Use Functional Programming In Python

You can replace the above for loop in Python in the following manner. Functions as first class objects, which means that you should be able to apply all the constructs of using data, to functions as well.

Objects have internal state and support methods that query or modify this internal state in some way. C++ and Python are languages that support object-oriented programming, but don’t force the use of object-oriented features. You have no guarantee that any of the code you rely on (pip modules or your collaborators’ code) is functional and pure. A popular Python feature that appears prominently in Functional Programming Languages is list comprehensions. Like the map and filter functions, list comprehensions allow us to modify data in a concise, expressive way. Take for example creating a List with 3 items and storing it in a variable my_list.

Functional Programming in Python

What is the meaning of using functions as first class objects. Every map and filter expression can be expressed as a list comprehension.

The term “first class” is closely related to “higher order”, which is more of a mathematical term to denote that an individual function operates on functions. As you can see, the second time we call last, passing the same immutable value t, we receive the cached result, rather than recomputing the result all over. Recursion is a method of breaking a problem into subproblems which are essentially of the same type as the original problem. Loops come into the picture when you want to loop over a collection of objects and apply some kind of logic or function. Recall that Higher Order Functions either accept a function as an argument or return a function for further processing. Functional languages are declarative languages, they tell the computer what result they want. This is usually contrasted with imperative languages that tell the computer what steps to take to solve a problem.

What Is Functional Programming?

As you work on a functional-style program, you’ll write a number of functions with varying inputs and outputs. Some of these functions will be unavoidably specialized to a particular application, but others will be useful in a wide variety of programs.

• It even has support for many common functional features like Lambda Expressions and the map and filter functions.
• A pure function is a function whose output value follows solely from its input values, without any observable side effects.
• Next, it’s time to delve into functional programming in Python.
• This makes unit testing a lot easier – you avoid having to do as much set-up, tear-down, and mocking, and the tests are more likely to be predictable regardless of the order they run in.
• Other languages are multi-paradigm languages that support several different approaches.

For space/memory efficiency reasons, these functions return an iterator instead of a list. Functional Programming is a popular programming paradigm closely linked to computer science’s mathematical foundations. While there is no strict definition of what constitutes a functional language, we consider them to be languages that use functions to transform data. Mertz describes ways to avoid Python’s imperative-style flow control, the nuances of callable functions, how to work lazily with iterators, and the use of higher-order functions. He also lists several third-party Python libraries useful for functional programming. Once a generator’s code was invoked to create an iterator, there was no way to pass any new information into the function when its execution is resumed. You could hack together this ability by making the generator look at a global variable or by passing in some mutable object that callers then modify, but these approaches are messy.

Map, Filter, and Reduce

Pure functional languages don’t support control flow such as for and while loops. Please note that this does not actually run the functions but returns a lazy map object.

Avoiding side effects means not using data structures that get updated as a program runs; every function’s output must only depend on its input. Any realistic, large and complex system has occasions when it will have to fail and retry.

Lambda Expressions (Single Expression Functions)

Note that lambda functions must be one-liners and must not contain a return statement written by the programmer. Like many intermediate or advanced Python techniques, this is very powerful and often confusing. The name of the function you called will be different from the name in the stack traces, unless you use the functools.wraps decorator to annotate. I have seen decorators do very complicated or important things, like parse values out of json blobs or handle authentication. I’ve also seen multiple layers of decorators on the same function or method definition, which requires knowing the decorator application order to understand.

The base case can be considered as a condition that tells the compiler or interpreter to exit from the function. Guido actually advocated for eliminating all three of reduce(), map(), and filter() from Python. As it happens, the previously mentioned list comprehension covers the functionality provided by all these functions and much more. You can learn more by reading When to Use a List Comprehension in Python. Now you know how to define an anonymous function with lambda.

Pure functions also make it easier to write parallel/concurrent applications. With the building blocks of pure functions and immutable values, programmers can create logical structures. Iteration can be replaced with recursion, because it is the functional way to cause the same action to occur multiple times. The function calls itself, with new inputs, until the parameters meet a termination condition.

We’ll first look at lambda expressions to better utilize these built-in functions. The description at the beginning of this section states that reduce() combines elements to produce a single result. But that result can be a composite object like a list or a tuple. For that reason, reduce() is a very generalized higher-order function from which many other functions can be implemented. Map(str, ) returns an iterator that yields the list of string objects [“1”, “2”, “3”, “4”, “5”], and you can then successfully pass that list to .join().

Concepts of Functional Programming

With Python, it’s easy to write code in a functional style, which may provide the best solution for the task at hand. They may be stored in data structures, passed as arguments, or used in control structures. A programming language is said to support first-class functions if it treats functions as first-class objects. Many programming languages support some degree of functional programming. In some languages, virtually all code follows the functional paradigm. Python, by contrast, does support functional programming but contains features of other programming models as well. Thus, we can assign them to variables, pass them as arguments to other functions, store them in other data structures such as dictionaries and use them as return values for other functions.

• Generator expressions return an iterator that computes the values as necessary, not needing to materialize all the values at once.
• What if you could later resume the function where it left off?
• But, what if the local variables weren’t thrown away on exiting a function?
• Some languages recycle the current execution frame whenever a tail call is made.
• You can replace the above for loop in Python in the following manner.
• For example, in the following example you can pass the int function as a parameter to map function.

Debugging is simplified because functions are generally small and clearly specified. When a program doesn’t work, each function is an interface point where you can check that the data are correct. You can look at the intermediate inputs and outputs to quickly isolate the function that’s responsible for a bug. For a long time researchers have been interested in finding ways to mathematically prove programs correct. Note that the above just gets you started, although thoroughly, with functional programming in Python. Let’s say you want to define a fibonacci function, fib, which takes a single argument, n, and we have to return the nth fibonacci number.

A possible way of defining such a function is with the use of a helper function that tracks the previous two terms of the fibonacci sequence . Some languages recycle the current execution frame whenever a tail call is made. Guido van Rossum tried to explain the reason why python doesn’t have tail call optimization. This makes the function very easy to use, and allows you to reason about the function locally. If you take this idea and apply it to the sequential execution of functions, you get the following construct. In your Python interpreter, enter import this and you will see “The Zen of Python”. Python generally encourages code to be written in the most obvious way possible.