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Python For Beginners

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.

Before getting started, you may want to find out which IDEs and text editors are tailored to make Python editing easy, browse the list of introductory books , or look at code samples that you might find helpful.

There is a list of tutorials suitable for experienced programmers on the BeginnersGuide/Tutorials page. There is also a list of resources in other languages which might be useful if English is not your first language.

The online documentation is your first port of call for definitive information. There is a fairly brief tutorial that gives you basic information about the language and gets you started. You can follow this by looking at the library reference for a full description of Python's many libraries and the language reference for a complete (though somewhat dry) explanation of Python's syntax. If you are looking for common Python recipes and patterns, you can browse the ActiveState Python Cookbook

Looking for Something Specific?

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.

Frequently Asked Questions

If you have a question, it's a good idea to try the FAQ , which answers the most commonly asked questions about Python.

Looking to Help?

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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• The Python Tutorial
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The Python Tutorial ¶

Python is an easy to learn, powerful programming language. It has efficient high-level data structures and a simple but effective approach to object-oriented programming. Python’s elegant syntax and dynamic typing, together with its interpreted nature, make it an ideal language for scripting and rapid application development in many areas on most platforms.

The Python interpreter and the extensive standard library are freely available in source or binary form for all major platforms from the Python web site, https://www.python.org/ , and may be freely distributed. The same site also contains distributions of and pointers to many free third party Python modules, programs and tools, and additional documentation.

The Python interpreter is easily extended with new functions and data types implemented in C or C++ (or other languages callable from C). Python is also suitable as an extension language for customizable applications.

This tutorial introduces the reader informally to the basic concepts and features of the Python language and system. It helps to have a Python interpreter handy for hands-on experience, but all examples are self-contained, so the tutorial can be read off-line as well.

For a description of standard objects and modules, see The Python Standard Library . The Python Language Reference gives a more formal definition of the language. To write extensions in C or C++, read Extending and Embedding the Python Interpreter and Python/C API Reference Manual . There are also several books covering Python in depth.

This tutorial does not attempt to be comprehensive and cover every single feature, or even every commonly used feature. Instead, it introduces many of Python’s most noteworthy features, and will give you a good idea of the language’s flavor and style. After reading it, you will be able to read and write Python modules and programs, and you will be ready to learn more about the various Python library modules described in The Python Standard Library .

The Glossary is also worth going through.

1. Whetting Your Appetite

• 2.1.1. Argument Passing
• 2.1.2. Interactive Mode
• 2.2.1. Source Code Encoding
• 3.1.1. Numbers
• 3.1.2. Text
• 3.1.3. Lists
• 3.2. First Steps Towards Programming
• 4.1. if Statements
• 4.2. for Statements
• 4.3. The range() Function
• 4.4. break and continue Statements, and else Clauses on Loops
• 4.5. pass Statements
• 4.6. match Statements
• 4.7. Defining Functions
• 4.8.1. Default Argument Values
• 4.8.2. Keyword Arguments
• 4.8.3.1. Positional-or-Keyword Arguments
• 4.8.3.2. Positional-Only Parameters
• 4.8.3.3. Keyword-Only Arguments
• 4.8.3.4. Function Examples
• 4.8.3.5. Recap
• 4.8.4. Arbitrary Argument Lists
• 4.8.5. Unpacking Argument Lists
• 4.8.6. Lambda Expressions
• 4.8.7. Documentation Strings
• 4.8.8. Function Annotations
• 4.9. Intermezzo: Coding Style
• 5.1.1. Using Lists as Stacks
• 5.1.2. Using Lists as Queues
• 5.1.3. List Comprehensions
• 5.1.4. Nested List Comprehensions
• 5.2. The del statement
• 5.3. Tuples and Sequences
• 5.5. Dictionaries
• 5.6. Looping Techniques
• 5.7. More on Conditions
• 5.8. Comparing Sequences and Other Types
• 6.1.1. Executing modules as scripts
• 6.1.2. The Module Search Path
• 6.1.3. “Compiled” Python files
• 6.2. Standard Modules
• 6.3. The dir() Function
• 6.4.1. Importing * From a Package
• 6.4.2. Intra-package References
• 6.4.3. Packages in Multiple Directories
• 7.1.1. Formatted String Literals
• 7.1.2. The String format() Method
• 7.1.3. Manual String Formatting
• 7.1.4. Old string formatting
• 7.2.1. Methods of File Objects
• 7.2.2. Saving structured data with json
• 8.1. Syntax Errors
• 8.2. Exceptions
• 8.3. Handling Exceptions
• 8.4. Raising Exceptions
• 8.5. Exception Chaining
• 8.6. User-defined Exceptions
• 8.7. Defining Clean-up Actions
• 8.8. Predefined Clean-up Actions
• 8.9. Raising and Handling Multiple Unrelated Exceptions
• 8.10. Enriching Exceptions with Notes
• 9.1. A Word About Names and Objects
• 9.2.1. Scopes and Namespaces Example
• 9.3.1. Class Definition Syntax
• 9.3.2. Class Objects
• 9.3.3. Instance Objects
• 9.3.4. Method Objects
• 9.3.5. Class and Instance Variables
• 9.4. Random Remarks
• 9.5.1. Multiple Inheritance
• 9.6. Private Variables
• 9.7. Odds and Ends
• 9.8. Iterators
• 9.9. Generators
• 9.10. Generator Expressions
• 10.1. Operating System Interface
• 10.2. File Wildcards
• 10.3. Command Line Arguments
• 10.4. Error Output Redirection and Program Termination
• 10.5. String Pattern Matching
• 10.6. Mathematics
• 10.7. Internet Access
• 10.8. Dates and Times
• 10.9. Data Compression
• 10.10. Performance Measurement
• 10.11. Quality Control
• 10.12. Batteries Included
• 11.1. Output Formatting
• 11.2. Templating
• 11.3. Working with Binary Data Record Layouts
• 11.4. Multi-threading
• 11.5. Logging
• 11.6. Weak References
• 11.7. Tools for Working with Lists
• 11.8. Decimal Floating Point Arithmetic
• 12.1. Introduction
• 12.2. Creating Virtual Environments
• 12.3. Managing Packages with pip
• 13. What Now?
• 14.1. Tab Completion and History Editing
• 14.2. Alternatives to the Interactive Interpreter
• 15.1. Representation Error
• 16.1.1. Error Handling
• 16.1.2. Executable Python Scripts
• 16.1.3. The Interactive Startup File
• 16.1.4. The Customization Modules

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Python and Its Basics: A Beginner’s Guide (2021)

Computer programmers use hundreds of languages to develop software and mobile applications, build websites, and teach computers how to learn. Because it performs all of those functions, Python is among the most popular and important programming languages for aspiring coders and accomplished developers to learn.

Python is a versatile programming language that employers find highly attractive; it can open doors whether you’re looking for a new job, seeking to grow your skills, or hoping to switch industries entirely.

In this article, we’ll explore the Python basics, including answers to the following questions:

What is Python?

• What can you do with Python?
• What are Python’s basic uses?
• Which careers benefit from a working knowledge of Python?

Read on to learn more about this well-loved programming language.

Python is a multipurpose, high-level, object-oriented programming language — three properties that make it popular with coders and developers. Python is multipurpose because it can be used to create software and apps, design websites, and automate repetitive tasks. Web developers and data scientists like Python for its broad range of companion libraries, accessible syntax, and portability. The library tools and packages help developers shorten and streamline their coding time, and many programmers appreciate that Python requires less time to build projects.

As a high-level language, Python uses an easy-to-read command syntax that it converts to machine code. It also works on the Mac, Windows, and Linux platforms, making it accessible to nearly every programmer.

As an object-oriented language, Python organizes programs into objects and classes that can be reused throughout a project.

Object-Oriented Programming

In object-oriented programming, related variables and functions are grouped into units (or objects). These objects contain data and procedures that determine how they act. In Python, everything is considered an object — and functions are created to determine objects’ actions.

