Functional Programming with Python (1)

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Functional Programming with Python A programming
paradigm known as functional programming avoids
state and changeable data and regards computing
as the evaluation of mathematical functions. In
other words, immutable variables and
side-effect-free code are encouraged by
functional programming (FP). It uses a
declarative programming approach. In contrast to
an imperative style, where the primary focus is
"how to solve," it places a greater emphasis on
"what to solve." You can attend the Python
online classes to learn more about Functional
Programming (FP) in Python.

• Concepts of Functional Programming
• 1. Pure Functions
• Two characteristics of pure functions are
• They have no negative consequences.
• If given the same input, they always create the
  same output. This indicates that no changeable
  state can be a dependency of the function.

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• The following causes of side effects include-
• altering data that is present outside the
  function's boundaries.
• Changing the value of arguments in the function.
• causing an error or throwing an exception.
• receiving user input or printing to the terminal.
• examining or adding to a file.
• Immutability
• A variable's value cannot be changed in
  functional programming once it has been
  initialised. This means that instead of altering
  the old list when we need to modify values in it,
  we must construct a new list with the updated
  values. The immutability of variables in FP is
  advantageous since it maintains the state as a
  program is run. Python supports immutable data
  types such as int, float, complex, text, tuple,
  frozen set, and bytes.
• Recursion
• In FP, if-else clauses and loops are avoided
  because they produce distinct results with each
  iteration. All iteration tasks in functional
  programs employ recursion instead of loops.
  Recursion is a function that repeatedly invokes
  itself up until an exit condition is satisfied.
• First-Class and higher-order functions are
  available

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• Map as a higher-order function.
• There are two arguments for the map function.
  Iterable and function are the first and second
  arguments, respectively. The passed function is
  subsequently applied to each item in the
  iterable.
• Filter as a Higher Order Function
• Based on the criteria you specify, the filter
  function makes it simple to isolate matched
  records from a broader set of data. There must
  be two arguments. Iterable and function are the
  first and second arguments, respectively. For
  each iterable element for which the filter
  function returns true, a sequence is returned.
• Reduce as a Higher Order Function
• Reduce takes a function and a sequence as inputs
  and outputs a single value that is calculated as
  follows
• The function is initially called with the first
  two elements in the sequence, and the output is
  then returned.
• Using the output from step 1 and the subsequent
  value in the sequence, the function is then
  called once more. Until all of the items in the
  sequence have been iterated, this
• process is repeated.

• Functional programming Advantages
• Pure functions always yield the same results
  they are unaffected by outside variables.
  Programs are simpler to test and debug as a
  result.
• Because they execute separately without changing
  state, parallel or concurrent programs can be
  written effectively.
• It supports the idea of lazy evaluation, where
  values are only evaluated and stored when
  necessary.
• With Pure Functions, FP enables stronger
  encapsulation than OOP.
• Since FP programs are composed entirely of pure
  functions, we can readily reuse them.
• Functional programming Disadvantages
• Recursion and immutable values could have a
  negative impact on performance.
• Sometimes writing pure functions can make the
  code harder to read.
• The state in functional programming is absent.
  Instead of altering existing things, they always
  create new ones in order to carry out their
  operations. FP Applications use a lot of memory
  as a result.
• Writing pure functions is simple the challenge
  comes in putting them together into a
  comprehensive application.

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Conclusion This article gives you a brief
overview of functional programming and should
help you understand its advantages. Python
enables us to code declaratively and
functionally. Even more, it supports a variety
of widely used functional constructs like lambda,
map(), filter(), and reduce() that may be used
to create parallelizable, compact code. Check out
the Python online training to learn more.
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