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A visual introduction to code using the Python programming language and Turtles.

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Summarize News Articles with Machine Learning in Python

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Introduction to Python: Python Syntax Cheatsheet | Codecademy

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Battlefield 2 uses python as its main programming language

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Battlefield 2 uses python as its main programming language

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Code Minecraft With Python in 57 Seconds

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Tools for Python - Learning Python Series Pt. 1

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Top 5  Python App Development Tools |  Best Python Frameworks for App Development

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Python | Definition, Language, History, & Facts | Britannica

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Python is a general-purpose high-level computer programming language valued for its English-like syntax and powerful built-in data analysis and data science functions and libraries.

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If you're struggling to learn to code, you must watch this

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If I could give my former self one piece of advice when I was struggling to learn Python as a beginner, it would be this: create more Python projects.

Learning Python can be difficult. You can spend time reading a textbook or watching videos but then struggle to actually put what you've learned into practice.

Or you might spend a ton of time learning syntax and get bored or lose motivation. (That happened to me. A lot).

To increase your chances of success, you must build Python projects. That way, you're learning by actually doing what you want to do!

Before You Build Your First Python Project

If you have some programming experience, you might be able to dive straight into building a project. However, If you're just starting out, it's important you learn Python basics first (we recommend our beginner Python courses here).

The idea is to learn the basics as quickly as possible so you can start doing things that are actually fun, like Python projects!

At Dataquest, we include guided projects in virtually every course, which we've designed to help bridge the gap between learning and doing.

Check out our full list of courses here.

Beginner Python Project Examples

Now, let's look at a few Python project examples. See if you can find one that piques your interest.

Guided Python Projects to Help Build Your Portfolio (Recommended):

Fun Python Project Ideas for Building Data Skills:

Find out How Much Money You've Spent on Amazon - Dig into your own spending habits with this beginner-level tutorial!

Analyze Your Own Netflix Data - Another beginner-to-intermediate tutorial that gets you working with your own personal dataset.

Analyze Your Personal Facebook Posting Habits - Are you spending too much time posting on Facebook? The numbers don't lie, and you can find them in this beginner-to-intermediate Python data project.

Analyze Survey Data - This walk-through will show you how to set up Python and how to filter survey data from any dataset (or just use the sample data linked in the article).

Dataquest's Guided Projects - These guided projects walk you through building real-world data projects of increasing complexity, with suggestions for how to expand each project.

Analyze Everything - Grab a free dataset that interests you, and start poking around! If you get stuck or aren't sure where to start, our Python courses are here to help, and you can try them for free!

Cool Python Projects for Game Devs:

Rock, Paper, Scissors - Start your Python learning journey with a simple-but-fun game that everybody knows.

Build a Text Adventure Game - This is a classic Python beginner project (it also pops up in this book) that'll teach you many basic game setup concepts that are useful for more advanced games.

Guessing Game - This is another beginner-level project that'll help you learn and practice the basics.

Mad Libs - Learn how to make interactive Python Mad Libs!

Hangman - Another childhood classic that you can make in Python to stretch your skills.

Snake - This is a bit more complex, but it's a classic (and surprisingly fun) game to make and play.

Simple Python Projects for Beginner Web Devs:

URL shortener - This free video course will show you how to build your own URL shortener like Bit.ly using Python and Django.

Build a Simple Web Page with Django - This is a very in-depth, from-scratch tutorial for building a website with Python and Django that even has cartoon illustrations!

Easy Python Projects for Aspiring Developers:

Additional Python Project Ideas

Still haven't found a project idea that appeals to you? Here are many more, separated (roughly) by experience level.

These aren't tutorials; they're ideas that you'll have to dig into and research on your own, but that's part of the fun! And it's part of the natural process of learning to code and working as a programmer.

The pros use Google for answers all the time - so don't be afraid to dive in and get your hands dirty!

Python Project Ideas: Beginner Level

Create a code generator. This can that take text as input, replaces each letter with another letter, and outputs the "encoded" message.

Build a countdown calculator. Write some code that can take two dates as input, and then calculate the amount of time between them. This will be a great way to familiarize yourself with Python's datetime module.

Write a sorting method. Given a list, can you write some code that sorts it alphabetically, or numerically? Yes, Python has this functionality built-in, but see if you can do it without using sort()!

Build an interactive quiz. Which Avenger are you? Build a personality or recommendation quiz that asks users some questions, stores their answers, and then performs some kind of calculation to give the user a personalized result based on their answers

Tic-Tac-Toe by Text. Build a Tic-Tac-Toe game that's playable like a text adventure. Can you make it print a text-based representation of the board after each move?

Make a temperature/measurement converter. Write a script that can convert Fahrenheit to Celcius and back, or inches to centimeters and back, etc. How far can you take it?

Build a counter app. Take your first steps into the world of UI by building a very simple app that counts up by one each time a user clicks a button.

Build a number-guessing game. Think of this as a bit like a text adventure, but with numbers. How far can you take it?

Build an alarm clock. This is borderline beginner/intermediate, but it's worth trying to build an alarm clock for yourself. Can you create different alarms? A snooze function?

Python Project Ideas: Intermediate Level

Build an upgraded code generator. Starting with the project mentioned in the beginner section, see what you can do to make it more sophisticated. Can you make it generate different kinds of codes? Can you create a "decoder" app that reads encoded messages if the user inputs a secret key? Can you create a more sophisticated code that goes beyond simple letter-replacement?

Make your Tic-Tac-Toe game clickable. Building off the beginner project, now make a version of Tic-Tac-Toe that has an actual UI you'll use by clicking on open squares. Challenge: can you write a simple "AI" opponent for a human player to play against?

Scrape some data to analyze. This could really be anything, from any website you like. The web is full of interesting data, and if you learn a little about web-scraping, you can collect some really unique datasets.

Build a clock website. How close can you get it to real-time? Can you implement different time zone selectors, and add in the "countdown calculator" functionality to calculate lengths of time?

Automate some of your job. This will vary, but many jobs have some kind of repetitive process that you can automate!

Automate your personal habits. Do you want to remember to stand up once every hour during work? How about writing some code that generates unique workout plans based on your goals and preferences? There are a variety of simple apps you can build for yourself to automate or enhance different aspects of your life.

Create a simple web browser. Build a simple UI that accepts URLs and loads webpages. PyWt will be helpful here! Can you add a "back" button, bookmarks, and other cool features?

Write a notes app. Create an app that helps people write and store notes. Can you think of some interesting and unique features to add?

Build a typing tester. This should show the user some text, and then challenge them to type it - while timing them and scoring them on for accuracy.

Create a "site updated" notification system. Ever get annoyed when you have to refresh a website to see if an out-of-stock product has been relisted, or to see if any news has been posted? Write a Python script that automatically checks a given URL for updates and informs you when it identifies one. (Be careful not to overload the servers of whatever site you're checking, though - keep the time interval reasonable between each check).

Recreate your favorite board game in Python. There are tons of options here, from something simple like Checkers all the way up to Risk - or even more modern and advanced games like Ticket to Ride or Settlers of Catan. How close can you get to the real thing?

Build a Wikipedia explorer. Build an app that displays a random Wikipedia page. The challenge here is in the details: can you add user-selected categories? Can you try a different "rabbit hole" version of the app, wherein each article is randomly selected from the articles linked in the previous article? This might seem simple, but it can actually require some serious web-scraping skills.

Python Project Ideas: Advanced Level

Build a stock market prediction app. For this one, you'll need a source of stock market data and some machine learning skills, but many people have tried this, so there's plenty of source code out there to work from.

Build a chatbot. The challenge here isn't so much making the chatbot as it is making it good. Can you, for example, implement some natural language processing techniques to make it sound more natural and spontaneous?

Program a robot. This requires some hardware (which isn't usually free), but there are many affordable options out there - and many learning resources, too. Definitely look into Raspberry Pi if you're not already thinking along those lines.

Build an image recognition app. Starting with handwriting recognition is a good idea - Dataquest even has a guided project to help with that! - but once you've learned it, you can take it to the next level.

Make a price prediction model. Select an industry or product that interests you, and build a machine learning model that predicts price changes.

Create your own sentiment analysis model. Sure, there are many pre-built ones available, but can you collect a large corpus of text data and build one of your own? (Or, less challenging: optimize an existing sentiment analysis model for the particular text you're analyzing.)

Create an interactive map. This will require a mix of data skills and UI creation skills. Your map can display whatever you'd like - bird migrations, traffic data, crime reports - but it should be interactive in some way. How far can you take it?

Next Steps

Each of the examples in the previous section built on the practice of choosing a great Python project for beginners. Next, you can advance to the following:

Think about what interests you, and choose a project that overlaps with your interests.

Think about your Python learning goals, and make sure your project moves you closer to achieving those goals.

Start small. Once you've built a small project, you can either expand it or build another one.

Now you're ready to get started. If you haven't learned the basics of Python yet, I recommend diving in with Dataquest's Python Fundamentals course.

If you already know the basics, there's no reason to hesitate! Now is the time to dive in and find your perfect Python project.

(If you're at a loss for ideas, this article contains many suggestions as well as some resources for structured projects.)

About the author

Celeste Grupman

Celeste is the Director of Operations at Dataquest. She is passionate about creating affordable access to high-quality skills training for students across the globe.

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55 Fun (and Unique) Python Project Ideas for Beginners in 2022

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If I could give my former self one piece of advice when I was struggling to learn Python as a beginner, it would be this: create more Python projects. Learning Python can be difficult. You can spend time reading a textbook or watching videos but then struggle to actually put what you've learned into practice.

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Build an AMAZING Computer Vision Project in 11 Minutes!

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Copyright (c) 2001-2020 Python Software Foundation. All rights reserved.

See the end of this file for further copyright and license information.

For more complete instructions on contributing to CPython development, see the Developer Guide.

Installable Python kits, and information about using Python, are available at python.org.

On Unix, Linux, BSD, macOS, and Cygwin:

./configure
make
make test
sudo make install

This will install Python as python3.

You can pass many options to the configure script; run ./configure --help to find out more. On macOS case-insensitive file systems and on Cygwin, the executable is called python.exe; elsewhere it's just python.

Building a complete Python installation requires the use of various additional third-party libraries, depending on your build platform and configure options. Not all standard library modules are buildable or useable on all platforms. Refer to the Install dependencies section of the Developer Guide for current detailed information on dependencies for various Linux distributions and macOS.

On macOS, there are additional configure and build options related to macOS framework and universal builds. Refer to Mac/README.rst.

On Windows, see PCbuild/readme.txt.

If you wish, you can create a subdirectory and invoke configure from there. For example:

mkdir debug
cd debug
../configure --with-pydebug
make
make test

(This will fail if you also built at the top-level directory. You should do a make clean at the top-level first.)

To get an optimized build of Python, configure --enable-optimizations before you run make. This sets the default make targets up to enable Profile Guided Optimization (PGO) and may be used to auto-enable Link Time Optimization (LTO) on some platforms. For more details, see the sections below.

PGO takes advantage of recent versions of the GCC or Clang compilers. If used, either via configure --enable-optimizations or by manually running make profile-opt regardless of configure flags, the optimized build process will perform the following steps:

The entire Python directory is cleaned of temporary files that may have resulted from a previous compilation.

An instrumented version of the interpreter is built, using suitable compiler flags for each flavour. Note that this is just an intermediary step. The binary resulting from this step is not good for real life workloads as it has profiling instructions embedded inside.

After the instrumented interpreter is built, the Makefile will run a training workload. This is necessary in order to profile the interpreter execution. Note also that any output, both stdout and stderr, that may appear at this step is suppressed.

The final step is to build the actual interpreter, using the information collected from the instrumented one. The end result will be a Python binary that is optimized; suitable for distribution or production installation.

Enabled via configure's --with-lto flag. LTO takes advantage of the ability of recent compiler toolchains to optimize across the otherwise arbitrary.o file boundary when building final executables or shared libraries for additional performance gains.

We have a comprehensive overview of the changes in the What's New in Python 3.10 document. For a more detailed change log, read Misc/NEWS, but a full accounting of changes can only be gleaned from the commit history.

If you want to install multiple versions of Python, see the section below entitled "Installing multiple versions".

Documentation for Python 3.10 is online, updated daily.

It can also be downloaded in many formats for faster access. The documentation is downloadable in HTML, PDF, and reStructuredText formats; the latter version is primarily for documentation authors, translators, and people with special formatting requirements.

For information about building Python's documentation, refer to Doc/README.rst.

Significant backward incompatible changes were made for the release of Python 3.0, which may cause programs written for Python 2 to fail when run with Python 3. For more information about porting your code from Python 2 to Python 3, see the Porting HOWTO.

To test the interpreter, type make test in the top-level directory. The test set produces some output. You can generally ignore the messages about skipped tests due to optional features which can't be imported. If a message is printed about a failed test or a traceback or core dump is produced, something is wrong.

By default, tests are prevented from overusing resources like disk space and memory. To enable these tests, run make testall.

If any tests fail, you can re-run the failing test(s) in verbose mode. For example, if test_os and test_gdb failed, you can run:

make test TESTOPTS="-v test_os test_gdb"

If the failure persists and appears to be a problem with Python rather than your environment, you can file a bug report and include relevant output from that command to show the issue.

See Running & Writing Tests for more on running tests.

On Unix and Mac systems if you intend to install multiple versions of Python using the same installation prefix (--prefix argument to the configure script) you must take care that your primary python executable is not overwritten by the installation of a different version. All files and directories installed using make altinstall contain the major and minor version and can thus live side-by-side. make install also creates${prefix}/bin/python3 which refers to ${prefix}/bin/pythonX.Y. If you intend to install multiple versions using the same prefix you must decide which version (if any) is your "primary" version. Install that version using make install. Install all other versions using make altinstall.

For example, if you want to install Python 2.7, 3.6, and 3.10 with 3.10 being the primary version, you would execute make install in your 3.10 build directory and make altinstall in the others.

Bug reports are welcome! You can use the issue tracker to report bugs, and/or submit pull requests on GitHub.

You can also follow development discussion on the python-dev mailing list.

If you have a proposal to change Python, you may want to send an email to the comp.lang.python or python-ideas mailing lists for initial feedback. A Python Enhancement Proposal (PEP) may be submitted if your idea gains ground. All current PEPs, as well as guidelines for submitting a new PEP, are listed at python.org/dev/peps/.

See PEP 596 for Python 3.9 release details.

Copyright (c) 2001-2020 Python Software Foundation. All rights reserved.

Copyright (c) 2000 BeOpen.com. All rights reserved.

Copyright (c) 1995-2001 Corporation for National Research Initiatives. All rights reserved.

Copyright (c) 1991-1995 Stichting Mathematisch Centrum. All rights reserved.

See the LICENSE for information on the history of this software, terms & conditions for usage, and a DISCLAIMER OF ALL WARRANTIES.

This Python distribution contains no GNU General Public License (GPL) code, so it may be used in proprietary projects. There are interfaces to some GNU code but these are entirely optional.

All trademarks referenced herein are property of their respective holders.

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Copyright (c) 2001-2020 Python Software Foundation. All rights reserved. See the end of this file for further copyright and license information. For more complete instructions on contributing to CPython development, see the Developer Guide. Installable Python kits, and information about using Python, are available at python.org.

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Python is a widely used general-purpose, high-level programming language. It was initially designed by Guido van Rossum in 1991 and developed by Python Software Foundation. It was mainly developed for emphasis on code readability, and its syntax allows programmers to express concepts in fewer lines of code.

Who invented Python?

In the late 1980s, history was about to be written. It was that time when working on Python started. Soon after that, Guido Van Rossum began doing its application-based work in December of 1989 at Centrum Wiskunde & Informatica (CWI) which is situated in the Netherlands. It was started as a hobby project because he was looking for an interesting project to keep him occupied during Christmas.

The programming language in which is said to have succeeded is ABC Programming Language, which had interfacing with the Amoeba Operating System and had the feature of exception handling. He had already helped create ABC earlier in his career and had seen some issues with ABC but liked most of the features. After that what he did was very clever. He had taken the syntax of ABC, and some of its good features. It came with a lot of complaints too, so he fixed those issues completely and created a good scripting language that had removed all the flaws. The inspiration for the name came from the BBC's TV Show - ' Monty Python's Flying Circus', as he was a big fan of the TV show and also he wanted a short, unique and slightly mysterious name for his invention and hence he named it Python! He was the "Benevolent dictator for life" (BDFL) until he stepped down from the position as the leader on 12th July 2018. For quite some time he used to work for Google, but currently, he is working at Dropbox.

Evolution of Python

The language was finally released in 1991. When it was released, it used a lot fewer codes to express the concepts, when we compare it with , & . Its design philosophy was quite good too. Its main objective is to provide code readability and advanced developer productivity. When it was released, it had more than enough capability to provide classes with inheritance, several core data types of exception handling and functions.
Following are the illustrations of different versions of Python along with the timeline.

Python 3.12.1 is the latest stable version.

The two of the most used versions has to Python 2.x & 3.x. There is a lot of competition between the two and both of them seem to have quite a number of different fanbases. You can also refer to mentioned below article to learn How you can download python latest version in your system.

Download and Install Python 3 Latest Version

For various purposes such as developing, scripting, generation, and software testing , this language is utilized. Due to its elegance and simplicity, top technology organizations like Dropbox, Google, Quora, Mozilla, Hewlett-Packard, Qualcomm, IBM, and Cisco have implemented Python.

Python has come a long way to become the most popular coding language in the world. Python has just turned 30 and just recently at pycon22(python conference) a new feature was released by Anaconda foundation it's known as pyscript with this now python can be written and run in the browser like javascript which was previously not possible, but it still has that unknown charm & X factor.

Python has been an inspiration for many other coding languages such as , Cobra, Boo, CoffeeScript ECMAScript, Groovy, Swift Go, OCaml, , etc.

History of Python - FAQ's

Q1. Who developed Python programming language?

It was initially designed by Guido van Rossum in 1991 and developed by Python Software Foundation.

Q2. Who invented Python programming language?

In the late 1980s, history was about to be written. It was that time when working on Python started. Soon after that, Guido Van Rossum began doing its application-based work in December of 1989 at Centrum Wiskunde & Informatica (CWI) which is situated in the Netherlands. It was started firstly as a hobby project because he was looking for an interesting project to keep him occupied during Christmas

Q3. What are the uses of Python?

One more question asises that, Is Python Programming or Scripting?, According to Linux Journal readers the Python is a programming as well as a scripting language. Also, This language has various purposes such as developing, scripting, generation, and software testing.

Don't miss your chance to ride the wave of the data revolution! Every industry is scaling new heights by tapping into the power of data. Sharpen your skills and become a part of the hottest trend in the 21st century.

Dive into the future of technology - explore the Complete Machine Learning and Data Science Program by GeeksforGeeks and stay ahead of the curve.

Last Updated : 26 Dec, 2023

Share your thoughts in the comments

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A Computer Science portal for geeks. It contains well written, well thought and well explained computer science and programming articles, quizzes and practice/competitive programming/company interview Questions.

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Flappy Bird Game Using Python With Source Code - CodeWithCurious

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Flappy Bird Game Using Python With Source Code Introduction: In this project, we have created a game using the “Pygame” module in python. The game is named “Flappy Bird”. Most of you have played this game on your mobile phones and now it’s time to code this game by yourself. If you haven’t played this […]

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Top 10 Python tools you should know about

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Top 10 Python tools you should know about

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How To Make Minecraft in Python | 3D Game Tutorial - Coding With Russ

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Learn how to make minecraft in python with this tutorial. This will be done using a 3D game engine for python called Ursina.

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The official Python documentation is an essential resource

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The official Python documentation is an essential resource

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Welcome! This is the official documentation for Python 3.12.1.

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The Origins of Python: A Journey Through Its History and Evolution

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Introduction Python is a widely popular high-level programming language, known for its…

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Programming with Python | Python for Beginners [1 of 44]

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Exercise 10 - News App in Python | Source Code | Explanation | English | Day #90

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The biggest games that use Python. Such as World Of Tanks, The Sims 4, Battlefield 2, ect.

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OpenTelemetry and Python: A complete instrumentation guide

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Guest post originally published on the Timescale blog by James Blackwood-Sewell, Timescale OpenTelemetry is considered by many the future of instrumentation, and it’s not hard to understand why.

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The Story of Python, by Its Creator, Guido van Rossum

# data[].attributes.attachment_link.readable_text
Python is a widely used general-purpose, high-level programming language. It was initially designed by Guido van Rossum in 1991 and developed by Python Software Foundation. It was mainly developed for emphasis on code readability, and its syntax allows programmers to express concepts in fewer lines of code.

Who invented Python?

In the late 1980s, history was about to be written. It was that time when working on Python started. Soon after that, Guido Van Rossum began doing its application-based work in December of 1989 at Centrum Wiskunde & Informatica (CWI) which is situated in the Netherlands. It was started as a hobby project because he was looking for an interesting project to keep him occupied during Christmas.

The programming language in which is said to have succeeded is ABC Programming Language, which had interfacing with the Amoeba Operating System and had the feature of exception handling. He had already helped create ABC earlier in his career and had seen some issues with ABC but liked most of the features. After that what he did was very clever. He had taken the syntax of ABC, and some of its good features. It came with a lot of complaints too, so he fixed those issues completely and created a good scripting language that had removed all the flaws. The inspiration for the name came from the BBC's TV Show - ' Monty Python's Flying Circus', as he was a big fan of the TV show and also he wanted a short, unique and slightly mysterious name for his invention and hence he named it Python! He was the "Benevolent dictator for life" (BDFL) until he stepped down from the position as the leader on 12th July 2018. For quite some time he used to work for Google, but currently, he is working at Dropbox.

Evolution of Python

The language was finally released in 1991. When it was released, it used a lot fewer codes to express the concepts, when we compare it with , & . Its design philosophy was quite good too. Its main objective is to provide code readability and advanced developer productivity. When it was released, it had more than enough capability to provide classes with inheritance, several core data types of exception handling and functions.
Following are the illustrations of different versions of Python along with the timeline.

Python 3.12.1 is the latest stable version.

The two of the most used versions has to Python 2.x & 3.x. There is a lot of competition between the two and both of them seem to have quite a number of different fanbases. You can also refer to mentioned below article to learn How you can download python latest version in your system.

Download and Install Python 3 Latest Version

For various purposes such as developing, scripting, generation, and software testing , this language is utilized. Due to its elegance and simplicity, top technology organizations like Dropbox, Google, Quora, Mozilla, Hewlett-Packard, Qualcomm, IBM, and Cisco have implemented Python.

Python has come a long way to become the most popular coding language in the world. Python has just turned 30 and just recently at pycon22(python conference) a new feature was released by Anaconda foundation it's known as pyscript with this now python can be written and run in the browser like javascript which was previously not possible, but it still has that unknown charm & X factor.

Python has been an inspiration for many other coding languages such as , Cobra, Boo, CoffeeScript ECMAScript, Groovy, Swift Go, OCaml, , etc.

History of Python - FAQ's

Q1. Who developed Python programming language?

It was initially designed by Guido van Rossum in 1991 and developed by Python Software Foundation.

Q2. Who invented Python programming language?

In the late 1980s, history was about to be written. It was that time when working on Python started. Soon after that, Guido Van Rossum began doing its application-based work in December of 1989 at Centrum Wiskunde & Informatica (CWI) which is situated in the Netherlands. It was started firstly as a hobby project because he was looking for an interesting project to keep him occupied during Christmas

Q3. What are the uses of Python?

One more question asises that, Is Python Programming or Scripting?, According to Linux Journal readers the Python is a programming as well as a scripting language. Also, This language has various purposes such as developing, scripting, generation, and software testing.

Don't miss your chance to ride the wave of the data revolution! Every industry is scaling new heights by tapping into the power of data. Sharpen your skills and become a part of the hottest trend in the 21st century.

Dive into the future of technology - explore the Complete Machine Learning and Data Science Program by GeeksforGeeks and stay ahead of the curve.

Last Updated : 26 Dec, 2023

Share your thoughts in the comments

# data[].attributes.attachment_link.description
A Computer Science portal for geeks. It contains well written, well thought and well explained computer science and programming articles, quizzes and practice/competitive programming/company interview Questions.

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Master Python by making 5 games [the new ultimate introduction to pygame]

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Learn to produce and consume data from Redpanda using Python in just five steps. Perfect for budding developers joining data engineering and ML.

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PyPI is the primary source for discovering and installing Python libraries and frameworks.

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PyPI is the primary source for discovering and installing Python libraries and frameworks.

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The Python Package Index (PyPI) is a repository of software for the Python programming language.

PyPI helps you find and install software developed and shared by the Python community. Learn about installing packages.

Package authors use PyPI to distribute their software. Learn how to package your Python code for PyPI.

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The Python Package Index (PyPI) is a repository of software for the Python programming language.

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The Importance of Coding in 21st Century Education - 21K School India

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We are living in an era of digitalization, where we are surrounded by technologies. Read this blog which discusses the importance of coding.

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In this post I will share the code to summarize a news article using Python's Natural Language Toolkit (NLTK)

For this example I'll be extracting an article from The Hindu using BeautifulSoup and summarize the article using word frequency distribution.

Final output looks like this;

Hope this helps!

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News article summarization with Python NLTK — arundhaj

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News Article Summarization with Python NLTK

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>>>, The default Python prompt of the interactive shell. Often seen for code examples which can be executed interactively in the interpreter.,,..., Can refer to:- The default Python prompt of the i…

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Planet Python aggregates news, articles, and blog posts about Python. 

