Real Python

What's New in Python 3.13

2024-10-08T14:00:00+00:00

Python 3.13 was published on October 7, 2024. This new version is a major step forward for the language, although several of the biggest changes are happening under the hood and won’t be immediately visible to you.

In a sense, Python 3.13 is laying the groundwork for some future improvements, especially to the language’s performance. As you watch the course, you’ll learn more about the background for this and dive into some new features that are fully available now.

In this video course, you’ll learn about some of the improvements in the new version, including:

Improvements made to the interactive interpreter (REPL)
Clearer error messages that can help you fix common mistakes
Advancements done in removing the global interpreter lock (GIL) and making Python free-threaded
The implementation of an experimental Just-In-Time (JIT) compiler
A host of minor upgrades to Python’s static type system

In this video course, you’ll explore these changes and see how this new version of Python can work for you.

If you want to try any of the examples in this video course, then you’ll need to use Python 3.13. The Python 3 Installation & Setup Guide and How Can You Install a Pre-Release Version of Python? walk you through several options for adding a new version of Python to your system.

[ 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 ]

Python News Roundup: October 2024

2024-10-07T14:00:00+00:00

October is always an important month for Python, as this is when a new major version is released. Python 3.13 is the new version this year, and it brings several new features that lay the groundwork for other changes in the future. As one version of Python comes to life, another is put to rest. Python 3.8 is already five years old, which means that this version won’t be supported any longer.

There are also exciting developments happening in the wider Python community. In this newsletter, you can read about Polars’ improved support for plotting, as well as how Django developers gathered for the annual DjangoCon US conference.

Time to jump in and read about what’s happening in the world of Python!

Python 3.13 Release Slightly Delayed

The release of Python 3.13, the newest version of Python, was originally scheduled for October 1, 2024. However, a few days before that date, release manager Thomas Wouters decided to postpone the release until October 7, 2024:

I’m a little concerned with the impact of the incremental GC change in 3.13, which recently showed up. It’s not clear that the incremental GC provides significant improvements (although the smaller pauses are probably desirable), it clearly has slightly more overhead in common cases, and we’re still discovering pathological cases.

I don’t think we should release 3.13.0 with the incremental GC. (Source)

The incremental garbage collector was a small improvement slated for Python 3.13. In many cases, the new garbage collection algorithm improves performance. Unfortunately, it was found to slow down Python significantly in some rare cases.

As a result, the core developers decided to revert the implementation and use the traditional garbage collector in Python 3.13. At the same time, the new implementation is being scrutinized and currently the goal is to include incremental garbage collection in Python 3.14.

Delaying a major Python release is never an easy choice. However, erring on the side of caution is a good approach, and it’s great to see that the Python 3.13 release is being handled responsibly.

Python 3.13 Highlights

As always, a new Python release brings many improvements and new features. You can explore these in-depth in Python 3.13: Cool New Features for You to Try. In particular, the new release includes:

A brand new interactive interpreter (REPL)
Colored tracebacks and improved error messages
A separate, free-threaded version of Python that runs without the global interpreter lock (GIL)
An experimental just-in-time (JIT) compiler
Several improvements to Python’s static type system

For free threading and the JIT compiler, you need to compile Python with special build flags. Read Python 3.13 Preview: Free Threading and a JIT Compiler to learn more about how to explore these two new features. Additionally, Python 3.13 Preview: A Modern REPL provides more detail on the new REPL.

Read the full article at https://realpython.com/python-news-october-2024/ »

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Quiz: Iterators and Iterables in Python: Run Efficient Iterations

2024-10-04T12:00:00+00:00

In this quiz, you’ll test your understanding of Python’s Iterators and Iterables.

By working through this quiz, you’ll revisit how to create and work with iterators and iterables, understand the differences between them, and review how to use generator functions and the yield statement.

[ 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 ]

Quiz: Python import: Advanced Techniques and Tips

2024-10-03T12:00:00+00:00

In this quiz, you’ll test your understanding of Python’s import statement and related topics.

By working through this quiz, you’ll revisit how to use modules in your scripts and import modules dynamically at runtime.

[ 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 ]

A Guide to Modern Python String Formatting Tools

2024-10-02T14:00:00+00:00

When working with strings in Python, you may need to interpolate values into your string and format these values to create new strings dynamically. In modern Python, you have f-strings and the .format() method to approach the tasks of interpolating and formatting strings.

In this tutorial, you’ll learn how to:

Use f-strings and the .format() method for string interpolation
Format the interpolated values using replacement fields
Create custom format specifiers to format your strings

To get the most out of this tutorial, you should know the basics of Python programming and the string data type.

Get Your Code: Click here to download the free sample code that shows you how to use modern string formatting tools in Python.

Take the Quiz: Test your knowledge with our interactive “A Guide to Modern Python String Formatting Tools” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

A Guide to Modern Python String Formatting Tools

You can take this quiz to test your understanding of modern tools for string formatting in Python. These tools include f-strings and the .format() method.

Getting to Know String Interpolation and Formatting in Python

Python has developed different string interpolation and formatting tools over the years. If you’re getting started with Python and looking for a quick way to format your strings, then you should use Python’s f-strings.

Note: To learn more about string interpolation, check out the String Interpolation in Python: Exploring Available Tools tutorial.

If you need to work with older versions of Python or legacy code, then it’s a good idea to learn about the other formatting tools, such as the .format() method.

In this tutorial, you’ll learn how to format your strings using f-strings and the .format() method. You’ll start with f-strings to kick things off, which are quite popular in modern Python code.

Using F-Strings for String Interpolation

Python has a string formatting tool called f-strings, which stands for formatted string literals. F-strings are string literals that you can create by prepending an f or F to the literal. They allow you to do string interpolation and formatting by inserting variables or expressions directly into the literal.

Creating F-String Literals

Here you’ll take a look at how you can create an f-string by prepending the string literal with an f or F:

Python
      
   👇
>>> f"Hello, Pythonista!"
'Hello, Pythonista!'

   👇
>>> F"Hello, Pythonista!"
'Hello, Pythonista!'

Using either f or F has the same effect. However, it’s a more common practice to use a lowercase f to create f-strings.

Just like with regular string literals, you can use single, double, or triple quotes to define an f-string:

Python
      
        
      
    
    👇
>>> f'Single-line f-string with single quotes'
'Single-line f-string with single quotes'

    👇
>>> f"Single-line f-string with double quotes"
'Single-line f-string with single quotes'
     👇
>>> f'''Multiline triple-quoted f-string
... with single quotes'''
'Multiline triple-quoted f-string\nwith single quotes'

     👇
>>> f"""Multiline triple-quoted f-string
... with double quotes"""
'Multiline triple-quoted f-string\nwith double quotes'

Up to this point, your f-strings look pretty much the same as regular strings. However, if you create f-strings like those in the examples above, you’ll get complaints from your code linter if you have one.

The remarkable feature of f-strings is that you can embed Python variables or expressions directly inside them. To insert the variable or expression, you must use a replacement field, which you create using a pair of curly braces.

