**NumPy** is the fundamental Python library for numerical computing. Its most important type is an **array type** called `ndarray`

. NumPy offers a lot of array creation routines for different circumstances. ** arange()** is one such function based on

**numerical ranges**. It’s often referred to as

`np.arange()`

because `np`

is a widely used abbreviation for NumPy.Creating NumPy arrays is important when you’re working with other Python libraries that rely on them, like SciPy, Pandas, Matplotlib, scikit-learn, and more. NumPy is suitable for creating and working with arrays because it offers useful routines, enables performance boosts, and allows you to write concise code.

**By the end of this course, you’ll know:**

- What
`np.arange()`

is - How to use
`np.arange()`

- How
`np.arange()`

compares to the Python built-in class`range`

- Which routines are similar to
`np.arange()`

Let’s see ** np.arange()** in action!