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Planet Python aggregates news, articles, and blog posts about Python. 

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Last update: May 03, 2024 07:43 AM UTC

May 02, 2024

This tutorial looks at how to build server-side UI components in Django.

June 2024 marks 10 incredible years of Django Girls magic! 🥳✨

We couldn't have reached this milestone without YOU! Whether you attended a workshop, volunteered, financially supported us, or cheered us on, you've been vital. From the bottom of our hearts, thank you for being part of the Django Girls community. 💕

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Python's variables are not buckets that contain objects; they're pointers. Assignment statements don't copy: they point a variable to a value (and multiple variables can "point" to the same value).

Changing two lists at once...?

Here we have a variable a that points to a list:

Let's make a new variable b and assign it to a:

If we append a new item to b, what will its length be?

Initially, the b list had four items, so now it should have five items. And it does:

How many items do you think a has? What's your guess?

Is it five, the same as b? Or is it still four, as it was before?

The a list also has five items:

What's going on here?

Well, the variables a and b, both point to the same list.

If we look up the unique ID for the object that each of these variables points to, we'll see that they both point to the same object:

This is possible because variables in Python are not buckets, but pointers.

Variables are separate from objects

Let's say we've made three ...

In this episode, we invite the founder of Buttondown, a Python-based Software as a Service (SaaS) application for creating and managing newsletters.

Mike Driscoll, the host of the show, chats with Justin about the following topics:

Why he created a SaaS with Python

Favorite Python packages or modules

Python web frameworks

Entrepreneurship

AI and programming

and more!

The post The Python Show Podcast Ep 39 - Buttondown - A Python SaaS with Justin Duke appeared first on Mouse Vs Python.

In this quiz, you'll test your understanding of creating calendars in Python using the calendar module.

By working through this quiz, you'll revisit the fundamental functions and methods provided by the calendar module.
[ Improve Your Python With 🐍 Python Tricks 💌 - Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]
In this tutorial, you will learn to write your first Python program.

Do you find yourself or your team building internal apps frequently for your company? Are you familiar with the term "forms over data"? They are super empowering for your org but they can be pretty repetitive and you might find yourself spending more time than you'd like working on them rather than core products and services. I invited Jimmy Chan from Dropbase to tell us about their service who's tagline is "Build internal web apps with just Python." It's a cool service and a fun conversation.<br/> <br/> <strong>Episode sponsors</strong><br/> <br/> <a href='https://talkpython.fm/mailtrap'>Mailtrap</a><br> <a href='https://talkpython.fm/training'>Talk Python Courses</a><br/> <br/> <strong>Links from the show</strong><br/> <br/> <div><b>Build internal web apps with just Python.</b>: <a href="https://www.dropbase.io" target="_blank" rel="noopener">dropbase.io</a><br/> <b>Dropbase on Github</b>: <a href="https://github.com/DropbaseHQ/dropbase" target="_blank" rel="noopener">github.com</a><br/> <b>Dropbase @ LinkedIn</b>: <a href="https://www.linkedin.com/company/dropbase" target="_blank" rel="noopener">linkedin.com</a><br/> <b>Dropbase on Twitter</b>: <a href="https://twitter.com/dropbasehq" target="_blank" rel="noopener">twitter.com</a><br/> <b>Jimmy Chan</b>: <a href="https://www.linkedin.com/in/jimmyechan/" target="_blank" rel="noopener">linkedin.com</a><br/> <b>Jimmy on Twitter</b>: <a href="https://twitter.com/jimmyechan" target="_blank" rel="noopener">twitter.com</a><br/> <b>Dropbase Docs</b>: <a href="https://docs.dropbase.io" target="_blank" rel="noopener">docs.dropbase.io</a><br/> <b>Dropbase</b>: <a href="https://dropbase.io" target="_blank" rel="noopener">dropbase.io</a><br/> <b>Watch this episode on YouTube</b>: <a href="https://www.youtube.com/watch?v=0VULU3g8wqU" target="_blank" rel="noopener">youtube.com</a><br/> <b>Episode transcripts</b>: <a href="https://talkpython.fm/episodes/transcript/460/dropbase-build-internal-tools-with-python" target="_blank" rel="noopener">talkpython.fm</a><br/> <br/> <b>--- Stay in touch with us ---</b><br/> <b>Subscribe to us on YouTube</b>: <a href="https://talkpython.fm/youtube" target="_blank" rel="noopener">youtube.com</a><br/> <b>Follow Talk Python on Mastodon</b>: <a href="https://fosstodon.org/web/@talkpython" target="_blank" rel="noopener"><i class="fa-brands fa-mastodon"></i>talkpython</a><br/> <b>Follow Michael on Mastodon</b>: <a href="https://fosstodon.org/web/@mkennedy" target="_blank" rel="noopener"><i class="fa-brands fa-mastodon"></i>mkennedy</a><br/></div>

Isolating risk in the CPython release process

This critical role would not be possible without funding from the Alpha-Omega project. Massive thank-you to Alpha-Omega for investing in the security of the Python ecosystem!

The first stage of the CPython release process produces source and docs artifacts. In terms of "supply chain integrity", the source artifacts are the most important artifact produced by this process. These tarballs are what propagates down into containers, pyenv, and operating system distributions, so reducing the risk that these artifacts are modified in-flight is critical.

A few weeks ago I published that CPythons' release process for source and docs artifacts was moved from developers machines onto GitHub Actions, which provides an isolated build environment.

This already reduces risk of artifacts being accidentally or maliciously modified during the release process. The layout of the build and release process before used a build script which built the software from source, built the docs, and then ran tests all in the same isolated job. This was totally fine on a developers' machine where there isn't any isolation possible between different stages.

Build Dependenci...Build SourceSource Artifa...Docs ArtifactsSource...Docs DependenciesBuild Dependenci...Build SourceSource Artifa...Docs ArtifactsSource...Docs DependenciesBuild DocsTestingBuild DocsTestingSource Artifa... Text is not SVG - cannot display
Before and after splitting up build stages

With GitHub Actions we can isolate each stage from the others and remove the need to install all dependencies for all jobs into the same stage. This drastically reduces the number of dependencies, each representing a small amount of risk, for the stages that are critical for supply chain security of CPython (specifically the building of source artifacts).

Above you can see on the left the previous process which pulls all dependencies into the same job (represented as a gray box) and the right being the new process having split up the builds and testing and the source and docs builds.

After doing this split the "Build Source" task only needs ~170 dependencies instead of over 800 dependencies (mostly for documentation LaTeX and PDFs) and all of those dependencies either come with the operating system and thus can't be reduced further or are pinned in a lock file.

The testing stage still has access to the source artifacts, but only after they've been uploaded to GitHub Action Artifacts and aren't able to modify them.

I plan to write a separate in-depth article about dependencies, pinning, and related topics, stay tuned for that.

SOSS Community Day 2024 recordings

The recordings for my talk and the OpenSSF tabletop session have been published to YouTube:

Embrace the Differences: Securing open source software ecosystems where they are

In the talk I discuss the technical and also social aspects of why it's sometimes difficult to adopt security changes into an open source ecosystem. Ecosystem-agnostic work (think memory safety, provenance, reproducible builds, vulnerabilities) tends to operate at a much higher level than the individual ecosystems where the work ends up being applied.
OpenSSF Tabletop Session
The tabletop session had many contributors representing all the different aspects of discovering, debugging, disclosing, fixing, and patching a zero-day vulnerability in an open source component that's affecting production systems.

Tabletop Session moderated by Dana Wang

Mentoring for Google Summer of Code

Google Summer of Code 2024 recently published its program and among the projects and contributors accepted was CPython's project for adopting the Hardened Compiler Options Guide for C/C++. I'm mentoring the contributor through the process of contributing to CPython and hopefully being successful in adopting hardened compiler options.

Other items

That's all for this week! 👋 If you're interested in more you can read last week's report.

Thanks for reading! ♡ Did you find this article helpful and want more content like it? Get notified of new posts by subscribing to the RSS feed or the email newsletter.

May 01, 2024

We are proud to announce the 7.2 release of Tryton.
This release provides many bug fixes, performance improvements and some fine tuning. It also adds 5 new modules.
You can give it a try on the demo server, use the docker image or download it here.
As usual upgrading from previous series is fully supported but some manual steps are needed to update from 7.0 to 7.2.

Here is a list of the most noticeable changes:

Changes for the User

Clients

You can now request to reset your password from the login dialog. Doing this sends a temporary password to your email address.

The PYSON widgets display the value using operators which are more user-friendly.

Web Client

The binary and image widgets now support drag and drop to set their value.

Desktop Client

On list and tree views, there is now a contextual menu that allows you to copy the contents of a cell or a column.

Accounting

It is now possible to modify the dates of a period even if it contains posted moves as long as the existing moves stay inside the new period dates. This useful to correct mistakes or even extend a period.

A warning is now raised when you validate an invoice for which some lines do not have the expected default taxes. This helps to detect mistakes.

When an invoice in another currency is paid, the currency exchange amount is now booked automatically into a configured account.

You can now enter the amount of the transaction in a second currency on statements. This makes it easier to do the reconciliation between the statement and invoices based on a second currency.

Company

Employees are now automatically deactivated once their end date has passed.

It is now possible to use some placeholders in the header and footer of company reports like the company name, phone, website etc.

Marketing

Some reports are now available on marketing scenario and activities. They calculate and display the open, click and click-through rates.

UTM parameters can be added to marketing emails so you can follow their results.

Product

You can now store the Manufacturer Part Number and brand as a product identifier.

Tryton now supports to adding images to product categories.

You can now use non-square images on products. The module resizes the images to fit the requested size but keeps the aspect ratio.

Production

The production number is now only set when the order progresses to waiting. This prevents the supply module from consuming number for production request that are subsequently deleted.

Purchase

It is now possible to remove ignored invoices and stock moves from purchases. This is useful when you have ignored the invoice or shipping exception by mistake and need to correct it.

Sale

It is now possible to remove the ignored invoices and stock moves from sales. This is useful when you have ignored the invoice or shipping exception by mistake and need to correct it.

The product on sale opportunity lines can be omitted, a description and a note can be used instead.

Stock

The drop shipment (like the other shipments) can now be split. This is useful to match exactly how the supplier shipped the products.

The shipment numbers are now only set when it progresses to a waiting state. This prevents consuming sequences numbers for requests that are going to be deleted.

The lot trace now optionally displays the source and destination locations. This can be useful when investigating the traceability of a lot.

Web Shop

It is now possible to limit a web shop by country.

The web shop supports price lists to calculate the sale price and the non sale price.

New Modules

Stock Product Location Place

The Stock Product Location Place Module allows defining the place where each product is stored within each location.

Account SYSCOHADA

The Account SYSCOHADA Module provides templates for the chart of account for OHADA countries.

Account Export

The Account Export Module provides the basis to allow accounting moves to be exported to external accounting software.

Account Export WinBooks

The Account Export WinBooks Module adds support to export accounting data to WinBooks.

Web Shop Product Data Feed

The Web Shop Product Data Feed Module exposes web shop products as a data feed for Google Merchant and Meta for business.

Changes for the System Administrator

Server

It is now possible to update the database without updating the indexes or to create the indexes concurrently. These are useful options when updating busy system.

It is possible to define a timeout for some RPC calls. This helps preventing users from overloading the system with expensive requests.

Changes for the Developer

Server

We added send_message methods to simplify sending emails using python's Message.

A new kind of field fmany2one is now available, which is a type of many2one field but stores a different field to the id. It is used mainly in the infrastructure to create foreign keys based on a model or field name.

The read-only relational fields are no longer copied by default. This was source of various bugs as developers often forgot to disable these from the copy.

Clients

The clients read the xxx2many fields using dotted notation. This avoids making multiple requests when displaying a form with these fields.

The XML ID of a record is now displayed in the log window.

Script

It is possible to configure the scripting client to skip any warning.

Product

It is now possible to generate barcodes for a product using a different type than the one on the identifier.

Stock

The done buttons have been renamed to do.

Location name fields have been added to stock moves. This is useful to customize the information displayed in reports about the source and destination locations.
3 posts - 2 participants Read full topic

Note: I've been awfully silent here for the past ... (checks notes) oh dear, 3 months! But that's not because I've been idle, quite the contrary, I've been very busy but just didn't have time to write about anything. So I've taken it upon myself to write something about my work this week, and published this post on the Tor blog which I copy here for a broader audience. Let me know if you like this or not.

Tor has finally completed a long migration from legacy Git infrastructure ( Gitolite and GitWeb) to our self-hosted GitLab server.

Git repository addresses have therefore changed. Many of you probably have made the switch already, but if not, you will need to change:

https://git.torproject.org/

to:

https://gitlab.torproject.org/

In your Git configuration.

The GitWeb front page is now an archived listing of all the repositories before the migration. Inactive git repositories were archived in GitLab legacy/gitolite namespace and the gitweb.torproject.org and git.torproject.org web sites now redirect to GitLab.

Best effort was made to reproduce the original gitolite repositories faithfully and also avoid duplicating too much data in the migration. But it's possible that some data present in Gitolite has not migrated to GitLab.

User repositories are particularly at risk, because they were massively migrated, and they were "re-forked" from their upstreams, to avoid wasting disk space. If a user had a project with a matching name it was assumed to have the right data, which might be inaccurate.

The two virtual machines responsible for the legacy service ( cupani for git-rw.torproject.org and vineale for git.torproject.org and gitweb.torproject.org) have been shutdown. Their disks will remain for 3 months (until the end of July 2024) and their backups for another year after that (until the end of July 2025), after which point all the data from those hosts will be destroyed, with only the GitLab archives remaining.

The rest of this article expands on how this was done and what kind of problems we faced during the migration.

Normally, nothing should be lost. All repositories in gitolite have been either explicitly migrated by their owners, forcibly migrated by the sysadmin team ( TPA), or explicitly destroyed at their owner's request.

An exhaustive rewrite map translates gitolite projects to GitLab projects. Some of those projects actually redirect to their parent in cases of empty repositories that were obvious forks. Destroyed repositories redirect to the GitLab front page.

Because the migration happened progressively, it's technically possible that commits pushed to gitolite were lost after the migration. We took great care to avoid that scenario. First, we adopted a proposal ( TPA-RFC-36) in June 2023 to announce the transition. Then, in March 2024, we locked down all repositories from any further changes. Around that time, only a handful of repositories had changes made after the adoption date, and we examined each repository carefully to make sure nothing was lost.

Still, we built a diff of all the changes in the git references that archivists can peruse to check for data loss. It's large (6MiB+) because a lot of repositories were migrated before the mass migration and then kept evolving in GitLab. Many other repositories were rebuilt in GitLab from parent to rebuild a fork relationship which added extra references to those clones.

A note to amateur archivists out there, it's probably too late for one last crawl now. The Git repositories now all redirect to GitLab and are effectively unavailable in their original form.

That said, the GitWeb site was crawled into the Internet Archive in February 2024, so at least some copy of it is available in the Wayback Machine. At that point, however, many developers had already migrated their projects to GitLab, so the copies there were already possibly out of date compared with the repositories in GitLab.

Software Heritage also has a copy of all repositories hosted on Gitolite since June 2023 and have continuously kept mirroring the repositories, where they will be kept hopefully in eternity. There's an issue where the main website can't find the repositories when you search for gitweb.torproject.org, instead search for git.torproject.org.

In any case, if you believe data is missing, please do let us know by opening an issue with TPA.

This is an old project in the making. The first discussion about migrating from gitolite to GitLab started in 2020 (almost 4 years ago). But going further back, the first GitLab experiment was in 2016, almost a decade ago.

The current GitLab server dates from 2019, replacing Trac for issue tracking in 2020. It was originally supposed to host only mirrors for merge requests and issue trackers but, naturally, one thing led to another and eventually, GitLab had grown a container registry, continuous integration (CI) runners, GitLab Pages, and, of course, hosted most Git repositories.

There were hesitations at moving to GitLab for code hosting. We had discussions about the increased attack surface and ways to mitigate that, but, ultimately, it seems the issues were not that serious and the community embraced GitLab.

TPA actually migrated its most critical repositories out of shared hosting entirely, into specific servers (e.g. the Puppet Git repository is just on the Puppet server now), leveraging Git's decentralized nature and removing an entire attack surface from our infrastructure. Some of those repositories are mirrored back into GitLab, but the authoritative copy is not on GitLab.

In any case, the proposal to migrate from Gitolite to GitLab was effectively just formalizing a fait accompli.

The progressive migration was a challenge. If you intend to migrate between hosting platforms, we strongly recommend to make a "flag day" during which you migrate all repositories at once. This ensures a smoother transition and avoids elaborate rewrite rules.

When Gitolite access was shutdown, we had repositories on both GitLab and Gitolite, without a clear relationship between the two. A priori, the plan then was to import all the remaining Gitolite repositories into the legacy/gitolite namespace, but that seemed wasteful, particularly for large repositories like Tor Browser which uses nearly a gigabyte of disk space. So we took special care to avoid duplicating repositories.

When the mass migration started, only 71 of the 538 Gitolite repositories were Migrated to GitLab in the gitolite.conf file. So, given that we had hundreds of repositories to migrate:, we developed some automation to " save time". We already automate similar ad-hoc tasks with Fabric, so we used that framework here as well. (Our normal configuration management tool is Puppet, which is a poor fit here.)

So a relatively large amount of Python code was produced to basically do the following:

1. check if all on-disk repositories are listed in gitolite.conf (and vice versa) and either add missing repositories or delete them from disk if garbage

2. for each repository in gitolite.conf, if its category is markedMigrated to GitLab, skip, otherwise;

3. find a matching GitLab project by name, prompt the user for multiple matches

4. if a match is found, redirect if the repository is non-empty

we have GitLab projects that look like the real thing, but are only present to host migrated Trac issues

in such cases we cloned the Gitolite project locally and pushed to the existing repository instead

5. otherwise, a new repository is created in the legacy/gitolite namespace, using the "import" mechanism in GitLab to automatically import the repository from Gitolite, creating redirections and updatinggitolite.conf to document the change

User repositories (those under the user/ directory in Gitolite) were handled specially. First, the existing redirection map was checked to see if a similarly named project was migrated (so that, e.g. user/dgoulet/tor is properly treated as a fork of tpo/core/tor). Then the parent project was forked in GitLab and the Gitolite project force-pushed to the fork. This allows us to show the fork relationship in GitLab and, more importantly, benefit from the "pool" feature in GitLab which deduplicates disk usage between forks.

Sometimes, we found no such relationships. Then we simply imported multiple repositories with similar names in the legacy/gitolite namespace, sometimes creating forks between user repositories, on a first-come-first-served basis from the gitolite.conf order.

The code used in this migration is now available publicly. We encourage other groups planning to migrate from Gitolite/GitWeb to GitLab to use (and contribute to) our fabric-tasks repository, even though it does have its fair share of hard-coded assertions.

The main entry point is the gitolite.mass-repos-migration task. A typical migration job looked like:

anarcat@angela:fabric-tasks$ fab -H cupani.torproject.org gitolite.mass-repos-migration [...] INFO: skipping project project/help/infra in category Migrated to GitLab INFO: skipping project project/help/wiki in category Migrated to GitLab INFO: skipping project project/jenkins/jobs in category Migrated to GitLab INFO: skipping project project/jenkins/tools in category Migrated to GitLab INFO: searching for projects matching fastlane INFO: Successfully connected to https://gitlab.torproject.org import gitolite project project/tor-browser/fastlane into gitlab legacy/gitolite/project/tor-browser/fastlane with desc 'Tor Browser app store and deployment configuration for Fastlane'? [Y/n] INFO: importing gitolite project project/tor-browser/fastlane into gitlab legacy/gitolite/project/tor-browser/fastlane with desc 'Tor Browser app store and deployment configuration for Fastlane' INFO: building a new connect to cupani INFO: defaulting name to fastlane INFO: importing project into GitLab INFO: Successfully connected to https://gitlab.torproject.org INFO: loading group legacy/gitolite/project/tor-browser INFO: archiving project INFO: creating repository fastlane (fastlane) in namespace legacy/gitolite/project/tor-browser from https://git.torproject.org/project/tor-browser/fastlane into https://gitlab.torproject.org/legacy/gitolite/project/tor-browser/fastlane INFO: migrating Gitolite repository project/tor-browser/fastlane to GitLab project legacy/gitolite/project/tor-browser/fastlane INFO: uploading 399 bytes to /srv/git.torproject.org/repositories/project/tor-browser/fastlane.git/hooks/pre-receive INFO: making /srv/git.torproject.org/repositories/project/tor-browser/fastlane.git/hooks/pre-receive executable INFO: adding entry to rewrite_map /home/anarcat/src/tor/tor-puppet/modules/profile/files/git/gitolite2gitlab.txt INFO: modifying gitolite.conf to add: "config gitweb.category = Migrated to GitLab" INFO: rewriting gitolite config /home/anarcat/src/tor/gitolite-admin/conf/gitolite.conf to change project project/tor-browser/fastlane to category Migrated to GitLab INFO: skipping project project/bridges/bridgedb-admin in category Migrated to GitLab [...]

In the above, you can see migrated repositories skipped then the fastlane project being archived into GitLab. Another example with a later version of the script, processing only user repositories and showing the interactive prompt and a force-push into a fork:

$ fab -H cupani.torproject.org gitolite.mass-repos-migration --include 'user/.*' --exclude '.*tor-?browser.*' INFO: skipping project user/aagbsn/bridgedb in category Migrated to GitLab [...] INFO: skipping project user/phw/atlas in category Migrated to GitLab INFO: processing project user/phw/obfsproxy (Philipp's obfsproxy repository) in category Users' development repositories (Attic) INFO: Successfully connected to https://gitlab.torproject.org INFO: user repository detected, trying to find fork phw/obfsproxy WARNING: no existing fork found, entering user fork subroutine INFO: found 6 GitLab projects matching 'obfsproxy' (https://gitweb.torproject.org/user/phw/obfsproxy.git) 0 legacy/gitolite/debian/obfsproxy 1 legacy/gitolite/debian/obfsproxy-legacy 2 legacy/gitolite/user/asn/obfsproxy 3 legacy/gitolite/user/ioerror/obfsproxy 4 tpo/anti-censorship/pluggable-transports/obfsproxy 5 tpo/anti-censorship/pluggable-transports/obfsproxy-legacy select parent to fork from, or enter to abort: ^G4 INFO: repository is not empty: in-pack: 2104, packs: 1, size-pack: 414 fork project tpo/anti-censorship/pluggable-transports/obfsproxy into legacy/gitolite/user/phw/obfsproxy^G [Y/n] INFO: loading project tpo/anti-censorship/pluggable-transports/obfsproxy INFO: forking project user/phw/obfsproxy into namespace legacy/gitolite/user/phw INFO: waiting for fork to complete... INFO: fork status: started, sleeping... INFO: fork finished INFO: cloning and force pushing from user/phw/obfsproxy to legacy/gitolite/user/phw/obfsproxy INFO: deleting branch protection: <class 'gitlab.v4.objects.branches.ProjectProtectedBranch'> => {'id': 2723, 'name': 'master', 'push_access_levels': [{'id': 2864, 'access_level': 40, 'access_level_description': 'Maintainers', 'deploy_key_id': None}], 'merge_access_levels': [{'id': 2753, 'access_level': 40, 'access_level_description': 'Maintainers'}], 'allow_force_push': False} INFO: cloning repository git-rw.torproject.org:/srv/git.torproject.org/repositories/user/phw/obfsproxy.git in /tmp/tmp6orvjggy/user/phw/obfsproxy Cloning into bare repository '/tmp/tmp6orvjggy/user/phw/obfsproxy'... INFO: pushing to GitLab: https://gitlab.torproject.org/legacy/gitolite/user/phw/obfsproxy remote: remote: To create a merge request for bug_10887, visit: remote: https://gitlab.torproject.org/legacy/gitolite/user/phw/obfsproxy/-/merge_requests/new?merge_request%5Bsource_branch%5D=bug_10887 remote: [...] To ssh://gitlab.torproject.org/legacy/gitolite/user/phw/obfsproxy + 2bf9d09...a8e54d5 master -> master (forced update) * [new branch] bug_10887 -> bug_10887 [...] INFO: migrating repo INFO: migrating Gitolite repository https://gitweb.torproject.org/user/phw/obfsproxy.git to GitLab project https://gitlab.torproject.org/legacy/gitolite/user/phw/obfsproxy INFO: adding entry to rewrite_map /home/anarcat/src/tor/tor-puppet/modules/profile/files/git/gitolite2gitlab.txt INFO: modifying gitolite.conf to add: "config gitweb.category = Migrated to GitLab" INFO: rewriting gitolite config /home/anarcat/src/tor/gitolite-admin/conf/gitolite.conf to change project user/phw/obfsproxy to category Migrated to GitLab INFO: processing project user/phw/scramblesuit (Philipp's ScrambleSuit repository) in category Users' development repositories (Attic) INFO: user repository detected, trying to find fork phw/scramblesuit WARNING: no existing fork found, entering user fork subroutine WARNING: no matching gitlab project found for user/phw/scramblesuit INFO: user fork subroutine failed, resuming normal procedure INFO: searching for projects matching scramblesuit import gitolite project user/phw/scramblesuit into gitlab legacy/gitolite/user/phw/scramblesuit with desc 'Philipp's ScrambleSuit repository'?^G [Y/n] INFO: checking if remote repo https://git.torproject.org/user/phw/scramblesuit exists INFO: importing gitolite project user/phw/scramblesuit into gitlab legacy/gitolite/user/phw/scramblesuit with desc 'Philipp's ScrambleSuit repository' INFO: importing project into GitLab INFO: Successfully connected to https://gitlab.torproject.org INFO: loading group legacy/gitolite/user/phw INFO: creating repository scramblesuit (scramblesuit) in namespace legacy/gitolite/user/phw from https://git.torproject.org/user/phw/scramblesuit into https://gitlab.torproject.org/legacy/gitolite/user/phw/scramblesuit INFO: archiving project INFO: migrating Gitolite repository https://gitweb.torproject.org/user/phw/scramblesuit.git to GitLab project https://gitlab.torproject.org/legacy/gitolite/user/phw/scramblesuit INFO: adding entry to rewrite_map /home/anarcat/src/tor/tor-puppet/modules/profile/files/git/gitolite2gitlab.txt INFO: modifying gitolite.conf to add: "config gitweb.category = Migrated to GitLab" INFO: rewriting gitolite config /home/anarcat/src/tor/gitolite-admin/conf/gitolite.conf to change project user/phw/scramblesuit to category Migrated to GitLab [...]