Interpolating Variables Into F-Strings

The variable that you insert in a replacement field is evaluated and converted to its string representation. The result is interpolated into the original string at the replacement field’s location:

Python
      
>>> site = "Real Python"

                   👇
>>> f"Welcome to {site}!"
'Welcome to Real Python!'

In this example, you’ve interpolated the site variable into your string. Note that Python treats anything outside the curly braces as a regular string.

Read the full article at https://realpython.com/python-formatted-output/ »

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Quiz: A Guide to Modern Python String Formatting Tools

2024-10-02T12:00:00+00:00

Test your understanding of Python’s tools for string formatting, including f-strings and the .format() method.

Take this quiz after reading our A Guide to Modern Python String Formatting Tools tutorial.

[ 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 ]

Differences Between Python's Mutable and Immutable Types

2024-10-01T14:00:00+00:00

As a Python developer, you’ll have to deal with mutable and immutable objects sooner or later. Mutable objects are those that allow you to change their value or data in place without affecting the object’s identity. In contrast, immutable objects don’t allow this kind of operation. You’ll just have the option of creating new objects of the same type with different values.

In Python, mutability is a characteristic that may profoundly influence your decision when choosing which data type to use in solving a given programming problem. Therefore, you need to know how mutable and immutable objects work in Python.

In this video course, you’ll:

Understand how mutability and immutability work under the hood in Python
Explore immutable and mutable built-in data types in Python
Identify and avoid some common mutability-related gotchas
Understand and control how mutability affects your custom classes

[ 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 ]

Quiz: When to Use a List Comprehension in Python

2024-10-01T12:00:00+00:00

In this quiz, you’ll test your understanding of List Comprehension in Python.

By working through this quiz, you’ll revisit how to rewrite loops as list comprehensions, how to choose when to use list comprehensions, how you can use conditional logic in your comprehensions, and how to profile your code to resolve performance questions.

[ 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 ]

Python 3.13: Cool New Features for You to Try

2024-09-30T14:00:00+00:00

In a sense, Python 3.13 is laying the groundwork for some future improvements, especially to the language’s performance. As you read on, you’ll learn more about the background for this and dive into some new features that are fully available now.

In this tutorial, you’ll learn about some of the improvements in the new version, including:

Improvements made to the interactive interpreter (REPL)
Clearer error messages that can help you fix common mistakes
Advancements done in removing the global interpreter lock (GIL) and making Python free-threaded
The implementation of an experimental Just-In-Time (JIT) compiler
A host of minor upgrades to Python’s static type system

If you want to try any of the examples in this tutorial, then you’ll need to use Python 3.13. The tutorials Python 3 Installation & Setup Guide and How Can You Install a Pre-Release Version of Python? walk you through several options for adding a new version of Python to your system.

Note: Python 3.13 was originally planned to be released on October 1, 2024. The developers found some issues during testing and decided to push back the release to October 7.

In addition to learning more about the new features coming to the language, you’ll also get some advice about what to consider before upgrading to the new version. Click the link below to download code examples demonstrating the new capabilities of Python 3.13:

Get Your Code: Click here to download the free sample code that shows you how to use the new features in Python 3.13.

Take the Quiz: Test your knowledge with our interactive “Python 3.13: Cool New Features for You to Try” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Python 3.13: Cool New Features for You to Try

In this quiz, you'll test your understanding of the new features introduced in Python 3.13. By working through this quiz, you'll review the key updates and improvements in this version of Python.

An Improved Interactive Interpreter (REPL)

If you run Python without specifying any script or code, you’ll find yourself inside Python’s interactive interpreter. This interpreter is informally called the REPL because it’s based on a read-evaluate-print loop. The REPL reads your input, evaluates it, and prints the result before looping back and doing the same thing again.

The Python REPL has been around for decades, and it supports an explorative workflow that makes Python a beginner-friendly language. Unfortunately, the interpreter has been missing several features you may have come to expect, including multiline editing and efficient pasting of code.

Note: Experienced Python developers often install a third-party interactive interpreter instead of relying on the built-in REPL. You can learn more about the alternatives in these tutorials:

You can also read more about alternative REPLs in the guide to the standard REPL.

Begin by starting the REPL. You can do this by typing python in your terminal. Depending on your setup, you may have to write py, python3, or even python3.13 instead. One way to recognize that you’re using the new interpreter shipping with Python 3.13 is that the prompt consisting of three chevrons (>>>) is subtly colored:

One improvement is that you can now use REPL-specific commands without calling them with parentheses as if they are Python functions. Here are some of the commands and keyboard shortcuts you can use:

exit or quit: Exit the interpreter
clear: Clear the screen
help or F1: Access the help system
F2: Open the history browser
F3: Enter paste mode

You can learn more about these options in Python 3.13 Preview: A Modern REPL.

Recalling code you’ve written earlier has been cumbersome in the REPL before Python 3.13, especially if you’re working with a block of code spanning several lines. Traditionally, you’ve had to bring back each line one by one by repeatedly pressing Up. Now in 3.13, you can bring back the whole block of code with a single Up keystroke.

To try this for yourself, enter the following code in your REPL:

Python
      
        
      
    
>>> numbers = range(3, 13)
>>> [
...     (number - 3)**3 for number in numbers
...     if number % 2 == 1
... ]
[0, 8, 64, 216, 512]

You’re creating a somewhat complex list comprehension that calculates an offset cube of a range of numbers, but only if the numbers are odd. The important part is that for readability, you split the list comprehension over several lines. Now try hitting that Up key! The interpreter recalls all four lines at once, and you can continue to use your arrow keys to move around inside of the expression.

You can make changes to your code and run it again. To execute the updated code, you need to move your cursor to the end of the last line in the code block. If you press Enter inside the expression, you’ll create a new empty line instead:

The ability to recall and edit multiline statements is a huge time-saver and will make you more efficient when working with the REPL.

Read the full article at https://realpython.com/python313-new-features/ »

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Quiz: Python 3.13: Cool New Features for You to Try

2024-09-28T12:00:00+00:00

In this quiz, you’ll test your understanding of Python 3.13: Cool New Features for You to Try. By working through this quiz, you’ll review the key updates and improvements in this version of Python.

[ 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 ]

Quiz: Syntactic Sugar: Why Python Is Sweet and Pythonic

2024-09-28T12:00:00+00:00

Test your understanding of Python’s most common pieces of syntactic sugar and how they make your code more Pythonic and readable.

Take this quiz after reading our Syntactic Sugar: Why Python is Sweet and Pythonic tutorial.

[ 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 ]

The Real Python Podcast – Episode #222: Using Virtual Environments in Docker & Comparing Python Dev Tools

2024-09-27T12:00:00+00:00

Should you use a Python virtual environment in a Docker container? What are the advantages of using the same development practices locally and inside a container? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

[ 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 ]

Python 3.13 Preview: A Modern REPL

2024-09-25T14:00:00+00:00

One of Python’s strong points is its interactive capabilities. By running python you start the interactive interpreter, or REPL, which allows you to perform quick calculations or explore and experiment with your code. In Python 3.13, the interactive interpreter has been completely redesigned with new modern features.