Python Basics: How Does Python Work?

While Python has become an essential language and a very useful skill for data analysts , it requires some basic terminology to get started. If you have a limited programming background, here are a few important terms to know:

Use a hashtag to leave comments, or notes, to yourself or others explaining elements of your code. In Python, comments are ignored so they are not incorrectly included in the final product.

Every programming language relies on certain words to convey meanings or perform specific functions. For example, True and False are used to represent the truth value of an expression within the Python Boolean, one of Python’s built-in data types.

Built-in Data Types

Because variables can store different types of data, it’s important to input the correct data types while programming. Python uses several data types, including numerics, strings, Booleans, lists, and tuples.

Loops simplify the process of repeating an action for a specific number of steps or until a condition is met. Python presents two types of loops when code needs to be repeated: for and while .

How to Install Python

Python is simple to install; in fact, it might already be installed on your computer. To check, open a command-line window and type “Python.” If the language is installed, a Python interpreter will respond by showing a version number. If not, a link to a free download will likely appear.

If you need to download Python, the language’s free site offers instructions to easily download the latest version for Windows, Mac OS, and Linux.

Here are a few more key related terms to be familiar with as you get started using Python:

Conditionals

Generally speaking, conditional statements facilitate decision-making within a program and perform an action depending on whether a defined condition is true. The primary conditional commands in Python are if and else . Since Python supports common math conditions (e.g., a = 1, b = 2, b > a), the if and else commands deliver instructions based on those conditions. For instance, “print(“b is greater than a”) might follow the conditional “if b > a.” Else might lead to the instruction “print(“b is not greater than a”).

A function is a block of code that runs when the program commands it to do so. To run the function, programmers simply call it by entering the function’s name into their code. Functions in Python are defined using the “def” keyword, followed by a block of code that defines an action. A program might consist of the line “def coding_function():” which is followed by the function’s steps. To perform the function, a programmer simply enters its name (coding_function).

In Python, operators perform many tasks: arithmetic functions, assignment of values to variables, comparison of values, combining conditional statements, and more. Common operators include + for adding numbers, * for multiplication, and / for division. Words such as “and,” “or,” “not,” “is,” and “is not” also serve as operators to define and compare variables.

Strings are sequences of characters that form words or phrases that we can read. In Python, strings are defined in quotation marks, so the line print(“Good morning”) tells the computer to print the string “Good morning.”

Values are stored in variables. In a simple example, “x = 100,” x is the variable, and 100 is the value. Programmers often name variables more descriptively to provide context to the data they’re referencing. When using Python, programmers don’t need to declare the variable; rather, it’s created very simply: for instance, name = “Mark” assigns the string value “Mark” to the variable “name.”

Mutual Exclusion

Programmers often write programs that use shared files or resources. A mutual exclusion program stops one process from using those files while another process is using them. In Python, programmers can add a mutual exclusion — or “mutex” — to lock one process while another continues to reach its output.

Race Conditions

Python supports multiple processes and multi-threads, but errors can occur. A race condition occurs when two or more threads in a program try to access shared data at the same time; they then try to “race”, which can create instability.

Locks, like mutual exclusions, can help solve problems caused by race conditions. In Python, locks can be used to synchronize threads so they work together; use the terms acquire and release to move threads between their locked and unlocked states.

Deadlocks, which can result from flawed implementation of thread locks, are instances programmers want to avoid in Python. They occur when a locked thread isn’t released, which then locks the entire program.

Echo Command

Echo is a common programming command that tells the computer to display an output. In Python, this command is known as print — so the line print(‘Hello’) tells the computer to display the word Hello .

Enhancing With HTTP

Python encourages its community members to introduce new features or provide input on existing features. These Python Enhancement Proposals (or PEPs) go through a lengthy review and approval process but contribute to the sustainability and longevity of Python.

Debugging Tools

Python offers a number of other editing and debugging tools in its library, including pdb , which is accessed with the import pdb instruction.

How to Use Python

Python consistently ranks among the most in-demand languages because so many programmers use it across a variety of industries. In fact, developers consider Python among their most-loved languages, according to a Stack Overflow survey , and rate it highly on their must-learn lists, according to HackerRank (PDF, 2.4 MB) .

Mining Social Media Data

Python users can access a variety of tools to generate insight from data, making the language a favorite for data analysis. For example, Python can be used to write a program that studies Twitter or Facebook data.

Game Design

Python features many game libraries — developers are supported by a series of modules found within the language’s libraries.

Web Development

Django was designed and introduced to support Python. The development framework also contains many of the essential tools for web development, making Python a useful tool for building websites.

Machine Learning

Python has a collection of libraries designed specifically to classify and analyze data, key components of machine learning and artificial intelligence. As a result, Python is considered among the top languages for machine learning.

Explore Columbia Engineering Data Analytics Boot Camp which covers the specialized skills needed to tap into the data field.

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Potential Careers Using Python

Programmers, web and application developers, and data scientists with a knowledge of Python have a variety of careers available to them. Where can a coding career using Python lead you? Here are a few job opportunities, with outlook and salary data from the U.S. Bureau of Labor Statistics .

Computer Programmer

Programmers rely on several languages, including Python, to write code for software and applications. The median wage for programmers is $89,190 , with some software publishers making over $100,000.

For more information, see our guide on how to become a computer programmer .

Software Developer

Developers are involved in the entire process of software development, from design to implementation; it’s a fast-growing field with an expected job growth of 22 percent by 2029 — and a median salary of $110,140 .

For more information, see our guide on how to become a software engineer (a type of software developer)

Web Developer

Creating and maintaining websites is another promising job path. This industry projects an 8 percent job growth by 2029, and the continued growth of e-commerce is just one reason for this positive outlook. The median developer salary is $77,200 , with top-end developers making more than $140,000.

For more information, see our guide on how to become a web developer .

Database Administrator

With 10 percent expected growth, database administration has a bright outlook — primarily because many businesses rely on data management. In fact, healthcare has become chief among these fields as more records are digitized. The current median salary for this career path is $98,860 .

Computer Network Architect

Network architects build and maintain corporate data communication networks, including intranets. Job opportunities in this field are expected to grow by 5 percent by 2029, as more specialists will be needed to update existing networks. The current median salary for architects is $116,780 .

What Are Some Tips I Should Consider When Learning Python?

Starting a Python tutorial from scratch? Here are a few suggestions to get started.

• Verse yourself in the basics of coding: Familiarize yourself with general terminology and concepts such as data types (strings and numbers), variables, functions, and control structures. Don’t worry if the information doesn’t make sense at first; you’re learning a new language, which can take time — so approach it with patience.
• Consider the role you want: Writing game software is different from developing a website, which is different from maintaining a database. Consider the role you wish to pursue with the applicable knowledge you will gain from learning Python.
• Consider joining a bootcamp: Bootcamps offer a concentrated, structured way to begin a career in coding. For example, Columbia’s online Coding Boot Camp offers 12- and 24-week courses that cover in-demand skills (learners who sign up for optional continuation courses will gain experience with Python) and also provide practical experience to help launch your career. If you’re interested in the data science uses and implications of Python, consider Columbia Engineering Data Analytics Boot Camp .

Python Programming FAQ

How long does it take to learn python.

The answer depends on your levels of experience and comfort with coding. Since many of its keywords are recognizable, Python is considered easier to grasp than other languages, and the basics can usually be learned in a few weeks. However, if you lack any coding experience, give yourself time to learn at your own pace, sticking to a schedule and setting goals that are reflective of reality.

What exercises can I complete in Python?

Start small and practice coding simple programs or apps. Building a calculator app, a program that tracks cryptocurrency values, or a password generator that requires user input and produces a strong password are all great ways to test your skills.

What else should I learn besides Python?