Acute eyes will notice the bell used as a notification mechanism as well in this transcript.

A lot of the code is now useless for us, but some, like "commit and push" or is-repo-empty live on in the git module and, of course, the gitlab module has grown some legs along the way. We've also found fun bugs, like a file descriptor exhaustion in bash, among other oddities. The retirement milestone and issue 41215 has a detailed log of the migration, for those curious.

This was a challenging project, but it feels nice to have this behind us. This gets rid of 2 of the 4 remaining machines running Debian "old-old-stable", which moves a bit further ahead in our late bullseye upgrades milestone.

Full transparency: we tested GPT-3.5, GPT-4, and other large language models to see if they could answer the question "write a set of rewrite rules to redirect GitWeb to GitLab". This has become a standard LLM test for your faithful writer to figure out how good a LLM is at technical responses. None of them gave an accurate, complete, and functional response, for the record.

The actual rewrite rules as of this writing follow, for humans that actually like working answers provided by expert humans instead of artificial intelligence which currently seem to be, glorified, mansplaining interns.

git.torproject.org rewrite rules

Those rules are relatively simple in that they rewrite a single URL to its equivalent GitLab counterpart in a 1:1 fashion. It relies on the rewrite map mentioned above, of course.

RewriteEngine on # this RewriteMap connects the gitweb projects to their GitLab # equivalent RewriteMap gitolite2gitlab "txt:/etc/apache2/gitolite2gitlab.txt" # if this becomes a performance bottleneck, convert to a DBM map with: # # $ httxt2dbm -i mapfile.txt -o mapfile.map # # and: # # RewriteMap mapname "dbm:/etc/apache/mapfile.map" # # according to reports lavamind found online, we hit such a # performance bottleneck only around millions of entries, which is not our case # those two rules can go away once all the projects are # migrated to GitLab # # this matches the request URI so we can check the RewriteMap # for a match next # # WARNING: this won't match URLs without .git in them, which # *do* work now. one possibility would be to match the request # URI (without query string!) with: # # /git/(.*)(.git)?/(((branches|hooks|info|objects/).*)|git-.*|upload-pack|receive-pack|HEAD|config|description)?. # # I haven't been able to figure out the actual structure of # those URLs, so it's really hard to figure out the boundaries # of the project name here. I stopped after pouring around the # http-backend.c code in git # itself. https://www.git-scm.com/docs/http-protocol is also # kind of incomplete and unsatisfying. RewriteCond %{REQUEST_URI} ^/(git/)?(.*).git/.*$ # this makes the RewriteRule match only if there's a match in # the rewrite map RewriteCond ${gitolite2gitlab:%2|NOT_FOUND} !NOT_FOUND RewriteRule ^/(git/)?(.*).git/(.*)$ https://gitlab.torproject.org/${gitolite2gitlab:$2}.git/$3 [R=302,L] # Fallback everything else to GitLab RewriteRule (.*) https://gitlab.torproject.org [R=302,L]

gitweb.torproject.org rewrite rules

Those are the vastly more complicated GitWeb to GitLab rewrite rules.

Note that we say "GitWeb" but we were actually not running GitWeb but cgit, as the former didn't actually scale for us.

RewriteEngine on # this RewriteMap connects the gitweb projects to their GitLab # equivalent RewriteMap gitolite2gitlab "txt:/etc/apache2/gitolite2gitlab.txt" # special rule to process targets of the old spec.tpo site and # bring them to the right redirect on the new spec.tpo site. that should turn, for example: # # https://gitweb.torproject.org/torspec.git/tree/address-spec.txt # # into: # # https://spec.torproject.org/address-spec RewriteRule ^/torspec.git/tree/(.*).txt$ https://spec.torproject.org/$1 [R=302] # list of endpoints taken from cgit's cmd.c # those two RewriteCond are necessary because we don't move # all repositories at once. once the migration is completed, # they can be removed. # # and yes, they are copied all over the place below # # create a match for the project name to check if the project # has been moved to GitLab RewriteCond %{REQUEST_URI} ^/(.*).git(/.*)?$ # this makes the RewriteRule match only if there's a match in # the rewrite map RewriteCond ${gitolite2gitlab:%1|NOT_FOUND} !NOT_FOUND # main project page, like summary below RewriteRule ^/(.*).git/?$ https://gitlab.torproject.org/${gitolite2gitlab:$1}/ [R=302,L] # summary RewriteCond %{REQUEST_URI} ^/(.*).git/.*$ RewriteCond ${gitolite2gitlab:%1|NOT_FOUND} !NOT_FOUND RewriteRule ^/(.*).git/summary/?$ https://gitlab.torproject.org/${gitolite2gitlab:$1}/ [R=302,L] # about RewriteCond %{REQUEST_URI} ^/(.*).git/.*$ RewriteCond ${gitolite2gitlab:%1|NOT_FOUND} !NOT_FOUND RewriteRule ^/(.*).git/about/?$ https://gitlab.torproject.org/${gitolite2gitlab:$1}/ [R=302,L] # commit RewriteCond %{REQUEST_URI} ^/(.*).git/.*$ RewriteCond ${gitolite2gitlab:%1|NOT_FOUND} !NOT_FOUND RewriteCond "%{QUERY_STRING}" "(.*(?:^|&))id=([^&]*)(&.*)?$" RewriteRule ^/(.*).git/commit/? https://gitlab.torproject.org/${gitolite2gitlab:$1}/-/commit/%2 [R=302,L,QSD] RewriteCond %{REQUEST_URI} ^/(.*).git/.*$ RewriteCond ${gitolite2gitlab:%1|NOT_FOUND} !NOT_FOUND RewriteRule ^/(.*).git/commit/? https://gitlab.torproject.org/${gitolite2gitlab:$1}/-/commits/HEAD [R=302,L] # diff, incomplete because can diff arbitrary refs and files in cgit but not in GitLab, hard to parse RewriteCond %{REQUEST_URI} ^/(.*).git/.*$ RewriteCond ${gitolite2gitlab:%1|NOT_FOUND} !NOT_FOUND RewriteCond %{QUERY_STRING} id=([^&]*) RewriteRule ^/(.*).git/diff/? https://gitlab.torproject.org/${gitolite2gitlab:$1}/-/commit/%1 [R=302,L,QSD] # patch RewriteCond %{REQUEST_URI} ^/(.*).git/.*$ RewriteCond ${gitolite2gitlab:%1|NOT_FOUND} !NOT_FOUND RewriteCond %{QUERY_STRING} id=([^&]*) RewriteRule ^/(.*).git/patch/? https://gitlab.torproject.org/${gitolite2gitlab:$1}/-/commit/%1.patch [R=302,L,QSD] # rawdiff, incomplete because can show only one file diff, which GitLab cannot RewriteCond %{REQUEST_URI} ^/(.*).git/.*$ RewriteCond ${gitolite2gitlab:%1|NOT_FOUND} !NOT_FOUND RewriteCond %{QUERY_STRING} id=([^&]*) RewriteRule ^/(.*).git/rawdiff/?$ https://gitlab.torproject.org/${gitolite2gitlab:$1}/-/commit/%1.diff [R=302,L,QSD] # log RewriteCond %{REQUEST_URI} ^/(.*).git/.*$ RewriteCond ${gitolite2gitlab:%1|NOT_FOUND} !NOT_FOUND RewriteCond %{QUERY_STRING} h=([^&]*) RewriteRule ^/(.*).git/log/?$ https://gitlab.torproject.org/${gitolite2gitlab:$1}/-/commits/%1 [R=302,L,QSD] RewriteCond %{REQUEST_URI} ^/(.*).git/.*$ RewriteCond ${gitolite2gitlab:%1|NOT_FOUND} !NOT_FOUND RewriteRule ^/(.*).git/log/?$ https://gitlab.torproject.org/${gitolite2gitlab:$1}/-/commits/HEAD [R=302,L] RewriteCond %{REQUEST_URI} ^/(.*).git/.*$ RewriteCond ${gitolite2gitlab:%1|NOT_FOUND} !NOT_FOUND RewriteRule ^/(.*).git/log(/?.*)$ https://gitlab.torproject.org/${gitolite2gitlab:$1}/-/commits/HEAD$2 [R=302,L] # atom RewriteCond %{REQUEST_URI} ^/(.*).git/.*$ RewriteCond ${gitolite2gitlab:%1|NOT_FOUND} !NOT_FOUND RewriteCond %{QUERY_STRING} h=([^&]*) RewriteRule ^/(.*).git/atom/?$ https://gitlab.torproject.org/${gitolite2gitlab:$1}/-/commits/%1 [R=302,L,QSD] RewriteCond %{REQUEST_URI} ^/(.*).git/.*$ RewriteCond ${gitolite2gitlab:%1|NOT_FOUND} !NOT_FOUND RewriteRule ^/(.*).git/atom/?$ https://gitlab.torproject.org/${gitolite2gitlab:$1}/-/commits/HEAD [R=302,L,QSD] # refs, incomplete because two pages in GitLab, defaulting to "tags" RewriteCond %{REQUEST_URI} ^/(.*).git/.*$ RewriteCond ${gitolite2gitlab:%1|NOT_FOUND} !NOT_FOUND RewriteRule ^/(.*).git/refs/?$ https://gitlab.torproject.org/${gitolite2gitlab:$1}/-/tags [R=302,L] RewriteCond %{REQUEST_URI} ^/(.*).git/.*$ RewriteCond ${gitolite2gitlab:%1|NOT_FOUND} !NOT_FOUND RewriteCond %{QUERY_STRING} h=([^&]*) RewriteRule ^/(.*).git/tag/? https://gitlab.torproject.org/${gitolite2gitlab:$1}/-/tags/%1 [R=302,L,QSD] # tree RewriteCond %{REQUEST_URI} ^/(.*).git/.*$ RewriteCond ${gitolite2gitlab:%1|NOT_FOUND} !NOT_FOUND RewriteCond %{QUERY_STRING} id=([^&]*) RewriteRule ^/(.*).git/tree(/?.*)$ https://gitlab.torproject.org/${gitolite2gitlab:$1}/-/tree/%1$2 [R=302,L,QSD] RewriteCond %{REQUEST_URI} ^/(.*).git/.*$ RewriteCond ${gitolite2gitlab:%1|NOT_FOUND} !NOT_FOUND RewriteRule ^/(.*).git/tree(/?.*)$ https://gitlab.torproject.org/${gitolite2gitlab:$1}/-/tree/HEAD$2 [R=302,L] # /-/tree has no good default in GitLab, revert to HEAD which is a good # approximation (we can't assume "master" here anymore) RewriteCond %{REQUEST_URI} ^/(.*).git/.*$ RewriteCond ${gitolite2gitlab:%1|NOT_FOUND} !NOT_FOUND RewriteRule ^/(.*).git/tree/?$ https://gitlab.torproject.org/${gitolite2gitlab:$1}/-/tree/HEAD [R=302,L] # plain RewriteCond %{REQUEST_URI} ^/(.*).git/.*$ RewriteCond ${gitolite2gitlab:%1|NOT_FOUND} !NOT_FOUND RewriteCond %{QUERY_STRING} h=([^&]*) RewriteRule ^/(.*).git/plain(/?.*)$ https://gitlab.torproject.org/${gitolite2gitlab:$1}/-/raw/%1$2 [R=302,L,QSD] RewriteCond %{REQUEST_URI} ^/(.*).git/.*$ RewriteCond ${gitolite2gitlab:%1|NOT_FOUND} !NOT_FOUND RewriteRule ^/(.*).git/plain(/?.*)$ https://gitlab.torproject.org/${gitolite2gitlab:$1}/-/raw/HEAD$2 [R=302,L] # blame: disabled #RewriteCond %{REQUEST_URI} ^/(.*).git/.*$ #RewriteCond ${gitolite2gitlab:%1|NOT_FOUND} !NOT_FOUND #RewriteCond %{QUERY_STRING} h=([^&]*) #RewriteRule ^/(.*).git/blame(/?.*)$ https://gitlab.torproject.org/${gitolite2gitlab:$1}/-/blame/%1$2 [R=302,L,QSD] # same default as tree above #RewriteCond %{REQUEST_URI} ^/(.*).git/.*$ #RewriteCond ${gitolite2gitlab:%1|NOT_FOUND} !NOT_FOUND #RewriteRule ^/(.*).git/blame(/?.*)$ https://gitlab.torproject.org/${gitolite2gitlab:$1}/-/blame/HEAD/$2 [R=302,L] # stats RewriteCond %{REQUEST_URI} ^/(.*).git/.*$ RewriteCond ${gitolite2gitlab:%1|NOT_FOUND} !NOT_FOUND RewriteRule ^/(.*).git/stats/?$ https://gitlab.torproject.org/${gitolite2gitlab:$1}/-/graphs/HEAD [R=302,L] # still TODO: # repolist: once migration is complete # # cannot be done: # atom: needs a feed token, user must be logged in # blob: no direct equivalent # info: not working on main cgit website? # ls_cache: not working, irrelevant? # objects: undocumented? # snapshot: pattern too hard to match on cgit's side # special case, we keep a copy of the main index on the archive RewriteRule ^/?$ https://archive.torproject.org/websites/gitweb.torproject.org.html [R=302,L] # Fallback: everything else to GitLab RewriteRule .* https://gitlab.torproject.org [R=302,L]

The reference copy of those is available in our (currently private) Puppet git repository.

The jump in interest rates to the highest level in over 20 years that hit in summer 2023 for the US, UK and many other countries is still impacting the Software industry. Rates may be due to drop soon, but currently it has choked off investment, upped borrowing costs and lead to many software companies making engineers redundant to please the markets.

For the UK the estimate is around 8% of software industry jobs made redundant. Although strangely, the overall trend in vacancies for software engineers continues to march upwards, the initial surge after the pandemic dipped last summer but has now recovered.
But if you work in the industry you are bound to have colleagues and friends who have been made redundant, if you are lucky enough to have not been impacted personally.

Given recent history, I thought it may be worth reflecting on my personal experience of the whole hiring and firing process, in the tech industry. It is a UK centric view, but the companies I have worked for in the last 8 years are US software companies.

I have been fired, hired and conducted technical interviews to hire others. Giving me a few different perspectives.

This post is NOT about getting your first Software job

I first got a coding job in the public sector and it was as a self taught web developer in the 1990s, before web development was a thing you could get a degree in. So I initially got a job in IT support, volunteered to act up (ie no pay increase) and built some websites that were needed, then became a full time web developer through a portfolio of work, ie sites.

Today junior developers may have to prove themselves suitable by artificial measures. I skipped these, so I do not have any professional certifications, or any to recommend. I also don't know how to ace coding algorithm or personality profile assessments.

Once you are 5-10 years in to a software career - none of those approaches are used for hiring decisions.

Only large companies are likely to subject you to them, and that is really out of fairness on the juniors who have to go through them, and to screen out dodgy applicants. Screening just needs to be passed, it will not have any input into whether you get the job. Hence Acing the coding interview as promoted by sites such as LeetCode is not even a thing, only passing coding exercise systems in order to start, or switch to, a career as a developer. I would recommend starting an open source project instead, to demonstrate you can actually code.

The majority of small to medium software companies and of job vacancies require experience and in effect have no vacancies for the most junior software grades with less than 3 years under their belt. So they tend not to use any of these filtering methods. They just want to see proof that you are already a developer, and usually base that on face to face interviews and examples of your code you provide them. So much like how I was originally hired back in the 1990s.

I have only been subject to a LeetCode style test once, which was for a generic job application, ie hiring for numbers of SREs of various seniority, for a FANNG.

When you get that unexpected one to one Zoom call with you manager appear in your calendar these days, it is unlikely to be great news 😓

In the majority of cases the firing process, or to be more polite, redundancy, is all about balancing the finances of the whole company or institution. As such it is very unlikely to be about you.

Of course people are also fired as individuals for various reasons, one of which is actually not being any good at their job, failing to get along with their manager, being a bad culture fit, jobs turning out not to be what was advertised, or expressing political views. Since unlike where I once worked, in the UK Education sector, where 50% of staff are union members, US software companies will have less than 1% membership, so don't tend to respond well to dissent.

Mostly this happens via failing probation, at around 15% then maybe another 5% annually for disciplinary / performance improvement failure.

If you want to try getting individually fired then go the overemployed route. Get two or three jobs at once and test how long it takes before the company notices you giving 110% is now only 40% and fire you. The rule of thumb is the larger the company, the longer it takes!

But this post's focus isn't about individual firing, its about organizational hiring and firing.

1. A company may be doing badly in a slow long term way, so it has to chop as part of a restructure and downsize to attempt to fix that.

2. Alternatively the company could be doing really well. So it gets the attention of a big investment company and is bought up and merged with its rival. To fix overlap and justify the merger - both companies lose 20% of staff.

3. Maybe it needs to pivot towards a new area (currently likely to be AI) and so chop 20% of its staff so it can hire 15% experienced, and pricey, AI developers.

4. Or it may just have had a one off external impacting event that hit it financially. So to balance the earnings for that year and keep its share price good, it chops a bunch of staff. It will rehire next year, when it suits the balance sheet. This is the example in which I was made redundant along with 5% of staff, it was a big company, so that was a few thousand people globally.

5. Finally it may be an industry wide phenomenon as it is with the current redundancies in the software industry. A world clamp down on easy cheap loans means investment company driven industries such as tech. are no longer awash with spare cash. Cut backs look good right now, and keep the share price high.
Hence redundancies that are nothing to do with the industry itself or its future prospects.

That is mirrored in who is fired. Companies do not keep a log book of gold stars and black marks against each employee. They do not use organizational triggered rounds of redundancies to select individuals to fire. They certainly have not got the capability to accurately determine all the best employees and only fire the worst ones. You will be fired based on what part of the organization you are in, how much it is valued in the current strategy and how much you cost vs others who could do your job. If you are currently between teams / or in a new team or role which has yet to establish itself, when the music stops - like musical chairs, bad luck you are out.

The only personal element may be if a whole team is seen as under performing or difficult to manage it might be axed. No matter that it contains a star performer. Decisions may also be geographic. Lets axe the Greek office, save by withdrawing engineering from a country, which is again how I was made redundant, the rest of my team was in Greece.
Alternatively it may be, fire under 20 staff from each country, to avoid more burdensome regulation for bulk layoffs.

The organization could create an insecure / downturn atmosphere to encourage staff to leave. Because its a lot cheaper for people to leave than the company paying out redundancy settlements.

Redundancy keeps the average employees 😐

As a result in response to significant redundancies an organisation will tend to lose more of the best employees - since they are the most able to move, the most likely to get a big pay rise if they move and the least likely to want to stick around if they see negative organisational change. The software industry has very high staff turnover at almost 20%. Out weighing any nominal idea of removing less efficient staff.

If a company handles things well it may only lose a representative productivity range of staff from best to worst. But a bulk redundancy process is likely to lead to the biggest loss in the top talent, get rid of slightly more of the bottom dwellers and so result in maximising the mediocre!

In summary the answer to 'Why me?' in group redundancies is "because you were there" ... and you didn't have a personal friendship with the CEO 😉. Of course that is why new CEOs are often brought in to restructure - the first step of which is to take an axe to the current C-suite.

Some of the best software engineers I have worked with have been made redundant at some point in their career. Group redundancies are not about you or how well you do your job. But taking it personally and challenging the messenger, with why me?, as demonstrated by recent viral videos, is an understandable emotional response to rejection, and the misguided belief that work aims to be some form of meritocracy, in the same way college might.

LIFO rather than FIFO is the norm in Firing. New hires are less likely to have established themselves as essential to the company, and have less personal connections within it. More importantly many countries redundancy legislation doesn't kick in until over 2 years of employment and the longer you have been employed the more the company will have to pay to terminate you.

Which means a new hire who has uprooted for their new tech job, will be the most likely to find themselves losing that job when bulk redundancies hit.
But FIFO has its place, next would be older engineers. Some companies don't even hire hands on engineers much over the age of 40, anyway. But staff near retirement have at most only a few years left to contribute and may cost more for the same grade. So encouraging early retirement can be part of the bulk redundancy process.

Whilst redundancy is all about costs and not about your personal performance. That is not to say companies who pass the redundancy choices down to junior managers may not end up with firing disproportionate numbers of workers who are not from the same background as their manager, ie white USA males, ideally younger than the manager. But prejudice is not personal either. That is pretty much what defines it as prejudice, a pre-judgement of people based on physical characteristics rather than their ability at the job. Also people are least likely to fire staff that they have the most in common with, resulting in prejudicial firing.
Unfortunately it seems many companies with a good diversity policy for hiring, may not have adequate ones for firing. Again resulting in losing more of the higher performing staff.

I have heard of a case where someone got a new manager, who on joining was told to cut from his team, so he fired everyone outside the USA. The worker was so keen to stay at their current employer they went over the head of their manager to senior management and asked for their redundancy to be repealed. Since they had been at the company many years and personally knew senior management, this worked.
Alternatively a more purely cost based restructure may hire all developers from cheaper countries and fire most of them in the US. As happened with Google's Python team recently.

The company may set up a pool process for bulk redundancies if numbers are high enough per country, where you can fight for a place on the lifeboat of remaining positions.

In both cases I would recommend that you don't waste time on a company that doesn't value you. If you do stay you risk, dealing with the bulk redundancy aftermath. Which will be present unless the redundancies were for a pivot (3) or one off event (4).
An increased workload, pay freezes, no bonus, needing to over work to justify being kept on, plus a negative work atmosphere.

In a case where I stayed after the department I was in was axed, I had to reapply for a new job which was moved to a different division. The work was less worthwhile and at the time, the employment of in-house software developers as a whole, was questioned as being unnecessary for the organisation. I outstayed my welcome for 18 months of legacy commercial software support, before getting the message and quitting.

Lesson learnt, if you must ask to to stay in your company, via senior management, a pool or reapplication. Make sure you look around and apply for other jobs outside of it at the same time.

You also miss out on a minimum of a couple of months tax free pay as a settlement.

On the other hand, if the redundancy round is for a more minor pivot, and you are happy in the role, it may be well worth staying around to see how things pan out.

Of course you may get no choice in the matter, in which case, get straight into GET HIRED mode, and start the job search. If you can manage it fast enough, you will benefit financially from the whole process. Although if the reason is (5) a sector wide reduction, then it will be take longer and be harder to obtain the usual 20% pay increase that a new position can offer.

Why change jobs (aside from being fired!)

It is a lot easier to get a pay rise or promotion by changing companies, than being promoted internally. To fast track your career to a principal or architect top IC role. Or just get a pay rise.

Changing jobs gives you much wider experience, of different technology, approaches and cultures. Making you a better engineer.

If you have been in your current job over 10 years without significant internal promotions or changes of role then it is detrimental to your CV, indicating you are stuck in a rut and unable to handle change, eg. new technology.

You want to shift sectors.
I changed from public sector web developer, to commercial cloud engineer with one move.

You want to get into new technology that is not used in your current role.
I changed from a Python, Ruby config management automation engineer to a Kubernetes Golang engineer with another.

You want to change your role in tech, or leave it entirely. For example get out of sales as a solution architect and back into a more technical role as an SRE.

On that basis many software engineers change jobs every 2 or 3 years for part of their careers. Its expected, the average engineer in a FAANG stays less than 3 years.

Of course you probably need to be in a job for at least 2 years to fully master it.
If your CV has loads of similar positions where you barely make it past the probation period, its marking you out as a failure at those roles == Fail hiring at the first step, the HR CV check.

The other problem is that changing jobs to change roles, even if its just to use a new language or framework can be blocked by roles requiring experience in that area on the CV to get interviewed for the job in the first place. For software engineering that is less of an issue. Since tech changes faster than any other sector.
You just need to prove you have a range of experience and software languages and are willing to learn, early in a technology boom. To catch the cloud engineer bus, I got a job in it in 2016. The US cloud sector was $8 billion back then. It is $600 billion now. Similarly to get on board with Golang and Kubernetes in 2019. In the first few years of a tech boom most companies will initially have to cross train engineers without direct experience. The corollary of that is that in the current downturn attempting to pivot to an established technology, which k8s has become, is going to be much harder.

