Python

Python

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.

Martin has been part of the Europython Conferences volunteers since 2017. Some have "met" him the first time in a response on an issue sent to the helpdesk or during the organisation meetings of the ops team.Others interacted with him as a volunteer at reception, out in the halls, a tutor in a Humble Data tutorial, a session chair, or room manager in a tutorial or talk.

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.

caniscrape checks a website for common anti-bot mechanisms and reports: A difficulty score (0-10) Which protections are active (e.g., Cloudflare, Akamai, hCaptcha, etc.) What tools you'll likely need (headless browsers, proxies, CAPTCHA solvers, etc.) Whether using a scraping API might be better This helps you decide the right scraping approach before you waste time building a bot that keeps getting blocked.

Free-threaded CPython (PEP 703): A build mode that removes the GIL so CPU-bound threads can run in parallel. Expect ecosystem work to make C extensions safe here. (Python Enhancement Proposals (PEPs)) uv and uvx: A faster, modern installer and environment manager that makes "clean, repeatable" installs feel instant. (Astral Docs) Docker + Compose: Containers capture your app and its services so dev and prod stay in sync. (Docker Documentation)

Asyncpg is the connector for PostgreSQL and asyncio-flavored Python. Here's how to use it without other libraries on FastAPI and Air projects. Recently I've been on a few projects using PostgreSQL where SQLAlchemy and SQLModel felt like overkill. Instead of using those libraries I leaned on writing SQL queries and running those directly in [asyncpg](https://pypi.org/project/asyncpg/) instead of using an ORM powered by asyncpg. Here's how I got it to work

Interactive Quiz ⋅ 10 QuestionsBy Ian Eyre

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.

Descriptors are a specific Python feature that power a lot of the magic hidden under the language's hood. If you've ever thought that Python descriptors are an advanced topic with few practical applications, then this video course is the perfect tool to help you understand this powerful feature. You'll come to understand why Python descriptors are such an interesting topic and discover the kinds of use cases where you can apply them.

Sentry is a great tool for monitoring celery tasks, and alerting when they fail or don't run on time. But it requires a bit of work to setup properly. Below is some sample code for setting up sentry monitoring of periodic tasks, followed by an explanation. import math import sentry_sdk from celery import signals from sentry_sdk import monitor from sentry_sdk.integrations.celery import CeleryIntegration @signals.beat_init.connect # if you use beats @signals.celeryd_init.connect def init_sentry(**kwargs): sentry_sdk.init( dsn=..., integrations=[ CeleryIntegration(monitor_beat_tasks=False) ] ) @signals.worker_shutdown.connect @signals.task_postrun.connect def flush_sentry(**kwargs): sentry_sdk.flush(timeout=5) def add_periodic_task(celery, schedule, task): max_runtime = math.ceil(schedule * 4 / 60) monitor_config = { "recovery_threshold": 1, "failure_issue_threshold": 10, "checkin_margin": max_runtime, "max_runtime": max_runtime, "schedule": { "type": "interval", "value": math.ceil(schedule / 60.0) "unit": "minute" } } name = task.__name__ task = monitor(monitor_slug=name, monitor_config=monitor_config)(task) celery.add_periodic_task(schedule, celery.task(task).s(), name=name)