Summary
In this chapter, you learned a lot about NumPy fundamentals: data types and arrays. Arrays have several attributes describing them. You learned that one of these attributes is the data type, which, in NumPy, is represented by a fully-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, Getting Familiar with Commonly Used Functions, which includes basic statistical and mathematical functions.