Python’s REPL has remained largely unchanged for decades. Instead, alternative interpreters like IPython, bpython, and ptpython have addressed some of the built-in REPL’s shortcomings, providing more convenient interactive workflows for developers. As you’re about to learn, Python 3.13 brings many significant improvements to the interactive interpreter.

In this tutorial, you’ll:

Run Python 3.13 and explore the new REPL
Browse through the help system
Work with multiline statements
Paste code into your REPL session
Navigate through your interpreter history

The upgraded REPL is just one of the new features coming in Python 3.13. You can read about all the changes in the what’s new section of Python’s changelog. Additionally, you can dig deeper into the work done on free threading and a Just-In-Time compiler.

Get Your Code: Click here to download the free sample code that shows you how to use some of the new features in Python 3.13.

A New Interactive Interpreter (REPL) in Python 3.13

To try out the new REPL for yourself, you need to get your hands on a version of Python 3.13. Before the official release in October 2024, you can install a pre-release version. After October 2024, you should be able to install Python 3.13 through any of the regular channels.

A REPL, or a Read-Eval-Print Loop, is a program that allows you to work with code interactively. The REPL reads your input, evaluates it, and prints the result before looping back and doing the same thing again.

Note: To learn about the built-in REPL in Python and how it works in general, check out The Python Standard REPL: Try Out Code and Ideas Quickly.

In any version of Python, you can start this interactive shell by typing the name of your Python executable in your terminal. Typically, this will be python, but depending on your operating system and your setup, you may have to use something like py or python3 instead.

Once you start the REPL in Python 3.13, you’ll see a small but noticeable difference. The familiar Python interactive shell prompt, consisting of three right angle brackets (>>>), is now colored differently from the rest of the text:

The color difference indicates that the shell now supports color. In the new shell, color is mainly used to highlight output in tracebacks. If your terminal doesn’t display color, then the new REPL will automatically detect this and fall back to its plain, colorless display.

If you prefer to keep your interpreter free of color even when it’s supported, you can disable this new feature. One option is to set the new environment variable PYTHON_COLORS to 0:

Shell
      
$ PYTHON_COLORS=0 python
Python 3.13.0rc2 (main, Sep 13 2024, 17:09:27) [GCC 9.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>>

Setting PYTHON_COLORS=0 disables color in the REPL. If you set the environment variable to 1, you’ll get the colored prompt and output. However, since it’s the default, this is rarely necessary.

Before going any further, you’ll exit the REPL. As you may know, the old REPL, that you’ve used on Python 3.12 and earlier, has been widely commented on for the following idiosyncrasy:

Python
      
>>> exit
Use exit() or Ctrl-D (i.e. EOF) to exit

The shell clearly understands your intention to end the session. Still, it makes you jump through hoops and add parentheses to your command. In Python 3.13, the REPL now understands special commands that you can write without any parentheses:

exit or quit: Exit the interpreter
help or F1: Access the help system
clear: Clear the screen

Having these commands more easily available is a small thing, but it removes some friction when using the interactive interpreter. You can still use parentheses and type something like exit() if you prefer. These commands are not reserved though. That means that you could shadow them with variable assignments:

Python
      
        
      
    
>>> exit = True
>>> exit
True
>>> exit()
Traceback (most recent call last):
  ...
TypeError: 'bool' object is not callable

>>> del exit
>>> exit

Here you create a variable named exit, effectively disabling the exit command. To end your REPL session, you can either delete the exit variable or use one of the alternative ways to exit the interpreter.

Note: Even with the new improvements, the built-in REPL is not as powerful as some of the third-party alternatives. If you’re already using any of these, there’s no immediate reason to revert back to the standard interactive interpreter.

The main advantage of the new REPL is that it’s installed together with Python. In other words, it’ll always be available for you. This is great if you find yourself in a situation where you can’t or don’t want to install a third-party library.

You can learn more about the alternative interactive interpreters in the following tutorials:

You can also read more about alternative REPLs in the guide to the standard REPL.

Read the full article at https://realpython.com/python313-repl/ »

[ 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 ]

Advanced Python import Techniques

2024-09-24T14:00:00+00:00

In Python, you use the import keyword to make code in one module available in another. Imports in Python are important for structuring your code effectively. Using imports properly will make you more productive, allowing you to reuse code while keeping your projects maintainable.

This video course provides a comprehensive overview of Python’s import statement and how it works. The import system is powerful, and this course will teach you how to harness this power. While you’ll cover many of the concepts behind Python’s import system, this video course is mostly example driven, so you’ll learn from the numerous code examples shared throughout.

In this video course, you’ll learn how to:

Use modules, packages, and namespace packages
Manage namespaces and avoid shadowing
Avoid circular imports
Import modules dynamically at runtime
Customize Python’s import system

[ 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 ]

Python Virtual Environments: A Primer

2024-09-23T14:00:00+00:00

In this tutorial, you’ll learn how to work with Python’s venv module to create and manage separate virtual environments for your Python projects. Each environment can use different versions of package dependencies and different versions of Python.

Once you’ve learned to work with virtual environments, you’ll be able to help other programmers reproduce your development setup and make sure that your projects never create dependency conflicts.

By the end of this tutorial, you’ll know how to:

Create and activate a Python virtual environment
Explain why you want to isolate external dependencies
Visualize what Python does when you create a virtual environment
Customize your virtual environments using optional arguments to venv
Deactivate and remove virtual environments
Choose additional tools for managing your Python versions and virtual environments

Working with virtual environments is a common and effective technique used in Python development. Gaining a better understanding of how they work, why you need them, and what you can do with them will help you master your Python programming workflow.

Throughout the tutorial, you can select code examples for either Windows, Ubuntu Linux, or macOS. Pick your platform at the top right of the relevant code blocks to get the commands that you need, and feel free to switch between them if you want to learn how to work with virtual environments on other operating systems.

Free Bonus: Click here to download a free cheat sheet that summarizes the main venv commands you’ll learn about in this tutorial.

Take the Quiz: Test your knowledge with our interactive “Python Virtual Environments: A Primer” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Python Virtual Environments: A Primer

In this quiz, you'll test your understanding of Python virtual environments. With this knowledge, you'll be able to avoid dependency conflicts and help other developers reproduce your development environment.

How Can You Work With a Python Virtual Environment?

If you just need to get a virtual environment up and running to continue working on your favorite project, then this section is for you.

This tutorial uses Python’s venv module to create virtual environments. This module is part of Python’s standard library, and it’s been the officially recommended way to create virtual environments since Python 3.5.

Note: There are other great third-party tools for creating virtual environments, such as conda and virtualenv, that you’ll learn more about later in this tutorial. Either of these tools can help you set up a virtual environment and also go beyond just that.

For basic usage, venv is an excellent choice because it already comes packaged with your Python installation. With that in mind, you’re ready to create your first virtual environment.

