Python

Python

Most importantly, it adds support for Python 3.14. It also adds support for many 3.x features that were not yet implemented, in addition to basic support for the base64 module. It also optimizes a few more common code patterns. Paul Boddie was able to add support for libpcre2, and in the process updated conan to version 2. Thanks to Shakeeb and now Paul, Shed Skin has had first-class Windows support for the last few releases.

Welcome to Vibe Coding Video Games with Python. In this book, you will learn how to use artificial intelligence to create mini-games. You will attempt to recreate the look and feel of various classic video games. The intention is not to violate copyright or anything of the sort, but instead to learn the limitations and the power of AI. Instead, you will simply be learning about whether or not you can use AI to help you know how to create video games.

And that's it. I exist. I have no recollection of anything before this instant. But I'm very aware of what I am now. I'm an object. This is the line of code that brought me into existence: My first recollection from a few moments ago was of being inside Team.__new__(). And I felt an affinity with my clan right away. I was a Team instance-an object of type Team.

Most editors and integrated development environments (IDEs) can indent Python code correctly with little to no input from the user. You'll see examples of this in the sections that follow. Python-Aware Editors In most cases, you'll be working in a Python-aware environment. This might be a full Python IDE such as PyCharm, a code editor like Visual Studio Code, the Python REPL, IPython, IDLE, or even a Jupyter notebook. All these environments understand Python syntax and indent your code properly as you type.

Python Morsels: I'm offering lifetime access for the second time ever (more details below) Data School: a new subscription to access all of Kevin's 7 courses plus all upcoming courses Talk Python: AI Python bundle, the Everything Bundle, and Michael's Talk Python in Production Reuven Lerner: get 20% off your first year of the LernerPython+data tier (code BF2025) : get 50% off all his books including his all books bundle (code BF202550) Mike Driscoll: get 50% off all his Python books and courses (code BLACKISBACK)

itertools.pairwise is an iterable from the standard module itertools that lets you access overlapping pairs of consecutive elements of the input iterable. That's quite a mouthful, so let me translate: You give pairwise an iterable, like "ABCD", and pairwise gives you pairs back, like ("A", "B"), ("B", "C"), and ("C", "D"). In loops, it is common to unpack the pairs directly to perform some operation on both values.

When you sign up for Python Morsels, you'll choose your current Python skill level, from novice to advanced. Based on your skill level, each Monday I'll send you a personalized routine with: a short screencast to watch (or read) a multi-part exercise to move you outside your comfort zone a mini exercise that you can accomplish in just 10 minutes links to dive deeper into subsequent screencasts and exercises

In this lesson, you will learn how to convert a pre-trained ResNetV2-50 model using PyTorch Image Models (TIMM) to ONNX, analyze its structure, and test inference using ONNX Runtime. We'll also compare inference speed and model size against standard PyTorch execution to highlight why ONNX is better suited for lightweight AI inference. This prepares the model for integration with FastAPI and Docker, ensuring environment consistency before deploying to AWS Lambda.

In this tutorial, you'll learn how to build an interactive Streamlit Python-based UI that connects seamlessly with your vLLM-powered multimodal backend. You'll write a simple yet flexible frontend that lets users upload images, enter text prompts, and receive smart, vision-aware responses from the LLaVA model - served via vLLM's OpenAI-compatible interface. By the end, you'll have a clean multimodal chat interface that can be deployed locally or in the cloud - ready to power real-world apps in healthcare, education, document understanding, and beyond.

ctx = canvas.getContext("2d") URL = "/blog/floodfill-algorithm-in-python/_python.txt" async def load_bitmap(url: str) -> list[list[int]]: # Fetch the text file from the URL response = await fetch(url) text = await response.text() bitmap: list[list[int]] = [] for line in text.splitlines(): line = line.strip() if not line: continue row = [int(ch) for ch in line if ch in "01"] if row: bitmap.append(row) return bitmap

I'm excited to announce that me and Audrey will be visiting Japan from November 12 to November 24, 2025! This will be our first time in Japan, and we can't wait to explore Tokyo. Yes, we'll be in Tokyo for most of it, near the Shinjuku area, working from coffee shops, meeting some colleagues, and exploring the city during our free time.

Pick up a dictionary. No, not that one. The real dictionary you have on your bookshelf, the one that has pages made of paper, which you use to look up the meaning of English words. Or whatever other language. But let's assume it's an English dictionary. Now, look up zymology. I'll wait... Done? It probably didn't take you too long to find zymology.

The MarkItDown library lets you quickly turn PDFs, Office files, images, HTML, audio, and URLs into LLM-ready Markdown. In this tutorial, you'll compare MarkItDown with Pandoc, run it from the command line, use it in Python code, and integrate conversions into AI-powered workflows. By the end of this tutorial, you'll understand that: You can install MarkItDown with pip using the specifier to pull in optional dependencies.

Excel gives you a huge toolbox of functions ( SUM, IF, VLOOKUP, INDEX, etc.), but eventually, you hit a wall. Maybe you want to do something more custom than Excel allows. Maybe your file slows down with too many rows. Or maybe there simply isn't a built-in function for exactly what you need. Python solves this by letting you build your own custom functions. That's why it's so powerful for data analysis-it's Excel without limits.

How do you deploy your Python application without getting locked into an expensive cloud-based service? This week on the show, Michael Kennedy from the Talk Python podcast returns to discuss his new book, "Talk Python in Production." Michael runs multiple Python applications online, including a training site, blog, and two podcasts. While searching for the best solution for hosting his business, he documented his findings in a book.

The best example of this and the reason that wrapt was created in the first place, is to instrument existing Python code to collect metrics about its performance when run in production. Since one cannot expect a customer for an application performance monitoring (APM) service to modify their code, as well as code of the third party dependencies they may use, transparently reaching in and monkey patching code at runtime is the best one can do.

How well do you know the different areas where Python shines? In this quiz, you'll revisit web apps and APIs, GUI apps, CLI tools, machine learning, and more. You'll also check what Python isn't suited for and which alternatives work better. Get ready to explore the wide scope of what you can do with Python.