All You Need to Know to get started with NumPy

Everything you need to know to get started with NumPy. The world runs on data and everyone should know how to work with it. It's hard to imagine a modern, tech-literate business that doesn't use data analysis, data science, machine learning, or artificial intelligence in some form. NumPy is at the core of all of those fields. While it's impossible to know exactly how many people are learning to analyze and work with data, it's a pretty safe assumption that tens of thousands (if not millions) of people need to understand NumPy and how to use it. Because of that, I've spent the last three months putting together what I hope is the best introductory guide to NumPy yet! If there's anything you want to see included in this tutorial, please leave a note in the comments or reach out any time! NumPy (Numerical Python) is an open-source Python library that's used in almost every field of science and engineering. NumPy users include everyone from beginning coders to experienced researchers doing state-of-the-art scientific and industrial research and development. The NumPy API is used extensively in Pandas, SciPy, Matplotlib, scikit-learn, scikit-image and most other data science and scientific Python packages. The NumPy library contains multidimensional array and matrix data structures (you'll find more information about this in later sections). It provides ndarray, a homogeneous n-dimensional array object, with methods to efficiently operate on it.