Python's reduce(): From Functional to Pythonic Style – Real Python

Exploring Functional Programming in Python

Functional programming is a programming paradigm based on breaking down a problem into a set of individual functions. Ideally, every function only takes a set of input arguments and produces an output.

In functional programming, functions don’t have any internal state that affects the output that they produce for a given input. This means that anytime you call a function with the same set of input arguments, you’ll get the same result or output.

In a functional program, input data flows through a set of functions. Each function operates on its input and produces some output. Functional programming tries to avoid mutable data types and state changes as much as possible. It works with the data that flow between functions.

Other core features of functional programming include the following:

  • The use of recursion rather than loops or other structures as a primary flow control structure
  • A focus on lists or arrays processing
  • A focus on what is to be computed rather than on how to compute it
  • The use of pure functions that avoid side effects
  • The use of higher-order functions

There are several important concepts in this list. Here’s a closer look to some of them:

  • Recursion is a technique in which functions call themselves, either directly or indirectly, in order to loop. It allows a program to loop over data structures that have unknown or unpredictable lengths.
  • Pure functions are functions that have no side effects at all. In other words, they’re functions that do not update or modify any global variable, object, or data structure in the program. These functions produce an output that depends only on the input, which is closer to the concept of a mathematical function.
  • Higher-order functions are functions that operate on other functions by taking functions as arguments, returning functions, or both, as with Python decorators.

Since Python is a multi-paradigm programming language, it provides some tools that support a functional programming style:

  • Functions as first-class objects
  • Recursion capabilities
  • Anonymous functions with [lambda](https://realpython.com/python-lambda/)
  • Iterators and generators
  • Standard modules like [functools](https://docs.python.org/3/library/functools.html#module-functools) and [itertools](https://realpython.com/python-itertools/)
  • Tools like [map()](https://docs.python.org/3/library/functions.html#map)[filter()](https://docs.python.org/3/library/functions.html#filter)[reduce()](https://docs.python.org/3/library/functools.html#functools.reduce)[sum()](https://docs.python.org/3/library/functions.html#sum)[len()](https://docs.python.org/3/library/functions.html#len)[any()](https://realpython.com/any-python/)[all()](https://docs.python.org/3/library/functions.html#all)[min()](https://docs.python.org/3/library/functions.html#min)[max()](https://docs.python.org/3/library/functions.html#max), and so on

Even though Python isn’t heavily influenced by functional programming languages, back in 1993 there was a clear demand for some of the functional programming features listed above.

In response, several functional tools were added to the language. According to Guido van Rossum, they were contributed by a community member:

Python acquired lambdareduce()filter() and map(), courtesy of (I believe) a Lisp hacker who missed them and submitted working patches. (Source)

Over the years, new features such as list comprehensionsgenerator expressions, and built-in functions like sum()min()max()all(), and any() were viewed as Pythonic replacements for map()filter(), and reduce(). Guido planned to remove map()filter()reduce(), and even lambda from the language in Python 3.

Luckily, this removal didn’t take effect, mainly because the Python community didn’t want to let go of such popular features. They’re still around and still widely used among developers with a strong functional programming background.

In this tutorial, you’ll cover how to use Python’s reduce() to process iterables and reduce them to a single cumulative value without using a [for](https://realpython.com/python-for-loop/) loop. You’ll also learn about some Python tools that you can use in place of reduce() to make your code more Pythonic, readable, and efficient.

#python #functional #pythonic style

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Python's reduce(): From Functional to Pythonic Style – Real Python
Ray  Patel

Ray Patel

1619510796

Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map

Ray  Patel

Ray Patel

1619518440

top 30 Python Tips and Tricks for Beginners

Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.

1) swap two numbers.

2) Reversing a string in Python.

3) Create a single string from all the elements in list.

4) Chaining Of Comparison Operators.

5) Print The File Path Of Imported Modules.

6) Return Multiple Values From Functions.

7) Find The Most Frequent Value In A List.

8) Check The Memory Usage Of An Object.

#python #python hacks tricks #python learning tips #python programming tricks #python tips #python tips and tricks #python tips and tricks advanced #python tips and tricks for beginners #python tips tricks and techniques #python tutorial #tips and tricks in python #tips to learn python #top 30 python tips and tricks for beginners

Python's reduce(): From Functional to Pythonic Style – Real Python

Exploring Functional Programming in Python

Functional programming is a programming paradigm based on breaking down a problem into a set of individual functions. Ideally, every function only takes a set of input arguments and produces an output.