To enhance your marketability for coding-related roles, consider learning HTML, CSS, or JavaScript. Python learners also might consider Django (website building), Bootstrap (front end development), or Node.js (application development) as companion disciplines to learn. After becoming fluent in Python, you might consider studying how you can use it in machine learning. Bootcamp courses are an effective means of learning programming languages and preparing for a career in coding or data analytics.

Get started learning Python with DataCamp's free Intro to Python tutorial . Learn Data Science by completing interactive coding challenges and watching videos by expert instructors. Start Now !

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Welcome to the LearnPython.org interactive Python tutorial.

Whether you are an experienced programmer or not, this website is intended for everyone who wishes to learn the Python programming language.

You are welcome to join our group on Facebook for questions, discussions and updates.

After you complete the tutorials, you can get certified at LearnX and add your certification to your LinkedIn profile.

Just click on the chapter you wish to begin from, and follow the instructions. Good luck!

Learn the Basics

• Hello, World!
• Variables and Types
• Basic Operators
• String Formatting
• Basic String Operations
• Classes and Objects
• Dictionaries
• Modules and Packages

Data Science Tutorials

• Numpy Arrays
• Pandas Basics

Advanced Tutorials

• List Comprehensions
• Lambda functions
• Multiple Function Arguments
• Regular Expressions
• Exception Handling
• Serialization
• Partial functions
• Code Introspection
• Map, Filter, Reduce

Other Python Tutorials

• DataCamp has tons of great interactive Python Tutorials covering data manipulation, data visualization, statistics, machine learning, and more
• Read Python Tutorials and References course from After Hours Programming

Contributing Tutorials

Read more here: Contributing Tutorials

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Get started using Python on Windows for beginners

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The following is a step-by-step guide for beginners interested in learning Python using Windows.

Set up your development environment

For beginners who are new to Python, we recommend you install Python from the Microsoft Store . Installing via the Microsoft Store uses the basic Python3 interpreter, but handles set up of your PATH settings for the current user (avoiding the need for admin access), in addition to providing automatic updates. This is especially helpful if you are in an educational environment or a part of an organization that restricts permissions or administrative access on your machine.

If you are using Python on Windows for web development , we recommend a different set up for your development environment. Rather than installing directly on Windows, we recommend installing and using Python via the Windows Subsystem for Linux. For help, see: Get started using Python for web development on Windows . If you're interested in automating common tasks on your operating system, see our guide: Get started using Python on Windows for scripting and automation . For some advanced scenarios (like needing to access/modify Python's installed files, make copies of binaries, or use Python DLLs directly), you may want to consider downloading a specific Python release directly from python.org or consider installing an alternative , such as Anaconda, Jython, PyPy, WinPython, IronPython, etc. We only recommend this if you are a more advanced Python programmer with a specific reason for choosing an alternative implementation.

Install Python

To install Python using the Microsoft Store:

Go to your Start menu (lower left Windows icon), type "Microsoft Store", select the link to open the store.

Once the store is open, select Search from the upper-right menu and enter "Python". Select which version of Python you would like to use from the results under Apps. We recommend using the most recent unless you have a reason not to (such as aligning with the version used on a pre-existing project that you plan to work on). Once you've determined which version you would like to install, select Get .

Once Python has completed the downloading and installation process, open Windows PowerShell using the Start menu (lower left Windows icon). Once PowerShell is open, enter Python --version to confirm that Python3 has installed on your machine.

The Microsoft Store installation of Python includes pip , the standard package manager. Pip allows you to install and manage additional packages that are not part of the Python standard library. To confirm that you also have pip available to install and manage packages, enter pip --version .

Install Visual Studio Code

By using VS Code as your text editor / integrated development environment (IDE), you can take advantage of IntelliSense (a code completion aid), Linting (helps avoid making errors in your code), Debug support (helps you find errors in your code after you run it), Code snippets (templates for small reusable code blocks), and Unit testing (testing your code's interface with different types of input).

VS Code also contains a built-in terminal that enables you to open a Python command line with Windows Command prompt, PowerShell, or whatever you prefer, establishing a seamless workflow between your code editor and command line.

To install VS Code, download VS Code for Windows: https://code.visualstudio.com .

Once VS Code has been installed, you must also install the Python extension. To install the Python extension, you can select the VS Code Marketplace link or open VS Code and search for Python in the extensions menu (Ctrl+Shift+X).

Python is an interpreted language, and in order to run Python code, you must tell VS Code which interpreter to use. We recommend using the most recent version of Python unless you have a specific reason for choosing something different. Once you've installed the Python extension, select a Python 3 interpreter by opening the Command Palette (Ctrl+Shift+P), start typing the command Python: Select Interpreter to search, then select the command. You can also use the Select Python Environment option on the bottom Status Bar if available (it may already show a selected interpreter). The command presents a list of available interpreters that VS Code can find automatically, including virtual environments. If you don't see the desired interpreter, see Configuring Python environments .

To open the terminal in VS Code, select View > Terminal , or alternatively use the shortcut Ctrl+` (using the backtick character). The default terminal is PowerShell.

Inside your VS Code terminal, open Python by simply entering the command: python

Try the Python interpreter out by entering: print("Hello World") . Python will return your statement "Hello World".

Install Git (optional)

If you plan to collaborate with others on your Python code, or host your project on an open-source site (like GitHub), VS Code supports version control with Git . The Source Control tab in VS Code tracks all of your changes and has common Git commands (add, commit, push, pull) built right into the UI. You first need to install Git to power the Source Control panel.

Download and install Git for Windows from the git-scm website .

An Install Wizard is included that will ask you a series of questions about settings for your Git installation. We recommend using all of the default settings, unless you have a specific reason for changing something.

If you've never worked with Git before, GitHub Guides can help you get started.

Hello World tutorial for some Python basics

Python, according to its creator Guido van Rossum, is a “high-level programming language, and its core design philosophy is all about code readability and a syntax which allows programmers to express concepts in a few lines of code.”

Python is an interpreted language. In contrast to compiled languages, in which the code you write needs to be translated into machine code in order to be run by your computer's processor, Python code is passed straight to an interpreter and run directly. You just type in your code and run it. Let's try it!

With your PowerShell command line open, enter python to run the Python 3 interpreter. (Some instructions prefer to use the command py or python3 , these should also work). You will know that you're successful because a >>> prompt with three greater-than symbols will display.

There are several built-in methods that allow you to make modifications to strings in Python. Create a variable, with: variable = 'Hello World!' . Press Enter for a new line.

Print your variable with: print(variable) . This will display the text "Hello World!".

Find out the length, how many characters are used, of your string variable with: len(variable) . This will display that there are 12 characters used. (Note that the blank space it counted as a character in the total length.)

Convert your string variable to upper-case letters: variable.upper() . Now convert your string variable to lower-case letters: variable.lower() .

Count how many times the letter "l" is used in your string variable: variable.count("l") .

Search for a specific character in your string variable, let's find the exclamation point, with: variable.find("!") . This will display that the exclamation point is found in the 11th position character of the string.

Replace the exclamation point with a question mark: variable.replace("!", "?") .

To exit Python, you can enter exit() , quit() , or select Ctrl-Z.

Hope you had fun using some of Python's built-in string modification methods. Now try creating a Python program file and running it with VS Code.

Hello World tutorial for using Python with VS Code

The VS Code team has put together a great Getting Started with Python tutorial walking through how to create a Hello World program with Python, run the program file, configure and run the debugger, and install packages like matplotlib and numpy to create a graphical plot inside a virtual environment.

Open PowerShell and create an empty folder called "hello", navigate into this folder, and open it in VS Code:

Once VS Code opens, displaying your new hello folder in the left-side Explorer window, open a command line window in the bottom panel of VS Code by pressing Ctrl+` (using the backtick character) or selecting View > Terminal . By starting VS Code in a folder, that folder becomes your "workspace". VS Code stores settings that are specific to that workspace in .vscode/settings.json, which are separate from user settings that are stored globally.