Clearly ML ops and AI data science are current booming areas. The demand so far outstrips current supply that for switching to a more junior Python AI role in them may pay as well as a senior Django web developer for example. Note that geographic software pay differences are why you often come across engineers of other nationalities emigrating to, and working in the higher paying countries, USA, Canada and Australia. I have worked with many people from the UK and Europe who live in the USA, and Indians who live there or Europe, for example.

Of course as a cheap foreign worker myself, I too stick with US companies partly because they pay rather more than UK ones, even if a lot lower than what I would get if I moved there 😉

Now is the time when such a switch will be easier to accomplish without having to work nights doing courses, certifications and personal projects. The usual means of demonstrating your ability without any work experience.
So around £60k for a junior role, but in 3-4 years it should jump to at least £100k for a senior AI engineer. Of course for US salaries add 30%, plus usually free medical, life insurance etc. The lower tax rates cancels out the higher cost of living in the US ... so its UK salary +30% in real terms*. Researching the going rate for the particular role, technical skills and sector you are applying for is a necessary part of the hiring process. In order that you don't let recruitment bargain you down too low.

*

The caveat here is that moving jobs in a down turn, as we are arguably experiencing currently, can depress the market salary rates and if you are already at the top of those when made redundant, can mean you have to take a pay cut for a year or two rather than face the cost of long term unemployment.

The hiring process for an experienced software engineer role

The process will consist of a technical assessment task and at least 3 interviews, screening, manager and technical. With another for introduction to team mates / office etc. which is unlikely to have any effect on the hiring decision unless you are your potential new manager take an instant personal dislike to each other.

The HR screening interview, just checks you are a genuine candidate for the job.

The Manager interview similarly is more about checking you will fit in with the company and team, plus that you have basic personal communication skills.

Being a Technical Interviewer

A phrase you'll often hear is that everything in Python is an object, and every object has a type. This points to the importance of data types in Python. However, often what an object can do is more important than what it is. So, it's useful to discuss categories of data types and one of the main categories is Python's sequence.
In this tutorial, you'll learn about:

Basic characteristics of a sequence

Operations that are common to most sequences

Special methods associated with sequences

Abstract base classes Sequence and MutableSequence

User-defined mutable and immutable sequences and how to create them

This tutorial assumes that you're familiar with Python's built-in data types and with the basics of object-oriented programming.

Building Blocks of Python Sequences

It's likely you used a Python sequence the last time you wrote Python code, even if you don't know it. The term sequence doesn't refer to a specific data type but to a category of data types that share common characteristics.

Characteristics of Python Sequences

A sequence is a data structure that contains items arranged in order, and you can access each item using an integer index that represents its position in the sequence. You can always find the length of a sequence. Here are some examples of sequences from Python's basic built-in data types:

Lists, tuples, and strings are among Python's most basic data types. Even though they're different types with distinct characteristics, they have some common traits. You can summarize the characteristics that define a Python sequence as follows:

A sequence is an iterable, which means you can iterate through it.

A sequence has a length, which means you can pass it to to get its number of elements.

An element of a sequence can be accessed based on its position in the sequence using an integer index. You can use the square bracket notation to index a sequence.

There are other built-in data types in Python that also have all of these characteristics. One of these is the range object:

You can iterate through a range object, which makes it iterable. You can also find its length using len() and fetch items through indexing. Therefore, a range object is also a sequence.

You can also verify that bytes and bytearray objects, two of Python's built-in data structures, are also sequences. Both are sequences of integers. A bytes sequence is immutable, while a bytearray is mutable.

Special Methods Associated With Python Sequences

In Python, the key characteristics of a data type are determined using special methods, which are defined in the class definitions. The special methods associated with the properties of sequences are the following:

: This special method makes an object iterable using Python's preferred iteration protocol. However, it's possible for a class without an .__iter__() special method to create iterable objects if the class has a .__getitem__() special method that supports iteration. Most sequences have an .__iter__() special method, but it's possible to have a sequence without this method.

: This special method defines the length of an object, which is normally the number of elements contained within it. The len() built-in function calls an object's .__len__() special method. Every sequence has this special method.

: This special method enables you to access an item from a sequence. The square brackets notation can be used to fetch an item. The expression countries[0] is equivalent to countries.__getitem__(0). For sequences, .__getitem__() should accept integer arguments starting from zero. Every sequence has this special method. This method can also ensure an object is iterable if the .__iter__() special method is missing.

Therefore, all sequences have a .__len__() and a .__getitem__() special method and most also have .__iter__().

However, it's not sufficient for an object to have these special methods to be a sequence. For example, many mappings also have these three methods but mappings aren't sequences.

A dictionary is an example of a mapping. You can find the length of a dictionary and iterate through its keys using a for loop or other iteration techniques. You can also fetch an item from a dictionary using the square brackets notation.

This characteristic is defined by .__getitem__(). However, .__getitem__() needs arguments that are dictionary keys and returns their matching values. You can't index a dictionary using integers that refer to an item's position in the dictionary. Therefore, dictionaries are not sequences.

Slicing in Python Sequences
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53rd issue of Andrei Neagoie's must-read monthly Python Newsletter: Python Security Best Practices, State of Python 2024, Prompt Engineering, and much more. Read the full newsletter to get up-to-date with everything you need to know from last month.

April 30, 2024

Unlock the power of Pieces, right in your terminal! Our open-source CLI plugin helps you manage code snippets, chat with your on-device AI copilot, and even auto-generate commit messages. Join our community to refine your Python skills and influence a product used by 1000s of devs. Contribute today →
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Talk Python interviews Tony Sherman and they discuss the current state of serverless computing in the Python world, including some of the newer tools and best practices.
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Discussions

Articles & Tutorials

The async and await keywords that form Python's coroutine mechanism can be used for class methods as well as the more common case of functions. This article shows you how you can use asyncio with your objects.
JASON BROWNLEE

In this video course, you'll learn about Python lists and tuples, including how to define and manipulate them in your code. By the end of the course, you'll be ready to effectively use lists and tuples in your programming projects.
REAL PYTHON course

This "Things I've Learned" post discusses how to take advantage of test parameterisation in pytest.
RODRIGO GIRÃO SERRÃO

Projects & Code

Events

Happy Pythoning!
This was PyCoder's Weekly Issue #627.
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April 30, 2024 07:30 PM UTC

Enhancements in the Endpoints tool window, extended GitHub gists support for notebooks, and navigation and refactoring across notebooks and scripts - these are just some of the improvements you'll find in PyCharm 2024.1.1!

You can download the latest version from our download page or update your current version through our free Toolbox App.

Key features

JetBrains AI Assistant in PyCharm Community Edition

JetBrains AI Assistant is now available in version 2024.1.1 of PyCharm Community Edition! With features ranging from smart suggestions to code generation, now Community Edition users can also enhance their coding journey with AI Assistant.

Improvements to the Endpoints tool window

Search URLs faster and more efficiently with the improved Endpoints tool window in PyCharm 2024.1.1. Use the dedicated Endpoints tab in Search Anywhere to have all your endpoints grouped by application with their routes displayed.

Navigation and refactoring across notebooks and scripts

Enjoy navigation and refactoring between notebooks and Python scripts within a single project in PyCharm. Find declarations or usages easily, use the Rename refactoring, and have our full spectrum of code inspections at your disposal. Changes are synchronized across file types, so if you employ any of these features in a notebook, they will automatically be applied to the related script, and vice versa.

Create gists from Jupyter notebooks

You can share Jupyter notebooks seamlessly and quickly now that PyCharm offers full support for GitHub gists. Create a gist for a single notebook or select several files in the Project tool window and create a Git repo with all of them at once.

DataFrame statistics and distribution histograms

Review essential statistics directly within DataFrame headers in both Jupyter notebooks and Python scripts. Gain instant insights into how your data is distributed via the histograms provided in the DataFrame header.

IPython config file in the console

Save time by configuring your IPython console automatically using config files. Eliminate the need to import dependencies manually every time you use the console.

And that's not all! Please visit our What's New page to discover other improvements in PyCharm 2024.1.1. You can also check out our full release notes for all the details to ensure you don't miss out on trying any of the enhancements.

Thank you for your continued support as we strive to improve your PyCharm experience. Please report any bugs through our issue tracker so we can take care of them as soon as possible. Connect with us on X (formerly Twitter) to share your valuable feedback on PyCharm 2024.1.1!

April 30, 2024 06:28 PM UTC

Matplotlib is one of the most popular data visualization packages for the Python programming language. It allows you to create many different charts and graphs. This tutorial focuses on adding a "watermark" to your graph. If you need to learn the basics, you might want to check out Matplotlib- An Intro to Creating Graphs with Python.

Let's get started!

Installing Matplotlib

If you don't have Matplotlib on your computer, you must install it. Fortunately, you can use pip, the Python package manager utility that comes with Python.

Open up your terminal or command prompt and run the following command:

python -m pip install matplotlib

Pip will now install Matplotlib and any dependencies that Matplotlib needs to work properly. Assuming that Matplotlib installs successfully, you are good to go!

Watermarking Your Graph

Adding a watermark to a graph is a fun way to learn how to use Matplotlib. For this example, you will create a simple bar chart and then add some text. The text will be added at an angle across the graph as a watermark.

Open up your favorite Python IDE or text editor and create a new Python file. Then add the following code:

import matplotlib.pyplot as plt


def bar_chart(numbers, labels, pos):
    fig = plt.figure(figsize=(5, 8))
    plt.bar(pos, numbers, color="red")

    # add a watermark
    fig.text(1, 0.15, "Mouse vs Python",
             fontsize=45, color="blue",
             ha="right", va="bottom", alpha=0.4,
             rotation=25)

    plt.xticks(ticks=pos, labels=labels)
    plt.show()


if __name__ == "__main__":
    numbers = [2, 1, 4, 6]
    labels = ["Electric", "Solar", "Diesel", "Unleaded"]
    pos = list(range(4))
    bar_chart(numbers, labels, pos)

Your bar_chart() function takes in some numbers, labels and a list of positions for where the bars should be placed. You then create a figure to put your plot into. Then you create the bar chart using the list of bar positions and the numbers. You also tell the chart that you want the bars to be colored "red".

The next step is to add a watermark. To do that, you call fig.text() which lets you add text on top of your plot. Here is a quick listing of the arguments that you need to pass in:

x, y (the first two arguments are the x/y coordinates for the text)

fontsize - The size of the font

color - The color of the text

ha - Horizontal alignment

va - Vertical alignment

alpha - How transparent the text should be

rotation - How many degrees to rotate the text

The last bit of code in bar_chart() adds the ticks and labels to the bottom of the plot.

When you run this code, you will see something like this:

Isn't that neat? You now have a simple plot, and you know how to add semi-transparent text to it, too!

Wrapping Up

Proper attribution is important in academics and business. Knowing how to add a watermark to your data visualization can help you do that. You now have that knowledge when using Matplotlib.

The Matplotlib package can do many other types of plots and provides much more customization than what it covered here. Check out its documentation to learn more!

The post How to Watermark a Graph with Matplotlib appeared first on Mouse Vs Python.

April 30, 2024 03:01 PM UTC

A global variable is a variable that you can use from any part of a program, including within functions. Using global variables inside your Python functions can be tricky. You'll need to differentiate between accessing and changing the values of the target global variable if you want your code to work correctly.

Global variables can play a fundamental role in many software projects because they enable data sharing across an entire program. However, you should use them judiciously to avoid issues.
In this video course, you'll:

Understand global variables and how they work in Python

Access global variables within your Python functions directly

Modify and create global variables within functions using the global keyword

Access, create, and modify global variables within your functions with the globals() function

Explore strategies to avoid using global variables in Python code

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April 30, 2024 02:00 PM UTC

Recently I became suddenly curious about the sizes of buildings in Southampton, UK, where I live. I don't know what triggered this sudden curiosity, but I wanted to know what the largest buildings in Southampton are. In this context, I'm using "largest" to mean largest in terms of land area covered - ie. the area of the outline when viewed in plan view. Luckily I know about useful sources of geographic data, and also know how to use GeoPandas, so I could answer this question pretty quickly - in fact, in only five lines of code. I'll take you through this code below, as an example of a very simple GIS analysis.

First I needed to get hold of the data. I know Ordnance Survey release data on buildings in Great Britain, but to make this even easier we can go to Alastair Rae's website where he has split the OS data up into Local Authority areas. We need to download the buildings data for Southampton, so we go here and download a GeoPackage file.

Then we need to create a Python environment to do the analysis in. You can do this in various ways - with virtualenvs, conda environments or whatever - but you just need to ensure that Jupyter and GeoPandas are installed. Then create a new notebook and you're ready to start coding.

First, we import geopandas:

import geopandas as gpd

and then load the buildings data:

buildings = gpd.read_file("Southampton_buildings.gpkg")

The buildings GeoDataFrame has a load of polygon geometries, one for each building. We can calculate the area of a polygon with the .area property - so to create a new 'area' column in the GeoDataFrame we can run:

buildings['area'] = buildings.geometry.area

I'm only interested in the largest buildings, so we can now sort by this new area column, and take the first twenty entries:

top20 = buildings.sort_values('area', ascending=False).head(20)

We can then use the lovely explore function to show these buildings on a map. This will load an interactive map in the Jupyter notebook:

top20.explore()

If you'd like to save the interactive map to a standalone HTML file then you can do this instead:

top20.explore().save("map.html")

I've done that, and uploaded that HTML file to my website - and you can view it here.

So, putting all the code together, we have:

import geopandas as gpd buildings = gpd.read_file("Southampton_buildings.gpkg") buildings['area'] = buildings.geometry.area top20 = buildings.sort_values('area', ascending=False).head(20) top20.explore()

Five lines of code, with a simple analysis, resulting in an interactive map, and all with the power of GeoPandas.

Hopefully in a future post I'll do a bit more work on this data - I'd like to make a prettier map, and I'd like to try and find some way to test my friends and see if they can work out what buildings they are.

April 30, 2024 11:30 AM UTC

<strong>Topics covered in this episode:</strong><br> <ul> <li><a href="https://wasmer.io/posts/py2wasm-a-python-to-wasm-compiler"><strong>Announcing py2wasm: A Python to Wasm compiler</strong></a></li> <li><strong>Exploring Python packages with</strong> <a href="https://oven.fming.dev"><strong>Oven</strong></a> <strong>and</strong> <a href="https://pypi-browser.org"><strong>PyPI Browser</strong></a></li> <li><a href="https://www.youtube.com/watch?v=DLBiJ5kYUFg">PyCharm Local LLM</a></li> <li><strong>Google shedding Python devs</strong> <strong>(at</strong> <strong>least in the US).</strong></li> <li><strong>Extras</strong></li> <li><strong>Joke</strong></li> </ul><a href='https://www.youtube.com/watch?v=KlLuQ7UT4t8' style='font-weight: bold;'data-umami-event="Livestream-Past" data-umami-event-episode="381">Watch on YouTube</a><br> <p><strong>About the show</strong></p> <p>Sponsored by ScoutAPM: <a href="https://pythonbytes.fm/scout">pythonbytes.fm/scout</a></p> <p><strong>Connect with the hosts</strong></p> <ul> <li>Michael: <a href="https://fosstodon.org/@mkennedy"><strong>@mkennedy@fosstodon.org</strong></a></li> <li>Brian: <a href="https://fosstodon.org/@brianokken"><strong>@brianokken@fosstodon.org</strong></a></li> <li>Show: <a href="https://fosstodon.org/@pythonbytes"><strong>@pythonbytes@fosstodon.org</strong></a></li> </ul> <p>Join us on YouTube at <a href="https://pythonbytes.fm/stream/live"><strong>pythonbytes.fm/live</strong></a> to be part of the audience. Usually Tuesdays at 11am PT. Older video versions available there too.</p> <p>Finally, if you want an artisanal, hand-crafted digest of every week of </p> <p>the show notes in email form? Add your name and email to <a href="https://pythonbytes.fm/friends-of-the-show">our friends of the show list</a>, we'll never share it.</p> <p><strong>Michael #1:</strong> <a href="https://wasmer.io/posts/py2wasm-a-python-to-wasm-compiler"><strong>Announcing py2wasm: A Python to Wasm compiler</strong></a></p> <ul> <li>py2wasm converts your Python programs to WebAssembly, running them at 3x faster speeds</li> <li>thanks to <a href="https://nuitka.net/">Nuitka</a></li> </ul> <p><strong>Brian #2:</strong> <strong>Exploring Python packages with</strong> <a href="https://oven.fming.dev"><strong>Oven</strong></a> <strong>and</strong> <a href="https://pypi-browser.org"><strong>PyPI Browser</strong></a></p> <ul> <li><a href="https://pypi.org">pypi.org</a> is great, but there are some handy alternatives</li> <li><a href="https://oven.fming.dev"><strong>Oven</strong></a> <ul> <li>Shows how to install stuff with pip, pdm, rye, and poetry</li> <li>Similar meta and description as PyPI</li> <li>Includes README.md view (no tables yet, though)</li> <li>Nice listing of versions</li> <li>Ability to look at what files are in wheels and tarballs (very cool) </li> <li>Can deploy yourself. Node/Remix app.</li> <li>Really slick.</li> </ul></li> <li><a href="https://pypi-browser.org">PyPI Browser</a> <ul> <li>View versions</li> <li>View wheel and tarball contents.</li> <li>Metadata and contents.</li> <li>No README view</li> <li>Is a Starlette app that you can deploy on your on with a private registry. So that's cool.</li> </ul></li> </ul> <p><strong>Michael #3:</strong> <a href="https://www.youtube.com/watch?v=DLBiJ5kYUFg">PyCharm Local LLM</a></p> <ul> <li>Pretty awesome full line completer based on a local LLM for PyCharm</li> <li>Requires PyCharm Professional</li> <li>An example, given this partial function in Flask: <pre><code>@blueprint.get('/listing') def listing(): videos = video_service.all_videos() </code></pre></li> </ul> <p>Typing ret →</p> <p><img src="https://python-bytes-static.nyc3.digitaloceanspaces.com/llm-complete.png" alt="img" /></p> <p>That is, typing ret autocompletes to:</p> <pre><code>return flask.render_template('home/listing.html', videos=videos) </code></pre> <p>Which is pretty miraculous, and correct.</p> <p><strong>Brian #4:</strong> <strong>Google shedding Python devs</strong> <strong>(at</strong> <strong>least in the US).</strong></p> <ul> <li><a href="https://techcrunch.com/2024/04/29/google-lays-off-staff-from-flutter-dart-python-weeks-before-its-developer-conference/">Google lays off staff from Flutter, Dart and Python teams weeks before its developer conference</a> - techcrunch</li> <li><a href="https://www.theregister.com/2024/04/29/google_python_flutter_layoffs/">Python, Flutter teams latest on the Google chopping block</a> - The Register <ul> <li>"Despite Alphabet last week <a href="https://www.theregister.com/2024/04/26/register_kettle_ai/">reporting</a> a 57 percent year-on-year jump in net profit to $23.66 billion for calendar Q1, more roles are being expunged as the mega-corp cracks down on costs."</li> <li>"As for the Python team, the current positions have <a href="https://social.coop/@Yhg1s/112332127058328855">reportedly</a> been "reduced" in favor of a new team based in Munich."</li> </ul></li> <li>MK: Related and timely: <a href="https://www.wheresyoured.at/the-men-who-killed-google/">How one power-hungry leader destroyed Google search</a></li> </ul> <p><strong>Extras</strong> </p> <p>Brian:</p> <ul> <li><a href="https://andrewwegner.com/python-gotcha-strip-functions-unexpected-behavior.html">Python Gotcha: strip, lstrip, rstrip can remove more than expected</a> <ul> <li>Reminder: You probably want .removesuffix() and .removeprefix() </li> </ul></li> </ul> <p>Michael:</p> <ul> <li><a href="https://lmstudio.ai/blog/llama-3">Using Llama3</a> in <a href="https://lmstudio.ai">LMStudio</a></li> </ul> <p><strong>Joke:</strong> <a href="https://devhumor.com/media/broken-system">Broken System</a></p>

April 30, 2024 08:00 AM UTC

April 29, 2024

We can't wait to welcome Jay Miller, Kate Chapman, Simon Willison, and Sumana Harihareswara to our stage as PyCon US keynote speakers this year.

We asked each of our keynote speakers:

What excites them about the Python community?

What they're looking forward to doing at PyCon US?

What can we expect from their keynote speech?

And any advice they'd like to share with the Python community.

Check out our interviews with each of our keynote speakers below!

Jay Miller

"I fully believe I owe my entire tech career to the Python community. I met so many amazing people that I would become long lasting friends with."

April 29, 2024 02:45 PM UTC

The Python standard library ships with a testing framework named , which you can use to write automated tests for your code. The unittest package has an object-oriented approach where test cases derive from a base class, which has several useful methods.

The framework supports many features that will help you write consistent unit tests for your code. These features include test cases, fixtures, test suites, and test discovery capabilities.
In this tutorial, you'll learn how to:

Write unittest tests with the class

Explore the assert methods that TestCase provides

Use unittest from the command line

Group test cases using the TestSuite class

Create fixtures to handle setup and teardown logic

To get the most out of this tutorial, you should be familiar with some important Python concepts, such as object-oriented programming, inheritance, and assertions. Having a good understanding of code testing is a plus.

Testing Your Python Code

Code testing or software testing is a fundamental part of a modern software development cycle. Through code testing, you can verify that a given software project works as expected and fulfills its requirements. Testing enforces code quality and robustness.

You'll do code testing during the development stage of an application or project. You'll write tests that isolate sections of your code and verify its correctness. A well-written battery or suite of tests can also serve as documentation for the project at hand.

You'll find several different concepts and techniques around testing. Most of them surpass the scope of this tutorial. However, unit test is an important and relevant concept. A unit test is a test that operates on an individual unit of software. A unit test aims to validate that the tested unit works as designed.

A unit is often a small part of a program that takes a few inputs and produces an output. Functions, methods, and other callables are good examples of units that you'd need to test.

In Python, there are several tools to help you write, organize, run, and automate your unit test. In the Python standard library, you'll find two of these tools:

Python's doctest module is a lightweight testing framework that provides quick and straightforward test automation. It can read the test cases from your project's documentation and your code's docstrings. This framework is shipped with the Python interpreter as part of the batteries-included philosophy.

The unittest package is also a testing framework. However, it provides a more complete solution than doctest. In the following sections, you'll learn and work with to create suitable unit tests for your Python code.

Getting to Know Python's unittest

The unittest package provides a unit test framework inspired by JUnit, which is a unit test framework for the Java language. The unittest framework is directly available in the standard library, so you don't have to install anything to use this tool.

The framework uses an object-oriented approach and supports some essential concepts that facilitate test creation, organization, preparation, and automation:

Test case: An individual unit of testing. It examines the output for a given input set.

Test suite: A collection of test cases, test suites, or both. They're grouped and executed as a whole.

Test fixture: A group of actions required to set up an environment for testing. It also includes the teardown processes after the tests run.

Test runner: A component that handles the execution of tests and communicates the results to the user.

In the following sections, you'll dive into using the unittest package to create test cases, suites of tests, fixtures, and, of course, run your tests.

Organizing Your Tests With the TestCase Class

The unittest package defines the TestCase class, which is primarily designed for writing unit tests. To start writing your test cases, you just need to import the class and subclass it. Then, you'll add methods whose names should begin with test. These methods will test a given unit of code using different inputs and check for the expected results.

Here's a quick test case that tests the built-in function:
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April 29, 2024 02:00 PM UTC

On behalf of the Nikola team, I am pleased to announce the immediate availability of Nikola v8.3.1. This release fixes some small bugs, including some introduced by the new Nikola Plugin Manager.

The minimum Python version supported is now 3.8, and we have adopted a formal policy to define the Python versions supported by Nikola.

What is Nikola?

Nikola is a static site and blog generator, written in Python. It can use Mako and Jinja2 templates, and input in many popular markup formats, such as reStructuredText and Markdown - and can even turn Jupyter Notebooks into blog posts! It also supports image galleries, and is multilingual. Nikola is flexible, and page builds are extremely fast, courtesy of doit (which is rebuilding only what has been changed).

Find out more at the website: https://getnikola.com/

Downloads

Install using pip install Nikola.

Changes

Features

Bugfixes

Remove insecure HTTP fallback from nikola plugin

Fix the nikola plugin command not working (Issue #3736, #3737)

Fix nikola new_post --available-formats crashing with TypeError (Issue #3750)

Fix the new plugin manager not loading plugins if the plugin folder is a symlink (Issue #3741)

Fix the nikola plugin command not working (Issue #3736)

Remove no longer used leftovers of annotations support (Issue #3764)

Other

Nikola now requires Python 3.8 or newer.

Nikola has adopted a policy for Python version support, promising support for versions supported by the Python core team, Ubuntu LTS, or Debian stable, and taking into consideration Debian oldstable and PyPy.

Remove polyfill from polyfill.io.

April 29, 2024 12:11 PM UTC

2024-04-29, by Dariusz Suchojad

How to test APIs in pure English

Do you have 20 minutes to learn how to test APIs in pure English, without any programming needed?

Great, the API testing tutorial is here.

Right after you complete it, you'll be able to write API tests as the one below.

