NumPy is a Python library used for working with arrays. It also has functions for working in domain of linear algebra, fourier transform, and matrices. NumPy was created in 2005 by Travis Oliphant.

We have created 43 tutorial pages for you to learn more about NumPy. Starting with a basic introduction and ends up with creating and plotting random data sets, and working with NumPy functions: Basic

Installation of NumPy. If you have Python and PIP already installed on a system, then installation of NumPy is very easy. Install it using this command:

SciPy is a scientific computation library that uses NumPy underneath. SciPy stands for Scientific Python. It provides more utility functions for optimization, stats and signal processing. Like NumPy, SciPy is open source so we can use it freely. SciPy was created by NumPy's creator Travis Olliphant.

In NumPy we work with arrays, and you can use the two methods from the above examples to make random arrays. Integers. The randint() method takes a size parameter where you can specify the shape of an array.

NumPy (Python) Editor. With our "Try it Yourself" editor, you can edit Python code and use the NumPy module, and view the result in your browser.

The NumPy module has a method for this. Learn about the NumPy module in our NumPy Tutorial.

Create a NumPy ndarray Object. NumPy is used to work with arrays. The array object in NumPy is called ndarray. We can create a NumPy ndarray object by using the array() function.

NumPy provides the ufuncs sin(), cos() and tan() that take values in radians and produce the corresponding sin, cos and tan values.

NumPy provides powerful tools for scientific calculations and analysis. Machine Learning: NumPy is fundamental for handling numerical data in Machine Learning applications.