Summary
In this chapter, we learned how to manipulate NumPy arrays and how to write fast mathematical expressions using array broadcasting. This knowledge will help you write more concise, expressive code and, at the same time, obtain substantial performance gains. We also introduced the numexpr library to further speed up NumPy calculations with minimal effort.
pandas implements efficient data structures that are useful when analyzing large datasets. In particular, pandas shines when the data is indexed by non-integer keys and provides very fast hashing algorithms.
NumPy and pandas work well when handling large, homogenous inputs, but they are not suitable when the expressions become complex and the operations cannot be expressed using the tools provided by these libraries. xarray comes in handy as an alternative option where we need to work with labeled, multidimensional data.
In combination, the three libraries offer Python users powerful APIs and flexible functionalities...
