In this chapter, we found out a heap about the NumPy basics--data types and arrays. Arrays have various properties that describe them. You learned that one of these properties is the data type, which, in NumPy, is represented by a full-fledged object.
NumPy arrays can be sliced and indexed in an effective way, compared to standard Python lists. NumPy arrays have the extra ability to work with multiple dimensions.
The shape of an array can be modified in multiple ways, such as stacking, resizing, reshaping, and splitting. A large number of convenience functions for shape manipulation were presented in this chapter.
Having picked up the fundamentals, it's time to proceed to data analysis with the commonly used functions in Chapter 4, Statistics and Linear Algebra. This includes the usage of staple statistical and numerical functions.
The reader is encouraged to read the books mentioned in the References section for exploring NumPy in further detail and depth.