#python

#python

Your code is easiest to read just after you've written it. Your future self will find your code far less readable days, weeks, or months after you've written it. When it comes to code readability, whitespace is your friend. Whitespace around operators Compare this: To this: I find that second one more readable because the operations we're performing are more obvious (as is the order of operations). Too much whitespace can hurt readability though: This seems like a step backward because we've lost those three groups we had before. With both typography and visual design, more whitespace isn't always better.

Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: Deprecations via warnings Michael #2: docs

You provide three tiers to your customers: Gold, Silver, and Bronze. And one of the perks of the higher tiers is priority over the others when your customers need you. Gold customers get served first. When no Gold customers are waiting, you serve Silver customers. Bronze customers get served when there's no one in the upper tiers waiting. How do you set up this queue in your Python program?

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.

To get the most out of this tutorial, you should have a basic knowledge of Python. Familiarity with general GUI programming concepts, such as event handling, widgets, and layouts, will also be beneficial. Installing NiceGUI Before using any third-party library like NiceGUI, you must install it in your working environment. Installing NiceGUI is as quick as running the python -m pip install nicegui command in your terminal or command line. This command will install the library from the Python Package Index (PyPI).

Easily create classes with rich, declarative attributes - Parameter objects - that include extended metadata for various purposes such as runtime type and range validation, documentation strings, default values or factories, nullability, etc. In this sense, Param is conceptually similar to libraries like Pydantic, Python's dataclasses, or Traitlets. A suite of expressive and composable APIs for reactive programming, enabling automatic updates on attribute changes, and declaring complex reactive dependencies and expressions that can be introspected by other frameworks to implement their own reactive workflows.

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.

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

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.

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.

These terms included affirming the statement that we 'do not, and will not during the term of this financial assistance award, operate any programs that advance or promote DEI [diversity, equity, and inclusion], or discriminatory equity ideology in violation of Federal anti-discrimination laws,' Crary noted.

Here's the task. I want to create a class to represent a track and field event held in a competition. This entry allows you to enter the raw results as reported by the officials-the athlete's bib number and the time they clocked in a race. It then computes the event's full results. There's more we could add to make this class more complete. But I won't. You can create a list of athletes that are competing in the track and field meeting:

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.

If you spend long enough in the programming world, you'll come across the term singleton at some point. And if you hadn't seen this term yet, well, now you have! In Python, you don't need singleton classes. Almost never. But creating one is a great exercise in understanding how Python creates objects. And discussing alternatives to a singleton class helps you explore other aspects of Python.

Do not overcrowd your functions with logic for four or five different things. A function should do a single thing, and it should do it well, and the name of the function should clearly tell you what your function does. If you are unsure about whether some piece of code should be a single function or multiple functions, it's best to err on the side of too many functions. That is because a function is a modular piece of code, and the smaller your functions are, the easier it is to compose them together to create more complex behaviours.

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. We share techniques for finding motivation, building projects, and learning the fundamentals. We provide advice on installing Python and not obsessing over finding the perfect editor. We also examine incorporating LLMs into learning to code and practicing asking good questions.

Let's say we have some text that was retrieved from a database, and the original text came from a form submission in a web browser. Web browsers often represent line breaks as a carriage return character, followed by a line feed character: That's what we see in our text as well: \r followed by \n. This is often called CRLF (carriage return and line feed) whereas \n is called LF (line feed).

Python's asyncio.gather function is great for I/O bound parallel processing. There's a simple utility function I like to use that I call gather_in_batches: async def gather_in_batches(tasks, batch_size=100, return_exceptions=False): for i in range(0, len(tasks), batch_size): batch = tasks[i:i+batch_size] for result in await asyncio.gather(*batch, return_exceptions=return_exceptions): yield result