Next steps:

April 29, 2024 08:00 AM UTC

April 28, 2024

The DSF Board are pleased to introduce Baptiste Mispelon as a new member of the Ops team. Baptiste will join the team who maintains Django's infrastructure.

Baptiste speaking at Django Under the Hood 2015

Baptiste (IPA pronunciation /ba.tist/) is a long-time Django contributor, having been a member of the community for over a decade now.

He was an initial board member of the Django Girls Foundation, co-created the Django Under the Hood, series of conferences, and was chair of the DjangoCon Europe 2016 edition. More recently, he's taken up the maintenance of Django's venerable ticket tracker: code.djangoproject.com.

He currently lives in the Norwegian countryside where he works part time as a Django developer while studying for a degree in linguistics at the local university.

You can learn more about Baptiste on his website.

I'm also taking this time to thanks the Ops team on the behalf of the DSF board for their efforts on the maintenance during all the time of their service.

Please join me in welcoming Baptiste in the Ops team!

April 28, 2024 12:45 PM UTC

In the past few years python has gained a huge popularity as a programming language in data science world. Many banks and pharma organisations have started using Python and some of them are in transition stage, migrating SAS syntax library to Python.

Many big organisations have been using SAS since early 2000 and they developed a hundreds of SAS codes for various tasks ranging from data extraction to model building and validation. Hence it's a marathon task to migrate SAS code to any other programming language. Migration can only be done in phases so day to day tasks would not be hit by development and testing of python code. Since Python is open source it becomes difficult sometimes in terms of maintaining the existing code. Some SAS procedures are very robust and powerful in nature its alternative in Python is still not implemented, might be doable but not a straightforward way for average developer or analyst.

Do you wish to run both SAS and Python programs in the same environment (IDE)? If yes, you are not the only one. Many analysts have been desiring the same. It is possible now via python package called saspy developed by SAS. It allows flexibility to transfer data between Pandas Dataframe and SAS Dataset. Imagine a situation when you have data in pandas dataframe and you wish to run SAS statistical procedure on the same without switching between SAS and Python environment.

Access to SAS Software for free

First and Foremost is to have access to SAS either via cloud or server/desktop version of software.

If you don't have SAS software, you don't need to worry. You can get it for free without installation via SAS OnDemand for Academics It is available for free for everyone (not restricted to students or academicians). It includes access to all the commonly used SAS modules like SAS STAT, SAS ETS, SAS SQL etc. You just need to do registration once and it does not take more than 5 minutes.

saspy python package has the following dependencies :

Python 3.4 or higher

SAS 9.4 or higher

Steps to access SAS in Python (Jupyter)

Please follow the steps below to make SAS run in Jupyter Notebook.

To install saspy package you can run the following command in Python.

!pip install saspy

To read this article in full, please click here

April 28, 2024 09:50 AM UTC

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Python | Definition, Language, History, & Facts | Britannica

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Python is a general-purpose high-level computer programming language valued for its English-like syntax and powerful built-in data analysis and data science functions and libraries.

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Start coding Python in Minecraft with MakeCode! Learn the basics of Python, including syntax, data types, variables, conditionals, 'for' and conditional loops, lists and functions.

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Learn the basics of the world's fastest growing and most popular programming language used by software engineers, analysts, data scientists, and machine learning engineers alike.

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Welcome! Are you completely new to programming? If not then we presume you will be looking for information about why and how to get started with Python. Fortunately an experienced programmer in any programming language (whatever it may be) can pick up Python very quickly. It's also easy for beginners to use and learn, so jump in!

Installing Python is generally easy, and nowadays many Linux and UNIX distributions include a recent Python. Even some Windows computers (notably those from HP) now come with Python already installed. If you do need to install Python and aren't confident about the task you can find a few notes on the BeginnersGuide/Download wiki page, but installation is unremarkable on most platforms.

If you want to know whether a particular application, or a library with particular functionality, is available in Python there are a number of possible sources of information. The Python web site provides a Python Package Index (also known as the Cheese Shop, a reference to the Monty Python script of that name). There is also a search page for a number of sources of Python-related information. Failing that, just Google for a phrase including the word ''python'' and you may well get the result you need. If all else fails, ask on the python newsgroup and there's a good chance someone will put you on the right track.

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If you want to help to develop Python, take a look at the developer area for further information. Please note that you don't have to be an expert programmer to help. The documentation is just as important as the compiler, and still needs plenty of work!

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Flappy Bird is a 2013 casual mobile game developed by Vietnamese video game artist and programmer Dong Nguyen, under his game development company .Gears. The game is a side-scroller where the player controls a bird, Faby, attempting to fly between columns of green pipes without hitting them

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Python is a very simple language, and has a very straightforward syntax. It encourages programmers to program without boilerplate (prepared) code. The simplest directive in Python is the "print" directive - it simply prints out a line (and also includes a newline, unlike in C).

There are two major Python versions, Python 2 and Python 3. Python 2 and 3 are quite different. This tutorial uses Python 3, because it more semantically correct and supports newer features.

For example, one difference between Python 2 and 3 is the print statement. In Python 2, the "print" statement is not a function, and therefore it is invoked without parentheses. However, in Python 3, it is a function, and must be invoked with parentheses.

To print a string in Python 3, just write:

print("This line will be printed.")

Indentation

Python uses indentation for blocks, instead of curly braces. Both tabs and spaces are supported, but the standard indentation requires standard Python code to use four spaces. For example:

x = 1 if x == 1: # indented four spaces print("x is 1.")

Exercise

Use the "print" function to print the line "Hello, World!".

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Hello, World! - Learn Python - Free Interactive Python Tutorial

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Python is a very simple language, and has a very straightforward syntax. It encourages programmers to program without boilerplate (prepared) code. The simplest directive in Python is the "print" directive - it simply prints out a line (and also includes a newline, unlike in C).

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PyGame is a set of Python modules designed for writing video games.

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PyGame is a set of Python modules designed for writing video games.

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Learning a programming language can be hard. Learning with materials in a different human language is even harder. This is why we have started the process of converting pygame learning materials into another language. With the help of a professional qualified educator we have begun the process with…

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https://www.datacamp.com/blog/python-in-healthcare-ai-applications-in-hospitals

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Get all the latest news about Python, one of the most user-friendly and popular programming languages in the world.

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Get all the latest news about Python, one of the most user-friendly and popular programming languages in the world.

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World of Tanks | Realistic Online Tank Game | Play for Free

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World of Tanks | Realistic Online Tank Game | Play for Free

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WoT - go on the official website, watch realistic videos of the best MMO game. Choose to play online multiplayer after the registration or download it for free on your PC.

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i guess

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Python is a widely used general-purpose, high-level programming language. It was initially designed by Guido van Rossum in 1991 and developed by Python Software Foundation. It was mainly developed for emphasis on code readability, and its syntax allows programmers to express concepts in fewer lines of code.

Who invented Python?

In the late 1980s, history was about to be written. It was that time when working on Python started. Soon after that, Guido Van Rossum began doing its application-based work in December of 1989 at Centrum Wiskunde & Informatica (CWI) which is situated in the Netherlands. It was started as a hobby project because he was looking for an interesting project to keep him occupied during Christmas.

The programming language in which is said to have succeeded is ABC Programming Language, which had interfacing with the Amoeba Operating System and had the feature of exception handling. He had already helped create ABC earlier in his career and had seen some issues with ABC but liked most of the features. After that what he did was very clever. He had taken the syntax of ABC, and some of its good features. It came with a lot of complaints too, so he fixed those issues completely and created a good scripting language that had removed all the flaws. The inspiration for the name came from the BBC's TV Show - ' Monty Python's Flying Circus', as he was a big fan of the TV show and also he wanted a short, unique and slightly mysterious name for his invention and hence he named it Python! He was the "Benevolent dictator for life" (BDFL) until he stepped down from the position as the leader on 12th July 2018. For quite some time he used to work for Google, but currently, he is working at Dropbox.

Evolution of Python

The language was finally released in 1991. When it was released, it used a lot fewer codes to express the concepts, when we compare it with , & . Its design philosophy was quite good too. Its main objective is to provide code readability and advanced developer productivity. When it was released, it had more than enough capability to provide classes with inheritance, several core data types of exception handling and functions.
Following are the illustrations of different versions of Python along with the timeline.

Python 3.12.1 is the latest stable version.

The two of the most used versions has to Python 2.x & 3.x. There is a lot of competition between the two and both of them seem to have quite a number of different fanbases. You can also refer to mentioned below article to learn How you can download python latest version in your system.

Download and Install Python 3 Latest Version

For various purposes such as developing, scripting, generation, and software testing , this language is utilized. Due to its elegance and simplicity, top technology organizations like Dropbox, Google, Quora, Mozilla, Hewlett-Packard, Qualcomm, IBM, and Cisco have implemented Python.

Python has come a long way to become the most popular coding language in the world. Python has just turned 30 and just recently at pycon22(python conference) a new feature was released by Anaconda foundation it's known as pyscript with this now python can be written and run in the browser like javascript which was previously not possible, but it still has that unknown charm & X factor.

Python has been an inspiration for many other coding languages such as , Cobra, Boo, CoffeeScript ECMAScript, Groovy, Swift Go, OCaml, , etc.

History of Python - FAQ's

Q1. Who developed Python programming language?

It was initially designed by Guido van Rossum in 1991 and developed by Python Software Foundation.

Q2. Who invented Python programming language?

In the late 1980s, history was about to be written. It was that time when working on Python started. Soon after that, Guido Van Rossum began doing its application-based work in December of 1989 at Centrum Wiskunde & Informatica (CWI) which is situated in the Netherlands. It was started firstly as a hobby project because he was looking for an interesting project to keep him occupied during Christmas

Q3. What are the uses of Python?

One more question asises that, Is Python Programming or Scripting?, According to Linux Journal readers the Python is a programming as well as a scripting language. Also, This language has various purposes such as developing, scripting, generation, and software testing.

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Last Updated : 26 Dec, 2023

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A Computer Science portal for geeks. It contains well written, well thought and well explained computer science and programming articles, quizzes and practice/competitive programming/company interview Questions.

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The Mind at Work: Guido van Rossum on how Python makes thinking in code easier

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A conversation with the creator of the world’s most popular programming language on removing brain friction for better work.

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Space X and Nasa use Python to send stuff to space.

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Python in Space: How Python is Powering Satellite Technology and Space Exploration [SpaceX & NASA]

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Discover how Python propels space exploration from SpaceX to NASA. Journey with us through the cosmos with code. 🚀🌌💻

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Researchers unveil LLM tool to find Python zero-days

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: The static analyzer uses Claude AI to identify vulns and suggest exploit code

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What Is Python Used For? A Beginner’s Guide

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Python is a general-purpose language, which means it’s designed to be used in a range of applications, including data science, software and web development, automation, and generally getting stuff done.

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The Story of Python, by Its Creator, Guido van Rossum

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Python in Game Development: Popular Games Made with Python

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Delve into the world of Python game development and transform your programming journey to the next level with the 98thPercentile.

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 Real Python offers a vast collection of tutorials, tips, and best practices to improve your Python skills.

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 Real Python offers a vast collection of tutorials, tips, and best practices to improve your Python skills.

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In this step-by-step tutorial, you'll learn how to create an installable Django app. You'll cover everything you need to know, from extracting your app from a Django project to turning it into a package that's available on PyPI and installable through pip.

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In this step-by-step tutorial, you'll learn how to create an installable Django app. You'll cover everything you need to know, from extracting your app from a Django project to turning it into a package that's available on PyPI and installable through pip.

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Start coding Python in Minecraft with MakeCode! Learn the basics of Python, including syntax, data types, variables, conditionals, 'for' and conditional loops, lists and functions.

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SciPy is a free and open-source Python library used for scientific computing and technical computing. SciPy contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.

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Python games are easier to make than ever with Trinket. We share two examples, an Asteroids clone and a drawing program, with more to come!

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Grace Hopper's A-0 compiler paved the way for modern programming languages

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Python | Definition, Language, History, & Facts | Britannica

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Python is a general-purpose high-level computer programming language valued for its English-like syntax and powerful built-in data analysis and data science functions and libraries.

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Python

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Paradigm

Multi-paradigm: object-oriented,[1] procedural ( imperative), functional, structured, reflective

Designed by
Guido van Rossum

Developer
Python Software Foundation

First appeared

20 February 1991; 32 years ago [2]

Stable release

3.11[3] / 24 October 2022; 11 months ago

Preview release

3.12.0rc3[4] / 19 September 2023; 6 days ago

Typing discipline

Duck, dynamic, strong typing;[5] gradual (since 3.5, but ignored in CPython)[6]

OS

Windows, macOS, Linux/UNIX, Android[7][8] and a few other platforms[9]

License
Python Software Foundation License

Filename extensions

.py, .pyi, .pyc, .pyd, .pyw, .pyz (since 3.5),[10] .pyo (prior to 3.5)[11]

Website

python.org

CPython, PyPy, Stackless Python, MicroPython, CircuitPython, IronPython, Jython

Cython, RPython, Starlark[12]

ABC,[13] Ada,[14] ALGOL 68,[15]
APL,[16] C,[17] C++,[18] CLU,[19] Dylan,[20]
Haskell,[21][16] Icon,[22] Lisp,[23]
Modula-3,[15][18] Perl,[24] Standard ML[16]

Apache Groovy, Boo, Cobra, CoffeeScript,[25] D, F#, GDScript, Genie,[26] Go, JavaScript,[27][28] Julia,[29] Mojo,[30] Nim, Ring,[31] Ruby,[32] Swift[33]

Python is a high-level, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation.

Python is dynamically typed and garbage-collected. It supports multiple programming paradigms, including structured (particularly procedural), object-oriented and functional programming. It is often described as a "batteries included" language due to its comprehensive standard library.

Guido van Rossum began working on Python in the late 1980s as a successor to the ABC programming language and first released it in 1991 as Python 0.9.0. Python 2.0 was released in 2000. Python 3.0, released in 2008, was a major revision not completely backward-compatible with earlier versions. Python 2.7.18, released in 2020, was the last release of Python 2.

The designer of Python, Guido van Rossum, at OSCON 2006

Python was conceived in the late 1980s by Guido van Rossum at Centrum Wiskunde & Informatica (CWI) in the Netherlands as a successor to the ABC programming language, which was inspired by SETL, capable of exception handling and interfacing with the Amoeba operating system. Its implementation began in December 1989. Van Rossum shouldered sole responsibility for the project, as the lead developer, until 12 July 2018, when he announced his "permanent vacation" from his responsibilities as Python's " benevolent dictator for life", a title the Python community bestowed upon him to reflect his long-term commitment as the project's chief decision-maker. In January 2019, active Python core developers elected a five-member Steering Council to lead the project.

Python 2.0 was released on 16 October 2000, with many major new features such as list comprehensions, cycle-detecting garbage collection, reference counting, and Unicode support. Python 3.0, released on 3 December 2008, with many of its major features backported to Python 2.6.x and 2.7.x. Releases of Python 3 include the 2to3 utility, which automates the translation of Python 2 code to Python 3.

Python 2.7's end-of-life was initially set for 2015, then postponed to 2020 out of concern that a large body of existing code could not easily be forward-ported to Python 3. No further security patches or other improvements will be released for it. Currently only 3.8 and later are supported (2023 security issues were fixed in e.g. 3.7.17, the final 3.7.x release). In 2021, Python 3.9.2 and 3.8.8 were expedited as all versions of Python (including 2.7) had security issues leading to possible remote code execution and web cache poisoning.

In 2022, Python 3.10.4 and 3.9.12 were expedited and 3.8.13, because of many security issues. When Python 3.9.13 was released in May 2022, it was announced that the 3.9 series (joining the older series 3.8 and 3.7) would only receive security fixes in the future. On September 7, 2022, four new releases were made due to a potential denial-of-service attack: 3.10.7, 3.9.14, 3.8.14, and 3.7.14.

As of November 2022, Python 3.11 is the stable release. Notable changes from 3.10 include increased program execution speed and improved error reporting.

Since 27 June 2023, Python 3.8 is the oldest supported version of Python (albeit in the 'security support' phase), due to Python 3.7 reaching end-of-life.

The first release candidate of Python 3.12 was offered on 6 August 2023.

Design philosophy and features

Python is a multi-paradigm programming language. Object-oriented programming and structured programming are fully supported, and many of their features support functional programming and aspect-oriented programming (including metaprogramming and metaobjects). Many other paradigms are supported via extensions, including design by contract and logic programming.

Python uses dynamic typing and a combination of reference counting and a cycle-detecting garbage collector for memory management. It uses dynamic name resolution ( late binding), which binds method and variable names during program execution.

Its design offers some support for functional programming in the Lisp tradition. It has filter, mapand reduce functions; list comprehensions, dictionaries, sets, and generator expressions. The standard library has two modules ( itertools and functools) that implement functional tools borrowed from Haskell and Standard ML.

Its core philosophy is summarized in the document The Zen of Python ( PEP 20), which includes aphorisms such as:

Beautiful is better than ugly.

Explicit is better than implicit.

Simple is better than complex.

Complex is better than complicated.

Readability counts.

Rather than building all of its functionality into its core, Python was designed to be highly extensible via modules. This compact modularity has made it particularly popular as a means of adding programmable interfaces to existing applications. Van Rossum's vision of a small core language with a large standard library and easily extensible interpreter stemmed from his frustrations with ABC, which espoused the opposite approach.

Python strives for a simpler, less-cluttered syntax and grammar while giving developers a choice in their coding methodology. In contrast to Perl's " there is more than one way to do it" motto, Python embraces a "there should be one-and preferably only one-obvious way to do it" philosophy. Alex Martelli, a Fellow at the Python Software Foundation and Python book author, wrote: "To describe something as 'clever' is not considered a compliment in the Python culture."

Python's developers strive to avoid premature optimization and reject patches to non-critical parts of the CPython reference implementation that would offer marginal increases in speed at the cost of clarity. Execution speed can be improved by moving speed-critical functions to extension modules written in languages such as C, or by using a just-in-time compiler like PyPy. It is also possible to cross-compile to other languages, but it either doesn't provide the full speed-up that might be expected, since Python is a very dynamic language, and/or a restricted subset of Python is compiled, and possibly semantics are slightly changed. Python's developers aim for it to be fun to use. This is reflected in its name-a tribute to the British comedy group Monty Python-and in occasionally playful approaches to tutorials and reference materials, such as the use of the terms "spam" and "eggs" (a reference to a Monty Python sketch) in examples, instead of the often-used "foo" and "bar".

A common neologism in the Python community is pythonic, which has a wide range of meanings related to program style. "Pythonic" code may use Python idioms well, be natural or show fluency in the language, or conform with Python's minimalist philosophy and emphasis on readability. Code that is difficult to understand or reads like a rough transcription from another programming language is called unpythonic.

Syntax and semantics

Python is meant to be an easily readable language. Its formatting is visually uncluttered and often uses English keywords where other languages use punctuation. Unlike many other languages, it does not use curly brackets to delimit blocks, and semicolons after statements are allowed but rarely used. It has fewer syntactic exceptions and special cases than C or Pascal.

Indentation

Python uses whitespace indentation, rather than curly brackets or keywords, to delimit blocks. An increase in indentation comes after certain statements; a decrease in indentation signifies the end of the current block. Thus, the program's visual structure accurately represents its semantic structure. This feature is sometimes termed the off-side rule. Some other languages use indentation this way; but in most, indentation has no semantic meaning. The recommended indent size is four spaces.

Statements and control flow

Python's statements include:

The assignment statement, using a single equals sign =

The statement, which conditionally executes a block of code, along with else and elif (a contraction of else-if)

The statement, which iterates over an iterable object, capturing each element to a local variable for use by the attached block

The statement, which executes a block of code as long as its condition is true

The statement, which allows exceptions raised in its attached code block to be caught and handled by except clauses (or new syntax except* in Python 3.11 for exception groups); it also ensures that clean-up code in a finally block is always run regardless of how the block exits

The raise statement, used to raise a specified exception or re-raise a caught exception

The class statement, which executes a block of code and attaches its local namespace to a class, for use in object-oriented programming

The def statement, which defines a function or method

The statement, which encloses a code block within a context manager (for example, acquiring a lock before it is run, then releasing the lock; or opening and closing a file), allowing resource-acquisition-is-initialization (RAII)-like behavior and replacing a common try/finally idiom

The statement, which exits a loop

The continue statement, which skips the rest of the current iteration and continues with the next

The del statement, which removes a variable-deleting the reference from the name to the value, and producing an error if the variable is referred to before it is redefined

The pass statement, serving as a NOP, syntactically needed to create an empty code block

The statement, used in debugging to check for conditions that should apply

The yield statement, which returns a value from a generator function (and also an operator); used to implement coroutines

The return statement, used to return a value from a function

The and from statements, used to import modules whose functions or variables can be used in the current program

The assignment statement (=) binds a name as a reference to a separate, dynamically allocated object. Variables may subsequently be rebound at any time to any object. In Python, a variable name is a generic reference holder without a fixed data type; however, it always refers to some object with a type. This is called dynamic typing-in contrast to statically-typed languages, where each variable may contain only a value of a certain type.

Python does not support tail call optimization or first-class continuations, and, according to Van Rossum, it never will. However, better support for coroutine-like functionality is provided by extending Python's generators. Before 2.5, generators were lazy iterators; data was passed unidirectionally out of the generator. From Python 2.5 on, it is possible to pass data back into a generator function; and from version 3.3, it can be passed through multiple stack levels.

Expressions

Python's expressions include:

The +, -, and * operators for mathematical addition, subtraction, and multiplication are similar to other languages, but the behavior of division differs. There are two types of divisions in Python: floor division (or integer division) // and floating-point/division. Python uses the ** operator for exponentiation.

Python uses the + operator for string concatenation. Python uses the * operator for duplicating a string a specified number of times.

The @ infix operator. It is intended to be used by libraries such as NumPy for matrix multiplication.

The syntax :=, called the "walrus operator", was introduced in Python 3.8. It assigns values to variables as part of a larger expression.

In Python, == compares by value. Python's is operator may be used to compare object identities (comparison by reference), and comparisons may be chained-for example, .

Python uses and, or, and not as Boolean operators.

Python has a type of expression called a list comprehension, as well as a more general expression called a generator expression.

Anonymous functions are implemented using lambda expressions; however, there may be only one expression in each body.

Conditional expressions are written as (different in order of operands from the operator common to many other languages).

Python makes a distinction between lists and tuples. Lists are written as , are mutable, and cannot be used as the keys of dictionaries (dictionary keys must be immutable in Python). Tuples, written as , are immutable and thus can be used as keys of dictionaries, provided all of the tuple's elements are immutable. The + operator can be used to concatenate two tuples, which does not directly modify their contents, but produces a new tuple containing the elements of both. Thus, given the variable t initially equal to , executing first evaluates , which yields , which is then assigned back to t-thereby effectively "modifying the contents" of t while conforming to the immutable nature of tuple objects. Parentheses are optional for tuples in unambiguous contexts.

Python features sequence unpacking where multiple expressions, each evaluating to anything that can be assigned (to a variable, writable property, etc.) are associated in an identical manner to that forming tuple literals-and, as a whole, are put on the left-hand side of the equal sign in an assignment statement. The statement expects an iterable object on the right-hand side of the equal sign that produces the same number of values as the provided writable expressions; when iterated through them, it assigns each of the produced values to the corresponding expression on the left.

Strings in Python can be concatenated by "adding" them (with the same operator as for adding integers and floats), e.g. returns "spameggs". If strings contain numbers, they are added as strings rather than integers, e.g. returns "22".

Python has various string literals:

Delimited by single or double quote marks; unlike in Unix shells, Perl, and Perl-influenced languages, single and double quote marks work the same. Both use the backslash (\) as an escape character. String interpolation became available in Python 3.6 as "formatted string literals".

Triple-quoted (beginning and ending with three single or double quote marks), which may span multiple lines and function like here documents in shells, Perl, and Ruby.

Raw string varieties, denoted by prefixing the string literal with r. Escape sequences are not interpreted; hence raw strings are useful where literal backslashes are common, such as regular expressions and Windows-style paths. (Compare "@-quoting" in C#.)

Python has array index and array slicing expressions in lists, denoted as a[key], or . Indexes are zero-based, and negative indexes are relative to the end. Slices take elements from the start index up to, but not including, the stop index. The third slice parameter called step or stride, allows elements to be skipped and reversed. Slice indexes may be omitted-for example, returns a copy of the entire list. Each element of a slice is a shallow copy.

In Python, a distinction between expressions and statements is rigidly enforced, in contrast to languages such as Common Lisp, Scheme, or Ruby. This leads to duplicating some functionality. For example:

List comprehensions vs. for-loops

Conditional expressions vs. if blocks

The eval() vs. exec() built-in functions (in Python 2, exec is a statement); the former is for expressions, the latter is for statements

Statements cannot be a part of an expression-so list and other comprehensions or lambda expressions, all being expressions, cannot contain statements. A particular case is that an assignment statement such as cannot form part of the conditional expression of a conditional statement. This has the advantage of avoiding a classic C error of mistaking an assignment operator = for an equality operator == in conditions: is syntactically valid (but probably unintended) C code, but causes a syntax error in Python.

Methods on objects are functions attached to the object's class; the syntax is, for normal methods and functions, syntactic sugar for . Python methods have an explicit parameter to access instance data, in contrast to the implicit self (or this) in some other object-oriented programming languages (e.g., C++, Java, Objective-C, Ruby). Python also provides methods, often called dunder methods (due to their names beginning and ending with double-underscores), to allow user-defined classes to modify how they are handled by native operations including length, comparison, in arithmetic operations and type conversion.

