Introduction to NumPy

In this chapter, we’ll introduce to the basics of using NumPy (Numerical Python) which should be sufficient for following along the rest of the book. While it is not necessary to have deep understanding of NumPy for many data analytical applications, becoming proficient in array-oriented programming and thinking is a key step along the way to becoming a scientific Python guru. A fundamental requirement in data scienceData science is to manage and process large numerical arrays. The NumPy package ensures the efficient storage and processing of numerical arrays. Efficient storage and manipulation of numerical arrays is absolutely fundamental to the process of doing data scienceData science. Python has specialized tools for handling such numerical arrays: the NumPy package and the Pandas package. NumPy, short for Numerical Python, is one of the most important foundational packages for numerical computation in Python. Most computational packages providing scientific functionality use NumPy’s array objects for data exchange.