Functional Programming. Part II:
The Essence of Functional Programming: Practical Implementation
Part I has discussed the functional programming paradigm in general, with its concepts, differences from the declarative paradigm, and advantages and disadvantages. Though all of this is hard to implement in one language, it is possible to write a program in any programming language using functional style. Here we will observe the practical side of functional programming and try to implement its concepts in Python in simple examples.
As the simplest example, I would like to present the one below. Here we find out the length of each value in the list. This is done by using map and list functions instead of using loops.
This is also another small example of functional programming in Python using the reduce and lambda functions. As it was described in Part I, the lambda function has no name, and it serves as a template to be used several times. Here is an example of such use:
Here we observe the pure function doubleNumber(). It is a pure function because the input is specified, as well as the output value, and it does not depend on any outside value, thus it will not cause any side-effects.
This is an example of higher order functions, as here the method first() returns another function that can be used later in the code.
[‘JaneJane’, ‘PaulPaul’, ‘VinVin’]
If we write this piece of code imperatively, it would look like the following:
Here we see a loop, which is the first enemy of functional programming.
The theoretical aspect is crucial for any discipline, but it can’t exist separately from practice. In Part I of our guide on functional programming, one of our experts provides some theoretical knowledge: languages, concepts, and pros and cons. In Part II, you’ve seen functional programming examples in Python.
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