The standard type hierarchy in Python 3

Python uses duck typing and has typed objects but untyped variable names. Type constraints are not checked at compile time; rather, operations on an object may fail, signifying that it is not of a suitable type. Despite being dynamically typed, Python is strongly typed, forbidding operations that are not well-defined (for example, adding a number to a string) rather than silently attempting to make sense of them.

Python allows programmers to define their own types using classes, most often used for object-oriented programming. New instances of classes are constructed by calling the class (for example, or ), and the classes are instances of the metaclass type (itself an instance of itself), allowing metaprogramming and reflection.

Before version 3.0, Python had two kinds of classes (both using the same syntax): old-style and new-style, current Python versions only support the semantics new style.

Python supports gradual typing. Python's syntax allows specifying static types, but they are not checked in the default implementation, CPython. An experimental optional static type-checker, mypy, supports compile-time type checking.

Arithmetic operations

Python has the usual symbols for arithmetic operators (+, -, *, /), the floor division operator // and the modulo operation % (where the remainder can be negative, e.g. 4 % -3 == -2). It also has ** for exponentiation, e.g. 5**3 == 125 and 9**0.5 == 3.0, and a matrix‑multiplication operator @ . These operators work like in traditional math; with the same precedence rules, the operators infix (+ and - can also be unary to represent positive and negative numbers respectively).

The division between integers produces floating-point results. The behavior of division has changed significantly over time:

The floor division // operator was introduced. So 7//3 == 2, -7//3 == -3, 7.5//3 == 2.0 and -7.5//3 == -3.0. Adding causes a module used in Python 2.7 to use Python 3.0 rules for division (see above).

In Python terms, / is true division (or simply division), and // is floor division. / before version 3.0 is classic division.

Rounding towards negative infinity, though different from most languages, adds consistency. For instance, it means that the equation is always true. It also means that the equation is valid for both positive and negative values of a. However, maintaining the validity of this equation means that while the result of a%b is, as expected, in the half-open interval [0, b), where b is a positive integer, it has to lie in the interval ( b, 0] when b is negative.

Python provides a round function for rounding a float to the nearest integer. For tie-breaking, Python 3 uses round to even: round(1.5) and round(2.5) both produce 2. Versions before 3 used round-away-from-zero: round(0.5) is 1.0, round(-0.5) is −1.0.

Python allows Boolean expressions with multiple equality relations in a manner that is consistent with general use in mathematics. For example, the expression a < b < c tests whether a is less than b and b is less than c. C-derived languages interpret this expression differently: in C, the expression would first evaluate a < b, resulting in 0 or 1, and that result would then be compared with c.

Python uses arbitrary-precision arithmetic for all integer operations. The Decimal type/class in the decimal module provides decimal floating-point numbers to a pre-defined arbitrary precision and several rounding modes. The Fraction class in the fractions module provides arbitrary precision for rational numbers.

Due to Python's extensive mathematics library, and the third-party library NumPy that further extends the native capabilities, it is frequently used as a scientific scripting language to aid in problems such as numerical data processing and manipulation.

Programming examples

Hello world program:

Program to calculate the factorial of a positive integer:

Python's large standard library provides tools suited to many tasks and is commonly cited as one of its greatest strengths. For Internet-facing applications, many standard formats and protocols such as MIME and HTTP are supported. It includes modules for creating graphical user interfaces, connecting to relational databases, generating pseudorandom numbers, arithmetic with arbitrary-precision decimals, manipulating regular expressions, and unit testing.

Some parts of the standard library are covered by specifications-for example, the Web Server Gateway Interface (WSGI) implementation wsgiref follows PEP 333-but most are specified by their code, internal documentation, and test suites. However, because most of the standard library is cross-platform Python code, only a few modules need altering or rewriting for variant implementations.

As of 14 November 2022, the Python Package Index (PyPI), the official repository for third-party Python software, contains over 415,000 packages with a wide range of functionality, including:

Development environments

Most Python implementations (including CPython) include a read-eval-print loop (REPL), permitting them to function as a command line interpreter for which users enter statements sequentially and receive results immediately.

Python also comes with an Integrated development environment (IDE) called IDLE, which is more beginner-oriented.

Other shells, including IDLE and IPython, add further abilities such as improved auto-completion, session state retention, and syntax highlighting.

As well as standard desktop integrated development environments, there are web browser-based IDEs, including SageMath, for developing science- and math-related programs; PythonAnywhere, a browser-based IDE and hosting environment; and Canopy IDE, a commercial IDE emphasizing scientific computing.

Implementations

Reference implementation

CPython is the reference implementation of Python. It is written in C, meeting the C89 standard (Python 3.11 uses C11) with several select C99 features. CPython includes its own C extensions, but third-party extensions are not limited to older C versions-e.g. they can be implemented with C11 or C++.) It compiles Python programs into an intermediate bytecode which is then executed by its virtual machine. CPython is distributed with a large standard library written in a mixture of C and native Python, and is available for many platforms, including Windows (starting with Python 3.9, the Python installer deliberately fails to install on Windows 7 and 8; Windows XP was supported until Python 3.5) and most modern Unix-like systems, including macOS (and Apple M1 Macs, since Python 3.9.1, with experimental installer) and unofficial support for e.g. VMS. Platform portability was one of its earliest priorities. (During Python 1 and 2 development, even OS/2 and Solaris were supported, but support has since been dropped for many platforms.)

Other implementations

Unsupported implementations

Other just-in-time Python compilers have been developed, but are now unsupported:

Google began a project named Unladen Swallow in 2009, with the aim of speeding up the Python interpreter fivefold by using the LLVM, and of improving its multithreading ability to scale to thousands of cores, while ordinary implementations suffer from the global interpreter lock.

Psyco is a discontinued just-in-time specializing compiler that integrates with CPython and transforms bytecode to machine code at runtime. The emitted code is specialized for certain data types and is faster than the standard Python code. Psyco does not support Python 2.7 or later.

PyS60 was a Python 2 interpreter for Series 60 mobile phones released by Nokia in 2005. It implemented many of the modules from the standard library and some additional modules for integrating with the Symbian operating system. The Nokia N900 also supports Python with GTK widget libraries, enabling programs to be written and run on the target device.

Cross-compilers to other languages

There are several compilers/ transpilers to high-level object languages, with either unrestricted Python, a restricted subset of Python, or a language similar to Python as the source language:

Codon compiles a subset of statically-typed Python to machine code (via LLVM) and supports native multithreading.

Cython compiles (a superset of) Python to C. The resulting code is also usable with Python via direct C-level API calls into the Python interpreter.

PyJL compiles/transpiles a subset of Python to "human-readable, maintainable, and high-performance Julia source code". Despite claiming high performance, no tool can claim to do that for arbitrary Python code; i.e. it's known not possible to compile to a faster language or machine code. Unless semantics of Python are changed, but in many cases speedup is possible with few or no changes in the Python code. The faster Julia source code can then be used from Python, or compiled to machine code, and based that way.

Numba uses LLVM to compile a subset of Python to machine code.

RPython can be compiled to C, and is used to build the PyPy interpreter of Python.

The Python → 11l → C++ transpiler compiles a subset of Python 3 to C++ (C++17).

Specialized:

Older projects (or not to be used with Python 3.x and latest syntax):

Performance

Performance comparison of various Python implementations on a non-numerical (combinatorial) workload was presented at EuroSciPy '13. Python's performance compared to other programming languages is also benchmarked by The Computer Language Benchmarks Game.

Development

Python's development is conducted largely through the Python Enhancement Proposal (PEP) process, the primary mechanism for proposing major new features, collecting community input on issues, and documenting Python design decisions. Python coding style is covered in PEP 8. Outstanding PEPs are reviewed and commented on by the Python community and the steering council.

Enhancement of the language corresponds with the development of the CPython reference implementation. The mailing list python-dev is the primary forum for the language's development. Specific issues were originally discussed in the Roundup bug tracker hosted at by the foundation. In 2022, all issues and discussions were migrated to GitHub. Development originally took place on a self-hosted source-code repository running Mercurial, until Python moved to GitHub in January 2017.

CPython's public releases come in three types, distinguished by which part of the version number is incremented:

Major or "feature" releases are largely compatible with the previous version but introduce new features. The second part of the version number is incremented. Starting with Python 3.9, these releases are expected to happen annually. Each major version is supported by bug fixes for several years after its release.

Bugfix releases, which introduce no new features, occur about every 3 months and are made when a sufficient number of bugs have been fixed upstream since the last release. Security vulnerabilities are also patched in these releases. The third and final part of the version number is incremented.

Many alpha, beta, and release-candidates are also released as previews and for testing before final releases. Although there is a rough schedule for each release, they are often delayed if the code is not ready. Python's development team monitors the state of the code by running the large unit test suite during development.

The major academic conference on Python is PyCon. There are also special Python mentoring programs, such as Pyladies.

Python 3.10 deprecated wstr (to be removed in Python 3.12; meaning Python extensions need to be modified by then), and added pattern matching to the language.

API documentation generators

Tools that can generate documentation for Python API include pydoc (available as part of the standard library), Sphinx, Pdoc and its forks, Doxygen and Graphviz, among others.

Python's name is derived from the British comedy group Monty Python, whom Python creator Guido van Rossum enjoyed while developing the language. Monty Python references appear frequently in Python code and culture; for example, the metasyntactic variables often used in Python literature are instead of the traditional . The official Python documentation also contains various references to Monty Python routines. Users of Python sometimes referred to as "Pythonistas".

The prefix Py- is used to show that something is related to Python. Examples of the use of this prefix in names of Python applications or libraries include Pygame, a binding of SDL to Python (commonly used to create games); PyQt and PyGTK, which bind Qt and GTK to Python respectively; and PyPy, a Python implementation originally written in Python.

Popularity

Since 2003, Python has consistently ranked in the top ten most popular programming languages in the TIOBE Programming Community Index where as of December 2022 it was the most popular language (ahead of C, C++, and Java). It was selected Programming Language of the Year (for "the highest rise in ratings in a year") in 2007, 2010, 2018, and 2020 (the only language to have done so four times as of 2020).

An empirical study found that scripting languages, such as Python, are more productive than conventional languages, such as C and Java, for programming problems involving string manipulation and search in a dictionary, and determined that memory consumption was often "better than Java and not much worse than C or C++".

Python Powered

Python can serve as a scripting language for web applications, e.g., via mod_wsgi for the Apache webserver. With Web Server Gateway Interface, a standard API has evolved to facilitate these applications. Web frameworks like Django, Pylons, Pyramid, TurboGears, web2py, Tornado, Flask, Bottle, and Zope support developers in the design and maintenance of complex applications. Pyjs and IronPython can be used to develop the client-side of Ajax-based applications. SQLAlchemy can be used as a data mapper to a relational database. Twisted is a framework to program communications between computers, and is used (for example) by Dropbox.

Libraries such as NumPy, SciPy, and Matplotlib allow the effective use of Python in scientific computing, with specialized libraries such as Biopython and Astropy providing domain-specific functionality. SageMath is a computer algebra system with a notebook interface programmable in Python: its library covers many aspects of mathematics, including algebra, combinatorics, numerical mathematics, number theory, and calculus. OpenCV has Python bindings with a rich set of features for computer vision and image processing.

Python is commonly used in artificial intelligence projects and machine learning projects with the help of libraries like TensorFlow, Keras, Pytorch, and scikit-learn. As a scripting language with a modular architecture, simple syntax, and rich text processing tools, Python is often used for natural language processing.

Python can also be used for graphical user interface (GUI) by using libraries like Tkinter.

Python can also be used to create games, with libraries such as Pygame, which can make 2D games.

Python has been successfully embedded in many software products as a scripting language, including in finite element method software such as Abaqus, 3D parametric modelers like FreeCAD, 3D animation packages such as 3ds Max, Blender, Cinema 4D, Lightwave, Houdini, Maya, modo, MotionBuilder, Softimage, the visual effects compositor Nuke, 2D imaging programs like GIMP, Inkscape, Scribus and Paint Shop Pro, and musical notation programs like scorewriter and capella. GNU Debugger uses Python as a pretty printer to show complex structures such as C++ containers. Esri promotes Python as the best choice for writing scripts in ArcGIS. It has also been used in several video games, and has been adopted as first of the three available programming languages in Google App Engine, the other two being Java and Go.

Many operating systems include Python as a standard component. It ships with most Linux distributions, AmigaOS 4 (using Python 2.7), FreeBSD (as a package), NetBSD, and OpenBSD (as a package) and can be used from the command line (terminal). Many Linux distributions use installers written in Python: Ubuntu uses the Ubiquity installer, while Red Hat Linux and Fedora Linux use the Anaconda installer. Gentoo Linux uses Python in its package management system, Portage.

Python is used extensively in the information security industry, including in exploit development.

Most of the Sugar software for the One Laptop per Child XO, developed at Sugar Labs as of 2008, is written in Python. The Raspberry Pi single-board computer project has adopted Python as its main user-programming language.

LibreOffice includes Python and intends to replace Java with Python. Its Python Scripting Provider is a core feature since Version 4.0 from 7 February 2013.

Languages influenced by Python

Python's design and philosophy have influenced many other programming languages:

Cobra uses indentation and a similar syntax, and its Acknowledgements document lists Python first among languages that influenced it.

CoffeeScript, a programming language that cross-compiles to JavaScript, has Python-inspired syntax.

ECMAScript/JavaScript borrowed iterators and generators from Python.

GDScript, a scripting language very similar to Python, built-in to the Godot game engine.

Groovy was motivated by the desire to bring the Python design philosophy to Java.

Mojo is currently a non-strict (aims to be a strict) superset of Python (e.g. still missing classes, and adding e.g. struct), and is up to 35,000x faster for some code (mandelbrot, since it is embarrassingly parallel), where static typing helps (and MLIR it is implemented with), and, e.g., 4000 times faster for matrix multiplication.

Ruby's creator, Yukihiro Matsumoto, has said: "I wanted a scripting language that was more powerful than Perl, and more object-oriented than Python. That's why I decided to design my own language."

Python's development practices have also been emulated by other languages. For example, the practice of requiring a document describing the rationale for, and issues surrounding, a change to the language (in Python, a PEP) is also used in Tcl, Erlang, and Swift.

References

1. File extension .pyo was removed in Python 3.5. See ^ PEP 0488 Archived 1 June 2020 at the Wayback Machine

2. ^ van Rossum, Guido (1993). "An Introduction to Python for UNIX/C Programmers". Proceedings of the NLUUG Najaarsconferentie (Dutch UNIX Users Group). CiteSeerX 10.1.1.38.2023. even though the design of C is far from ideal, its influence on Python is considerable.

3. ^ Simionato, Michele. "The Python 2.3 Method Resolution Order". Python Software Foundation. Archived from the original on 20 August 2020. The C3 method itself has nothing to do with Python, since it was invented by people working on Dylan and it is described in a paper intended for lispers

4. ^ Lattner, Chris (3 June 2014). "Chris Lattner's Homepage". Chris Lattner. Archived from the original on 25 December 2018. The Swift language is the product of tireless effort from a team of language experts, documentation gurus, compiler optimization ninjas, and an incredibly important internal dogfooding group who provided feedback to help refine and battle-test ideas. Of course, it also greatly benefited from the experiences hard-won by many other languages in the field, drawing ideas from Objective-C, Rust, Haskell, Ruby, Python, C#, CLU, and far too many others to list.

5. ^ Kuhlman, Dave. "A Python Book: Beginning Python, Advanced Python, and Python Exercises". Section 1.1. Archived from the original (PDF) on 23 June 2012.

6. "TIOBE Index". TIOBE. Archived from the original on 25 February 2018. The TIOBE Programming Community index is an indicator of the popularity of programming languages Updated as required.

7. ^ van Rossum, Guido (29 August 2000). "SETL (was: Lukewarm about range literals)". Python-Dev (Mailing list). Archived from the original on 14 July 2018.

8. ^ "Extending and Embedding the Python Interpreter: Reference Counts". Docs.python.org. Archived from the original on 18 October 2012. Since Python makes heavy use of malloc() and free(), it needs a strategy to avoid memory leaks as well as the use of freed memory. The chosen method is called reference counting.

9. ^ Martelli, Alex; Ravenscroft, Anna; Ascher, David (2005). Python Cookbook, 2nd Edition. O'Reilly Media. p. 230. ISBN 978-0-596-00797-3. Archived from the original on 23 February 2020.

10. "Transpiling Python to Julia using PyJL" (PDF). After manually modifying one line of code by specifying the necessary type information, we obtained a speedup of 52.6×, making the translated Julia code 19.5× faster than the original Python code.

11. ^ Guttag, John V. (12 August 2016). Introduction to Computation and Programming Using Python: With Application to Understanding Data. MIT Press. ISBN 978-0-262-52962-4.

12. ^ Sweigart, Al (2020). Beyond the Basic Stuff with Python: Best Practices for Writing Clean Code. No Starch Press. p. 322. ISBN 978-1-59327-966-0. Archived from the original on 13 August 2021.

13. ^ Guelton, Serge; Brunet, Pierrick; Amini, Mehdi; Merlini, Adrien; Corbillon, Xavier; Raynaud, Alan (16 March 2015). "Pythran: enabling static optimization of scientific Python programs". Computational Science & Discovery. IOP Publishing. 8 (1): 014001. Bibcode:2015CS&D....8a4001G. doi:10.1088/1749-4680/8/1/014001. ISSN 1749-4699.

14. ^ "GitHub - IronLanguages/ironpython3: Implementation of Python 3.x for .NET Framework that is built on top of the Dynamic Language Runtime". . Archived from the original on 28 September 2021.

15. ^ Murri, Riccardo (2013). Performance of Python runtimes on a non-numeric scientific code. European Conference on Python in Science (EuroSciPy). arXiv:. Bibcode:2014arXiv1404.6388M.

16. ^ Lutz, Mark (2009). Learning Python: Powerful Object-Oriented Programming. O'Reilly Media, Inc. p. 17. ISBN 9781449379322. Archived from the original on 17 July 2017.

17. ^ Oliphant, Travis (2007). "Python for Scientific Computing". Computing in Science and Engineering. 9 (3): 10-20. Bibcode:2007CSE.....9c..10O. CiteSeerX 10.1.1.474.6460. doi:10.1109/MCSE.2007.58. S2CID 206457124. Archived from the original on 15 June 2020.

18. ^ Millman, K. Jarrod; Aivazis, Michael (2011). "Python for Scientists and Engineers". Computing in Science and Engineering. 13 (2): 9-12. Bibcode:2011CSE....13b...9M. doi:10.1109/MCSE.2011.36. Archived from the original on 19 February 2019.

19. ^ Dean, Jeff; Monga, Rajat; et al. (9 November 2015). "TensorFlow: Large-scale machine learning on heterogeneous systems" (PDF). TensorFlow.org. Google Research. Archived (PDF) from the original on 20 November 2015.

20. ^ Jouppi, Norm. "Google supercharges machine learning tasks with TPU custom chip". Google Cloud Platform Blog. Archived from the original on 18 May 2016.

21. ^ CCP porkbelly (24 August 2010). "Stackless Python 2.7". EVE Community Dev Blogs. CCP Games. Archived from the original on 11 January 2014. As you may know, EVE has at its core the programming language known as Stackless Python.

22. ^ Caudill, Barry (20 September 2005). "Modding Sid Meier's Civilization IV". Sid Meier's Civilization IV Developer Blog. Firaxis Games. Archived from the original on 2 December 2010. we created three levels of tools ... The next level offers Python and XML support, letting modders with more experience manipulate the game world and everything in it.

23. ^ Lattner, Chris (3 June 2014). "Chris Lattner's Homepage". Chris Lattner. Archived from the original on 22 December 2015. I started work on the Swift Programming Language in July of 2010. I implemented much of the basic language structure, with only a few people knowing of its existence. A few other (amazing) people started contributing in earnest late in 2011, and it became a major focus for the Apple Developer Tools group in July 2013 [...] drawing ideas from Objective-C, Rust, Haskell, Ruby, Python, C#, CLU, and far too many others to list.

Further reading

External links

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Python (programming language) - Wikipedia

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Once upon a time, the world of computer programming was a mysterious and exclusive place. Only a select handful of people were considered computer programmers with cutting-edge coding skills. Today, many IT jobs require a solid grasp of the top programming languages, and yes, we mean more than one.

If your plans to advance your career or change careers completely requires you to master a programming language, you might wonder which one to learn. After all, it will take time and money to learn the language, so you want to make the right choice.

When making your decision, you should bear several considerations in mind, like the difficulty level you're willing to tackle, the programming language knowledge you already possess that meshes with your existing coding skills, or your reasons for learning a top programming language.

Whether you want to develop a mobile application, get certification for programming knowledge, or learn new skills, you need to learn the right programming language. Below you'll learn about the best programming languages in demand among employers in 2023. You'll be briefed about the details of each language, its complexity, and how it is used.

What is a Programming Language?

A programming language is a way for programmers (developers) to communicate with computers. Programming languages consist of a set of rules that allows string values to be converted into various ways of generating machine code, or, in the case of visual programming languages, graphical elements.

Generally speaking, a program is a set of instructions written in a particular language (C, C++, Java, Python) to achieve a particular task.

Unlock the synergy of programming languages for cyber security bootcamp. Delve into the realm of ethical hacking and defense strategies, all while mastering the programming languages that power digital landscapes. From Python to C++, equip yourself with the tools to detect vulnerabilities and fortify systems against cyber threats. Join us in building a solid foundation in both programming and cybersecurity to stand strong in the ever-evolving digital world.

Learn more about the top trending and in-demand programming languages and take a step towards upskilling today!

Major Types of Programming Languages

Procedural Programming Languages

A programming paradigm that uses procedures or functions to abbreviate and categorize the code into reusable blocks is a procedural programming language. C, Pascal and FORTRAN are the most in-demand programming languages supporting this paradigm.

In this programming, the program is divided into functions or procedures. They are primarily insular sub-programs that perform a specific task. These procedures can be called from other program parts, allowing for flexible programming and code reuse. The priority is the systematic execution of a program. Further, it emphasizes the series of instructions and influences data stored in variables.

These are the best programming languages to learn as they are widely used in the fields like engineering, gaming and finance. Procedural programming can be less flexible though this does not stop it from being one of the most significant programming paradigms taught in high-ranking computer science courses.

Functional Programming Languages

Unlike procedural languages, functional programming languages are more flexible. It is composed of a series of functions. This programming paradigm stresses the use of model computations and data transformation. Haskell, Clojure, Lisp and Scala are the languages supporting this paradigm.

Functional programming makes programs easier to reason with and increases their reliability. The functions solely operate on their input arguments. They are less popular, but they have experienced a colossal boom from the educational point of view. They are assigned to variables, passed as arguments to other functions and return results from other functions.

Functional programming is efficient parallel programming. They have no mutable state. You can program functions and parallelly work as instructions. These codes support nested functions and consist of independent units that run coherently. Hence, this is more efficient.

Object-oriented Programming Languages

In an object-oriented programming language, objects define the data and the behavior of objects. These objects typically include data attributes representing the object's state and method. This language enables users to make a complex system with interconnected objects.

This language hides implementation components from the outside world through encapsulation. This makes it possible to build large intricate systems without stressing about the internal workings of respective objects. The other benefit that makes this language so in demand is the feature of inheritance. It creates a ranking of classes that share common features while still allowing customization.

Some popular object-oriented programming languages are Java, Python, C++, and Ruby. All these languages are top programming languages, but they share the principle of being object-oriented.

Scripting Languages

Simple to learn with easy syntax and dynamic typing, the scripting language is the type of language that is interpreted rather than compiled. The two types of this language are server-side scripting languages and client-side scripting languages. These languages make communication possible with other programming languages.

Python - The easiest programming language used among developers is Python. It is an object-oriented programming language. The language has a high-Level data structure, and built-in libraries, that make it easy to use and suitable for rapid application development. It is easy, decoded and has a dynamic semantic language.

Perl - The language is dynamic with innovative features that make it popular and different from what is available on Linux and Windows Server. Websites with high traffic usually use Perl, including IMDB, as it helps in text manipulation tasks.

Bash - Bourne -Again, SHell is a scripting language that is the default command interpreter on most Linux/GNU operating systems. This language is easier than most of the other programming languages. Bash makes it easier to create script store documentation for others and provides useful reusable scripts.

Logic Programming Languages

As the name suggests, this is computer programming based on formal logic. This programming language program consists of a cluster of logical statements or rules that determine relationships among objects. It allows the system to extrapolate new information.

Artificial intelligence and expert systems commonly use this language where reasoning and conjecture are required. This language allows a concise and expressive program which is easier to reason about and maintain than programs that return to other paradigms.

To summarize, logical programming is a secure and flexible approach to solving problems in computer programming. It is suitable for every type of problem. It is a valuable tool for a few applications as well. One of the most popular logical languages is Prolog which consists of a set of facts and rules to describe a problem and reason about it.

Imperative Programming

In imperative programming, the programmer provides a set of instructions that the computer follows to manipulate the state of the program and the information structure within it. This paradigm describes the steps that a computer needs to take to solve a program rather than defining the mathematical function. C, C++, Java, and Python are some of the imperative programming languages,

The Imperative is the most popular programming language in software development for system programming and low-level programming tasks, which includes direct level control over hardware resources.