Create It

Any time you’re working on a Python project that uses external dependencies you’re installing with pip, it’s best to first create a virtual environment:

Windows PowerShell
      
PS> py -m venv venv\

This command allows the Python launcher for Windows to select an appropriate version of Python to execute. It comes bundled with the official installation and is the most convenient way to execute Python on Windows.

You can bypass the launcher and run the Python executable directly using the python command, but if you haven’t configured the PATH and PATHEXT variables, then you might need to provide the full path:

Windows PowerShell
      
PS> C:\Users\Name\AppData\Local\Programs\Python\Python312\python -m venv venv\

The system path shown above assumes that you installed Python 3.12 using the Windows installer provided by the Python downloads page. The path to the Python executable on your system might be different. Working with PowerShell, you can find the path using the where.exe python command.

Note: You don’t need to include the backslash (\) at the end of the name of your virtual environment, but it’s a helpful reminder that you’re creating a folder.

Shell
      
$ python3 -m venv venv/

Many Linux operating systems ship with a version of Python 3. If python3 doesn’t work, then you’ll have to first install Python and you may need to use the specific name of the executable version that you installed, for example, python3.12 for Python 3.12.x. If that’s the case for you, remember to replace mentions of python3 in the code blocks with your specific version number.

Note: You don’t need to include the slash (/) at the end of the name of your virtual environment, but it’s a helpful reminder that you’re creating a folder.

Shell
      
$ python3 -m venv venv/

Older versions of macOS come with a system installation of Python 2.7.x that you should never use to run your scripts. If you’re working on macOS < 12.3 and invoke the Python interpreter with python instead of python3, then you might accidentally start up the outdated system Python interpreter.

If running python3 doesn’t work, then you’ll have to first install a modern version of Python.

Note: You don’t need to include the slash (/) at the end of the name of your virtual environment, but it’s a helpful reminder that you’re creating a folder.

This command creates a new virtual environment named venv using Python’s built-in venv module. The first venv that you use in the command specifies the module, and the second venv/ sets the name for your virtual environment. You could name it differently, but calling it venv is a good practice for consistency.

Activate It

Great! Your project now has its own virtual environment. Generally, before you start to use it, you’ll activate the environment by executing a script that comes with the installation:

Windows PowerShell
      
PS> venv\Scripts\activate
(venv) PS>

If your attempt to run this command produces an error, then you’ll first have to loosen the execution policy.

Shell
      
$ source venv/bin/activate
(venv) $

Before you run this command, make sure that you’re in the folder containing the virtual environment you just created. If you’ve named your virtual environment something other than venv, then you’ll have to use that name in the path instead of venv when you source the activation script.

Note: You can also work with your virtual environment without activating it. To do this, you provide the full path to its Python interpreter when executing a command. However, you’ll likely want to activate the virtual environment after you create it to save yourself the effort of having to repeatedly type long pathnames.

Once you can see the name of your virtual environment in your command prompt—in this case (venv)—then you’ll know that your virtual environment is active. Now you’re all set and ready to install your external packages!

Read the full article at https://realpython.com/python-virtual-environments-a-primer/ »

[ 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 ]

The Real Python Podcast – Episode #221: Thriving as a Developer With ADHD

2024-09-20T12:00:00+00:00

What are strategies for being a productive developer with ADHD? How can you help your team members with ADHD to succeed and complete projects? This week on the show, we speak with Chris Ferdinandi about his website and podcast "ADHD For the Win!"

[ 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 ]

Python 3.13 Preview: Free Threading and a JIT Compiler

2024-09-18T14:00:00+00:00

Although the final release of Python 3.13 is scheduled for October 2024, you can download and install a preview version today to explore the new features. Notably, the introduction of free threading and a just-in-time (JIT) compiler are among the most exciting enhancements, both designed to give your code a significant performance boost.

In this tutorial, you’ll:

Compile a custom Python build from source using Docker
Disable the Global Interpreter Lock (GIL) in Python
Enable the Just-In-Time (JIT) compiler for Python code
Determine the availability of new features at runtime
Assess the performance improvements in Python 3.13
Make a C extension module targeting Python’s new ABI

Check out what’s new in the Python changelog for a complete list of the upcoming features and improvements. This document contains a quick summary of the release highlights as well as a detailed breakdown of the planned changes.

To download the sample code and other resources accompanying this tutorial, click the link below:

Get Your Code: Click here to download the free sample code that shows you how to work with the experimental free threading and JIT compiler in Python 3.13.

Take the Quiz: Test your knowledge with our interactive “Python 3.13: Free Threading and a JIT Compiler” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Python 3.13: Free Threading and a JIT Compiler

In this quiz, you'll test your understanding of the new features in Python 3.13. You'll revisit how to compile a custom Python build, disable the Global Interpreter Lock (GIL), enable the Just-In-Time (JIT) compiler, and more.

Free Threading and JIT in Python 3.13: What’s the Fuss?

Before going any further, it’s important to note that the majority of improvements in Python 3.13 will remain invisible to the average Joe. This includes free threading (PEP 703) and the JIT compiler (PEP 744), which have already sparked a lot of excitement in the Python community.

Keep in mind that they’re both experimental features aimed at power users, who must take extra steps to enable them at Python’s build time. None of the official channels will distribute Python 3.13 with these additional features enabled by default. This is to maintain backward compatibility and to prevent potential glitches, which should be expected.

Note: Don’t try to use Python 3.13 with the experimental features in a production environment! It may cause unexpected problems, and the Python Steering Council reserves the right to remove these features entirely from future Python releases if they prove to be unstable. Treat them as an experiment to gather real-world data.

In this section, you’ll get a birds-eye view of these experimental features so you can set the right expectations. You’ll find detailed explanations on how to enable them and evaluate their impact on Python’s performance in the remainder of this tutorial.

Free Threading Makes the GIL Optional

Free threading is an attempt to remove the Global Interpreter Lock (GIL) from CPython, which has traditionally been the biggest obstacle to achieving thread-based parallelism when performing CPU-bound tasks. In short, the GIL allows only one thread of execution to run at any given time, regardless of how many cores your CPU is equipped with. This prevents Python from leveraging the available computing power effectively.

There have been many attempts in the past to bypass the GIL in Python, each with varying levels of success. You can read about these attempts in the tutorial on bypassing the GIL. While previous attempts were made by third parties, this is the first time that the core Python development team has taken similar steps with the permission of the steering council, even if some reservations remain.

Note: Python 3.12 approached the GIL obstacle from a different angle by allowing the individual subinterpreters to have their independent GILs. This can improve Python’s concurrency by letting you run different tasks in parallel, but without the ability to share data cheaply between them due to isolated memory spaces. In Python 3.13, you’ll be able to combine subinterpreters with free threading.