Continue the tutorial in the VS Code docs: Create a Python Hello World source code file .

Create a simple game with Pygame

Pygame is a popular Python package for writing games - encouraging students to learn programming while creating something fun. Pygame displays graphics in a new window, and so it will not work under the command-line-only approach of WSL. However, if you installed Python via the Microsoft Store as detailed in this tutorial, it will work fine.

Once you have Python installed, install pygame from the command line (or the terminal from within VS Code) by typing python -m pip install -U pygame --user .

Test the installation by running a sample game : python -m pygame.examples.aliens

All being well, the game will open a window. Close the window when you are done playing.

Here's how to start writing your own game.

Open PowerShell (or Windows Command Prompt) and create an empty folder called "bounce". Navigate to this folder and create a file named "bounce.py". Open the folder in VS Code:

Using VS Code, enter the following Python code (or copy and paste it):

Save it as: bounce.py .

From the PowerShell terminal, run it by entering: python bounce.py .

Try adjusting some of the numbers to see what effect they have on your bouncing ball.

Read more about writing games with pygame at pygame.org .

Resources for continued learning

We recommend the following resources to support you in continuing to learn about Python development on Windows.

• Microsoft Dev Blogs: Python : Read the latest updates about all things Python at Microsoft.

Online resources for learning Python

Introduction to Python : Try the interactive Microsoft Learn platform and earn experience points for completing this module covering the basics on how to write basic Python code, declare variables, and work with console input and output. The interactive sandbox environment makes this a great place to start for folks who don't have their Python development environment set up yet.

Learning Python on LinkedIn.com : A basic introduction to Python.

Python Tutorial For Beginners : A complete and free Python tutorial with interactive (runnable) code examples, ideal for both complete beginners and those with prior experience.

LearnPython.org Tutorials : Get started on learning Python without needing to install or set anything up with these free interactive Python tutorials from the folks at DataCamp.

The Python.org Tutorials : Introduces the reader informally to the basic concepts and features of the Python language and system.

Working with Python in VS Code

Editing Python in VS Code : Learn more about how to take advantage of VS Code's autocomplete and IntelliSense support for Python, including how to customize their behavior... or just turn them off.

Linting Python : Linting is the process of running a program that will analyse code for potential errors. Learn about the different forms of linting support VS Code provides for Python and how to set it up.

Debugging Python : Debugging is the process of identifying and removing errors from a computer program. This article covers how to initialize and configure debugging for Python with VS Code, how to set and validate breakpoints, attach a local script, perform debugging for different app types or on a remote computer, and some basic troubleshooting.

Unit testing Python : Covers some background explaining what unit testing means, an example walkthrough, enabling a test framework, creating and running your tests, debugging tests, and test configuration settings.

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Python Code Example Handbook – Sample Script Coding Tutorial for Beginners

Hi! Welcome. If you are learning Python, then this article is for you. You will find a thorough description of Python syntax and lots of code examples to guide you during your coding journey.

What we will cover:

• Variable Definitions in Python
• Hello, World! Program in Python
• Data Types and Built-in Data Structures in Python
• Python Operators
• Conditionals in Python
• For Loops in Python
• While Loops in Python
• Nested Loops in Python
• Functions in Python
• Recursion in Python
• Exception Handling in Python
• Object-Oriented Programming in Python
• How to Work with Files in Python
• Import Statements in Python
• List and Dictionary Comprehension in Python
• and more...

Are you ready? Let's begin! 🔅

💡 Tip: throughout this article, I will use <> to indicate that this part of the syntax will be replaced by the element described by the text. For example, <var> means that this will be replaced by a variable when we write the code.

🔹 Variable Definitions in Python

The most basic building-block of any programming language is the concept of a variable, a name and place in memory that we reserve for a value.

In Python, we use this syntax to create a variable and assign a value to this variable:

For example:

If the name of a variable has more than one word, then the Style Guide for Python Code recommends separating words with an underscore "as necessary to improve readability."

💡 Tip: The Style Guide for Python Code (PEP 8) has great suggestions that you should follow to write clean Python code.

Here's an interactive scrim to help you understand variable definitions in Python:

🔸 hello, world program in python.

Before we start diving into the data types and data structures that you can use in Python, let's see how you can write your first Python program.

You just need to call the print() function and write "Hello, World!" within parentheses:

You will see this message after running the program:

💡 Tip: Writing a "Hello, World!" program is a tradition in the developer community. Most developers start learning how to code by writing this program.

Great. You just wrote your first Python program. Now let's start learning about the data types and built-in data structures that you can use in Python.

🔹 Data Types and Built-in Data Structures in Python

We have several basic data types and built-in data structures that we can work with in our programs. Each one has its own particular applications. Let's see them in detail.

Numeric Data Types in Python: Integers, Floats, and Complex

These are the numeric types that you can work with in Python:

Integers are numbers without decimals. You can check if a number is an integer with the type() function. If the output is <class 'int'> , then the number is an integer.

Floats are numbers with decimals. You can detect them visually by locating the decimal point. If we call type() to check the data type of these values, we will see this as the output:

Here we have some examples:

Complex numbers have a real part and an imaginary part denoted with j . You can create complex numbers in Python with complex() . The first argument will be the real part and the second argument will be the imaginary part.

These are some examples:

Strings in Python

Strings incredibly helpful in Python. They contain a sequence of characters and they are usually used to represent text in the code.

We can use both single quotes '' or double quotes "" to define a string. They are both valid and equivalent, but you should choose one of them and use it consistently throughout the program.

💡 Tip: Yes! You used a string when you wrote the "Hello, World!" program. Whenever you see a value surrounded by single or double quotes in Python, that is a string.

Strings can contain any character that we can type in our keyboard, including numbers, symbols, and other special characters.

💡 Tip: Spaces are also counted as characters in a string.

Quotes Within Strings

If we define a string with double quotes "" , then we can use single quotes within the string. For example:

If we define a string with single quotes '' , then we can use double quotes within the string. For example:

String Indexing

We can use indices to access the characters of a string in our Python program. An index is an integer that represents a specific position in the string. They are associated to the character at that position.

This is a diagram of the string "Hello" :

💡 Tip: Indices start from 0 and they are incremented by 1 for each character to the right.

We can also use negative indices to access these characters:

💡 Tip: we commonly use -1 to access the last character of a string.

String Slicing

We may also need to get a slice of a string or a subset of its characters. We can do so with string slicing.

This is the general syntax:

start is the index of the first character that will be included in the slice. By default, it's 0 .

• stop is the index of the last character in the slice (this character will not be included). By default, it is the last character in the string (if we omit this value, the last character will also be included).
• step is how much we are going to add to the current index to reach the next index.

We can specify two parameters to use the default value of step , which is 1 . This will include all the characters between start and stop (not inclusive):

💡 Tip: Notice that if the value of a parameter goes beyond the valid range of indices, the slice will still be presented. This is how the creators of Python implemented this feature of string slicing.

If we customize the step , we will "jump" from one index to the next according to this value.

We can also use a negative step to go from right to left:

And we can omit a parameter to use its default value. We just have to include the corresponding colon ( : ) if we omit start , stop , or both:

💡 Tip: The last example is one of the most common ways to reverse a string.

In Python 3.6 and more recent versions, we can use a type of string called f-string that helps us format our strings much more easily.

To define an f-string, we just add an f before the single or double quotes. Then, within the string, we surround the variables or expressions with curly braces {} . This replaces their value in the string when we run the program.

The output is:

Here we have an example where we calculate the value of an expression and replace the result in the string:

The values are replaced in the output:

We can also call methods within the curly braces and the value returned will be replaced in the string when we run the program:

String Methods

Strings have methods, which represent common functionality that has been implemented by Python developers, so we can use it in our programs directly. They are very helpful to perform common operations.

