In this tutorial, we will look at how to get the mathematical constant pi in python using the `numpy`

and `math`

libraries.

## How to get the value of pi in python?

You can use the numpy library’s `numpy.pi`

or the `math`

standard library’s `math.pi`

to get the value of the mathematical constant pi in python. The following is the syntax:

# using numpy import numpy as np # value of pi np.py # using math import math # value of pi math.pi

Let’s look at each of the two methods with the help of examples.

## Using numpy.pi

Numpy is a scientific computation library in python and has values for a number of numerical constants including pi. You can use `numpy.pi`

or `np.pi`

depending on how you import the library to get the value of pi. Let’s print out the value of pi obtained from numpy.

import numpy as np # get pi constant value print(np.pi)

Output:

3.141592653589793

Here’s a list of all the constants that are available in numpy – Numpy constants.

## Using math.pi

You can also use the standard library `math`

in python to get the value of pi. Let’s print out the value of pi obtained from `math.pi`

import math # get pi constant value print(math.pi)

Output:

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3.141592653589793

We get the value of pi as a floating-point value.

Here’s a list of all constants available in the math library – math constants.

`numpy.pi`

vs `math.pi`

The values obtained from `numpy.pi`

and `math.pi`

appear to be the same. Let’s check if they are exactly equal.

import math import numpy as np # check if value of pi from both the libraries is equal if math.pi == np.pi: print("Yes") else: print("No")

Output:

Yes

We see that both the values are equal.

Now, should you prefer one over the other?

Well, an argument can be made that since `math`

is a standard library in python, it makes sense to prefer it since it decreases the dependency on additional libraries. However, if you’re already using `numpy`

for other operations/tasks in your code then it’s totally fine to use `numpy.pi`

With this, we come to the end of this tutorial. The code examples and results presented in this tutorial have been implemented in a Jupyter Notebook with a python (version 3.8.3) kernel having numpy version 1.18.5

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