pi constant.

### Code and Explanation:

Since we are using numpy, we have to import it first.```
# Importing Numpy Library
import numpy
# Print pi in Python using the math module
print (numpy.pi)
```

## Closing thoughts - Pi in Python:

Both these methods work the same way. However, given it relates to mathematics the module you chose to work with would depend on your use case. If you are looking to work on large sets of data with numerous calculations I would recommend using numpy. If not, the math module would be a good choice.

There are other modules that help you achieve this as well. However, these methods are the most commonly used. In case you are curious to know more you can take a look at the scipy.pi methods from the scipy module.