Functions, Iterators, and Generators
The core of functional programming is the use of pure functions to map values from the input domain to the output range. A pure function has no side effects, a relatively easy threshold for us to achieve in Python.
Avoiding side effects can lead to reducing any dependence on variable assignment to maintain the state of our computations. We can't purge the assignment statement from the Python language, but we can reduce our dependence on stateful objects. This means choosing among the available Python built-in functions and data structures to select those that don't require stateful operations.
This chapter will present several Python features from a functional viewpoint, as follows:
- Pure functions, free of side effects
- Functions as objects that can be passed as arguments or returned as results
- The use of Python strings using object-oriented suffix notation and prefix notation
- Using tuples and named tuples as a way to create stateless objects
- Using iterable collections as our primary design tool for functional programming
We'll look at generators and generator expressions, since these are ways to work with collections of objects. As we noted in Chapter 2, Introducing Essential Functional Concepts, there are some boundary issues when trying to replace all generator expressions with recursions. Python imposes a recursion limit, and doesn't automatically handle Tail Call Optimization (TCO): we must optimize recursions manually using a generator expression.
We'll write generator expressions that will perform the following tasks:
- Conversions
- Restructuring
- Complex calculations
We'll take a quick survey of many of the built-in Python collections, and how we can work with collections while pursuing a functional paradigm. This may change our approach to working with lists, dicts, and sets. Writing functional Python encourages us to focus on tuples and immutable collections. In the next chapter, we'll emphasize more functional ways to work with specific kinds of collections.