This is the general syntax to call a string method:

To learn more about Python methods, I would recommend reading this article from the Python documentation.

💡 Tip: All string methods return copies of the string. They do not modify the string because strings are immutable in Python.

Booleans in Python

Boolean values are True and False in Python. They must start with an uppercase letter to be recognized as a boolean value.

If we write them in lowercase, we will get an error:

Lists in Python

Now that we've covered the basic data types in Python, let's start covering the built-in data structures. First, we have lists.

To define a list, we use square brackets [] with the elements separated by a comma.

💡 Tip: It's recommended to add a space after each comma to make the code more readable.

For example, here we have examples of lists:

Lists can contain values of different data types, so this would be a valid list in Python:

We can also assign a list to a variable:

Nested Lists

Lists can contain values of any data type, even other lists. These inner lists are called nested lists .

In this example, [1, 2, 3] and [4, 5, 6] are nested lists.

Here we have other valid examples:

We can access the nested lists using their corresponding index:

Nested lists could be used to represent, for example, the structure of a simple 2D game board where each number could represent a different element or tile:

List Length

We can use the len() function to get the length of a list (the number of elements it contains).

Update a Value in a List

We can update the value at a particular index with this syntax:

Add a Value to a List

We can add a new value to the end of a list with the .append() method.

Remove a Value from a List

We can remove a value from a list with the .remove() method.

💡 Tip: This will only remove the first occurrence of the element. For example, if we try to remove the number 3 from a list that has two number 3s, the second number will not be removed:

List Indexing

We can index a list just like we index strings, with indices that start from 0 :

List Slicing

We can also get a slice of a list using the same syntax that we used with strings and we can omit the parameters to use their default values. Now, instead of adding characters to the slice, we will be adding the elements of the list.

List Methods

Python also has list methods already implemented to help us perform common list operations. Here are some examples of the most commonly used list methods:

To learn more about list methods, I would recommend reading this article from the Python documentation.

Here's an interactive scrim to help you learn more about lists in Python:

Tuples in python.

To define a tuple in Python, we use parentheses () and separate the elements with a comma. It is recommended to add a space after each comma to make the code more readable.

We can assign tuples to variables:

Tuple Indexing

We can access each element of a tuple with its corresponding index:

We can also use negative indices:

Tuple Length

To find the length of a tuple, we use the len() function, passing the tuple as argument:

Nested Tuples

Tuples can contain values of any data type, even lists and other tuples. These inner tuples are called nested tuples .

In this example, we have a nested tuple (4, 5, 6) and a list. You can access these nested data structures with their corresponding index.

Tuple Slicing

We can slice a tuple just like we sliced lists and strings. The same principle and rules apply.

Tuple Methods

There are two built-in tuple methods in Python:

💡 Tip: tuples are immutable. They cannot be modified, so we can't add, update, or remove elements from the tuple. If we need to do so, then we need to create a new copy of the tuple.

Tuple Assignment

In Python, we have a really cool feature called Tuple Assignment. With this type of assignment, we can assign values to multiple variables on the same line.

The values are assigned to their corresponding variables in the order that they appear. For example, in a, b = 1, 2 the value 1 is assigned to the variable a and the value 2 is assigned to the variable b .

💡 Tip: Tuple assignment is commonly used to swap the values of two variables:

Dictionaries in Python

Now let's start diving into dictionaries. This built-in data structure lets us create pairs of values where one value is associated with another one.

To define a dictionary in Python, we use curly brackets {} with the key-value pairs separated by a comma.

The key is separated from the value with a colon : , like this:

You can assign the dictionary to a variable:

The keys of a dictionary must be of an immutable data type. For example, they can be strings, numbers, or tuples but not lists since lists are mutable.

• Strings: {"City 1": 456, "City 2": 577, "City 3": 678}
• Numbers: {1: "Move Left", 2: "Move Right", 3: "Move Up", 4: "Move Down"}
• Tuples: {(0, 0): "Start", (2, 4): "Goal"}

The values of a dictionary can be of any data type, so we can assign strings, numbers, lists, tuple, sets, and even other dictionaries as the values. Here we have some examples:

Dictionary Length

To get the number of key-value pairs, we use the len() function:

Get a Value in a Dictionary

To get a value in a dictionary, we use its key with this syntax:

This expression will be replaced by the value that corresponds to the key.

The output is the value associated to "a" :

Update a Value in a Dictionary

To update the value associated with an existing key, we use the same syntax but now we add an assignment operator and the value:

Now the dictionary is:

Add a Key-Value Pair to a Dictionary

The keys of a dictionary have to be unique. To add a new key-value pair we use the same syntax that we use to update a value, but now the key has to be new.

Now the dictionary has a new key-value pair:

Delete a Key-Value Pair in a Dictionary

To delete a key-value pair, we use the del statement:

Dictionary Methods

These are some examples of the most commonly used dictionary methods:

To learn more about dictionary methods, I recommend reading this article from the documentation.

And here's an interactive scrim to help you learn more about data types in Python:

🔸 python operators.

Great. Now you know the syntax of the basic data types and built-in data structures in Python, so let's start diving into operators in Python. They are essential to perform operations and to form expressions.

Arithmetic Operators in Python

These operators are:

Addition: +

💡 Tip: The last two examples are curious, right? This operator behaves differently based on the data type of the operands.

When they are strings, this operator concatenates the strings and when they are Boolean values, it performs a particular operation.

In Python, True is equivalent to 1 and False is equivalent to 0 . This is why the result is 1 + 0 = 1

Subtraction: -

Multiplication: *.

💡 Tip: you can "multiply" a string by an integer to repeat the string a given number of times.

Exponentiation: **

Division: /.

💡 Tip: this operator returns a float as the result, even if the decimal part is .0

If you try to divide by 0 , you will get a ZeroDivisionError :

Integer Division: //

This operator returns an integer if the operands are integers. If they are floats, the result will be a float with .0 as the decimal part because it truncates the decimal part.

Comparison Operators

• Greater than: >
• Greater than or equal to: >=
• Less than: <
• Less than or equal to: <=
• Equal to: ==
• Not Equal to: !=

These comparison operators make expressions that evaluate to either True or False . Here we have are some examples:

We can also use them to compare strings based on their alphabetical order:

We typically use them to compare the values of two or more variables:

💡 Tip: notice that the comparison operator is == while the assignment operator is = . Their effect is different. == returns True or False while = assigns a value to a variable.

Comparison Operator Chaining

In Python, we can use something called "comparison operator chaining" in which we chain the comparison operators to make more than one comparison more concisely.

For example, this checks if a is less than b and if b is less than c :

Logical Operators

There are three logical operators in Python: and , or , and not . Each one of these operators has its own truth table and they are essential to work with conditionals.

The and operator:

The or operator:

The not operator:

These operator are used to form more complex expressions that combine different operators and variables.

Assignment Operators

Assignment operators are used to assign a value to a variable.

They are: = , += , -= , *= , %= , /= , //= , **=

• The = operator assigns the value to the variable.
• The other operators perform an operation with the current value of the variable and the new value and assigns the result to the same variable.

💡 Tips: these operators perform bitwise operations before assigning the result to the variable: &= , |= , ^= , >>= , <<= .

Membership Operators

You can check if an element is in a sequence or not with the operators: in and not in . The result will be either True or False .

We typically use them with variables that store sequences, like in this example:

🔹 Conditionals in Python

Now let's see how we can write conditionals to make certain parts of our code run (or not) based on whether a condition is True or False .

if statements in Python

This is the syntax of a basic if statement:

If the condition is True , the code will run. Else, if it's False , the code will not run.

💡 Tip: there is a colon ( : ) at the end of the first line and the code is indented. This is essential in Python to make the code belong to the conditional.

False Condition

The condition is x > 9 and the code is print("Hello, World!") .

In this case, the condition is False , so there is no output.