Other Ways to Classify Programming Languages

Front-end vs. Back-end Languages

Front-end languages are called client-side languages, which are used to create virtual and interactive website elements that users can see. The front-end languages include HTML, CSS and JavaScript.

Backend languages are known as server-side languages. They create logic and functionality behind the scenes of the website, like processing, storing and managing the user's account and authentication. Python, Ruby, and Java are some of the backend languages.

High-level vs. Low-level Languages

High-level languages are easy to read and write since they are understandable. Developers can write code at a higher level of abstraction without worrying about the underlying hardware or operating system, as it is close to natural languages. They have built-in libraries and frameworks to simplify ordinary programming tasks like managing data structures or executing calculations. Examples of high-level languages are Python, Java or Ruby.

On the other hand, level languages are much more complex to understand and write. The code written in languages is very efficient and directly manipulates the hardware and memory of the computer. Yet the developer needs to rely on manual efforts and understanding. Assembly language and C are examples of this type of language.

Interpreted vs. Compiled Languages

The interpreter directly executes and reads the interpreted code line by line without compiling it into machine code. It can be executed immediately without requiring separate compilation steps, such as Python, Ruby and JavaScript.

In contrast, the compiler first translates the compiled languages into machine code. Further converts the human-readable code into a structure that computers can execute. Languages like C, C++, and Java fall into this category.

What Programming Language Should You Learn?

With rigorous updates in the tech market, a candidate should be up-to-date with new languages. Though, other factors play a significant role in the decision of what programming languages to learn.

Firstly, one needs to understand their goals and what career one wants to pursue after learning the programming language.

Next, conduct thorough market research and narrow down the field you want to enter.

After that, you need to understand the industry demand and the job portfolio of the desired profession. Learn the skills and languages that are demanded in the chosen field.

You might want to start one of the easiest programming languages and gradually elevate your difficulty level. Python and Ruby are great languages, to begin with.

Look for simply available material where you can learn quickly. Join communities and network with people at the top level of the field.

Lastly, keep reinventing and learning as the market is constantly changing.

What Are the Best Programming Languages to Learn in 2023?

What coding and programming language should i learn? JavaScript and Python, two of the most popular languages in the startup industry, are in high demand. Most startups use Python-based backend frameworks such as Django (Python), Flask (Python), and NodeJS (JavaScript). These languages are also considered to be the best programming languages to learn for beginners.

Below is a list of the most popular and best programming languages that will be in demand in 2023.

1. Javascript

JavaScript is a high-level programming language that is one of the core technologies of the World Wide Web. It is used as a client-side programming language by 97.8 percent of all websites. JavaScript was originally used only to develop web browsers, but they are now used for server-side website deployments and non-web browser applications as well.

Javascript was created in 1995 and was initially known as LiveScript. However, Java was a very popular language at that time, so it was advertised as a "younger brother" of Java. As it evolved over time, JavaScript became a fully independent language. Nowadays, JavaScript is often confused with Java, and although there are some similarities between them, the two languages are distinct.

Also Read: Top 10 Reasons to Learn JavaScript

Javascript is the most popular programming language in the world and is in high demand among various organizations. The average Java developer earns around $112,152 each year.

2. Python

Python is one of the most popular programming languages today and is easy for beginners to learn because of its readability. It is a free, open-source programming language with extensive support modules and community development, easy integration with web services, user-friendly data structures, and GUI-based desktop applications. It is a popular programming language for machine learning and deep learning applications.

Python is used to develop 2D imaging and 3D animation packages like Blender, Inkscape, and Autodesk. It has also been used to create popular video games, including Civilization IV, Vegas Trike, and Toontown. Python is used for scientific and computational applications like FreeCAD and Abacus and by popular websites like YouTube, Quora, Pinterest, and Instagram. Python developers earn average annual salaries of about $109,092.

Also Read: Introduction To Python Basics

3. Go

Go was developed by Google in 2007 for APIs and web applications. Go has recently become one of the fastest-growing programming languages due to its simplicity, as well as its ability to handle multicore and networked systems and massive codebases.

Go, also known as Golang, was created to meet the needs of programmers working on large projects. It has gained popularity among many large IT companies thanks to its simple and modern structure and syntax familiarity. Companies using Go as their programming language include Google, Uber, Twitch, Dropbox, among many others. Go is also gaining in popularity among data scientists because of its agility and performance.

Go developers can earn an average annual salary of $141,654, and hitting up to $261,000.

4. Java

Java is one of the most popular programming languages used today.

Owned by Oracle Corporation, this general-purpose programming language with its object-oriented structure has become a standard for applications that can be used regardless of platform (e.g., Mac, Windows, Android, iOS, etc.) because of its Write Once, Run Anywhere (WORA) capabilities. As a result, Java is recognized for its portability across platforms, from mainframe data centers to smartphones. Today there are more than 3 billion devices running applications built with Java.

Java is widely used in web and application development as well as big data. Java is also used on the backend of several popular websites, including Google, Amazon, Twitter, and YouTube. It is also extensively used in hundreds of applications. New Java frameworks like Spring, Struts, and Hibernate are also very popular. With millions of Java developers worldwide, there are hundreds of ways to learn Java. Also, Java programmers have an extensive online community and support each other to solve problems.

Java is a more complex language to learn, but experienced developers with Java coding skills are in high demand. The average Java developer earns around $109,225 each year.

5. Kotlin

Kotlin is a general-purpose programming language originally developed and unveiled as Project Kotlin by JetBrains in 2011. The first version was officially released in 2016. It is interoperable with Java and supports functional programming languages.

Kotlin is used extensively for Android apps, web application, desktop application, and server-side application development. Kotlin was built to be better than Java, and people who use this language are convinced. Most of the Google applications are based on Kotlin. Some companies using Kotlin as their programming language include Coursera, Pinterest, PostMates among many others.

Kotlin developers earn an average of $136,000 a year, with the potential to earn up to $171,500.

6. PHP

PHP is an open-source programming language created in 1990. Many web developers find it essential to learn PHP, as this language is used to build more than 80% of websites on the Internet, including notable sites like Facebook and Yahoo.

Programmers mainly use PHP mainly to write server-side scripts. But developers can also use this language to write command-line scripts, and programmers with high-level PHP coding skills can also use it to develop desktop applications.

PHP is considered a relatively easy language to learn for beginning developers. PHP professionals have access to several dedicated online communities, making it easy to get support and answers to questions.

On average, PHP programmers earn average annual salaries of about $107,409.

Also Read: Top 8 Practical Applications of PHP and Steps to Carve a Career in the Field

7. C#

Developed by Microsoft, C# rose to fame in the 2000s for supporting the concepts of object-oriented programming. It is one of the most used programming languages for the .NET framework. Anders Hejlsberg, the creator of C#, says the language is more like C++ than Java.

C# is best suited for applications on Windows, Android, and iOS, as it takes the help of the integrated development environment product, Microsoft Visual C++. C# is used on the back end of several popular websites like Bing, Dell, Visual Studio, and MarketWatch. C# developers earn around $ 111,760 per year.

8. Swift

A few years ago, Swift made the top 10 in the monthly TIOBE Index ranking of popular programming languages. Apple developed Swift in 2014 for Linux and Mac applications.

An open-source programming language that is easy to learn, Swift supports almost everything from the programming language Objective-C. Swift requires fewer coding skills compared with other programming languages, and it can be used with IBM Swift Sandbox and IBM Bluemix. Swift is used in popular iOS apps like WordPress, Mozilla Firefox, SoundCloud, and even in the game Flappy Bird. Professionals who develop iOS applications take home average annual salaries of around $106,652.

9. R

R is an open-source language that is essentially a different version of the S language. Much of the code that developers write for S runs on R without modification.

Applications built in R are used for processing statistics, including linear and nonlinear modeling, calculation, testing, visualization, and analysis. Applications coded using R can interface with a number of databases and process both structured and unstructured data.

R has a moderate learning curve and is not as easy for beginners to pick up as some other languages in this article. However, like other open-source programming languages, R boasts an active online community of developers, which is always a plus when learning new coding skills.

On average, R developers earn average annual salaries of about $90,940.

10. Ruby

If you want to start with a language that is known for being relatively simple to learn, consider Ruby. Developed in the 1990s, it was designed to have a more human-friendly syntax while still being flexible from the standpoint of its object-oriented architecture that supports procedural and functional programming notation. A web-application framework that is implemented in Ruby is Ruby on Rails ("RoR"). Ruby developers tout it for being an easy language to write in and also for the relatively short learning time required. These attributes have led to a large community of Ruby developers and a growing interest in the language among beginning developers. The average salary for a Ruby developer is around $121,000 per year.

11. C and C++

C is probably the oldest and popular programming language and is the root of other programming languages such as C#, Java, and JavaScript. C++ is an enhanced version of C. Many developers today skip learning C on its own, while others think learning C first provides a valuable foundation for C++ development. Both languages are widely used in computer science and programming.

C and C++ developers can make use of compilers for a wide variety of platforms, making applications developed in these languages largely transportable. Both C and C++ are considered high-performance languages. As such, they are widely used in developing applications where performance is a critical issue, such as client/server applications, commercial products like Firefox and Adobe, and video games. C and C++ developers earn an average of $118,366 each year.

12. Matlab

Matlab is a proprietary programming language owned by MathWorks and originally released in the mid-1980s. It is built specifically for use by scientists and engineers.

Programmers use Matlab to build machine learning and deep learning applications. Matlab-based programs enable users to analyze data, create algorithms, process images, and verify research.

Generally, Matlab is easier to learn than other programming languages on our list. MathWorks' website has an extensive section dedicated to answering questions about Matlab.

The average Matlab developer takes home an average salary of $100,760 each year.

13. TypeScript

TypeScript is a newcomer to top programming language lists, but it's making headway. It was developed in 2012 by Microsoft and is a typed version of JavaScript that is well suited for large code bases. TypeScript is used to create JavaScript-based projects with typing in both client-side and server-side development, making useful for catching errors and preventing systemic issues.

TypeScript was named "most adopted tech" in the 2021 State of JavaScript Survey. TypeScript developers typically earn an annual average of $75,199.

14. Scala

Scala is a general-purpose, type-safe Java virtual machine language that combines the best oop and functional programming languages into one special high language. Scala is ideal for reducing and removing bugs in large, complex applications. It supports both object-oriented and functional programming.

Programmers can use Scala for any task that they normally would use Java for. Scala is a complex language, but that complexity gives it a lot of flexibility. Companies that use Scala include Netflix, Twitter, and the New York Times.

Scala developers earn a yearly average of $127,005.

15. SQL

SQL is a standard database query language. It is used to access and manipulate data in databases. SQL is a declarative language that specifies the desired results, but not the steps to achieve those results. SQL is a powerful tool for accessing and manipulating data, and it is the world's most widely used database query language.

Benefits of SQL

Cons of SQL

16. HTML

HTML(HyperText Markup Language) is the standard language for creating web pages and applications. HTML is used to create web pages. You can use HTML to add images, links, and other types of content to your web page. HTML is a simple programming language; you don't need to know much about it to create a basic web page.

Images and other objects, such as interactive forms, can be embedded within the produced page using HTML structures. It enables the creation of structured documents by indicating structural semantics for text elements including as headings, paragraphs, lists, links, quotations, and other objects. HTML elements are delineated by tags, which are written in angle brackets.

Benefits of HTML

Benefits of using HTML include creating well-structured, standards-compliant web pages that are easy to maintain and update.

HTML is also easy to learn and is a great starting point for those new to web development.

HTML is used to structure and present content on the web and is typically used alongside CSS and JavaScript.

Additionally, HTML5 provides better multimedia and interactive content support, making it a powerful tool for creating engaging web experiences.

HTML5 is the latest version and includes new features like video and audio elements, local storage, and 2D/3D graphics. HTML5 is designed to be more user-friendly and efficient than previous versions of HTML.

Cons of HTML

A few potential drawbacks exist to using HTML as a web development language.

17. CSS

CSS (Cascading Style Sheets) is a style sheet language used to describe how a page that was produced in a markup language is presented. A style sheet, which is a set of rules for web browsers, can control an HTML or XML.

All HTML tags, including the text in the document's body, headings, paragraphs, and other text elements, are styled using CSS. The display of grid components, table elements, and picture

Benefits of CSS

CSS is used to style all HTML tags, including the body of the document, headings, paragraphs, and other material. CSS can also be used to style how table components, grid elements, and images are displayed.

Web developers use CSS to create responsive and accessible websites. CSS can make it easier for web developers to create websites that look good on all devices, including mobile phones and tablets.

CSS can also help make websites more accessible to people with disabilities.

CSS is easy to learn and use. Many tutorials and resources are available online, and anyone can start using CSS to style their web pages.

Con of CSS

CSS can be challenging to debug. When there are errors in a CSS file, it can be difficult to track down the source of the problem.

And it can be time-consuming to write. CSS files can be large and complex, and it can take a lot of time to create and maintain them.

Overall, the CSS is a powerful tool that can be used to style web documents. However, it is vital to know the potential drawbacks before using it.

18. NoSQL

NoSQL databases are non-relational databases designed to provide high performance and scalability. And NoSQL databases are often used in big data applications, where data is distributed across many nodes.

The four key categories of NoSQL databases are as follows:

key-value stores,

columnar stores,

document stores,

And graph databases.

Benefits of NoSQL

Cons of NoSQL

19. Rust

Rust is a programming language designed to be safe, concurrent, and practical. It is a systems programming language that runs blazingly fast, prevents segfaults, and guarantees thread safety. Rust is also memory-efficient: it uses minimal memory, making it ideal for embedded systems.

Benefits of Rust

Cons of Rust

While Rust has many features that make it an attractive language, there are some drawbacks to using it.

There needs to be more support or documentation available for other languages because it is a new language, making it difficult to learn and use Rust.

Additionally, Rust only supports some platforms, and it can be challenging to integrate with existing codebases.

20. Perl

Perl is an interpreted, high-level, general-purpose programming language. Although Perl is not officially an acronym, various backronyms exist, including "Practical Extraction and Report Language." Perl was originally developed by Larry Wall in 1987 as a general-purpose Unix scripting language to make report processing easier. Since then, it has undergone many changes and revisions.

Perl is widely regarded as the " Swiss Army knife" of programming languages because of its versatility and power. It is used for various tasks, including web development, network programming, system administration, and more.

Benefits of Perl

Cons of Perl

Although Perl is an interesting and feature-rich programming language, it also has some cons.

How to Get Started?

Although there are hundreds of programming languages, very few make it to the short list of languages you should know. The ones described above lead the pack, in our opinion, as the best programmer training options. If you want to start a career as a programmer, make a lateral move into another field, or advance up the ladder at your current job, learning one of these languages is an excellent place to begin your transition. And since courses range from Python for the beginner to Java for the experienced, you can find the right fit for you.

Once you've decided it's time to learn a new language, turn to Simplilearn for both training and certification. We offer courses in all seven of these languages (Java, Javascript, C, C#, Python, Swift, and Ruby), plus others, all with content developed by industry leaders, an emphasis on hands-on learning, and 24x7 support. You'll learn the language, plus get the credibility of certification, making it easier to land one of those high-paying programmer jobs. Explore Simplilearn Post Graduate Program in Full Stack Web Development course, and get your programming career off to a great start!

If you have any questions, feel free to post them in the comments section below. Our team will get back to you at the earliest.

FAQs

1. What are the top programming languages to learn in 2023?

In 2023, some of the best programming languages to learn include Python, JavaScript, Java, C++, and TypeScript.

2. Which programming language is best for beginners?

Python is an excellent choice for beginners due to its simple syntax, readability, and vast community that provides ample learning resources.

3. What is the most in-demand programming language for web development?

JavaScript remains the most in-demand language for web development, as it powers the interactivity and functionality of modern websites.

4. Which language is widely used for mobile app development?

Swift (for iOS) and Kotlin (for Android) are the most widely used programming languages for mobile app development.

5. What programming language is preferred for data analysis and machine learning?

Python is the preferred language for data analysis and machine learning due to its extensive libraries like NumPy, Pandas, and TensorFlow.

6. Which programming language is known for its speed and performance?

C++ is renowned for its speed and performance, making it a popular choice for system-level programming and resource-intensive applications.

7. Is learning older languages like C and Java in 2023 worth learning?

Yes, learning C and Java is still valuable as they form the backbone of many existing applications and systems and have extensive use in various industries.

8. What are the main advantages of learning a versatile language like Python?

Python's versatility lies in its applications across web development, data science, machine learning, automation, and scientific computing, making it a powerful language to learn.

9. Which programming language is best suited for game development?

Languages like C#, C++, and Python (with game development frameworks like Unity and Pygame) are commonly used for game development.

10. What are some popular languages for backend web development?

Popular languages for backend web development include Python (Django, Flask), JavaScript (Node.js), Ruby (Ruby on Rails), and PHP (Laravel).

11. Is Rust a good language to learn for systems programming?

Yes, Rust is an excellent choice for systems programming as it combines low-level control with high-level safety features, making it ideal for writing secure and fast system code.

12. Which language should I learn to start a career in cybersecurity?

For a career in cybersecurity, learning languages like Python or C is beneficial as they are widely used for scripting and exploit development.

13. Are there any beginner-friendly resources for learning popular programming languages?

Yes, several online platforms offer beginner-friendly tutorials and courses for popular languages like Python, Java, and JavaScript.

14. Is it necessary to learn multiple programming languages for a successful career?

While specializing in one language is common, having exposure to multiple languages can make you more adaptable and versatile in the ever-changing tech industry.

15. What is the best way to stay updated with the latest trends in programming languages?

To stay updated with the latest trends, follow industry blogs, attend tech conferences, participate in online forums, and join developer communities on social media.

What is a Programming Language?

A programming language is a way for programmers (developers) to communicate with computers. Programming languages consist of a set of rules that allows string values to be converted into various ways of generating machine code, or, in the case of visual programming languages, graphical elements.

Generally speaking, a program is a set of instructions written in a particular language (C, C++, Java, Python) to achieve a particular task.

Unlock the synergy of programming languages for cyber security bootcamp. Delve into the realm of ethical hacking and defense strategies, all while mastering the programming languages that power digital landscapes. From Python to C++, equip yourself with the tools to detect vulnerabilities and fortify systems against cyber threats. Join us in building a solid foundation in both programming and cybersecurity to stand strong in the ever-evolving digital world.

Learn more about the top trending and in-demand programming languages and take a step towards upskilling today!

Major Types of Programming Languages

Procedural Programming Languages

A programming paradigm that uses procedures or functions to abbreviate and categorize the code into reusable blocks is a procedural programming language. C, Pascal and FORTRAN are the most in-demand programming languages supporting this paradigm.

In this programming, the program is divided into functions or procedures. They are primarily insular sub-programs that perform a specific task. These procedures can be called from other program parts, allowing for flexible programming and code reuse. The priority is the systematic execution of a program. Further, it emphasizes the series of instructions and influences data stored in variables.

These are the best programming languages to learn as they are widely used in the fields like engineering, gaming and finance. Procedural programming can be less flexible though this does not stop it from being one of the most significant programming paradigms taught in high-ranking computer science courses.

Functional Programming Languages

Unlike procedural languages, functional programming languages are more flexible. It is composed of a series of functions. This programming paradigm stresses the use of model computations and data transformation. Haskell, Clojure, Lisp and Scala are the languages supporting this paradigm.

Functional programming makes programs easier to reason with and increases their reliability. The functions solely operate on their input arguments. They are less popular, but they have experienced a colossal boom from the educational point of view. They are assigned to variables, passed as arguments to other functions and return results from other functions.

Functional programming is efficient parallel programming. They have no mutable state. You can program functions and parallelly work as instructions. These codes support nested functions and consist of independent units that run coherently. Hence, this is more efficient.

Object-oriented Programming Languages

In an object-oriented programming language, objects define the data and the behavior of objects. These objects typically include data attributes representing the object's state and method. This language enables users to make a complex system with interconnected objects.

This language hides implementation components from the outside world through encapsulation. This makes it possible to build large intricate systems without stressing about the internal workings of respective objects. The other benefit that makes this language so in demand is the feature of inheritance. It creates a ranking of classes that share common features while still allowing customization.

Some popular object-oriented programming languages are Java, Python, C++, and Ruby. All these languages are top programming languages, but they share the principle of being object-oriented.

Scripting Languages

Simple to learn with easy syntax and dynamic typing, the scripting language is the type of language that is interpreted rather than compiled. The two types of this language are server-side scripting languages and client-side scripting languages. These languages make communication possible with other programming languages.

Python - The easiest programming language used among developers is Python. It is an object-oriented programming language. The language has a high-Level data structure, and built-in libraries, that make it easy to use and suitable for rapid application development. It is easy, decoded and has a dynamic semantic language.

Perl - The language is dynamic with innovative features that make it popular and different from what is available on Linux and Windows Server. Websites with high traffic usually use Perl, including IMDB, as it helps in text manipulation tasks.

Bash - Bourne -Again, SHell is a scripting language that is the default command interpreter on most Linux/GNU operating systems. This language is easier than most of the other programming languages. Bash makes it easier to create script store documentation for others and provides useful reusable scripts.

Logic Programming Languages

As the name suggests, this is computer programming based on formal logic. This programming language program consists of a cluster of logical statements or rules that determine relationships among objects. It allows the system to extrapolate new information.

Artificial intelligence and expert systems commonly use this language where reasoning and conjecture are required. This language allows a concise and expressive program which is easier to reason about and maintain than programs that return to other paradigms.

To summarize, logical programming is a secure and flexible approach to solving problems in computer programming. It is suitable for every type of problem. It is a valuable tool for a few applications as well. One of the most popular logical languages is Prolog which consists of a set of facts and rules to describe a problem and reason about it.

Imperative Programming

In imperative programming, the programmer provides a set of instructions that the computer follows to manipulate the state of the program and the information structure within it. This paradigm describes the steps that a computer needs to take to solve a program rather than defining the mathematical function. C, C++, Java, and Python are some of the imperative programming languages,

The Imperative is the most popular programming language in software development for system programming and low-level programming tasks, which includes direct level control over hardware resources.

Other Ways to Classify Programming Languages

Front-end vs. Back-end Languages

Front-end languages are called client-side languages, which are used to create virtual and interactive website elements that users can see. The front-end languages include HTML, CSS and JavaScript.

Backend languages are known as server-side languages. They create logic and functionality behind the scenes of the website, like processing, storing and managing the user's account and authentication. Python, Ruby, and Java are some of the backend languages.

High-level vs. Low-level Languages

High-level languages are easy to read and write since they are understandable. Developers can write code at a higher level of abstraction without worrying about the underlying hardware or operating system, as it is close to natural languages. They have built-in libraries and frameworks to simplify ordinary programming tasks like managing data structures or executing calculations. Examples of high-level languages are Python, Java or Ruby.

On the other hand, level languages are much more complex to understand and write. The code written in languages is very efficient and directly manipulates the hardware and memory of the computer. Yet the developer needs to rely on manual efforts and understanding. Assembly language and C are examples of this type of language.

Interpreted vs. Compiled Languages

The interpreter directly executes and reads the interpreted code line by line without compiling it into machine code. It can be executed immediately without requiring separate compilation steps, such as Python, Ruby and JavaScript.

In contrast, the compiler first translates the compiled languages into machine code. Further converts the human-readable code into a structure that computers can execute. Languages like C, C++, and Java fall into this category.

What Programming Language Should You Learn?

With rigorous updates in the tech market, a candidate should be up-to-date with new languages. Though, other factors play a significant role in the decision of what programming languages to learn.

Firstly, one needs to understand their goals and what career one wants to pursue after learning the programming language.

Next, conduct thorough market research and narrow down the field you want to enter.

After that, you need to understand the industry demand and the job portfolio of the desired profession. Learn the skills and languages that are demanded in the chosen field.

You might want to start one of the easiest programming languages and gradually elevate your difficulty level. Python and Ruby are great languages, to begin with.

Look for simply available material where you can learn quickly. Join communities and network with people at the top level of the field.

Lastly, keep reinventing and learning as the market is constantly changing.

What Are the Best Programming Languages to Learn in 2023?

What coding and programming language should i learn? JavaScript and Python, two of the most popular languages in the startup industry, are in high demand. Most startups use Python-based backend frameworks such as Django (Python), Flask (Python), and NodeJS (JavaScript). These languages are also considered to be the best programming languages to learn for beginners.

Below is a list of the most popular and best programming languages that will be in demand in 2023.

1. Javascript

JavaScript is a high-level programming language that is one of the core technologies of the World Wide Web. It is used as a client-side programming language by 97.8 percent of all websites. JavaScript was originally used only to develop web browsers, but they are now used for server-side website deployments and non-web browser applications as well.

Javascript was created in 1995 and was initially known as LiveScript. However, Java was a very popular language at that time, so it was advertised as a "younger brother" of Java. As it evolved over time, JavaScript became a fully independent language. Nowadays, JavaScript is often confused with Java, and although there are some similarities between them, the two languages are distinct.

Also Read: Top 10 Reasons to Learn JavaScript

Javascript is the most popular programming language in the world and is in high demand among various organizations. The average Java developer earns around $112,152 each year.

