Python supports multiple programming paradigms created to help programmers write better programs faster. In short, paradigms entail patterns, practices, or techniques to help programmers code more efficiently.
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Generally, there are three common paradigms in Python, imperative, object-oriented, and functional programming. Each provides a set of bylaws to help code better. In this article, we will discuss functional programming in Python and when and how to use it.
Functional programming in Python revolves around immutable data types and functions, acting as bound variables or parameters, free variables, and local variables. By default, they are stateless and cannot disrupt the output. Their output relies solely upon the input.
Moreover, using functional programming in Python requires building familiarity with immutable data types, as they help resolve issues relating to race conditions in Python.
In addition, functions further break into two types, pure and impure functions. The former doesn’t have side effects, while the latter has. Side effects usually occur by running external programs. Undoubtedly, they have their uses but can also break havoc.
Using functional programming in Python helps break complex problems into smaller bite-sized pieces and implement a fix, as the resulting programs are more modular in nature. As it goes, modular programs are relatively easy to debug and test in Python.
A function must have the ability to take another as an argument and also return another to its caller to use functional programming. Functions in Python can do both.
As it goes, everything in Python is an object, including functions. However, the fact doesn’t imply they are no special. In fact, Python treats them like first-class citizens, and they enjoy the same attributes as numbers and strings. In short, functions can do anything a string or integer does.
For instance, Python lets programmers link a function to a variable and use it as a function.
1 >>> def func(): 2 ... print("I am function func()!") 3 ... 4 5 >>> func() 6 I am function func()! 7 8 >>> another_name = func 9 >>> another_name() 10 I am function func()!
In addition, Python also displays a function with print(), adds it to a list as an element, and takes it as a dictionary key.
>>> def func(): ... print("I am function func()!") ... >>> print("cat", func, 42) cat <function func at 0x7f81b4d29bf8> 42 >>> objects = ["cat", func, 42] >>> objects[1] <function func at 0x7f81b4d29bf8> >>> objects[1]() I am function func()! >>> d = {"cat": 1, func: 2, 42: 3} >>> d[func] 2
The above example illustrates how Python treats func() in much the same way as values like cat and 42.
Passing a function as an argument to another functions enables programmers to bring into play function composition. The below syntax shows how to use functional programming in Python to perform function composition.
1 >>> def inner(): 2 ... print("I am function inner()!") 3 ... 4 5 >>> def outer(function): 6 ... function() 7 ... 8 9 >>> outer(inner) 10 I am function inner()!
As a first-class citizen, a function in Python can also designate another function as its new return value. A feature that helps write codes more efficiently.
1 >>> def outer(): 2 ... def inner(): 3 ... print(“I am function inner()!”) 4 ... 5 ... # Function outer() returns function inner() 6 ... return inner 7 ... 8 9 >>> function = outer() 10 >>> function 11 <function outer.<locals>.inner at 0x7f18bc85faf0> 12 >>> function() 13 I am function inner()! 14 15 >>> outer()() 16 I am function inner()!
The above example illustrates how Python can call a specific function indirectly through another or via a return value without in-between assignments.
Functional programming in Python helps resolve complex problems in a code efficiently by enhancing modularity. Naturally, Python works well with functions, and learning to use this high compatibility via functional programming in Python only helps write better programs.
Also Read: Understanding Type Annotation In Python
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