True Condition

Here we have another example. Now the condition is True :

Code After the Conditional

Here we have an example with code that runs after the conditional has been completed. Notice that the last line is not indented, which means that it doesn't belong to the conditional.

In this example, the condition x > 9 is False , so the first print statement doesn't run but the last print statement runs because it is not part of the conditional, so the output is:

However, if the condition is True , like in this example:

The output will be:

Examples of Conditionals

This is another example of a conditional:

In this case, the output will be:

But if we change the value of favorite_season :

There will be no output because the condition will be False .

if/else statements in Python

We can add an else clause to the conditional if we need to specify what should happen when the condition is False .

💡 Tip: notice that the two code blocks are indented ( if and else ). This is essential for Python to be able to differentiate between the code that belongs to the main program and the code that belongs to the conditional.

Let's see an example with the else clause:

When the condition of the if clause is True , this clause runs. The else clause doesn't run.

Now the else clause runs because the condition is False .

Now the output is:

if/elif/else statements in Python

To customize our conditionals even further, we can add one or more elif clauses to check and handle multiple conditions. Only the code of the first condition that evaluates to True will run.

💡 Tip: elif has to be written after if and before else .

First Condition True

We have two conditions x < 9 and x < 15 . Only the code block from the first condition that is True from top to bottom will be executed.

In this case, the output is:

Because the first condition is True : x < 9 .

Second Condition True

If the first condition is False , then the second condition will be checked.

In this example, the first condition x < 9 is False but the second condition x < 15 is True , so the code that belongs to this clause will run.

All Conditions are False

If all conditions all False , then the else clause will run:

Multiple elif Clauses

We can add as many elif clauses as needed. This is an example of a conditional with two elif clauses:

Each condition will be checked and only the code block of the first condition that evaluates to True will run. If none of them are True , the else clause will run.

Here's an interactive scrim to help you learn more about conditionals in Python:

🔸 for loops in python.

Now you know how to write conditionals in Python, so let's start diving into loops. For loops are amazing programming structures that you can use to repeat a code block a specific number of times.

This is the basic syntax to write a for loop in Python:

The iterable can be a list, tuple, dictionary, string, the sequence returned by range, a file, or any other type of iterable in Python. We will start with range() .

The range() function in Python

This function returns a sequence of integers that we can use to determine how many iterations (repetitions) of the loop will be completed. The loop will complete one iteration per integer.

💡 Tip: Each integer is assigned to the loop variable one at a time per iteration.

This is the general syntax to write a for loop with range() :

As you can see, the range function has three parameters:

• start : where the sequence of integers will start. By default, it's 0 .
• stop : where the sequence of integers will stop (without including this value).
• step : the value that will be added to each element to get the next element in the sequence. By default, it's 1 .

You can pass 1, 2, or 3 arguments to range() :

• With 1 argument, the value is assigned to the stop parameter and the default values for the other two parameters are used.
• With 2 arguments, the values are assigned to the start and stop parameters and the default value for step is used.
• With 3 arguments, the values are assigned to the start , stop , and step parameters (in order).

Here we have some examples with one parameter :

💡 Tip: the loop variable is updated automatically.

In the example below, we repeat a string as many times as indicated by the value of the loop variable:

We can also use for loops with built-in data structures such as lists:

💡 Tip: when you use range(len(<seq>)) , you get a sequence of numbers that goes from 0 up to len(<seq>)-1 . This represents the sequence of valid indices.

These are some examples with two parameters :

Now the list is: ['a', 'b', 'cc', 'd']

These are some examples with three parameters :

How to Iterate over Iterables in Python

We can iterate directly over iterables such as lists, tuples, dictionaries, strings, and files using for loops. We will get each one of their elements one at a time per iteration. This is very helpful to work with them directly.

Let's see some examples:

Iterate Over a String

If we iterate over a string, its characters will be assigned to the loop variable one by one (including spaces and symbols).

We can also iterate over modified copies of the string by calling a string method where we specify the iterable in the for loop. This will assign the copy of the string as the iterable that will be used for the iterations, like this:

Iterate Over Lists and Tuples

Iterate over the keys, values, and key-value pairs of dictionaries.

We can iterate over the keys, values, and key-value pairs of a dictionary by calling specific dictionary methods. Let's see how.

To iterate over the keys , we write:

We just write the name of the variable that stores the dictionary as the iterable.

💡 Tip: you can also write <dictionary_variable>.keys() but writing the name of the variable directly is more concise and it works exactly the same.

💡 Tip: you can assign any valid name to the loop variable.

To iterate over the values , we use:

To iterate over the key-value pairs , we use:

💡 Tip: we are defining two loop variables because we want to assign the key and the value to variables that we can use in the loop.

If we define only one loop variable, this variable will contain a tuple with the key-value pair:

Break and Continue in Python

Now you know how to iterate over sequences in Python. We also have loop control statements to customize what happens when the loop runs: break and continue .

The Break Statement

The break statement is used to stop the loop immediately.

When a break statement is found, the loop stops and the program returns to its normal execution beyond the loop.

In the example below, we stop the loop when an even element is found.

The Continue Statement

The continue statement is used to skip the rest of the current iteration.

When it is found during the execution of the loop, the current iteration stops and a new one begins with the updated value of the loop variable.

In the example below, we skip the current iteration if the element is even and we only print the value if the element is odd:

The zip() function in Python

zip() is an amazing built-in function that we can use in Python to iterate over multiple sequences at once, getting their corresponding elements in each iteration.

We just need to pass the sequences as arguments to the zip() function and use this result in the loop.

The enumerate() Function in Python

You can also keep track of a counter while the loop runs with the enum() function. It is commonly used to iterate over a sequence and get the corresponding index.

💡 Tip: By default, the counter starts at 0 .

If you start the counter from 0 , you can use the index and the current value in the same iteration to modify the sequence:

You can start the counter from a different number by passing a second argument to enumerate() :

The else Clause

For loops also have an else clause. You can add this clause to the loop if you want to run a specific block of code when the loop completes all its iterations without finding the break statement.

💡 Tip: if break is found, the else clause doesn't run and if break is not found, the else clause runs.

In the example below, we try to find an element greater than 6 in the list. That element is not found, so break doesn't run and the else clause runs.

However, if the break statement runs, the else clause doesn't run. We can see this in the example below:

🔹 While Loops in Python

While loops are similar to for loops in that they let us repeat a block of code. The difference is that while loops run while a condition is True .

In a while loop, we define the condition, not the number of iterations. The loop stops when the condition is False .

This is the general syntax of a while loop:

💡 Tip: in while loops, you must update the variables that are part of the condition to make sure that the condition will eventually become False .

Break and Continue

We can also use break and continue with while loops and they both work exactly the same:

• break stops the while loop immediately.
• continue stops the current iteration and starts the next one.

We can also add an else clause to a while loop. If break is found, the else clause doesn't run but if the break statement is not found, the else clause runs.

In the example below, the break statement is not found because none of the numbers are even before the condition becomes False , so the else clause runs.

This is the output:

But in this version of the example, the break statement is found and the else clause doesn't run:

Infinite While Loops

When we write and work with while loops, we can have something called an "infinite loop." If the condition is never False , the loop will never stop without external intervention.

This usually happens when the variables in the condition are not updated properly during the execution of the loop.

💡 Tip: you must make the necessary updates to these variables to make sure that the condition will eventually evaluate to False .

💡 Tip: to stop this process, type CTRL + C . You should see a KeyboardInterrupt message.

🔸 Nested Loops in Python

We can write for loops within for loops and while loops within while loops. These inner loops are called nested loops.

💡 Tip: the inner loop runs for each iteration of the outer loop.

Nested For Loops in Python

If we add print statements, we can see what is happening behind the scenes:

The inner loop completes two iterations per iteration of the outer loop. The loop variables are updated when a new iteration starts.

This is another example:

Nested While Loops in Python

Here we have an example of nested while loops. In this case, we have to update the variables that are part of each condition to guarantee that the loops will stop.

