Summary
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 like Python lists. NumPy arrays have the added ability of working with multiple dimensions.
The shape of an array can be manipulated in many ways—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 the study of commonly-used functions in Chapter 3, Get to Terms with Commonly Used Functions. This includes basic statistical and mathematical functions.