In functional programming, functions don’t have any internal state that affects the output that they produce for a given input. This means that anytime you call a function with the same set of input arguments, you’ll get the same result or output.

In a functional program, input data flows through a set of functions. Each function operates on its input and produces some output. Functional programming tries to avoid mutable data types and state changes as much as possible. It works with the data that flow between functions.

Other core features of functional programming include the following:

  • The use of recursion rather than loops or other structures as a primary flow control structure
  • A focus on lists or arrays processing
  • A focus on what is to be computed rather than on how to compute it
  • The use of pure functions that avoid side effects
  • The use of higher-order functions

There are several important concepts in this list. Here’s a closer look to some of them:

  • Recursion is a technique in which functions call themselves, either directly or indirectly, in order to loop. It allows a program to loop over data structures that have unknown or unpredictable lengths.
  • Pure functions are functions that have no side effects at all. In other words, they’re functions that do not update or modify any global variable, object, or data structure in the program. These functions produce an output that depends only on the input, which is closer to the concept of a mathematical function.
  • Higher-order functions are functions that operate on other functions by taking functions as arguments, returning functions, or both, as with Python decorators.

Since Python is a multi-paradigm programming language, it provides some tools that support a functional programming style:

  • Functions as first-class objects
  • Recursion capabilities
  • Anonymous functions with [lambda](https://realpython.com/python-lambda/)
  • Iterators and generators
  • Standard modules like [functools](https://docs.python.org/3/library/functools.html#module-functools) and [itertools](https://realpython.com/python-itertools/)
  • Tools like [map()](https://docs.python.org/3/library/functions.html#map)[filter()](https://docs.python.org/3/library/functions.html#filter)[reduce()](https://docs.python.org/3/library/functools.html#functools.reduce)[sum()](https://docs.python.org/3/library/functions.html#sum)[len()](https://docs.python.org/3/library/functions.html#len)[any()](https://realpython.com/any-python/)[all()](https://docs.python.org/3/library/functions.html#all)[min()](https://docs.python.org/3/library/functions.html#min)[max()](https://docs.python.org/3/library/functions.html#max), and so on

Even though Python isn’t heavily influenced by functional programming languages, back in 1993 there was a clear demand for some of the functional programming features listed above.

In response, several functional tools were added to the language. According to Guido van Rossum, they were contributed by a community member:

Python acquired lambdareduce()filter() and map(), courtesy of (I believe) a Lisp hacker who missed them and submitted working patches. (Source)

Over the years, new features such as list comprehensionsgenerator expressions, and built-in functions like sum()min()max()all(), and any() were viewed as Pythonic replacements for map()filter(), and reduce(). Guido planned to remove map()filter()reduce(), and even lambda from the language in Python 3.

Luckily, this removal didn’t take effect, mainly because the Python community didn’t want to let go of such popular features. They’re still around and still widely used among developers with a strong functional programming background.

In this tutorial, you’ll cover how to use Python’s reduce() to process iterables and reduce them to a single cumulative value without using a [for](https://realpython.com/python-for-loop/) loop. You’ll also learn about some Python tools that you can use in place of reduce() to make your code more Pythonic, readable, and efficient.

#python #functional #pythonic style

Art  Lind

Art Lind

1602968400

Python Tricks Every Developer Should Know

Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?

In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.

Let’s get started

Swapping value in Python

Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead

>>> FirstName = "kalebu"
>>> LastName = "Jordan"
>>> FirstName, LastName = LastName, FirstName 
>>> print(FirstName, LastName)
('Jordan', 'kalebu')

#python #python-programming #python3 #python-tutorials #learn-python #python-tips #python-skills #python-development

Art  Lind

Art Lind

1602666000

How to Remove all Duplicate Files on your Drive via Python

Today you’re going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates.

Intro

In many situations you may find yourself having duplicates files on your disk and but when it comes to tracking and checking them manually it can tedious.

Heres a solution

Instead of tracking throughout your disk to see if there is a duplicate, you can automate the process using coding, by writing a program to recursively track through the disk and remove all the found duplicates and that’s what this article is about.

But How do we do it?

If we were to read the whole file and then compare it to the rest of the files recursively through the given directory it will take a very long time, then how do we do it?

The answer is hashing, with hashing can generate a given string of letters and numbers which act as the identity of a given file and if we find any other file with the same identity we gonna delete it.

There’s a variety of hashing algorithms out there such as

  • md5
  • sha1
  • sha224, sha256, sha384 and sha512

#python-programming #python-tutorials #learn-python #python-project #python3 #python #python-skills #python-tips