💡 Tip: we can also have for loops within while loops and while loops within for loops.

🔹 Functions in Python

In Python, we can define functions to make our code reusable, more readable, and organized. This is the basic syntax of a Python function:

💡 Tip: a function can have zero, one, or multiple parameters.

Function with No Parameters in Python

A function with no parameters has an empty pair of parentheses after its name in the function definition. For example:

This is the output when we call the function:

💡 Tip: You have to write an empty pair of parentheses after the name of the function to call it.

Function with One Parameter in Python

A function with one or more parameters has a list of parameters surrounded by parentheses after its name in the function definition:

When we call the function, we just need to pass one value as argument and that value will be replaced where we use the parameter in the function definition:

Here we have another example – a function that prints a pattern made with asterisks. You have to specify how many rows you want to print:

You can see the different outputs for different values of num_rows :

Functions with Two or More Parameters in Python

To define two or more parameters, we just separate them with a comma:

Now when we call the function, we must pass two arguments:

We can adapt the function that we just saw with one parameter to work with two parameters and print a pattern with a customized character:

You can see the output with the customized character is that we call the function passing the two arguments:

How to Return a Value in Python

Awesome. Now you know how to define a function, so let's see how you can work with return statements.

We will often need to return a value from a function. We can do this with the return statement in Python. We just need to write this in the function definition:

💡 Tip: the function stops immediately when return is found and the value is returned.

Here we have an example:

Now we can call the function and assign the result to a variable because the result is returned by the function:

We can also use return with a conditional to return a value based on whether a condition is True or False .

In this example, the function returns the first even element found in the sequence:

If we call the function, we can see the expected results:

💡 Tip: if a function doesn't have a return statement or doesn't find one during its execution, it returns None by default.

The Style Guide for Python Code recommends using return statements consistently. It mentions that we should:

Be consistent in return statements. Either all return statements in a function should return an expression, or none of them should. If any return statement returns an expression, any return statements where no value is returned should explicitly state this as return None, and an explicit return statement should be present at the end of the function (if reachable)

Default Arguments in Python

We can assign default arguments for the parameters of our function. To do this, we just need to write <parameter>=<value> in the list of parameters.

💡 Tip: The Style Guide for Python Code mentions that we shouldn't "use spaces around the = sign when used to indicate a keyword argument."

In this example, we assign the default value 5 to the parameter b . If we omit this value when we call the function, the default value will be used.

If we call the function without this argument, you can see the output:

We confirm that the default argument 5 was used in the operation.

But we can also assign a custom value for b by passing a second argument:

💡 Tip: parameters with default arguments have to be defined at the end of the list of parameters. Else, you will see this error: SyntaxError: non-default argument follows default argument .

Here we have another example with the function that we wrote to print a pattern. We assign the default value "*" to the char parameter.

Now we have the option to use the default value or customize it:

Here's an interactive scrim to help you learn more about functions in Python:

🔸 recursion in python.

A recursive function is a function that calls itself. These functions have a base case that stops the recursive process and a recursive case that continues the recursive process by making another recursive call.

Here we have some examples in Python:

🔹 Exception Handling in Python

An error or unexpected event that that occurs while a program is running is called an exception . Thanks to the elements that we will see in just a moment, we can avoid terminating the program abruptly when this occurs.

Let's see the types of exceptions in Python and how we can handle them.

Common Exceptions in Python

This is a list of common exceptions in Python and why they occur:

• ZeroDivisionError: raised when the second argument of a division or modulo operation is zero.
• IndexError: raised when we try to use an invalid index to access an element of a sequence.
• KeyError: raised when we try to access a key-value pair that doesn't exist because the key is not in the dictionary.
• NameError: raised when we use a variable that has not been defined previously.
• RecursionError: raised when the interpreter detects that the maximum recursion depth is exceeded. This usually occurs when the process never reaches the base case.

In the example below, we will get a RecursionError . The factorial function is implemented recursively but the argument passed to the recursive call is n instead of n-1 . Unless the value is already 0 or 1 , the base case will not be reached because the argument is not being decremented, so the process will continue and we will get this error.

💡 Tip: to learn more about these exceptions, I recommend reading this article from the documentation.

try / except in Python

We can use try/except in Python to catch the exceptions when they occur and handle them appropriately. This way, the program can terminate appropriately or even recover from the exception.

This is the basic syntax:

For example, if we take user input to access an element in a list, the input might not be a valid index, so an exception could be raised:

If we enter an invalid value like 15, the output will be:

Because the except clause runs. However, if the value is valid, the code in try will run as expected.

Here we have another example:

How to Catch a Specific Type of Exception in Python

Instead of catching and handling all possible exceptions that could occur in the try clause, we could catch and handle a specific type of exception. We just need to specify the type of the exception after the except keyword:

How to Assign a Name to the Exception Object in Python

We can specify a name for the exception object by assigning it to a variable that we can use in the except clause. This will let us access its description and attributes.

We only need to add as <name> , like this:

This is the output if we enter 15 as the index:

This is the output if we enter the value 0 for b :

try / except / else in Python

We can add an else clause to this structure after except if we want to choose what happens when no exceptions occur during the execution of the try clause:

If we enter the values 5 and 0 for a and b respectively, the output is:

But if both values are valid, for example 5 and 4 for a and b respectively, the else clause runs after try is completed and we see:

try / except / else / finally in Python

We can also add a finally clause if we need to run code that should always run, even if an exception is raised in try .

If both values are valid, the output is the result of the division and:

And if an exception is raised because b is 0 , we see:

The finally clause always runs.

💡 Tip: this clause can be used, for example, to close files even if the code throws an exception.

🔸 Object-Oriented Programming in Python

In Object-Oriented Programming (OOP), we define classes that act as blueprints to create objects in Python with attributes and methods (functionality associated with the objects).

This is a general syntax to define a class:

💡 Tip: self refers to an instance of the class (an object created with the class blueprint).

As you can see, a class can have many different elements so let's analyze them in detail:

Class Header

The first line of the class definition has the class keyword and the name of the class:

💡 Tip: If the class inherits attributes and methods from another class, we will see the name of the class within parentheses:

In Python, we write class name in Upper Camel Case (also known as Pascal Case), in which each word starts with an uppercase letter. For example: FamilyMember

__init__ and instance attributes

We are going to use the class to create object in Python, just like we build real houses from blueprints.

The objects will have attributes that we define in the class. Usually, we initialize these attributes in __init__ . This is a method that runs when we create an instance of the class.

We specify as many parameters as needed to customize the values of the attributes of the object that will be created.

Here is an example of a Dog class with this method:

💡 Tip: notice the double leading and trailing underscore in the name __init__ .

How to Create an Instance

To create an instance of Dog , we need to specify the name and age of the dog instance to assign these values to the attributes:

Great. Now we have our instance ready to be used in the program.

Some classes will not require any arguments to create an instance. In that case, we just write empty parentheses. For example:

To create an instance:

💡 Tip: self is like a parameter that acts "behind the scenes", so even if you see it in the method definition, you shouldn't consider it when you pass the arguments.

Default Arguments

We can also assign default values for the attributes and give the option to the user if they would like to customize the value.

In this case, we would write <attribute>=<value> in the list of parameters.

This is an example:

Now we can create a Circle instance with the default value for the radius by omitting the value or customize it by passing a value:

How to Get an Instance Attribute

To access an instance attribute, we use this syntax:

How to Update an Instance Attribute

To update an instance attribute, we use this syntax:

How to Remove an Instance Attribute

To remove an instance attribute, we use this syntax:

How to Delete an Instance

Similarly, we can delete an instance using del :

Public vs. Non-Public Attributes in Python

In Python, we don't have access modifiers to functionally restrict access to the instance attributes, so we rely on naming conventions to specify this.

For example, by adding a leading underscore, we can signal to other developers that an attribute is meant to be non-public.