2. Python

Python is one of the most popular programming languages today and is easy for beginners to learn because of its readability. It is a free, open-source programming language with extensive support modules and community development, easy integration with web services, user-friendly data structures, and GUI-based desktop applications. It is a popular programming language for machine learning and deep learning applications.

Python is used to develop 2D imaging and 3D animation packages like Blender, Inkscape, and Autodesk. It has also been used to create popular video games, including Civilization IV, Vegas Trike, and Toontown. Python is used for scientific and computational applications like FreeCAD and Abacus and by popular websites like YouTube, Quora, Pinterest, and Instagram. Python developers earn average annual salaries of about $109,092.

Also Read: Introduction To Python Basics

3. Go

Go was developed by Google in 2007 for APIs and web applications. Go has recently become one of the fastest-growing programming languages due to its simplicity, as well as its ability to handle multicore and networked systems and massive codebases.

Go, also known as Golang, was created to meet the needs of programmers working on large projects. It has gained popularity among many large IT companies thanks to its simple and modern structure and syntax familiarity. Companies using Go as their programming language include Google, Uber, Twitch, Dropbox, among many others. Go is also gaining in popularity among data scientists because of its agility and performance.

Go developers can earn an average annual salary of $141,654, and hitting up to $261,000.

4. Java

Java is one of the most popular programming languages used today.

Owned by Oracle Corporation, this general-purpose programming language with its object-oriented structure has become a standard for applications that can be used regardless of platform (e.g., Mac, Windows, Android, iOS, etc.) because of its Write Once, Run Anywhere (WORA) capabilities. As a result, Java is recognized for its portability across platforms, from mainframe data centers to smartphones. Today there are more than 3 billion devices running applications built with Java.

Java is widely used in web and application development as well as big data. Java is also used on the backend of several popular websites, including Google, Amazon, Twitter, and YouTube. It is also extensively used in hundreds of applications. New Java frameworks like Spring, Struts, and Hibernate are also very popular. With millions of Java developers worldwide, there are hundreds of ways to learn Java. Also, Java programmers have an extensive online community and support each other to solve problems.

Java is a more complex language to learn, but experienced developers with Java coding skills are in high demand. The average Java developer earns around $109,225 each year.

5. Kotlin

Kotlin is a general-purpose programming language originally developed and unveiled as Project Kotlin by JetBrains in 2011. The first version was officially released in 2016. It is interoperable with Java and supports functional programming languages.

Kotlin is used extensively for Android apps, web application, desktop application, and server-side application development. Kotlin was built to be better than Java, and people who use this language are convinced. Most of the Google applications are based on Kotlin. Some companies using Kotlin as their programming language include Coursera, Pinterest, PostMates among many others.

Kotlin developers earn an average of $136,000 a year, with the potential to earn up to $171,500.

6. PHP

PHP is an open-source programming language created in 1990. Many web developers find it essential to learn PHP, as this language is used to build more than 80% of websites on the Internet, including notable sites like Facebook and Yahoo.

Programmers mainly use PHP mainly to write server-side scripts. But developers can also use this language to write command-line scripts, and programmers with high-level PHP coding skills can also use it to develop desktop applications.

PHP is considered a relatively easy language to learn for beginning developers. PHP professionals have access to several dedicated online communities, making it easy to get support and answers to questions.

On average, PHP programmers earn average annual salaries of about $107,409.

Also Read: Top 8 Practical Applications of PHP and Steps to Carve a Career in the Field

7. C#

Developed by Microsoft, C# rose to fame in the 2000s for supporting the concepts of object-oriented programming. It is one of the most used programming languages for the .NET framework. Anders Hejlsberg, the creator of C#, says the language is more like C++ than Java.

C# is best suited for applications on Windows, Android, and iOS, as it takes the help of the integrated development environment product, Microsoft Visual C++. C# is used on the back end of several popular websites like Bing, Dell, Visual Studio, and MarketWatch. C# developers earn around $ 111,760 per year.

8. Swift

A few years ago, Swift made the top 10 in the monthly TIOBE Index ranking of popular programming languages. Apple developed Swift in 2014 for Linux and Mac applications.

An open-source programming language that is easy to learn, Swift supports almost everything from the programming language Objective-C. Swift requires fewer coding skills compared with other programming languages, and it can be used with IBM Swift Sandbox and IBM Bluemix. Swift is used in popular iOS apps like WordPress, Mozilla Firefox, SoundCloud, and even in the game Flappy Bird. Professionals who develop iOS applications take home average annual salaries of around $106,652.

9. R

R is an open-source language that is essentially a different version of the S language. Much of the code that developers write for S runs on R without modification.

Applications built in R are used for processing statistics, including linear and nonlinear modeling, calculation, testing, visualization, and analysis. Applications coded using R can interface with a number of databases and process both structured and unstructured data.

R has a moderate learning curve and is not as easy for beginners to pick up as some other languages in this article. However, like other open-source programming languages, R boasts an active online community of developers, which is always a plus when learning new coding skills.

On average, R developers earn average annual salaries of about $90,940.

10. Ruby

If you want to start with a language that is known for being relatively simple to learn, consider Ruby. Developed in the 1990s, it was designed to have a more human-friendly syntax while still being flexible from the standpoint of its object-oriented architecture that supports procedural and functional programming notation. A web-application framework that is implemented in Ruby is Ruby on Rails ("RoR"). Ruby developers tout it for being an easy language to write in and also for the relatively short learning time required. These attributes have led to a large community of Ruby developers and a growing interest in the language among beginning developers. The average salary for a Ruby developer is around $121,000 per year.

11. C and C++

C is probably the oldest and popular programming language and is the root of other programming languages such as C#, Java, and JavaScript. C++ is an enhanced version of C. Many developers today skip learning C on its own, while others think learning C first provides a valuable foundation for C++ development. Both languages are widely used in computer science and programming.

C and C++ developers can make use of compilers for a wide variety of platforms, making applications developed in these languages largely transportable. Both C and C++ are considered high-performance languages. As such, they are widely used in developing applications where performance is a critical issue, such as client/server applications, commercial products like Firefox and Adobe, and video games. C and C++ developers earn an average of $118,366 each year.

12. Matlab

Matlab is a proprietary programming language owned by MathWorks and originally released in the mid-1980s. It is built specifically for use by scientists and engineers.

Programmers use Matlab to build machine learning and deep learning applications. Matlab-based programs enable users to analyze data, create algorithms, process images, and verify research.

Generally, Matlab is easier to learn than other programming languages on our list. MathWorks' website has an extensive section dedicated to answering questions about Matlab.

The average Matlab developer takes home an average salary of $100,760 each year.

13. TypeScript

TypeScript is a newcomer to top programming language lists, but it's making headway. It was developed in 2012 by Microsoft and is a typed version of JavaScript that is well suited for large code bases. TypeScript is used to create JavaScript-based projects with typing in both client-side and server-side development, making useful for catching errors and preventing systemic issues.

TypeScript was named "most adopted tech" in the 2021 State of JavaScript Survey. TypeScript developers typically earn an annual average of $75,199.

14. Scala

Scala is a general-purpose, type-safe Java virtual machine language that combines the best oop and functional programming languages into one special high language. Scala is ideal for reducing and removing bugs in large, complex applications. It supports both object-oriented and functional programming.

Programmers can use Scala for any task that they normally would use Java for. Scala is a complex language, but that complexity gives it a lot of flexibility. Companies that use Scala include Netflix, Twitter, and the New York Times.

Scala developers earn a yearly average of $127,005.

15. SQL

SQL is a standard database query language. It is used to access and manipulate data in databases. SQL is a declarative language that specifies the desired results, but not the steps to achieve those results. SQL is a powerful tool for accessing and manipulating data, and it is the world's most widely used database query language.

Benefits of SQL

Cons of SQL

16. HTML

HTML(HyperText Markup Language) is the standard language for creating web pages and applications. HTML is used to create web pages. You can use HTML to add images, links, and other types of content to your web page. HTML is a simple programming language; you don't need to know much about it to create a basic web page.

Images and other objects, such as interactive forms, can be embedded within the produced page using HTML structures. It enables the creation of structured documents by indicating structural semantics for text elements including as headings, paragraphs, lists, links, quotations, and other objects. HTML elements are delineated by tags, which are written in angle brackets.

Benefits of HTML

Benefits of using HTML include creating well-structured, standards-compliant web pages that are easy to maintain and update.

HTML is also easy to learn and is a great starting point for those new to web development.

HTML is used to structure and present content on the web and is typically used alongside CSS and JavaScript.

Additionally, HTML5 provides better multimedia and interactive content support, making it a powerful tool for creating engaging web experiences.

HTML5 is the latest version and includes new features like video and audio elements, local storage, and 2D/3D graphics. HTML5 is designed to be more user-friendly and efficient than previous versions of HTML.

Cons of HTML

A few potential drawbacks exist to using HTML as a web development language.

17. CSS

CSS (Cascading Style Sheets) is a style sheet language used to describe how a page that was produced in a markup language is presented. A style sheet, which is a set of rules for web browsers, can control an HTML or XML.

All HTML tags, including the text in the document's body, headings, paragraphs, and other text elements, are styled using CSS. The display of grid components, table elements, and picture

Benefits of CSS

CSS is used to style all HTML tags, including the body of the document, headings, paragraphs, and other material. CSS can also be used to style how table components, grid elements, and images are displayed.

Web developers use CSS to create responsive and accessible websites. CSS can make it easier for web developers to create websites that look good on all devices, including mobile phones and tablets.

CSS can also help make websites more accessible to people with disabilities.

CSS is easy to learn and use. Many tutorials and resources are available online, and anyone can start using CSS to style their web pages.

Con of CSS

CSS can be challenging to debug. When there are errors in a CSS file, it can be difficult to track down the source of the problem.

And it can be time-consuming to write. CSS files can be large and complex, and it can take a lot of time to create and maintain them.

Overall, the CSS is a powerful tool that can be used to style web documents. However, it is vital to know the potential drawbacks before using it.

18. NoSQL

NoSQL databases are non-relational databases designed to provide high performance and scalability. And NoSQL databases are often used in big data applications, where data is distributed across many nodes.

The four key categories of NoSQL databases are as follows:

key-value stores,

columnar stores,

document stores,

And graph databases.

Benefits of NoSQL

Cons of NoSQL

19. Rust

Rust is a programming language designed to be safe, concurrent, and practical. It is a systems programming language that runs blazingly fast, prevents segfaults, and guarantees thread safety. Rust is also memory-efficient: it uses minimal memory, making it ideal for embedded systems.

Benefits of Rust

Cons of Rust

While Rust has many features that make it an attractive language, there are some drawbacks to using it.

There needs to be more support or documentation available for other languages because it is a new language, making it difficult to learn and use Rust.

Additionally, Rust only supports some platforms, and it can be challenging to integrate with existing codebases.

20. Perl

Perl is an interpreted, high-level, general-purpose programming language. Although Perl is not officially an acronym, various backronyms exist, including "Practical Extraction and Report Language." Perl was originally developed by Larry Wall in 1987 as a general-purpose Unix scripting language to make report processing easier. Since then, it has undergone many changes and revisions.

Perl is widely regarded as the " Swiss Army knife" of programming languages because of its versatility and power. It is used for various tasks, including web development, network programming, system administration, and more.

Benefits of Perl

Cons of Perl

Although Perl is an interesting and feature-rich programming language, it also has some cons.

How to Get Started?

Although there are hundreds of programming languages, very few make it to the short list of languages you should know. The ones described above lead the pack, in our opinion, as the best programmer training options. If you want to start a career as a programmer, make a lateral move into another field, or advance up the ladder at your current job, learning one of these languages is an excellent place to begin your transition. And since courses range from Python for the beginner to Java for the experienced, you can find the right fit for you.

Once you've decided it's time to learn a new language, turn to Simplilearn for both training and certification. We offer courses in all seven of these languages (Java, Javascript, C, C#, Python, Swift, and Ruby), plus others, all with content developed by industry leaders, an emphasis on hands-on learning, and 24x7 support. You'll learn the language, plus get the credibility of certification, making it easier to land one of those high-paying programmer jobs. Explore Simplilearn Post Graduate Program in Full Stack Web Development course, and get your programming career off to a great start!

If you have any questions, feel free to post them in the comments section below. Our team will get back to you at the earliest.

FAQs

1. What are the top programming languages to learn in 2023?

In 2023, some of the best programming languages to learn include Python, JavaScript, Java, C++, and TypeScript.

2. Which programming language is best for beginners?

Python is an excellent choice for beginners due to its simple syntax, readability, and vast community that provides ample learning resources.

3. What is the most in-demand programming language for web development?

JavaScript remains the most in-demand language for web development, as it powers the interactivity and functionality of modern websites.

4. Which language is widely used for mobile app development?

Swift (for iOS) and Kotlin (for Android) are the most widely used programming languages for mobile app development.

5. What programming language is preferred for data analysis and machine learning?

Python is the preferred language for data analysis and machine learning due to its extensive libraries like NumPy, Pandas, and TensorFlow.

6. Which programming language is known for its speed and performance?

C++ is renowned for its speed and performance, making it a popular choice for system-level programming and resource-intensive applications.

7. Is learning older languages like C and Java in 2023 worth learning?

Yes, learning C and Java is still valuable as they form the backbone of many existing applications and systems and have extensive use in various industries.

8. What are the main advantages of learning a versatile language like Python?

Python's versatility lies in its applications across web development, data science, machine learning, automation, and scientific computing, making it a powerful language to learn.

9. Which programming language is best suited for game development?

Languages like C#, C++, and Python (with game development frameworks like Unity and Pygame) are commonly used for game development.

10. What are some popular languages for backend web development?

Popular languages for backend web development include Python (Django, Flask), JavaScript (Node.js), Ruby (Ruby on Rails), and PHP (Laravel).

11. Is Rust a good language to learn for systems programming?

Yes, Rust is an excellent choice for systems programming as it combines low-level control with high-level safety features, making it ideal for writing secure and fast system code.

12. Which language should I learn to start a career in cybersecurity?

For a career in cybersecurity, learning languages like Python or C is beneficial as they are widely used for scripting and exploit development.

13. Are there any beginner-friendly resources for learning popular programming languages?

Yes, several online platforms offer beginner-friendly tutorials and courses for popular languages like Python, Java, and JavaScript.

14. Is it necessary to learn multiple programming languages for a successful career?

While specializing in one language is common, having exposure to multiple languages can make you more adaptable and versatile in the ever-changing tech industry.

15. What is the best way to stay updated with the latest trends in programming languages?

To stay updated with the latest trends, follow industry blogs, attend tech conferences, participate in online forums, and join developer communities on social media.

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Top 20 Best Programming Languages To Learn in 2023 | Simplilearn

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Programmers use programming languages to communicate with computers. These languages have rules to convert string values into machine code in visual programming. Know more!

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This goes over a detailed history of Python.

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The programming language Python was conceived in the late 1980s, and its implementation was started in December 1989 by Guido van Rossum at CWI in the Netherlands as a successor to ABC capable of exception handling and interfacing with the Amoeba operating system. Van Rossum is Python's principal author, and his continuing central role in deciding the direction of Python is reflected in the title given to him by the Python community, Benevolent Dictator for Life (BDFL). (However, van Rossum stepped down as leader on July 12, 2018.) Python was named for the BBC TV show Monty Python's Flying Circus.

Python 2.0 was released on October 16, 2000, with many major new features, including a cycle-detecting garbage collector (in addition to reference counting) for memory management and support for Unicode. However, the most important change was to the development process itself, with a shift to a more transparent and community-backed process.

Python 3.0, a major, backwards-incompatible release, was released on December 3, 2008 after a long period of testing. Many of its major features have also been backported to the backwards-compatible, while by now unsupported, Python 2.6 and 2.7.

In February 1991, Van Rossum published the code (labeled version 0.9.0) to alt.sources. Already present at this stage in development were classes with inheritance, exception handling, functions, and the core datatypes of list, dict, str and so on. Also in this initial release was a module system borrowed from Modula-3; Van Rossum describes the module as "one of Python's major programming units". Python's exception model also resembles Modula-3's, with the addition of an else clause. In 1994 comp.lang.python, the primary discussion forum for Python, was formed, marking a milestone in the growth of Python's userbase.

Python reached version 1.0 in January 1994. The major new features included in this release were the functional programming tools , , and . Van Rossum stated that "Python acquired lambda, reduce(), filter() and map(), courtesy of a Lisp hacker who missed them and submitted working patches".

The last version released while Van Rossum was at CWI was Python 1.2. In 1995, Van Rossum continued his work on Python at the Corporation for National Research Initiatives (CNRI) in Reston, Virginia from where he released several versions.

By version 1.4, Python had acquired several new features. Notable among these are the Modula-3 inspired keyword arguments (which are also similar to Common Lisp's keyword arguments) and built-in support for complex numbers. Also included is a basic form of data hiding by name mangling, though this is easily bypassed.

During Van Rossum's stay at CNRI, he launched the Computer Programming for Everybody (CP4E) initiative, intending to make programming more accessible to more people, with a basic "literacy" in programming languages, similar to the basic English literacy and mathematics skills required by most employers. Python served a central role in this: because of its focus on clean syntax, it was already suitable, and CP4E's goals bore similarities to its predecessor, ABC. The project was funded by DARPA. As of 2007, the CP4E project is inactive, and while Python attempts to be easily learnable and not too arcane in its syntax and semantics, reaching out to non-programmers is not an active concern.

In 2000, the Python core development team moved to BeOpen.com to form the BeOpen PythonLabs team, under the direction of early Google alum Domenic Merenda. CNRI requested that a version 1.6 be released, summarizing Python's development up to the point at which the development team left CNRI. Consequently, the release schedules for 1.6 and 2.0 had a significant amount of overlap. Python 2.0 was the only release from BeOpen.com. After Python 2.0 was released by BeOpen.com, Guido van Rossum and the other PythonLabs developers joined Digital Creations.

The Python 1.6 release included a new CNRI license that was substantially longer than the CWI license that had been used for earlier releases. The new license included a clause stating that the license was governed by the laws of the State of Virginia. The Free Software Foundation argued that the choice-of-law clause was incompatible with the GNU General Public License. BeOpen, CNRI and the FSF negotiated a change to Python's free software license that would make it GPL-compatible. Python 1.6.1 is essentially the same as Python 1.6, with a few minor bug fixes, and with the new GPL-compatible license.

Python 2.0, released October 2000, introduced list comprehensions, a feature borrowed from the functional programming languages SETL and Haskell. Python's syntax for this construct is very similar to Haskell's, apart from Haskell's preference for punctuation characters and Python's preference for alphabetic keywords. Python 2.0 also introduced a garbage collection system capable of collecting reference cycles.

Python 2.1 was close to Python 1.6.1, as well as Python 2.0. Its license was renamed Python Software Foundation License. All code, documentation and specifications added, from the time of Python 2.1's alpha release on, is owned by the Python Software Foundation (PSF), a non-profit organization formed in 2001, modeled after the Apache Software Foundation. The release included a change to the language specification to support nested scopes, like other statically scoped languages. (The feature was turned off by default, and not required, until Python 2.2.)

Python 2.2 was released on December 2001; a major innovation was the unification of Python's types (types written in C) and classes (types written in Python) into one hierarchy. This single unification made Python's object model purely and consistently object oriented. Also added were generators which were inspired by Icon.

Python 2.5 was released on September 2006 and introduced the with statement, which encloses a code block within a context manager (for example, acquiring a lock before the block of code is run and releasing the lock afterwards, or opening a file and then closing it), allowing Resource Acquisition Is Initialization (RAII)-like behavior and replacing a common try/finally idiom.

Python 2.6 was released to coincide with Python 3.0, and included some features from that release, as well as a "warnings" mode that highlighted the use of features that were removed in Python 3.0. Similarly, Python 2.7 coincided with and included features from Python 3.1, which was released on June 26, 2009. Parallel 2.x and 3.x releases then ceased, and Python 2.7 was the last release in the 2.x series. In November 2014, it was announced that Python 2.7 would be supported until 2020, but users were encouraged to move to Python 3 as soon as possible. Python 2.7 support ended on January 1, 2020, along with code freeze of 2.7 development branch. A final release, 2.7.18, occurred on April 20, 2020, and included fixes for critical bugs and release blockers. This marks the end-of-life of Python 2.

Python 3.0 (also called "Python 3000" or "Py3K") was released on December 3, 2008. It was designed to rectify fundamental design flaws in the language-the changes required could not be implemented while retaining full backwards compatibility with the 2.x series, which necessitated a new major version number. The guiding principle of Python 3 was: "reduce feature duplication by removing old ways of doing things".

Python 3.0 was developed with the same philosophy as in prior versions. However, as Python had accumulated new and redundant ways to program the same task, Python 3.0 had an emphasis on removing duplicative constructs and modules, in keeping with "There should be one- and preferably only one -obvious way to do it".

Nonetheless, Python 3.0 remained a multi-paradigm language. Coders could still follow object-oriented, structured, and functional programming paradigms, among others, but within such broad choices, the details were intended to be more obvious in Python 3.0 than they were in Python 2.x.

Compatibility

Python 3.0 broke backward compatibility, and much Python 2 code does not run unmodified on Python 3. Python's dynamic typing combined with the plans to change the semantics of certain methods of dictionaries, for example, made perfect mechanical translation from Python 2.x to Python 3.0 very difficult. A tool called "2to3" does the parts of translation that can be done automatically. At this, 2to3 appeared to be fairly successful, though an early review noted that there were aspects of translation that such a tool would never be able to handle. Prior to the roll-out of Python 3, projects requiring compatibility with both the 2.x and 3.x series were recommended to have one source (for the 2.x series), and produce releases for the Python 3.x platform using 2to3. Edits to the Python 3.x code were discouraged for so long as the code needed to run on Python 2.x. This is no longer recommended; as of 2012 the preferred approach was to create a single code base that can run under both Python 2 and 3 using compatibility modules.

Some of the major changes included for Python 3.0 were:

Changing print so that it is a built-in function, not a statement. This made it easier to change a module to use a different print function, as well as making the syntax more regular. In Python 2.6 and 2.7 print() is available as a builtin but is masked by the print statement syntax, which can be disabled by entering from __future__ import print_function at the top of the file

Removal of the Python 2 input function, and the renaming of the raw_input function to input. Python 3's input function behaves like Python 2's raw_input function, in that the input is always returned as a string rather than being evaluated as an expression

Moving reduce (but not map or filter) out of the built-in namespace and into functools (the rationale being code that uses reduce is less readable than code that uses a for loop and accumulator variable)

Adding support for optional function annotations that can be used for informal type declarations or other purposes

Unifying the str/unicode types, representing text, and introducing a separate immutable bytes type; and a mostly corresponding mutable bytearray type, both of which represent arrays of bytes

Removing backward-compatibility features, including old-style classes, string exceptions, and implicit relative imports

A change in integer division functionality: in Python 2, 5 / 2 is 2; in Python 3, 5 / 2 is 2.5. (In both Python 2 (2.2 onwards) and Python 3, a separate operator exists to provide the old behavior: 5 // 2 is 2)

Subsequent releases in the Python 3.x series have included additional, substantial new features; all ongoing development of the language is done in the 3.x series.

Releases before numbered versions:

Table notes:

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The programming language Python was conceived in the late 1980s, and its implementation was started in December 1989 by Guido van Rossum at CWI in the Netherlands as a successor to ABC capable of exception handling and interfacing with the Amoeba operating system.

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Python Tutorial for Absolute Beginners #1 - What Are Variables?

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Python Emerges as Game-Changer in Modern Financial Technology

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In this fast growing digital world, the intersection of technology and finance has reached a transformative milestone, as evidenced by Venkata Reddy Mulam’s gro

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I made a simple calculator application in Python over the course of around an hour and a half today. Is there anything that I could improve on in terms of simplicity or better ways of getting the job done? Thanks! (I started learning Python earlier t…

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PyGame: The 2D video game creation tool in Python

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PyGame is a Python library dedicated to video game development. Discover everything you need to know about this tool, how it works, its advantages and

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An Introduction to Python: A Language for the Ages

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An Introduction to Python: A Language for the Ages

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Python is so popular, because it's the ideal programming language to learn the craft of software engineering. And given how flexible the language is, the sky's the limit with what you can make it do.

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This teaches beginners steps and Motivation for Python Coding.

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Discover the most effective way to learn Python with insights from Dataquest founder Vik Paruchuri. Start your coding journey the right way!

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Introducing Python for electrochemistry research – Physics World

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Available to watch now, The Electrochemical Society, in partnership with BioLogic and Gamry Instruments, explores the advantages of using Python in your electrochemical research

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Python just brushed past JavaScript to become the most popular programming language on GitHub – and a key factor is that AI developers love it

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The meteoric rise of Python shows no sign of stopping

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For the creation and evolution of the Python programming language and for leadership of its community.

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For the creation and evolution of the Python programming language and for leadership of its community.

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Why Programming Is The Core Skill Of The 21st Century

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Why Programming Is The Core Skill Of The 21st Century

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Why Programming Is The Core Skill Of The 21st Century

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It’s never been easier, more accessible, or more essential to learn coding skills.

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Adding Coding to the Curriculum (Published 2014)

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Around the world, students from elementary school to the Ph.D. level are increasingly getting acquainted with the basics of computer coding.

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Source code: Lib/idlelib/ IDLE is Python's Integrated Development and Learning Environment. IDLE has the following features: cross-platform: works mostly the same on Windows, Unix, and macOS, Pytho...