The removal of the GIL would have significant implications for the Python interpreter itself and especially for the large body of third-party code that relies on it. Because free threading essentially breaks backward compatibility, the long-term plan for its implementation is as follows:

Experimental: Free threading is introduced as an experimental feature and isn’t a part of the official Python distribution. You must make a custom Python build to disable the GIL.
Enabled: The GIL becomes optional in the official Python distribution but remains enabled by default to allow for a transition period.
Disabled: The GIL is disabled by default, but you can still enable it if needed for compatibility reasons.

There are no plans to completely remove the GIL from the official Python distribution at the moment, as that would cause significant disruption to legacy codebases and libraries. Note that the steps outlined above are just a proposal subject to change. Also, free threading may not pan out at all if it makes single-threaded Python run slower than without it.

Until the GIL becomes optional in the official Python distribution, which may take a few more years, the Python development team will maintain two incompatible interpreter versions. The vanilla Python build won’t support free threading, while the special free-threaded flavor will have a slightly different Application Binary Interface (ABI) tagged with the letter “t” for threading.

This means that C extension modules built for stock Python won’t be compatible with the free-threaded version and the other way around. Maintainers of those external modules will be expected to distribute two packages with each release. If you’re one of them, and you use the Python/C API, then you’ll learn how to target CPython’s new ABI in the final section of this tutorial.

JIT Compiles Python to Machine Code

As an interpreted language, Python takes your high-level code and executes it on the fly without the need for prior compilation. This has both pros and cons. Some of the biggest advantages of interpreted languages include better portability across different hardware architectures and a quick development time due to the lack of a compilation step. At the same time, interpretation is much slower than directly executing code native to your machine.

Note: To be more precise, Python interprets bytecode instructions, an intermediate binary representation between pure Python and machine code. The Python interpreter compiles your code to bytecode when you import a module and stores the resulting bytecode in the __pycache__ folder. This doesn’t inherently make your Python scripts run faster, but loading a pre-processed bytecode can indeed speed up their startup time.

Languages like C and C++ leverage Ahead-of-Time (AOT) compilation to translate your high-level code into machine code before you ship your software. The benefit of this is faster execution since the code is already in the computer’s mother tongue. While you no longer need a separate program to interpret the code, you must compile it separately for all target platforms that you want supported. You should also handle platform-specific differences yourself.

Read the full article at https://realpython.com/python313-free-threading-jit/ »

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Customizing VS Code Through Color Themes

2024-09-17T14:00:00+00:00

A well-designed coding environment not only enhances your focus and productivity but also makes coding sessions more enjoyable. In this Code Conversation, your instructor Philipp Acsany will guide you step-by-step through the process of finding, installing, and adjusting color themes in VS Code. You’ll explore the various options available in VS Code and learn how to make fine adjustments to create a setup that suits your personal preferences.

In this video course, you’ll:

Learn about Themes in VS Code
Find a VS Code Color Theme
Select a Theme
Install Your Theme
Make Additional Adjustments

By the end of the course, you’ll have a coding environment that not only looks great but also enhances your overall coding experience.

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Quiz: Using Python's pip to Manage Your Projects' Dependencies

2024-09-17T12:00:00+00:00

In this quiz, you’ll test your understanding of Python’s standard package manager, pip. You’ll revisit the concepts behind pip, important commands, and how to install packages.

[ 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 ]

Using Python's pip to Manage Your Projects' Dependencies

2024-09-16T14:00:00+00:00

The standard package manager for Python is pip. It allows you to install and manage packages that aren’t part of the Python standard library. If you’re looking for an introduction to pip, then you’ve come to the right place!

In this tutorial, you’ll learn how to:

Set up pip in your working environment
Fix common errors related to working with pip
Install and uninstall packages with pip
Manage projects’ dependencies using requirements files

You can do a lot with pip, but the Python community is very active and has created some neat alternatives to pip. You’ll learn about those later in this tutorial.

Get Your Cheat Sheet: Click here to download a free pip cheat sheet that summarizes the most important pip commands.

Take the Quiz: Test your knowledge with our interactive “Using Python's pip to Manage Your Projects' Dependencies” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Using Python's pip to Manage Your Projects' Dependencies

In this quiz, you'll test your understanding of Python's standard package manager, pip. You'll revisit the ideas behind pip, important commands, and how to install packages.

Getting Started With `pip`

So, what exactly does pip do? pip is a package manager for Python. That means it’s a tool that allows you to install and manage libraries and dependencies that aren’t distributed as part of the standard library. The name pip was introduced by Ian Bicking in 2008:

I’ve finished renaming pyinstall to its new name: pip. The name pip is [an] acronym and declaration: pip installs packages. (Source)

Package management is so important that Python’s installers have included pip since versions 3.4 and 2.7.9, for Python 3 and Python 2, respectively. Many Python projects use pip, which makes it an essential tool for every Pythonista.

The concept of a package manager might be familiar to you if you’re coming from another programming language. JavaScript uses npm for package management, Ruby uses gem, and the .NET platform uses NuGet. In Python, pip has become the standard package manager.

Finding `pip` on Your System

The Python installer gives you the option to install pip when installing Python on your system. In fact, the option to install pip with Python is checked by default, so pip should be ready for you to use after installing Python.

Note: On some Linux (Unix) systems like Ubuntu, pip comes in a separate package called python3-pip, which you need to install with sudo apt install python3-pip. It’s not installed by default with the interpreter.

You can verify that pip is available by looking for the pip3 executable on your system. Select your operating system below and use your platform-specific command accordingly:

Windows PowerShell
      
PS> where pip3

The where command on Windows will show you where you can find the executable of pip3. If Windows can’t find an executable named pip3, then you can also try looking for pip without the three (3) at the end.

Shell

$ which pip3

The which command on Linux systems and macOS shows you where the pip3 binary file is located.

On Windows and Unix systems, pip3 may be found in more than one location. This can happen when you have multiple Python versions installed. If you can’t find pip in any location on your system, then you may consider reinstalling pip.

Instead of running your system pip directly, you can also run it as a Python module. In the next section, you’ll learn how.

Running `pip` as a Module

When you run your system pip directly, the command itself doesn’t reveal which Python version pip belongs to. This unfortunately means that you could use pip to install a package into the site-packages of an old Python version without noticing. To prevent this from happening, you should run pip as a Python module:

Shell
      
$ python -m pip

Notice that you use python -m to run pip. The -m switch tells Python to run a module as an executable of the python interpreter. This way, you can ensure that your system default Python version runs the pip command. If you want to learn more about this way of running pip, then you can read Brett Cannon’s insightful article about the advantages of using python -m pip.

Note: Depending on how you installed Python, your Python executable may have a different name than python. You’ll see python used in this tutorial, but you may have to adapt the commands to use something like py or python3 instead.

Sometimes you may want to be more explicit and limit packages to a specific project. In situations like this, you should run pip inside a virtual environment.

Read the full article at https://realpython.com/what-is-pip/ »

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The Real Python Podcast – Episode #220: Configuring Git Pre-Commit Hooks & Estimating Software Projects

2024-09-13T12:00:00+00:00

How do you take advantage of Git pre-commit hooks? How do you build custom software checks and rules that run every time you commit your code? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

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Quiz: Python 3.13: Free Threading and a JIT Compiler

2024-09-12T12:00:00+00:00

In this quiz, you’ll test your understanding of the new features in Python 3.13.

