Python function type system

Some functional languages (such as Haskell and Scala) are statically compiled, using type declarations to determine the types of functions and their arguments. To achieve Python-like flexibility, these languages have developed their own type matching rules, allowing for abstract functions that operate on a set of similar classes.

In object-oriented Python, class inheritance is often used instead of complex function type matching, leveraging Python’s name matching rules to associate operators with the correct methods.

Because Python is so flexible, it doesn’t require the type matching rules of compiled functional languages. It can even be said that complex type matching rules are merely a workaround for statically compiled languages to allow for writing abstract functions. Python is a dynamic language and doesn’t require such workarounds.

Python 3 introduced type hints, allowing you to use tools like mypy to analyze type matching and identify potential problems. Using type hints is preferable to checking that the parameter a is an integer value through type testing (e.g., `assert isinstance(a, int)`). This is because `assert` statements increase runtime resource overhead. Running `mypy` to verify hints is part of a regular quality assurance process, often used in conjunction with unit testing tools like `pylint` to ensure software correctness.

Functional Programming

At this point, it’s easy to see that Python possesses most of the characteristics of functional programming. In fact, these functional programming techniques are even widely used in object-oriented programming.

As a special case, fluent application programming interfaces (APIs) embody the characteristics of functional programming. By taking the time to add return self() to each method in a class, you can write code like this:

some_object.foo().bar().yet_more()

Or you can write several closely related functions like this:

yet_more(bar(foo(some_object)))

This essentially replaces the traditional object-oriented postfix syntax with a more functional prefix syntax. Both notations can be used in Python, but the prefix form is primarily used for methods with special meaning. For example, the len() function is actually implemented by the class.__len__() method.

Of course, these class implementations may still contain stateful objects, but even so, this slight shift in perspective can help us flexibly apply functional methods in our programming practices and write concise and clear code.

Again, using functional programming does not mean that imperative programming has some serious flaws or that functional programming offers advanced technologies. The essence of functional programming lies in a change of perspective, and this change often leads to better coding.

Advanced Concepts

We’ll discuss some advanced concepts related to functional programming that are essential in pure functional languages. Since Python isn’t a pure functional language, but rather a hybrid functional programming approach, we won’t delve into these concepts in depth.

This discussion is particularly useful for Python beginners familiar with functional languages like Haskell, as Python handles these issues differently from other languages. Often, we’ll apply imperative programming techniques to problems, rather than being limited to functional approaches.

These concepts are outlined below.

• Referential Transparency: In compiled languages, ensuring multiple paths to the same object is essential for lazy evaluation and various optimizations. This isn’t particularly important in Python, which lacks compile-time optimizations.

• Currying: The type system uses currying to transform multi-argument functions into single-argument functions.

• Monad: Flexible chaining of operations to form a pure function. In some cases, imperative Python code can achieve the same effect. Alternatively, you can construct a monad using the Python library PyMonad.