# Get value of pi in python with np.pi and math.pi

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

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:

Introductory ⭐

Intermediate ⭐⭐⭐

🔎 Find Data Science Programs 👨‍💻 111,889 already enrolled

Disclaimer: Data Science Parichay is reader supported. When you purchase a course through a link on this site, we may earn a small commission at no additional cost to you. Earned commissions help support this website and its team of writers.

```# 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.

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.

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:

`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.

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

• 