By working through this quiz, you’ll revisit how to compile a custom Python build, disable the Global Interpreter Lock (GIL), enable the Just-In-Time (JIT) compiler, determine the availability of new features at runtime, assess the performance improvements in Python 3.13, and make a C extension module targeting Python’s new ABI.

[ 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 ]

How to Use Conditional Expressions With NumPy where()

2024-09-11T14:00:00+00:00

The NumPy where() function is a powerful tool for filtering array elements in lists, tuples, and NumPy arrays. It works by using a conditional predicate, similar to the logic used in the WHERE or HAVING clauses in SQL queries. It’s okay if you’re not familiar with SQL—you don’t need to know it to follow along with this tutorial.

You would typically use np.where() when you have an array and need to analyze its elements differently depending on their values. For example, you might need to replace negative numbers with zeros or replace missing values such as None or np.nan with something more meaningful. When you run where(), you’ll produce a new array containing the results of your analysis.

You generally supply three parameters when using where(). First, you provide a condition against which each element of your original array is matched. Then, you provide two additional parameters: the first defines what you want to do if an element matches your condition, while the second defines what you want to do if it doesn’t.

If you think this all sounds similar to Python’s ternary operator, you’re correct. The logic is the same.

Note: In this tutorial, you’ll work with two-dimensional arrays. However, the same principles can be applied to arrays of any dimension.

Before you start, you should familiarize yourself with NumPy arrays and how to use them. It will also be helpful if you understand the subject of broadcasting, particularly for the latter part of this tutorial.

In addition, you may want to use the data analysis tool Jupyter Notebook as you work through the examples in this tutorial. Alternatively, JupyterLab will give you an enhanced notebook experience, but feel free to use any Python environment.

The NumPy library is not part of core Python, so you’ll need to install it. If you’re using a Jupyter Notebook, create a new code cell and type !python -m pip install numpy into it. When you run the cell, the library will install. If you’re working at the command line, use the same command, only without the exclamation point (!).

With these preliminaries out of the way, you’re now good to go.

Get Your Code: Click here to download the free sample code that shows you how to use conditional expressions with NumPy where().

Take the Quiz: Test your knowledge with our interactive “How to Use Conditional Expressions With NumPy where()” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

How to Use Conditional Expressions With NumPy where()

This quiz aims to test your understanding of the np.where() function. You won't find all the answers in the tutorial, so you'll need to do additional research. It's recommended that you make sure you can do all the exercises in the tutorial before tackling this quiz. Enjoy!

How to Write Conditional Expressions With NumPy `where()`

One of the most common scenarios for using where() is when you need to replace certain elements in a NumPy array with other values depending on some condition.

Consider the following array:

Python
      
        
      
    
>>> import numpy as np

>>> test_array = np.array(
...     [
...         [3.1688358, 3.9091694, 1.66405549, -3.61976783],
...         [7.33400434, -3.25797286, -9.65148913, -0.76115911],
...         [2.71053173, -6.02410179, 7.46355805, 1.30949485],
...     ]
... )

To begin with, you need to import the NumPy library into your program. It’s standard practice to do so using the alias np, which allows you to refer to the library using this abbreviated form.

The resulting array has a shape of three rows and four columns, each containing a floating-point number.

Now suppose you wanted to replace all the negative numbers with their positive equivalents:

Python
      
        
      
    
>>> np.where(
...     test_array < 0,
...     test_array * -1,
...     test_array,
... )
array([[3.1688358 , 3.9091694 , 1.66405549, 3.61976783],
       [7.33400434, 3.25797286, 9.65148913, 0.76115911],
       [2.71053173, 6.02410179, 7.46355805, 1.30949485]])

The result is a new NumPy array with the negative numbers replaced by positives. Look carefully at the original test_array and then at the corresponding elements of the new all_positives array, and you’ll see that the result is exactly what you wanted.

Note: The above example gives you an idea of how the where() function works. If you were doing this in practice, you’d most likely use either the np.abs() or np.absolute() functions instead. Both do the same thing because the former is shorthand for the latter:

Python
      
>>> np.abs(test_array)
array([[3.1688358 , 3.9091694 , 1.66405549, 3.61976783],
       [7.33400434, 3.25797286, 9.65148913, 0.76115911],
       [2.71053173, 6.02410179, 7.46355805, 1.30949485]])

Once more, all negative values have been removed.

Before moving on to other use cases of where(), you’ll take a closer look at how this all works. To achieve your aim in the previous example, you passed in test_array < 0 as the condition. In NumPy, this creates a Boolean array that where() uses:

Python
      
>>> test_array < 0
array([[False, False, False,  True],
       [False,  True,  True,  True],
       [False,  True, False, False]])

The Boolean array, often called the mask, consists only of elements that are either True or False. If an element matches the condition, the corresponding element in the Boolean array will be True. Otherwise, it’ll be False.

Read the full article at https://realpython.com/numpy-where-conditional-expressions/ »

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Quiz: Python Virtual Environments: A Primer

2024-09-11T12:00:00+00:00

So you’ve been primed on Python virtual environments! Test your understanding of the tutorial here.

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When to Use .repr() vs .str() in Python

2024-09-10T14:00:00+00:00

One of the most common tasks that a computer program performs is to display data. The program often displays this information to the program’s user. However, a program also needs to show information to the programmer developing and maintaining it. The information a programmer needs about an object differs from how the program should display the same object for the user, and that’s where .__repr__() vs .__str__() comes in.

A Python object has several special methods that provide specific behavior. There are two similar special methods that describe the object using a string representation. These methods are .__repr__() and .__str__(). The .__repr__() method returns a detailed description for a programmer who needs to maintain and debug the code. The .__str__() method returns a simpler description with information for the user of the program.

The .__repr__() and .__str__() methods are two of the special methods that you can define for any class. They allow you to control how a program displays an object in several common forms of output, such as what you get from the print() function, formatted strings, and interactive environments.

In this video course, you’ll learn how to differentiate .__repr__() vs .__str__() and how to use these special methods in the classes you define. Defining these methods effectively makes the classes that you write more readable and easier to debug and maintain. So, when should you choose Python’s .__repr__() vs .__str__?

[ 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 ]

Python News Roundup: September 2024

2024-09-09T14:00:00+00:00

As the autumn leaves start to fall and signal the transition to cooler weather, the Python community has warmed up to a series of noteworthy developments. Last month, a new maintenance release of Python 3.12.5 was introduced, reinforcing the language’s ongoing commitment to stability and security.

On a parallel note, Python continues its reign as the top programming language according to IEEE Spectrum’s annual rankings. This sentiment is echoed by the Python Developers Survey 2023 results, which reveal intriguing trends and preferences within the community.

Looking ahead, PEP 750 has proposed the addition of tag strings in Python 3.14, inspired by JavaScript’s tagged template literals. This feature aims to enhance string processing, offering developers more control and expressiveness.

Also, EuroSciPy 2024 recently concluded in Poland after successfully fostering cross-disciplinary collaboration and learning. The event featured insightful talks and hands-on tutorials, spotlighting innovative tools and libraries that are advancing scientific computing with Python.

Let’s dive into the most significant Python news from the past month!

Python 3.12.5 Released

Early last month, Python 3.12.5 was released as the fifth maintenance update for the 3.12 series. Since the previous patch update in June, this release packs over 250 bug fixes, performance improvements, and documentation enhancements.

Here are the most important highlights:

Standard Library: Many modules in the standard library received crucial updates, such as fixes for crashes in ssl when the main interpreter restarts, and various corrections for error-handling mechanisms.
Core Python: The core Python runtime has several enhancements, including improvements to dictionary watchers, error messages, and fixes for edge-case crashes involving f-strings and multithreading.
Security: Key security improvements include the addition of missing audit events for interactive Python use and socket connection authentication within a fallback implementation on platforms such as Windows, where Unix inter-process communication is unavailable.
Tests: New test cases have been added and bug fixes have been applied to prevent random memory leaks during testing.
Documentation: Python documentation has been updated to remove discrepancies and clarify edge cases in multithreaded queues.

Additionally, Python 3.12.5 comes equipped with pip 24.2 by default, bringing a slew of significant improvements to enhance security, efficiency, and functionality. One of the most notable upgrades is that pip now defaults to using system certificates, bolstering security measures when managing and installing third-party packages.

Read the full article at https://realpython.com/python-news-september-2024/ »

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The Real Python Podcast – Episode #219: Astrophysics and Astronomy With Python & PyCon Africa 2024

2024-09-06T12:00:00+00:00

Are you interested in practicing your Python skills while learning how to solve astrophysics and astronomy problems? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

[ 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 ]

Lists vs Tuples in Python

2024-09-04T14:00:00+00:00

In Python, lists and tuples are versatile and useful data types that allow you to store data in a sequence. You’ll find them in virtually every nontrivial Python program. Learning about them is a core skill for you as a Python developer.

In this tutorial, you’ll:

Get to know lists and tuples
Explore the core characteristics of lists and tuples
Learn how to define and manipulate lists and tuples
Decide when to use lists or tuples in your code

To get the most out of this tutorial, you should know the basics of Python programming, including how to define variables.

Get Your Code: Click here to download the free sample code that shows you how to work with lists and tuples in Python.

Take the Quiz: Test your knowledge with our interactive “Lists vs Tuples in Python” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Lists vs Tuples in Python

Challenge yourself with this quiz to evaluate and deepen your understanding of Python lists and tuples. You'll explore key concepts, such as how to create, access, and manipulate these data types, while also learning best practices for using them efficiently in your code.

Getting Started With Python Lists and Tuples

In Python, a list is a collection of arbitrary objects, somewhat akin to an array in many other programming languages but more flexible. To define a list, you typically enclose a comma-separated sequence of objects in square brackets ([]), as shown below:

Python
      
>>> colors = ["red", "green", "blue", "yellow"]

>>> colors
['red', 'green', 'blue', 'yellow']

In this code snippet, you define a list of colors using string objects separated by commas and enclose them in square brackets.

Similarly, tuples are also collections of arbitrary objects. To define a tuple, you’ll enclose a comma-separated sequence of objects in parentheses (()), as shown below:

Python
      
>>> person = ("Jane Doe", 25, "Python Developer", "Canada")

>>> person
('Jane Doe', 25, 'Python Developer', 'Canada')

In this example, you define a tuple with data for a given person, including their name, age, job, and base country.

Up to this point, it may seem that lists and tuples are mostly the same. However, there’s an important difference:

Feature	List	Tuple
Is an ordered sequence	✅	✅
Can contain arbitrary objects	✅	✅
Can be indexed and sliced	✅	✅
Can be nested	✅	✅
Is mutable	✅	❌

Both lists and tuples are sequence data types, which means they can contain objects arranged in order. You can access those objects using an integer index that represents their position in the sequence.

Even though both data types can contain arbitrary and heterogeneous objects, you’ll commonly use lists to store homogeneous objects and tuples to store heterogeneous objects.

Note: In this tutorial, you’ll see the terms homogeneous and heterogeneous used to express the following ideas:

Homogeneous: Objects of the same data type or the same semantic meaning, like a series of animals, fruits, colors, and so on.
Heterogeneous: Objects of different data types or different semantic meanings, like the attributes of a car: model, color, make, year, fuel type, and so on.

You can perform indexing and slicing operations on both lists and tuples. You can also have nested lists and nested tuples or a combination of them, like a list of tuples.

The most notable difference between lists and tuples is that lists are mutable, while tuples are immutable. This feature distinguishes them and drives their specific use cases.

Essentially, a list doesn’t have a fixed length since it’s mutable. Therefore, it’s natural to use homogeneous elements to have some structure in the list. A tuple, on the other hand, has a fixed length so the position of elements can have meaning, supporting heterogeneous data.

Creating Lists in Python

In many situations, you’ll define a list object using a literal. A list literal is a comma-separated sequence of objects enclosed in square brackets:

Python
      
>>> countries = ["United States", "Canada", "Poland", "Germany", "Austria"]

>>> countries
['United States', 'Canada', 'Poland', 'Germany', 'Austria']

In this example, you create a list of countries represented by string objects. Because lists are ordered sequences, the values retain the insertion order.

Read the full article at https://realpython.com/python-lists-tuples/ »

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Quiz: Lists vs Tuples in Python

2024-09-04T12:00:00+00:00

Challenge yourself with this quiz to evaluate and deepen your understanding of Python lists and tuples. You’ll explore key concepts, such as how to create and manipulate these data types, while also learning best practices for using them efficiently in your code.

You can take this quiz after reading the Lists vs Tuples in Python tutorial.

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Using Pydantic to Simplify Python Data Validation

2024-09-03T14:00:00+00:00

Pydantic is a powerful data validation and settings management library for Python, engineered to enhance the robustness and reliability of your codebase. From basic tasks, such as checking whether a variable is an integer, to more complex tasks, like ensuring highly-nested dictionary keys and values have the correct data types, Pydantic can handle just about any data validation scenario with minimal boilerplate code.

In this video course, you’ll learn how to:

Work with data schemas with Pydantic’s BaseModel
Write custom validators for complex use cases
Validate function arguments with Pydantic’s @validate_call
Manage settings and configure applications with pydantic-settings

[ 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 ]

Using Astropy for Astronomy With Python

2024-08-27T14:00:00+00:00

This course covers two problems from introductory astronomy to help you play with some Python libraries. You’ll use Astropy, NumPy, Matplotlib, and pandas to find planet conjunctions, and graph the best viewing times for a star.

In this course you’ll learn about:

Astronomy concepts of conjunction and optimal viewing
The Python package Astropy
Using pandas to process data
Building graphs with Matplotlib
Python’s warning 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 ]

The Real Python Podcast – Episode #218: Exploring Robotics and Python Through Electronic Projects

2024-08-23T12:00:00+00:00

Are you interested in learning robotics with Python? Can physical electronics-based projects grow a child's interest in coding? This week on the show, we speak with author Marwan Alsabbagh about his book "Build Your Own Robot - Using Python, CRICKIT, and Raspberry Pi."

[ 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 ]

Exploring Astrophysics in Python With pandas and Matplotlib

2024-08-20T14:00:00+00:00

This course uses three problems often covered in introductory astro-physics courses to play in Python. Along the way you’ll learn some astronomy, and how to use a variety of datascience libraries like NumPy, Matplotlib, pandas, and pint.

In this video course you’ll learn about:

Introductory astrophysics topics
Working with dataframes in pandas
Writing code that uses scientific units
Visualizing information with Matplotlib

[ 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 ]

The Real Python Podcast – Episode #217: Packaging Data Analyses & Using pandas GroupBy

2024-08-16T12:00:00+00:00

What are the best practices for organizing data analysis projects in Python? What are the advantages of a more package-centric approach to data science? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

[ 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 ]

Sorting Dictionaries in Python: Keys, Values, and More

2024-08-13T14:00:00+00:00

You’ve got a dictionary, but you’d like to sort the key-value pairs. Perhaps you’ve tried passing a dictionary to the sorted() function but didn’t receive the results you expected. In this video course, you’ll go over everything you need to know to sort dictionaries in Python.

In this video course, you’ll:

Review how to use the sorted() function
Learn how to get dictionary views to iterate over
Understand how dictionaries are cast to lists during sorting
Learn how to specify a sort key to sort a dictionary by value, key, or nested attribute
Review dictionary comprehensions and the dict() constructor to rebuild your dictionaries
Consider alternative data structures for your key-value data

[ 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 ]

The Real Python Podcast – Episode #216: Learning Through Building the Black Python Devs Community

2024-08-09T12:00:00+00:00

What hurdles must be cleared when starting an international organization? How do you empower others in a community by sharing responsibilities? This week on the show, we speak with Jay Miller about Black Python Devs.

[ 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 ]

Interacting With REST APIs and Python

2024-08-06T14:00:00+00:00

There’s an amazing amount of data available on the Web. Many web services, like YouTube and GitHub, make their data accessible to third-party applications through an application programming interface (API). One of the most popular ways to build APIs is the REST architecture style. Python provides some great tools not only to get data from REST APIs but also to build your own Python REST APIs.

In this video course, you’ll learn:

What REST architecture is
How REST APIs provide access to web data
How to consume data from REST APIs using the requests library
What steps to take to build a REST API
What some popular Python tools are for building REST APIs

[ 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 ]

The Real Python Podcast – Episode #215: Using GraphQL in Django With Strawberry & Prototype Purgatory

2024-08-02T12:00:00+00:00

How do you integrate GraphQL into your Python web development? How about quickly building graph-based APIs inside Django's battery-included framework? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

[ 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 ]

The Real Python Podcast – Episode #214: Build Captivating Display Tables in Python With Great Tables

2024-07-26T12:00:00+00:00

Do you need help making data tables in Python look interesting and attractive? How can you create beautiful display-ready tables as easily as charts and graphs in Python? This week on the show, we speak with Richard Iannone and Michael Chow from Posit about the Great Tables Python library.

[ 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 ]

The Real Python Podcast – Episode #213: Constraint Programming & Exploring Python's Built-in Functions

2024-07-19T12:00:00+00:00

What are discrete optimization problems? How do you solve them with constraint programming in Python? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects

[ 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 ]

Real Python

What's New in Python 3.13

Python News Roundup: October 2024

Python 3.13 Release Slightly Delayed

Python 3.13 Highlights

Quiz: Iterators and Iterables in Python: Run Efficient Iterations

Quiz: Python import: Advanced Techniques and Tips

A Guide to Modern Python String Formatting Tools

Getting to Know String Interpolation and Formatting in Python

Using F-Strings for String Interpolation

Creating F-String Literals

Interpolating Variables Into F-Strings

Quiz: A Guide to Modern Python String Formatting Tools

Differences Between Python's Mutable and Immutable Types

Quiz: When to Use a List Comprehension in Python

Python 3.13: Cool New Features for You to Try

An Improved Interactive Interpreter (REPL)

Quiz: Python 3.13: Cool New Features for You to Try

Quiz: Syntactic Sugar: Why Python Is Sweet and Pythonic

The Real Python Podcast – Episode #222: Using Virtual Environments in Docker & Comparing Python Dev Tools

Python 3.13 Preview: A Modern REPL

A New Interactive Interpreter (REPL) in Python 3.13

Advanced Python import Techniques

Python Virtual Environments: A Primer

How Can You Work With a Python Virtual Environment?

Create It

Activate It

The Real Python Podcast – Episode #221: Thriving as a Developer With ADHD

Python 3.13 Preview: Free Threading and a JIT Compiler

Free Threading and JIT in Python 3.13: What’s the Fuss?

Free Threading Makes the GIL Optional

JIT Compiles Python to Machine Code

Customizing VS Code Through Color Themes

Quiz: Using Python's pip to Manage Your Projects' Dependencies

Using Python's pip to Manage Your Projects' Dependencies

Getting Started With pip

Finding pip on Your System

Running pip as a Module

The Real Python Podcast – Episode #220: Configuring Git Pre-Commit Hooks & Estimating Software Projects

Quiz: Python 3.13: Free Threading and a JIT Compiler

How to Use Conditional Expressions With NumPy where()

How to Write Conditional Expressions With NumPy where()

Quiz: Python Virtual Environments: A Primer

When to Use .__repr__() vs .__str__() in Python

Python News Roundup: September 2024

Python 3.12.5 Released

The Real Python Podcast – Episode #219: Astrophysics and Astronomy With Python & PyCon Africa 2024

Lists vs Tuples in Python

Getting Started With Python Lists and Tuples

Creating Lists in Python

Quiz: Lists vs Tuples in Python

Using Pydantic to Simplify Python Data Validation

Using Astropy for Astronomy With Python

The Real Python Podcast – Episode #218: Exploring Robotics and Python Through Electronic Projects

Exploring Astrophysics in Python With pandas and Matplotlib

The Real Python Podcast – Episode #217: Packaging Data Analyses & Using pandas GroupBy

Sorting Dictionaries in Python: Keys, Values, and More

The Real Python Podcast – Episode #216: Learning Through Building the Black Python Devs Community

Interacting With REST APIs and Python

The Real Python Podcast – Episode #215: Using GraphQL in Django With Strawberry & Prototype Purgatory

The Real Python Podcast – Episode #214: Build Captivating Display Tables in Python With Great Tables

The Real Python Podcast – Episode #213: Constraint Programming & Exploring Python's Built-in Functions

Getting Started With `pip`

Finding `pip` on Your System

Running `pip` as a Module

How to Write Conditional Expressions With NumPy `where()`

When to Use .repr() vs .str() in Python