The Python documentation mentions:

Use one leading underscore only for non-public methods and instance variables. Always decide whether a class's methods and instance variables (collectively: "attributes") should be public or non-public. If in doubt, choose non-public; it's easier to make it public later than to make a public attribute non-public. Non-public attributes are those that are not intended to be used by third parties; you make no guarantees that non-public attributes won't change or even be removed. - source

However, as the documentation also mentions:

We don't use the term "private" here, since no attribute is really private in Python (without a generally unnecessary amount of work). - source

💡 Tip: technically, we can still access and modify the attribute if we add the leading underscore to its name, but we shouldn't.

Class Attributes in Python

Class attributes are shared by all instances of the class. They all have access to this attribute and they will also be affected by any changes made to these attributes.

💡 Tip: usually, they are written before the __init__ method.

How to Get a Class Attribute

To get the value of a class attribute, we use this syntax:

💡 Tip: You can use this syntax within the class as well.

How to Update a Class Attribute

To update a class attribute, we use this syntax:

How to Delete a Class Attribute

We use del to delete a class attribute. For example:

How to Define Methods

Methods represent the functionality of the instances of the class.

💡 Tip: Instance methods can work with the attributes of the instance that is calling the method if we write self.<attribute> in the method definition.

This is the basic syntax of a method in a class. They are usually located below __init__ :

They may have zero, one, or more parameters if needed (just like functions!) but instance methods must always have self as the first parameter.

For example, here is a bark method with no parameters (in addition to self ):

To call this method, we use this syntax:

Here we have a Player class with an increment_speed method with one parameter:

To call the method:

💡 Tip: to add more parameters, just separate them with a comma. It is recommended to add a space after the comma.

Properties, Getters and Setters in Python

Getters and setters are methods that we can define to get and set the value of an instance attribute, respectively. They work as intermediaries to "protect" the attributes from direct changes.

In Python, we typically use properties instead of getters and setters. Let's see how we can use them.

To define a property, we write a method with this syntax:

This method will act as a getter, so it will be called when we try to access the value of the attribute.

Now, we may also want to define a setter:

And a deleter to delete the attribute:

💡 Tip: you can write any code that you need in these methods to get, set, and delete an attribute. It is recommended to keep them as simple as possible.

If we add descriptive print statements, we can see that they are called when we perform their operation:

🔹 How to Work with Files in Python

Working with files is very important to create powerful programs. Let's see how you can do this in Python.

How to Read Files in Python

In Python, it's recommended to use a with statement to work with files because it opens them only while we need them and it closes them automatically when the process is completed.

To read a file, we use this syntax:

We can also specify that we want to open the file in read mode with an "r" :

But this is already the default mode to open a file, so we can omit it like in the first example.

💡 Tip: that's right! We can iterate over the lines of the file using a for loop. The file path can be relative to the Python script that we are running or it can be an absolute path.

How to Write to a File in Python

There are two ways to write to a file. You can either replace the entire content of the file before adding the new content, or append to the existing content.

To replace the content completely, we use the "w" mode, so we pass this string as the second argument to open() . We call the .write() method on the file object passing the content that we want to write as argument.

When you run the program, a new file will be created if it doesn't exist already in the path that we specified.

This will be the content of the file:

How to Append to a File in Python

However, if you want to append the content, then you need to use the "a" mode:

This small change will keep the existing content of the file and it will add the new content to the end.

If we run the program again, these strings will be added to the end of the file:

How to Delete a File in Python

To delete a file with our script, we can use the os module. It is recommended to check with a conditional if the file exists before calling the remove() function from this module:

You might have noticed the first line that says import os . This is an import statement. Let's see why they are helpful and how you can work with them.

🔸 Import Statements in Python

Organizing your code into multiple files as your program grows in size and complexity is good practice. But we need to find a way to combine these files to make the program work correctly, and that is exactly what import statements do.

By writing an import statement, we can import a module (a file that contains Python definitions and statements) into another file.

These are various alternatives for import statements:

First Alternative:

💡 Tip: math is a built-in Python module.

If we use this import statement, we will need to add the name of the module before the name of the function or element that we are referring to in our code:

We explicitly mention in our code the module that the element belongs to.

Second Alternative:

In our code, we can use the new name that we assigned instead of the original name of the module:

Third Alternative:

With this import statement, we can call the function directly without specifiying the name of the module:

Fourth Alternative:

With this import statement, we can assign a new name to the element imported from the module:

Fifth Alternative:

This statement imports all the elements of the module and you can refer to them directly by their name without specifying the name of the module.

💡 Tip: this type of import statement can make it more difficult for us to know which elements belong to which module, particularly when we are importing elements from multiple modules.

According to the Style Guide for Python Code :

Wildcard imports (from <module> import *) should be avoided, as they make it unclear which names are present in the namespace, confusing both readers and many automated tools.

🔹 List and Dictionary Comprehension in Python

A really nice feature of Python that you should know about is list and dictionary comprehension. This is just a way to create lists and dictionaries more compactly.

List Comprehension in Python

The syntax used to define list comprehensions usually follows one of these four patterns:

💡 Tip: you should only use them when they do not make your code more difficult to read and understand.

List Comprehensions vs. Generator Expressions in Python

List comprehensions are defined with square brackets [] . This is different from generator expressions, which are defined with parentheses () . They look similar but they are quite different. Let's see why.

• List comprehensions generate the entire sequence at once and store it in memory.
• Generator expressions yield the elements one at a time when they are requested.

We can check this with the sys module. In the example below, you can see that their size in memory is very different:

We can use generator expressions to iterate in a for loop and get the elements one at a time. But if we need to store the elements in a list, then we should use list comprehension.

Dictionary Comprehension in Python

Now let's dive into dictionary comprehension. The basic syntax that we need to use to define a dictionary comprehension is:

Here we have some examples of dictionary comprehension:

This is an example with a conditional where we take an existing dictionary and create a new dictionary with only the students who earned a passing grade greater than or equal to 60:

I really hope you liked this article and found it helpful. Now you know how to write and work with the most important elements of Python.

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Use github copilot to write python.

Explore how you can modify a Python repository using code suggestions from GitHub Copilot to create an interactive HTML form and an Application Programming Interface (API) endpoint. By working with this repository, you'll quickly get hands-on with a Python web app that serves an HTTP API that generates a pseudo-random token, commonly used in for identification.

Requirements

• Enable your GitHub Copilot service
• Open this repository with Codespaces

💪🏽 Exercise

The API already has a single endpoint to generate a token. Let's update the API by adding a new endpoint that accepts text and returns a list of tokens.

🛠 Step 1: Add a Pydantic model

Go to the main.py file, and add a comment so that GitHub Copilot can generate a Pydantic model for you. The generated model should look like this:

🔎 Step 2: Generate a new endpoint

Next, generate a new endpoint with GitHub Copilot by adding the comment:

🐍 Step 3: Add necessary imports

The generated code causes the application to crash. The crash happens because the base64 and os modules aren't imported. Add the following lines to the top of the file:

Finally, verify the new endpoint is working by trying it out by going to the /docs endpoint and confirming that the endpoint shows up.

🚀 Congratulations, through the exercise, you haven't only used copilot to generate code but also done it in an interactive and fun way! You can use GitHub Copilot to not only generate code, but write documentation, test your applications and more.

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Microsoft and any contributors grant you a license to the Microsoft documentation and other content in this repository under the Creative Commons Attribution 4.0 International Public License , see the LICENSE file, and grant you a license to any code in the repository under the MIT License , see the LICENSE-CODE file.

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Open the file with "a" for appending, then add some text to the file:

Definition and Usage

The write() method writes a specified text to the file.

Where the specified text will be inserted depends on the file mode and stream position.

"a" :  The text will be inserted at the current file stream position, default at the end of the file.

"w" : The file will be emptied before the text will be inserted at the current file stream position, default 0.

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