We learned a lot in this chapter about the NumPy fundamentals: data types and arrays. Arrays have several attributes describing them. We learned that one of these attributes is the data type which, in NumPy, is represented by a full-fledged object.

NumPy arrays can be sliced and indexed in an efficient manner, just as in the case of Python lists. NumPy arrays have the added ability of working with multiple dimensions.

The shape of an array can be manipulated in many ways, such as stacking, resizing, reshaping, and splitting. A great number of convenience functions for shape manipulation were demonstrated in this chapter.

Having learned about the basics, it's time to move on to data analysis with commonly used functions in Chapter 3, Basic Data Analysis with NumPy. This includes the usage of basic statistical and mathematical functions.