Welcome to this lesson on understanding Numpy arrays! Today, we will be focusing on numerical computations with Numpy, specifically around the creation, manipulation, and operations of Numpy arrays.

Numpy, short for Numerical Python, is an essential library for performing numerical computations in Python. It has support for arrays (like lists in Python, but can store items of the same type), multidimensional arrays, matrices, and a large collection of high-level mathematical functions.

In the world of data manipulation using Python, understanding and being able to use Numpy arrays allows us to efficiently manage numerical data.

The most common way to create a Numpy array is by using the numpy.array() function. You can pass any sequence-like object into this function, and it will be converted into an array. Let's convert a regular Python list into a Numpy array: