Real Python

The Real Python Podcast – Episode #269: Python 3.14: Exploring the New Features

2025-10-10T12:00:00+00:00

Python 3.14 is here! Christopher Trudeau returns to discuss the new version with Real Python team member Bartosz Zaczyński. This year, Bartosz coordinated the series of preview articles with members of the Real Python team and wrote the showcase tutorial, "Python 3.14: Cool New Features for You to Try." Christopher's video course, "What's New in Python 3.14", covers the topics from the article and shows the new features in action.

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Python 3.14: Cool New Features for You to Try

2025-10-08T14:00:00+00:00

Python 3.14 was released on October 7, 2025. While many of its biggest changes happen under the hood, there are practical improvements you’ll notice right away. This version sharpens the language’s tools, boosts ergonomics, and opens doors to new capabilities without forcing you to rewrite everything.

In this tutorial, you’ll explore features like:

A smarter, more colorful REPL experience
Error messages that guide you toward fixes
Safer hooks for live debugging
Template strings (t-strings) for controlled interpolation
Deferred annotation evaluation to simplify typing
New concurrency options like subinterpreters and a free-threaded build

If you want to try out the examples, make sure you run Python 3.14 or a compatible preview release.

Note: On Unix systems, when you create a new virtual environment with the new Python 3.14, you’ll spot a quirky alias:

(venv) $ 𝜋thon
Python 3.14.0 (main, Oct  7 2025, 17:32:06) [GCC 14.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>>

This feature is exclusive to the 3.14 release as a tribute to the mathematical constant π (pi), whose rounded value, 3.14, is familiar to most people.

As you read on, you’ll find detailed examples and explanations for each feature. Along the way, you’ll get tips on how they can streamline your coding today and prepare you for what’s coming next.

Get Your Code: Click here to download the free sample code that you’ll use to learn about the new features in Python 3.14.

Take the Quiz: Test your knowledge with our interactive “Python 3.14: 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 3.14: Cool New Features for You to Try

In this quiz, you'll test your understanding of the new features introduced in Python 3.14. By working through this quiz, you'll review the key updates and improvements in this version of Python.

Developer Experience Improvements

Python 3.14 continues the trend of refining the language’s ergonomics. This release enhances the built-in interactive shell with live syntax highlighting and smarter autocompletion. It also improves syntax and runtime error messages, making them clearer and more actionable. While these upgrades don’t change the language itself, they boost your productivity as you write, test, and debug code.

Even Friendlier Python REPL

Python’s interactive interpreter, also known as the REPL, has always been the quickest way to try out a snippet of code, debug an issue, or explore a third-party library. It can even serve as a handy calculator or a bare-bones data analysis tool. Although your mileage may vary, you typically start the REPL by running the python command in your terminal without passing any arguments:

$ python
Python 3.14.0 (main, Oct  7 2025, 17:32:06) [GCC 14.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>>

The humble prompt, which consists of three chevrons (>>>), invites you to type a Python statement or an expression for immediate evaluation. As soon as you press Enter, you’ll instantly see the computed result without having to create any source files or configure a project workspace. After each result, the familiar prompt returns, ready to accept your next command:

>>> 2 + 2
4
>>>

For years, the stock Python REPL remained intentionally minimal. It was fast and reliable, but lacked the polish of alternative shells built by the community, like IPython, ptpython, or bpython.

That started to change in Python 3.13, which adopted a modern REPL based on PyREPL borrowed from the PyPy project. This upgrade introduced multiline editing, smarter history browsing, and improved Tab completion, while keeping the simplicity of the classic REPL.

Python 3.14 takes the interactive shell experience to the next level, introducing two new features:

Syntax highlighting: Real-time syntax highlighting with configurable color themes
Code completion: Autocompletion of module names inside import statements

Together, these improvements make the built-in REPL feel closer to a full-fledged code editor while keeping it lightweight and always available. The Python REPL now highlights code as you type. Keywords, strings, comments, numbers, and operators each get their own color, using ANSI escape codes similar to those that already color prompts and tracebacks in Python 3.13:

Python 3.14 Syntax Highlighting in the REPL

Notice how the colors shift as you type, once the interactive shell has enough context to parse your input. In particular, tokens such as the underscore (_) are recognized as soft keywords only in the context of pattern matching, and Python highlights them in a distinct color to set them apart. This colorful output also shows up in the Python debugger (pdb) when you set a breakpoint() on a given line of code, for example.

Additionally, a few of the standard-library modules can now take advantage of this new syntax-coloring capability of the Python interpreter:

Colorful Output in Python 3.14's Standard-Library Modules

The argparse module displays a colorful help message, the calendar module highlights the current day, the json module pretty-prints and colorizes JSON documents. Finally, the unittest module provides a colorful output for failed assertions to make reading and diagnosing them easier.

Read the full article at https://realpython.com/python314-new-features/ »

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Quiz: Python 3.14: Cool New Features for You to Try

2025-10-08T12:00:00+00:00

In this quiz, you’ll test your understanding of Python 3.14: 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 ]

What's New in Python 3.14

2025-10-07T14:00:00+00:00

Python 3.14 was published on October 7, 2025. While many of its biggest changes happen under the hood, there are practical improvements you’ll notice right away. This version sharpens the language’s tools, boosts ergonomics, and opens doors to new capabilities without forcing you to rewrite everything.

In this video course, you’ll explore features like:

A smarter, more colorful REPL experience
Error messages that guide you toward fixes
Safer hooks for live debugging
Template strings (t-strings) for controlled interpolation
Deferred annotation evaluation to simplify typing
New concurrency options like subinterpreters and a free-threaded build

[ 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 ]

It's Almost Time for Python 3.14 and Other Python News

2025-10-06T14:00:00+00:00

Python 3.14 nears release with new features in sight, and Django 6.0 alpha hints at what’s next for the web framework. Several PEPs have landed, including improvements to type annotations and support for the free-threaded Python effort.

Plus, the Python Software Foundation announced new board members, while Real Python dropped a bundle of fresh tutorials and updates. Read on to learn what’s new in the world of Python this month!

Join Now: Click here to join the Real Python Newsletter and you’ll never miss another Python tutorial, course, or news update.

Python 3.14 Reaches Release Candidate 3

Python 3.14.0rc3 was announced in September, bringing the next major version of Python one step closer to final release. This release candidate includes critical bug fixes, final tweaks to new features, and overall stability improvements.

Python 3.14 is expected to introduce new syntax options, enhanced standard-library modules, and performance boosts driven by internal C API changes. For the complete list of changes in Python 3.14, consult the official What’s new in Python 3.14 documentation.

The release also builds upon ongoing work toward making CPython free-threaded, an effort that will eventually allow better use of multicore CPUs. Developers are encouraged to test their projects with the RC to help identify regressions or issues before the official release.

The final release, 3.14.0, is scheduled for October 7. Check out Real Python’s series about the new features you can look forward to in Python 3.14.

Django 6.0 Alpha Released

Django 6.0 alpha 1 is out! This first public preview gives early access to the upcoming features in Django’s next major version. Although not production-ready, the alpha includes significant internal updates and deprecations, setting the stage for future capabilities.

Some of the early changes include enhanced async support, continued cleanup of old APIs, and the groundwork for upcoming improvements in database backend integration. Now is a great time for Django developers to test their apps and provide feedback before Django 6.0 is finalized.

Django 5.2.6, 5.1.12, and 4.2.24 were released separately with important security fixes. If you maintain Django applications, then these updates are strongly recommended.

Read the full article at https://realpython.com/python-news-october-2025/ »

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The Real Python Podcast – Episode #268: Advice on Beginning to Learn Python

2025-10-03T12:00:00+00:00

What's changed about learning Python over the last few years? What new techniques and updated advice should beginners have as they start their journey? This week on the show, Stephen Gruppetta and Martin Breuss return to discuss beginning to learn Python.

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Python 3.14 Preview: Better Syntax Error Messages

2025-10-01T14:00:00+00:00

Python 3.14 brings a fresh batch of improvements to error messages that’ll make debugging feel less like detective work and more like having a helpful colleague point out exactly what went wrong. These refinements build on the clearer tracebacks introduced in recent releases and focus on the mistakes Python programmers make most often.

By the end of this tutorial, you’ll understand that:

Python 3.14’s improved error messages help you debug code more efficiently.
There are ten error message enhancements in 3.14 that cover common mistakes, from keyword typos to misusing async with.
These improvements can help you catch common coding mistakes faster.
Python’s error messages have evolved from version 3.10 through 3.14.
Better error messages accelerate your learning and development process.

There are many other improvements and new features coming in Python 3.14. The highlights include the following:

To try any of the examples in this tutorial, you need to use Python 3.14. The tutorials How to Install Python on Your System: A Guide and Managing Multiple Python Versions With pyenv walk you through several options for adding a new version of Python to your system.

Get Your Code: Click here to download the free sample code that you’ll use to learn about the error message improvements in Python 3.14.

Take the Quiz: Test your knowledge with our interactive “Python 3.14 Preview: Better Syntax Error Messages” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Python 3.14 Preview: Better Syntax Error Messages

Explore how Python 3.14 improves error messages with clearer explanations, actionable hints, and better debugging support for developers.

Better Error Messages in Python 3.14

When Python 3.9 introduced a new parsing expression grammar (PEG) parser for the language, it opened the door to better error messages in Python 3.10. Python 3.11 followed with even better error messages, and that same effort continued in Python 3.12.

Python 3.13 refined these messages further with improved formatting and clearer explanations, making multiline errors more readable and adding context to complex error situations. These improvements build upon PEP 657, which introduced fine-grained error locations in tracebacks in Python 3.11.

Now, Python 3.14 takes another step forward, alongside other significant changes like PEP 779, which makes the free-threaded build officially supported, and PEP 765, which disallows using return, break, or continue to exit a finally block. What makes the error message enhancements in Python 3.14 special is their focus on common mistakes.

Each improved error message follows a consistent pattern:

It identifies the mistake.
It explains what’s wrong in plain English.
It suggests a likely fix when possible.

The error message improvements in Python 3.14 cover SyntaxError, ValueError, and TypeError messages. For an overview of the ten improvements you’ll explore in this tutorial, expand the collapsible section below:

Keyword Typos — SyntaxError
⭕️ Previous: invalid syntax
✅ Improved: invalid syntax. Did you mean 'for'?
elif After else — SyntaxError
⭕️ Previous: invalid syntax
✅ Improved: 'elif' block follows an 'else' block
Conditional Expressions — SyntaxError
⭕️ Previous: invalid syntax
✅ Improved: expected expression after 'else', but statement is given
String Closure — SyntaxError
⭕️ Previous: invalid syntax
✅ Improved: invalid syntax. Is this intended to be part of the string?
String Prefixes — SyntaxError
⭕️ Previous: invalid syntax
✅ Improved: 'b' and 'f' prefixes are incompatible
Unpacking Errors — ValueError
⭕️ Previous: too many values to unpack (expected 2)
✅ Improved: too many values to unpack (expected 2, got 3)
as Targets — SyntaxError
⭕️ Previous: invalid syntax
✅ Improved: cannot use list as import target
Unhashable Types — TypeError
⭕️ Previous: unhashable type: 'list'
✅ Improved: cannot use 'list' as a dict key (unhashable type: 'list')
math Domain Errors — ValueError
⭕️ Previous: math domain error
✅ Improved: expected a nonnegative input, got -1.0
async with Errors — TypeError
⭕️ Previous: 'TaskGroup' object does not support the context manager protocol
✅ Improved: object does not support the context manager protocol...Did you mean to use 'async with'?

The examples in this tutorial show both the error messages from Python 3.13 and the improved messages in Python 3.14, so you can see the differences even if you haven’t installed 3.14 yet.

For a general overview of Python’s exception system, see Python’s Built-in Exceptions: A Walkthrough With Examples, or to learn about raising exceptions, check out Python’s raise: Effectively Raising Exceptions in Your Code.

Clearer Keyword Typo Suggestions

A typo is usually a tiny mistake, sometimes just one extra letter, but it’s enough to break your code completely. Typos that involve Python keywords are among the most common syntax errors in Python code.

In Python 3.13 and earlier, a typo in a keyword produces a generic syntax error that offers no guidance about what might be wrong:

>>> forr i in range(5):
  File "<python-input-0>", line 1
    forr i in range(5):
         ^
SyntaxError: invalid syntax

The error points to the problem area with a helpful caret symbol (^), which at least tells you where Python found the error. However, it doesn’t suggest what you might have meant. The message “invalid syntax” is technically correct but not particularly helpful in practice.

You have to figure out on your own that forr should actually be for. It might be obvious once you spot it, but finding that single wrong letter can take a surprisingly long time when you’re focused on logic rather than spelling.

Python 3.14 recognizes when you type something close to a Python keyword and offers a helpful suggestion that immediately points you to the fix:

Read the full article at https://realpython.com/python314-error-messages/ »

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Quiz: Python 3.14 Preview: Better Syntax Error Messages

2025-10-01T12:00:00+00:00

This quiz helps you get familiar with the upgraded error messages in Python 3.14. You’ll review new keyword typo suggestions, improved math errors, string prefix feedback, and more.

Put your understanding to the test and discover how Python’s improved error messages can help you debug code faster.

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Quiz: Python MCP Server: Connect LLMs to Your Data

2025-10-01T12:00:00+00:00

This quiz helps you review the core ideas behind the Model Context Protocol (MCP). You will practice how MCP connects large language models with external systems, how to install it, and what role prompts, resources, and tools play.

You’ll also revisit best practices for defining tools, explore client-server setups like Cursor, and check your understanding of transports and testing. For a full walkthrough, see Python MCP Server: Connect LLMs to Your Data.

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Modern Python Linting With Ruff

2025-09-30T14:00:00+00:00

Linting is essential to writing clean and readable code that you can share with others. A linter, like Ruff, is a tool that analyzes your code and looks for errors, stylistic issues, and suspicious constructs. Linting allows you to address issues and improve your code quality before you commit your code and share it with others.

Ruff is a modern linter that’s extremely fast and has a simple interface, making it straightforward to use. It also aims to be a drop-in replacement for many other linting and formatting tools, such as Flake8, isort, and Black. It’s quickly becoming one of the most popular Python linters.

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

Install Ruff
Check your Python code for errors
Automatically fix your linting errors
Use Ruff to format your code
Add optional configurations to supercharge your linting

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Quiz: Modern Python Linting With Ruff

2025-09-30T12:00:00+00:00

Sharpen your code quality workflow with Ruff. In this quiz, you’ll review installation checks, continuous linting, and code formatting.

You’ll also revisit selecting rule codes, reading rule docs, applying safe fixes, and configuring TOML files. For a refresher, see Modern Python Linting With Ruff.

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Astral's ty: A New Blazing-Fast Type Checker for Python

2025-09-29T14:00:00+00:00

After Ruff and uv, the Astral team is back with another Rust-based tool called ty, short for type check. This new tool delivers a lightning-fast static type-checking experience in Python, aiming to outpace existing tools in both performance and convenience. By incorporating ty into your daily workflow, you’ll catch type-related bugs earlier and get clearer feedback.

But is ty suitable for you? Take a look at the table below to make a quick decision:

Use Case	Pick `ty`	Pick Other Tools
Development or experimentation	✅	❌
Production use	❌	✅

At the time of writing, ty is available as an early preview release with hundreds of open issues. Despite being actively developed and boasting over ten thousand stars on GitHub, it’s still missing essential features and might occasionally fail.

As such, it’s not ready for full adoption in production yet, nor is it going to be a drop-in replacement for any of its competitors. Bugs can take you by surprise in unexpected ways! Additionally, because ty’s implementation is moving fast, some of the information you’ll find in this tutorial may become outdated over time.

If you’d like to get familiar with a new, robust, and promising type checker in your personal projects, then by all means give ty a try! Ready to dive in? Click the link below to grab the sample code you’ll be working with in this tutorial:

Get Your Code: Click here to download the free sample code that you’ll use to learn about Astral’s ty.

Take the Quiz: Test your knowledge with our interactive “Astral's ty Type Checker for Python” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Astral's ty Type Checker for Python

Test your knowledge of Astral's ty—a blazing-fast, Rust-powered Python type checker. You'll cover installation, usage, rule configuration, and the tool's current limitations.

Start Using ty in Python Now

Python is a dynamically typed language, so it requires third-party tools to perform type checking and other kinds of static code analysis. Recently, ty joined the club of external type-checking tools for Python. Despite being created by a private company, Astral, the tool itself remains open source and MIT-licensed.

Although ty is mostly written in Rust, you don’t need the Rust compiler or its execution environment to type check your Python projects with it. To quickly get started with ty, you can install it directly from PyPI with pip, preferably within an activated virtual environment:

(venv) $ python -m pip install ty

As long as you’re on one of the three major operating systems—Windows, macOS, or Linux—this command will bring a hefty binary script called ty into your virtual environment. The script is already compiled to machine code suitable for your platform with all the necessary dependencies baked in, so you can run it directly, just like any other command-line program.

To verify the installation, run the following command in your terminal emulator app:

(venv) $ ty --version
ty 0.0.1-alpha.21

You should see a version number similar to the one above appear in the output. Alternatively, if that doesn’t work, then try to execute ty as a Python module using the interpreter’s -m option:

(venv) $ python -m ty --version
ty 0.0.1-alpha.21

When you do, Python runs a tiny wrapper script that looks for the ty binary executable in your path and invokes it for you.

Note that the pip install command only installs ty into the given virtual environment, which is usually associated with a specific project. To make ty available globally from any folder on your computer, you’ll need to use a different approach. Check out the official documentation for more installation options.

Note: If Visual Studio Code is your code editor of choice, then you’ll benefit from installing the official ty extension. This extension integrates ty directly into VS Code, letting you work without ever touching the command line. Unfortunately, users of PyCharm and other popular IDEs must wait until plugins for their tools catch up.

If your editor supports the Language Server Protocol, then you could leverage the ty server subcommand. For PyCharm, this requires installing the LSP4IJ plugin first, though this tutorial won’t cover its configuration.

Finally, if none of this works for you, then you may want to run the ty check subcommand with the optional --watch switch. This will have ty automatically rerun the checks for instant feedback whenever changes are detected upon saving one of the files in your project.

From now on, you won’t see the virtual environment’s name (venv) in the command prompts for the rest of this tutorial. To keep the code blocks concise, it’ll be assumed that ty is already installed globally and available in your system’s path.

Alright. It’s time for the fun part: letting ty scrutinize some Python code to catch bugs before they sneak in.

Catch Typing Errors With ty

The command-line interface of ty is pretty minimal and straightforward, as you’ll notice when you run ty without providing any subcommands or options:

$ ty
An extremely fast Python type checker.

Usage: ty <COMMAND>

Commands:
  check    Check a project for type errors
  server   Start the language server
  version  Display ty's version
  help     Print this message or the help of the given subcommand(s)

Options:
  -h, --help     Print help
  -V, --version  Print version

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

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Quiz: Astral's ty Type Checker for Python

2025-09-29T12:00:00+00:00

In this quiz, you’ll revisit the key concepts from Astral’s ty: A New Blazing-Fast Type Checker for Python. You’ll check your understanding of installing ty from PyPI, running type checks, and interpreting its structured diagnostics. You’ll also recall how to configure and silence specific rules, limit the scope of checks, and adjust Python version or platform settings.

By completing this quiz, you’ll cement your ability to experiment confidently with ty in personal or exploratory projects.

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The Real Python Podcast – Episode #267: Managing Feature Flags & Comparing Python Visualization Libraries

2025-09-26T12:00:00+00:00

What's a good way to enable or disable code paths without redeploying the software? How can you use feature flags to toggle functionality for specific users of your application? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

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Python MCP Server: Connect LLMs to Your Data

2025-09-24T14:00:00+00:00

The Model Context Protocol (MCP) is a new open protocol that allows AI models to interact with external systems in a standardized, extensible way. In this tutorial, you’ll install MCP, explore its client-server architecture, and work with its core concepts: prompts, resources, and tools. You’ll then build and test a Python MCP server that queries e-commerce data and integrate it with an AI agent in Cursor to see real tool calls in action.

By the end of this tutorial, you’ll understand:

What MCP is and why it was created
What MCP prompts, resources, and tools are
How to build an MCP server with customized tools
How to integrate your MCP server with AI agents like Cursor

You’ll get hands-on experience with Python MCP by creating and testing MCP servers and connecting your MCP to AI tools. To keep the focus on learning MCP rather than building a complex project, you’ll build a simple MCP server that interacts with a simulated e-commerce database. You’ll also use Cursor’s MCP client, which saves you from having to implement your own.

Note: If you’re curious about further discussions on MCP, then listen to the Real Python Podcast Episode 266: Dangers of Automatically Converting a REST API to MCP.

You’ll get the most out of this tutorial if you’re comfortable with intermediate Python concepts such as functions, object-oriented programming, and asynchronous programming. It will also be helpful if you’re familiar with AI tools like ChatGPT, Claude, LangChain, and Cursor.

Get Your Code: Click here to download the free sample code that shows you how to use Python MCP to connect your LLM With the World.

Take the Quiz: Test your knowledge with our interactive “Python MCP Server: Connect LLMs to Your Data” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Python MCP Server: Connect LLMs to Your Data

Test your knowledge of Python MCP. Practice installation, tools, resources, transports, and how LLMs interact with MCP servers.

Installing Python MCP

Python MCP is available on PyPI, and you can install it with pip. Open a terminal or command prompt, create a new virtual environment, and then run the following command:

(venv) $ python -m pip install "mcp[cli]"

This command will install the latest version of MCP from PyPI onto your machine. To verify that the installation was successful, start a Python REPL and import MCP:

>>> import mcp

If the import runs without error, then you’ve successfully installed MCP. You’ll also need pytest-asyncio for this tutorial:

(venv) $ python -m pip install pytest-asyncio

It’s a pytest plugin that adds asyncio support, which you’ll use to test your MCP server. With that, you’ve installed all the Python dependencies you need, and you’re ready to dive into MCP! You’ll start with a brief introduction to MCP and its core concepts.

What Is MCP?

Before diving into the code for this tutorial, you’ll learn what MCP is and the problem it tries to solve. You’ll then explore MCP’s core primitives—prompts, resources, and tools.

Understanding MCP

The Model Context Protocol is a protocol for AI language models, often referred to as large language models (LLMs), that standardizes how they interact with the outside world. This interaction most often involves performing actions like sending emails, writing and executing code, making API requests, browsing the web, and much more.

You might be wondering how LLMs are capable of this. How can an LLM that accepts text as input and returns text as output possibly perform actions? The key to this capability lies in function calling—a process through which LLMs execute predefined functions in a programming language like Python. At a high level, here’s what an LLM function-calling workflow might look like:

The diagram above illustrates how an LLM and Python process could interact to perform an action through function calling. Here’s a breakdown of each step:

User Prompt: The user first sends a prompt to the Python process. For example, the user might ask a question that requires a database query to answer, such as “How many customers have ordered our product today?”
Function Descriptions: The Python process can expose several functions to the LLM, allowing it to decide which one to call. You do this by type hinting your functions’ input arguments and writing thorough docstrings that describe what your functions do. An LLM framework like LangChain will convert your function definition into a text description and send it to an LLM along with the user prompt.
User Prompt: In combination with the function descriptions, the LLM also needs the user prompt to guide which function(s) it should call.
Function Call JSON: Once the LLM receives the user prompt and a description of each function available in your Python process, it can decide which function to call by sending back a JSON string. The JSON string must include the name of the function that the LLM wants to execute, as well as the inputs to pass to that function. If the JSON string is valid, then your Python process can convert it into a dictionary and call the respective function.
Function Output: If your Python process successfully executes the function call specified in the JSON string, it then sends the function’s output back to the LLM for further processing. This is useful because it allows the LLM to interpret and summarize the function’s output in a human-readable format.
Summarized Results: The LLM returns summarized results back to the Python process. For example, if the user prompt is “How many customers have ordered our product today?”, the summarized result might be “10 customers have placed orders today.” Despite all of the work happening between the Python process and the LLM to query the database in this example, the user experience is seamless.
Summarized Results: Lastly, the Python process gives the LLM’s summarized results to the user.

The power of MCP lies in its standardized architecture built around clients and servers. An MCP server is an API that hosts the prompts, resources, and tools you want to make available to an LLM. You’ll learn about prompts, resources, and tools shortly.

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

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Strip Characters From a Python String

2025-09-23T14:00:00+00:00

By default, Python’s .strip() method removes whitespace characters from both ends of a string. To remove different characters, you can pass a string as an argument that specifies a set of characters to remove. The .strip() method is useful for tasks like cleaning user input, standardizing filenames, and preparing data for storage.

By the end of this video course, you’ll understand that:

The .strip() method removes leading and trailing whitespace but doesn’t remove whitespace from the middle of a string.
You can use .strip() to remove specified characters from both ends of the string by providing these characters as an argument.
With the related methods .lstrip() and .rstrip(), you can remove characters from one side of the string only.
All three methods, .strip(), .lstrip(), and .rstrip(), remove character sets, not sequences.
You can use .removeprefix() and .removesuffix() to strip character sequences from the start or end of a string.

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Quiz: Strip Characters From a Python String

2025-09-23T12:00:00+00:00

Brush up on how Python’s strip, lstrip, and rstrip string methods work. You’ll practice how to remove whitespace, specific characters, and use the right tool for trimming string ends.

Try these questions to check your understanding of default stripping, custom character sets, and the difference between stripping characters and removing sequences. Need a refresher? See Strip Characters From a Python String.

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Get Started With FastAPI

2025-09-22T14:00:00+00:00

FastAPI is a web framework for building APIs with Python. It leverages standard Python type hints to provide automatic validation, serialization, and interactive documentation. When you’re deciding between Python web frameworks, FastAPI stands out for its speed, developer experience, and built-in features that reduce boilerplate code for API development:

Use Case	Pick FastAPI	Pick Flask or Django
You want to build an API-driven web app	✅	—
You need a full-stack web framework	—	✅
You value automatic API documentation	✅	—

Whether you’re building a minimal REST API or a complex backend service, understanding core features of FastAPI will help you make an informed decision about adopting it for your projects. To get the most from this tutorial, you’ll benefit from having basic knowledge of Python functions, HTTP concepts, and JSON handling.

Get Your Code: Click here to download the free sample code that shows you how to get started with FastAPI.

Take the Quiz: Test your knowledge with our interactive “Get Started With FastAPI” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Get Started With FastAPI

This hands-on quiz will test your knowledge of FastAPI basics, from installation and endpoints to automatic JSON responses and Swagger UI.

Install FastAPI the Right Way

Installing FastAPI correctly sets the foundation for a smooth development experience. Unlike many Python packages that you can install with just pip install <package>, FastAPI’s installation approach has evolved to provide better out-of-the-box functionality. Understanding these installation options will save you time and prevent common configuration issues.

Note: Before exploring any new package, it’s a good idea to create and activate a virtual environment. That way, you’re installing any project dependencies in your project’s virtual environment instead of system-wide.

The recommended way to install FastAPI is with the [standard] extra dependencies. This ensures you get all the tools you need for developing an API without having to hunt down additional packages later:

$ python -m pip install "fastapi[standard]"

The quotes around "fastapi[standard]" ensure the command works correctly across different terminals and operating systems. With the command above, you install several useful packages, including the FastAPI CLI and uvicorn, an ASGI server for running your application.

If you prefer a minimal installation without these extras, then you can install just the core framework. However, you’ll likely need to install uvicorn separately to run your application:

$ python -m pip install fastapi uvicorn

For this tutorial, you’ll use the [standard] installation to take advantage of all FastAPI’s built-in capabilities. You can verify your installation by checking the FastAPI version in the Python REPL:

>>> import fastapi
>>> fastapi.__version__
'0.116.1'

With FastAPI properly installed, you’re ready to create your first web API application. The next step is to build a minimal example that demonstrates FastAPI’s core capabilities.

Create the Most Minimal FastAPI App

Creating a basic FastAPI application requires just a few lines of code. You’ll start with a basic “Hello, World!” example that demonstrates the framework’s approach to building APIs.

You can name your FastAPI main file however you like. Common names are app.py, api.py, or main.py. To follow along, create a file called main.py:

from fastapi import FastAPI

app = FastAPI()

@app.get("/")
def home():
    return {"message": "Hello, FastAPI!"}

Read the full article at https://realpython.com/get-started-with-fastapi/ »

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Quiz: Get Started With FastAPI

2025-09-22T12:00:00+00:00

Brush up on core FastAPI concepts with this short quiz. You’ll revisit how to install FastAPI with extras, define API endpoints, and understand path and query parameters. See how FastAPI handles JSON responses and where to find the built-in API docs.

Want to review first? Check out Get Started With FastAPI for a practical walkthrough of these features.

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The Real Python Podcast – Episode #266: Dangers of Automatically Converting a REST API to MCP

2025-09-19T12:00:00+00:00

When converting an existing REST API to the Model Context Protocol, what should you consider? What anti-patterns should you avoid to keep an AI agent’s context clean? This week on the show, Kyle Stratis returns to discuss his upcoming book, "AI Agents with MCP".

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Quiz: What Can You Do With Python?

2025-09-18T12:00:00+00:00

Sharpen your sense of where Python fits. You’ll revisit web frameworks, CLIs and TUIs, GUIs, data work, and robotics.

Use hints when stuck, then read the explanations. For a quick big-picture refresher, watch What Can You Do With Python?.

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Quiz: Ways to Start Interacting With Python

2025-09-18T12:00:00+00:00

Want to revisit different ways to run Python code interactively? In this quiz, you’ll review concepts such as using the REPL, executing scripts, and working within IDEs.

Before starting, make sure you’ve gone through the Ways to Start Interacting With Python course to get the most from these questions.

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Python 3.14 Preview: REPL Autocompletion and Highlighting

2025-09-17T14:00:00+00:00

Python 3.14 introduces improvements to its interactive shell (REPL), bringing a more modern, colorful, and user-friendly environment. The new features make the Python 3.14 REPL a powerful tool for experimentation. Whether you’re testing a quick idea, exploring a new library, or debugging a tricky snippet, the REPL gives you instant feedback—no files, no setup, just type and run.

The default CPython REPL intentionally kept things minimal. It was fast, reliable, and available everywhere, but it lacked the richer, more ergonomic features found in tools like IPython or ptpython. That began to change in Python 3.13, when CPython adopted a modern PyREPL-based shell by default, adding multiline editing, better history navigation, and smarter Tab completion.

By the end of this tutorial, you’ll understand that:

In Python 3.14’s REPL, autocompletion is on by default. You just need to press Tab in the context of an import statement to see possible completion suggestions.
The REPL highlights Python syntax in real time if your terminal supports ANSI colors.
Python 3.14 allows you to customize the color theme with the _colorize.set_theme() experimental API and the PYTHONSTARTUP script.
You can disable the syntax highlighting by setting NO_COLOR=1 or PYTHON_COLORS=0 in your environment.

Autocompleting module names during import statements makes interactive coding smoother and faster, especially for learning or exploratory tasks. In addition, the colored syntax in the REPL improves readability, making it easier to spot typos and syntax issues.

Get Your Code: Click here to download the free sample code that you’ll use to learn about REPL autocompletion and highlighting in Python 3.14.

Take the Quiz: Test your knowledge with our interactive “Python 3.14 Preview: REPL Autocompletion and Highlighting” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Python 3.14 Preview: REPL Autocompletion and Highlighting

Take this quiz to explore Python 3.14's REPL upgrades! Test your knowledge of new autocompletion tools and built-in syntax highlighting.

The Interactive REPL Before Python 3.14

When you first install Python, one of the most immediate ways to try it out is through the interactive REPL (Read–Eval–Print Loop) in your command line or terminal. The REPL is a shell that lets you type Python code, run it instantly, and see the result. It’s a quick-start environment perfect for experimentation, learning, and debugging without the overhead of creating and running separate scripts.

For years, the default CPython REPL kept things lightweight and fairly minimal, with very few features. In contrast, tools like IPython, bpython, and ptpython have offered richer interactive experiences.

This landscape has been changing lately. Starting in Python 3.13, the default REPL is based on PyPy’s pyrepl, which is written in Python and designed for extensibility and safety. It’s also a more capable interactive shell with a modern set of features:

Color by default: Take advantage of colorized prompts and tracebacks. You can also control this behavior with the PYTHON_COLORS or NO_COLOR environment variables.
Quick REPL commands: Use exit, quit, help, and clear as commands rather than function calls with parentheses.
Built‑in help browser: Press F1 to open a help viewer in your pager so you can browse docs for Python, modules, and objects.
Persistent history browser: Press F2 to open your command history in a pager and keep it across sessions so you can copy and reuse code.
Multiline editing: Edit and rerun entire code blocks—functions, classes, loops—as a single unit. The block structure is preserved in your history.
Robust pasting and paste mode: Paste whole scripts or larger code blocks reliably by default. Optionally, you can access paste mode with F3, although direct pasting usually works without issue.
Smarter Tab completion: Completions update as you type and hide irrelevant suggestions to reduce noise.

In Python 3.14, the REPL has taken another leap forward by including the following improvements:

Extended autocompletion that now covers module and submodule names in import statements
Live syntax highlighting that makes your interactive code as readable as in your favorite code editor or IDE

You don’t need to perform any extra installation or configuration to start using these new features as long as your terminal supports ANSI color output.

In the sections ahead, you’ll explore what these improvements look like in practice, learn how to use them in your workflow, and pick up some troubleshooting tips for when things don’t behave quite as expected.

Autocompletion Improvements

Python 3.14 extends the autocompletion logic to recognize import contexts and suggest module or package names accordingly. Now, when you type an import or from ... import statement and press Tab, the REPL will search the import path for available module names matching the partial text.

For example, if you type import and then press Tab twice, you’ll get the complete list of currently available modules and packages. If you start typing a module’s name like pat and press Tab, the REPL will automatically complete the pathlib name because it matches uniquely with the partial text.

Read the full article at https://realpython.com/python-repl-autocompletion-highlighting/ »

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Quiz: Python 3.14 Preview: REPL Autocompletion and Highlighting

2025-09-17T12:00:00+00:00

In this quiz, you’ll test your understanding of Python 3.14 Preview: REPL Autocompletion and Highlighting.

With these skills, you’ll be able to take advantage of smarter autocompletion in import statements, real-time syntax highlighting, and even customize or disable the colors to fit your workflow.

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Python Project Management With uv

2025-09-16T14:00:00+00:00

The uv tool is a high-speed package and project manager for Python. It’s written in Rust and designed to streamline your workflow. It offers fast dependency installation and integrates various functionalities into a single tool.

With uv, you can install and manage multiple Python versions, create virtual environments, efficiently handle project dependencies, reproduce working environments, and even build and publish a project. These capabilities make uv an all-in-one tool for Python project management.

By the end of this video course, you’ll understand that:

uv is a Python package and project manager that integrates multiple functionalities into one tool, offering a comprehensive solution for managing Python projects.
uv is used for fast dependency installation, virtual environment management, Python version management, and project initialization, enhancing productivity and efficiency.
uv can build and publish Python packages to package repositories like PyPI, supporting a streamlined process from development to distribution.
uv automatically handles virtual environments, creating and managing them as needed to ensure clean and isolated project dependencies.

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What Does -> Mean in Python Function Definitions?

2025-09-15T14:00:00+00:00

In Python, the arrow symbol (->) appears in function definitions as a notation to indicate the expected return type. This notation is optional, but when you include it, you clarify what data type a function should return:

>>> def get_number_of_titles(titles: list[str]) -> int:
...     return len(titles)
...

You may have observed that not all Python code includes this particular syntax. What does the arrow notation mean? In this tutorial, you’ll learn what it is, how to use it, and why its usage sometimes seems so inconsistent.

Get Your Code: Click here to download the free sample code that you’ll use to learn what -> means in Python function definitions.

Take the Quiz: Test your knowledge with our interactive “What Does -> Mean in Python Function Definitions?” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

What Does -> Mean in Python Function Definitions?

Test your understanding of Python return type hints and learn how to use the -> arrow, annotate containers, and check code with static tools.

In Short: The `->` Notation Indicates a Function’s Return Type in Python

In Python, every value stored in a variable has a type. Because Python is dynamically typed, variables themselves don’t have fixed types—they can hold values of any type at different times. This means the same variable might store an integer at one moment and a string the next. In contrast, statically typed languages like C++ or Java require explicit type declarations, and variables are bound to a specific type throughout their lifetime.

You can see an example of a dynamically typed Python variable in the following code example:

>>> my_number = 32
>>> my_number = "32"

You start by declaring a variable called my_number, and setting it to the integer value 32. You then change the variable’s value to the string value "32". When you run this code in a Python environment, you don’t encounter any problems with the value change.

Dynamic typing also means you might not always know what data type a Python function will return, if it returns anything at all. Still, it’s often useful to know the return type. To address this, Python 3.5 introduced optional type hints, which allow developers to specify return types. To add a type hint, you place a -> after a function’s parameter list and write the expected return type before the colon.

You can also add type hints to function parameters. To do this, place a colon after the parameter’s name, followed by the expected type—for example, int or str.

Basic Type Hint Examples

To further explore type hints, suppose that you’re creating a Python application to manage inventory for a video game store. The program stores a list of game titles, tracks how many copies are in stock, and suggests new games for customers to try.

You’ve already seen the type hint syntax in the code example introduced earlier, which returns the number of titles in a list of games that the game store carries:

>>> def get_number_of_titles(titles: list[str]) -> int:
...     return len(titles)
...
>>> games = ["Dragon Quest", "Final Fantasy", "Age of Empires"]
>>> print("Number of titles:", get_number_of_titles(games))
Number of titles: 3

Here you see a type hint. You define a function called get_number_of_titles(), which takes a list of game titles as input. Next, you add a type hint for the titles parameter, indicating that the function takes a list of strings. Finally, you also add another type hint for the return type, specifying that the function returns an int value.

The function returns the length of the list, which is an integer. You test this out in the next line, where you create a variable that stores a list of three game titles. When you invoke the function on the list, you verify that the output is 3.

Note that in a real-world application, creating a separate function just to return a list’s length might be redundant. However, for instructional purposes, the function shown in the example is a straightforward way to demonstrate the type hint concept.

You can use type hints with any Python type. You’ve already seen an example with an int return type, but consider another example with a str return type. Suppose you want to recommend a random game for a customer to try. You could do so with the following short example function:

Read the full article at https://realpython.com/what-does-arrow-mean-in-python/ »

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The Real Python Podcast – Episode #265: Python App Hosting Choices & Documenting Python's History

2025-09-12T12:00:00+00:00

What are your options for hosting your Python application or scripts? What are the advantages of a platform as a service, container-based hosts, or setting up a virtual machine? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

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How to Drop Null Values in pandas With .dropna()

2025-09-10T14:00:00+00:00

Missing values can derail your analysis. In pandas, you can use the .dropna() method to remove rows or columns containing null values—in other words, missing data—so you can work with clean DataFrames. In this tutorial, you’ll learn how this method’s parameters let you control exactly which data gets removed. As you’ll see, these parameters give you fine-grained control over how much of your data to clean.

Dealing with null values is essential for keeping datasets clean and avoiding the issues they can cause. Missing entries can lead to misinterpreted column data types, inaccurate conclusions, and errors in calculations. Simply put, nulls can cause havoc if they find their way into your calculations.

By the end of this tutorial, you’ll understand that:

You can use .dropna() to remove rows and columns from a pandas DataFrame.
You can remove rows and columns based on the content of a subset of your DataFrame.
You can remove rows and columns based on the volume of null values within your DataFrame.

To get the most out of this tutorial, it’s recommended that you already have a basic understanding of how to create pandas DataFrames from files.

You’ll use the Python REPL along with a file named sales_data_with_missing_values.csv, which contains several null values you’ll deal with during the exercises. Before you start, extract this file from the downloadable materials by clicking the link at the end of this section.

The sales_data_with_missing_values.csv file is based on the publicly available and complete sales data file from Kaggle. Understanding the file’s content isn’t essential for this tutorial, but you can explore the Kaggle link above for more details if you’d like.

You’ll also need to install both the pandas and PyArrow libraries to make sure all code examples work in your environment:

PS> python -m pip install pandas pyarrow

$ python -m pip install pandas pyarrow

It’s time to refine your pandas skills by learning how to handle missing data in a variety of ways.

You’ll find all code examples and the sales_data_with_missing_values.csv file in the materials for this tutorial, which you can download by clicking the link below:

Get Your Code: Click here to download the free sample code that you’ll use to learn how to drop null values in pandas.

Take the Quiz: Test your knowledge with our interactive “How to Drop Null Values in pandas” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

How to Drop Null Values in pandas

Quiz yourself on pandas .dropna(): remove nulls, clean missing data, and prepare DataFrames for accurate analysis.

How to Drop Rows Containing Null Values With pandas `.dropna()`

Before you start dropping rows, it’s helpful to know what options .dropna() gives you. This method supports six parameters that let you control exactly what’s removed:

axis: Specifies whether to remove rows or columns containing null values.
thresh and how: Define how many missing values to remove or retain.
subset: Limits the removal of null values to specific parts of your DataFrame.
inplace: Determines whether the operation modifies the original DataFrame or returns a new copy.
ignore_index: Resets the DataFrame index after removing rows.

Don’t worry if any of these parameters don’t make sense to you just yet—you’ll learn why each is used during this tutorial. You’ll also get the chance to practice your skills.

Note: Although this tutorial teaches you how pandas DataFrames use .dropna(), DataFrames aren’t the only pandas objects that use it.

Series objects also have their own .dropna() method. However, the Series version contains only four parameters—axis, inplace, how, and ignore_index—instead of the six supported by the DataFrame version. Of these, only inplace and ignore_index are used, and they work the same way as in the DataFrame method. The rest are kept for compatibility with DataFrame, but have no effect.

Indexes also have a .dropna() method for removing missing index values, and it contains just one parameter: how.

Before using .dropna() to drop rows, you should first find out whether your data contains any null values:

>>> import pandas as pd

>>> pd.set_option("display.max_columns", None)

>>> sales_data = pd.read_csv(
...     "sales_data_with_missing_values.csv",
...     parse_dates=["order_date"],
...     date_format="%d/%m/%Y",
... ).convert_dtypes(dtype_backend="pyarrow")

>>> sales_data
    order_number           order_date       customer_name  \
0           <NA>  2025-02-09 00:00:00      Skipton Fealty
1          70041                 <NA>  Carmine Priestnall
2          70042  2025-02-09 00:00:00                <NA>
3          70043  2025-02-10 00:00:00     Lanni D'Ambrogi
4          70044  2025-02-10 00:00:00         Tann Angear
5          70045  2025-02-10 00:00:00      Skipton Fealty
6          70046  2025-02-11 00:00:00             Far Pow
7          70047  2025-02-11 00:00:00          Hill Group
8          70048  2025-02-11 00:00:00         Devlin Nock
9           <NA>                 <NA>                <NA>
10         70049  2025-02-12 00:00:00           Swift Inc

                product_purchased discount  sale_price
0    Chili Extra Virgin Olive Oil     True       135.0
1                            <NA>     <NA>       150.0
2       Rosemary Olive Oil Candle    False        78.0
3                            <NA>     True        19.5
4    Vanilla and Olive Oil Candle     <NA>       13.98
5    Basil Extra Virgin Olive Oil     True        <NA>
6    Chili Extra Virgin Olive Oil    False       150.0
7    Chili Extra Virgin Olive Oil     True       135.0
8   Lavender and Olive Oil Lotion    False       39.96
9                            <NA>     <NA>        <NA>
10  Garlic Extra Virgin Olive Oil     True       936.0

To make sure all columns appear on your screen, you configure pd.set_option("display.max_columns", None). By passing None as the second parameter, you make sure all columns are displayed.

You read the sales_data_with_missing_values.csv file into a DataFrame using the pandas read_csv() function, then view the data. The order dates are in the "%d/%m/%Y" format in the file, so to make sure the order_date data is read correctly, you use both the parse_dates and date_format parameters. The output reveals there are ten rows and six columns of data in your file.

Read the full article at https://realpython.com/how-to-drop-null-values-in-pandas/ »

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Python String Splitting

2025-09-09T14:00:00+00:00

Python’s .split() method lets you divide a string into a list of substrings based on a specified delimiter. By default, .split() separates at whitespace, including spaces, tabs, and newlines. You can customize .split() to work with specific delimiters using the sep parameter, and control the amount of splits with maxsplit.

By the end of this video course, you’ll understand that:

You split a string by spaces in Python using .split() without arguments.
Python’s .split() method can split on custom delimiters when you pass a character or string as an argument.
You limit splits using maxsplit to control the number of substrings Python extracts.
re.split() uses regular expressions for splitting strings based on complex patterns.

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The Python Documentary Celebrates History While Developer Surveys Celebrate Python

2025-09-08T14:00:00+00:00

Python is making history, and headlines. The much-anticipated Python: The Documentary premiered last month, capturing 34 years of programming history. But Python isn’t just looking back, it’s also moving forward at breakneck speed.

The recent developer surveys revealed Python’s explosive growth. At the same time, the Python Software Foundation paused its grants program amid unprecedented demand, PyPI implemented multiple security enhancements, while JetBrains announced a transition for PyCharm Community Edition into a unified product model.

Here’s what’s been happening in the world of Python!

Join Now: Click here to join the Real Python Newsletter and you’ll never miss another Python tutorial, course, or news update.

The Python Documentary Is Now Available on YouTube

At the end of August, Python: The Documentary premiered on YouTube, where you can watch it for free. It’s an 84-minute film tracing Python’s journey from Amsterdam side project to the world’s most popular programming language:

Produced by CultRepo (formerly Honeypot) and directed by Ida Bechtle, the documentary explores Python’s evolution and the community that shaped it. It features Guido van Rossum and key contributors like Mariatta and recent Real Python Podcast guest, Travis Oliphant.

The documentary also highlights the important role of PyLadies and addresses controversial topics including the Python 2 to 3 transition.

Note: If you want to take a deep dive into Python’s version history, then check out A History of Python Versions and Features.

The timing of Python: The Documentary couldn’t be better. While the film celebrates past achievements, the latest developer surveys show Python continues to thrive.

Developer Surveys Reveal Python’s Explosive Growth

JetBrains The State of Python 2025 and the Stack Overflow 2025 Developer Survey revealed Python’s remarkable growth:

Image: Most Popular Technologies 2025

Python usage jumped seven percentage points to reach 57.9% of all developers, marking one of the largest year-over-year gains for any established language. Another exciting survey result is that 50% of respondents have two years or less of professional coding experience:

Image: Years of Python Experience 2025

If you’re one of them and want to get started with Python, then this Introduction to Python Learning Path is perfect for you. One of the positively surprising results of the Stack Overflow survey is that uv came in first place as the most admired Stack Overflow tag:

Image: Most Admired Technology Tag 2025

If you want to give uv a spin yourself, then you can check out the tutorials Managing Python Projects With uv and uv vs pip: Managing Python Packages and Dependencies. Another notable development is FastAPI’s jump in usage from 29% to 38% among Python developers:

Image: Web Frameworks Usage 2025

Read the full article at https://realpython.com/python-news-september-2025/ »

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The Real Python Podcast – Episode #264: Large Language Models on the Edge of the Scaling Laws

2025-09-05T12:00:00+00:00

What's happening with the latest releases of large language models? Is the industry hitting the edge of the scaling laws, and do the current benchmarks provide reliable performance assessments? This week on the show, Jodie Burchell returns to discuss the current state of LLM releases.

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A History of Python Versions and Features

2025-09-02T14:00:00+00:00

This course gives you a direct, practical overview of how Python has evolved—what changed in each major version, why it matters, and how to use the features that define modern Python. You’ll see both the historical context and hands-on code, so you understand not just what changed, but why and how to use it today.

In this course, you’ll learn:

Key new features introduced in every major Python version, starting from early Python 1 up to Python 3.14
The story behind pivotal changes, like list comprehensions, exception handling, the with statement, defaultdict, and the transition from Python 2 to Python 3
Hands-on use of modern Python tools: f-strings, dataclasses, the walrus operator (:=), match/case structural pattern matching, and new types like enum and pathlib
How to leverage improvements in each Python release for cleaner, faster, and more maintainable code
What’s coming in the latest Python releases, including t-strings and REPL improvements

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The Real Python Podcast – Episode #263: Exploring Mixin Classes in Python

2025-08-29T12:00:00+00:00

What is a good way to add isolated, reusable functionality to Python classes? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

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Profiling Performance in Python

2025-08-26T14:00:00+00:00

Do you want to optimize the performance of your Python program to make it run faster or consume less memory? Before diving into any performance tuning, you should strongly consider using a technique called software profiling. It can help you decide whether optimizing the code is necessary and, if so, which parts of the code you should focus on.

Sometimes, the return on investment in performance optimizations just isn’t worth the effort. If you only run your code once or twice, or if it takes longer to improve the code than execute it, then what’s the point?

When it comes to improving the quality of your code, you’ll probably optimize for performance as a final step, if you do it at all. Often, your code will become speedier and more memory efficient thanks to other changes that you make. When in doubt, go through this short checklist to figure out whether to work on performance:

Testing: Have you tested your code to prove that it works as expected and without errors?
Refactoring: Does your code need some cleanup to become more maintainable and Pythonic?
Profiling: Have you identified the most inefficient parts of your code?

[ 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 #262: Travis Oliphant: SciPy, NumPy, and Fostering Scientific Python

2025-08-22T12:00:00+00:00

What went into developing the open-source Python tools data scientists use every day? This week on the show, we talk with Travis Oliphant about his work on SciPy, NumPy, Numba, and many other contributions to the Python scientific community.

[ 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 ]

Deep vs Shallow Copies in Python

2025-08-19T14:00:00+00:00

When working with Python objects, you’ll often need to make copies rather than modify the originals. In this video course, you’ll explore various ways to copy objects in Python, including using the built-in copy module. You’ll also learn the key differences between shallow and deep copies, with practical examples so you can safely duplicate objects in your own code.

By the end of this video course, you’ll understand that:

Shallow copying creates a new object but references the same nested objects, leading to shared changes.
Deep copying recursively duplicates all objects, ensuring full independence from the original.
Python’s copy module provides the copy() function for shallow copies and deepcopy() for deep copies.
Custom classes can implement .__copy__() and .__deepcopy__() for specific copying behavior.
Assignment in Python binds variable names to objects without copying, unlike some lower-level languages.

[ 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 #261: Selecting Inheritance or Composition in Python

2025-08-15T12:00:00+00:00

When considering an object-oriented programming problem, should you prefer inheritance or composition? Why wouldn't it just be simpler to use functions? 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 ]

Working With Python's .dict Attribute

2025-08-12T14:00:00+00:00

Python’s .__dict__ is a special attribute in classes and instances that acts as a namespace, mapping attribute names to their corresponding values. You can use .__dict__ to inspect, modify, add, or delete attributes dynamically, which makes it a versatile tool for metaprogramming and debugging.

In this video course, you’ll learn about using .__dict__ in various contexts, including classes, instances, and functions. You’ll also explore its role in inheritance with practical examples and comparisons to other tools for manipulating attributes.

By the end of this video course, you’ll understand that:

.__dict__ holds an object’s writable attributes, allowing for dynamic manipulation and introspection.
Both vars() and .__dict__ let you inspect an object’s attributes. The .__dict__ attribute gives you direct access to the object’s namespace, while the vars() function returns the object’s .__dict__.
Common use cases of .__dict__ include dynamic attribute management, introspection, serialization, and debugging in Python applications.

[ 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 #260: Harnessing the Power of Python Polars

2025-08-08T12:00:00+00:00

What are the advantages of using Polars for your Python data projects? When should you use the lazy or eager APIs, and what are the benefits of each? This week on the show, we speak with Jeroen Janssens and Thijs Nieuwdorp about their new book, _Python Polars: The Definitive Guide_.

[ 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 Python T-Strings

2025-08-05T14:00:00+00:00

Python 3.14’s t-strings allow you to intercept and transform input values before assembling them into a final representation. Unlike f-strings, which produce a str object, t-strings resolve to a Template instance, allowing you to safely process and customize dynamic content.

One of the key benefits of t-strings is their ability to help prevent security vulnerabilities like SQL injection and XSS attacks. They’re also valuable in other fields that rely on string templates, such as structured logging.

By the end of this video course, you’ll understand that:

Python t-strings are a generalization of f-strings, designed to safely handle and process input values.
The main components of a t-string include static string parts and interpolations, which are accessible through the Template class.
You process t-strings by iterating over their components, using attributes such as .strings, .interpolations, and .values for safe and customized handling.

[ 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

The Real Python Podcast – Episode #269: Python 3.14: Exploring the New Features

Python 3.14: Cool New Features for You to Try

Developer Experience Improvements

Even Friendlier Python REPL

Quiz: Python 3.14: Cool New Features for You to Try

What's New in Python 3.14

It's Almost Time for Python 3.14 and Other Python News

Python 3.14 Reaches Release Candidate 3

Django 6.0 Alpha Released

The Real Python Podcast – Episode #268: Advice on Beginning to Learn Python

Python 3.14 Preview: Better Syntax Error Messages

Better Error Messages in Python 3.14

Clearer Keyword Typo Suggestions

Quiz: Python 3.14 Preview: Better Syntax Error Messages

Quiz: Python MCP Server: Connect LLMs to Your Data

Modern Python Linting With Ruff

Quiz: Modern Python Linting With Ruff

Astral's ty: A New Blazing-Fast Type Checker for Python

Start Using ty in Python Now

Catch Typing Errors With ty

Quiz: Astral's ty Type Checker for Python

The Real Python Podcast – Episode #267: Managing Feature Flags & Comparing Python Visualization Libraries

Python MCP Server: Connect LLMs to Your Data

Installing Python MCP

What Is MCP?

Understanding MCP

Strip Characters From a Python String

Quiz: Strip Characters From a Python String

Get Started With FastAPI

Install FastAPI the Right Way

Create the Most Minimal FastAPI App

Quiz: Get Started With FastAPI

The Real Python Podcast – Episode #266: Dangers of Automatically Converting a REST API to MCP

Quiz: What Can You Do With Python?

Quiz: Ways to Start Interacting With Python

Python 3.14 Preview: REPL Autocompletion and Highlighting

The Interactive REPL Before Python 3.14

Autocompletion Improvements

Quiz: Python 3.14 Preview: REPL Autocompletion and Highlighting

Python Project Management With uv

What Does -> Mean in Python Function Definitions?

In Short: The -> Notation Indicates a Function’s Return Type in Python

Basic Type Hint Examples

The Real Python Podcast – Episode #265: Python App Hosting Choices & Documenting Python's History

How to Drop Null Values in pandas With .dropna()

How to Drop Rows Containing Null Values With pandas .dropna()

Python String Splitting

The Python Documentary Celebrates History While Developer Surveys Celebrate Python

The Python Documentary Is Now Available on YouTube

Developer Surveys Reveal Python’s Explosive Growth

The Real Python Podcast – Episode #264: Large Language Models on the Edge of the Scaling Laws

A History of Python Versions and Features

The Real Python Podcast – Episode #263: Exploring Mixin Classes in Python

Profiling Performance in Python

The Real Python Podcast – Episode #262: Travis Oliphant: SciPy, NumPy, and Fostering Scientific Python

Deep vs Shallow Copies in Python

The Real Python Podcast – Episode #261: Selecting Inheritance or Composition in Python

Working With Python's .__dict__ Attribute

The Real Python Podcast – Episode #260: Harnessing the Power of Python Polars

Exploring Python T-Strings

In Short: The `->` Notation Indicates a Function’s Return Type in Python

How to Drop Rows Containing Null Values With pandas `.dropna()`

Working With Python's .dict Attribute