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5 months agoAn Intro to Python 3.14's New Features - Mouse Vs Python
Python 3.14 introduces free-threaded builds, an experimental JIT compiler, and replaces GPG signatures with Sigstore verification.
Python makes it straightforward to download files from a URL with its robust set of libraries. For quick tasks, you can use the built-in urllib module or the requests library to fetch and save files. When working with large files, streaming data in chunks can help save memory and improve performance.
By working through this quiz, you'll revisit the descriptor protocol, how .__get__() and .__set__() control attribute access, and how to implement read only descriptors. You'll also explore data vs. non-data descriptors, attribute lookup order, and the .__set_name__() method.
A directory without an __init__.py file becomes a namespace package, which behaves differently from a regular package and may cause slower imports. You can use __init__.py to explicitly define a package's public API by importing specific modules or functions into the package namespace.
Mocking in Python with unittest.mock allows you to simulate complex logic or unpredictable dependencies, such as responses from external services. The Mock class can imitate real objects, and the patch() function lets you temporarily substitute mocks for real objects in your tests.
This quiz sharpens your intuition for Python's asyncio module. You'll decide when async is the right tool, see how the event loop schedules work, and understand how coroutines pause and resume around I/O.
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Writing clear, consistent docstrings in Python helps others understand your code's purpose, parameters, and outputs. In this video course, you'll learn about best practices, standard formats, and common pitfalls to avoid, ensuring your documentation is accessible to users and tools alike. By the end of this video course, you'll understand that:
In the previous lesson, you learned how to turn text into embeddings - compact, high-dimensional vectors that capture semantic meaning. By computing cosine similarity between these vectors, you could find which sentences or paragraphs were most alike. That worked beautifully for a small handcrafted corpus of 30-40 paragraphs. But what if your dataset grows to millions of documents or billions of image embeddings? Suddenly, your brute-force search breaks down - and that's where Approximate Nearest Neighbor (ANN) methods come to the rescue.
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Join us on March 4th 2026, for an unforgettable, non-stop event, streamed from our studio in Amsterdam. We'll be joined live by 15 well-known and beloved speakers from Python communities around the globe, including Carol Willing, Deb Nicholson, Sheena O'Connell, Paul Everitt, Marlene Mhangami, and Carlton Gibson. They'll be speaking about topics such as core Python, AI, community, web development and data science.
Prerequisites This guide is for all Python users who want to grow their Python knowledge, get involved with the Python community, or explore new professional opportunities. Your level of experience with Python doesn't matter, and neither does whether you use Python professionally or as a hobbyist-regularly or only from time to time. If you use Python, you're a Python developer, and Python conferences are for Python developers!
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EuroPython depends entirely on the dedication of volunteers who invest tremendous effort into bringing it to life. From managing sponsor relationships and designing the event schedule to handling registration systems and organizing social events, countless hours of passionate work go into ensuring each year surpasses the last. Discover our recent conversation with Rodrigo Girão Serrão, who served on the EuroPython 2025 Programme Team.
Bob and I have spent many years as Python devs, and 6 years coaching with Pybites and we can safely say that being a Senior Developer is only about 1/3 Python knowledge. The other 60% is the ecosystem. It's the tooling. It's all of the tech around Python that makes you stand out from the rest. This is the biggest blind spot keeping developers stuck in Tutorial Hell. You spend hours memorising obscure library features, but you crumble when asked to configure a CI/CD pipeline.
Kacper Borucki blogged about parameterizing exception testing, and linked to pytest docs and a StackOverflow answer with similar approaches. The common way to test exceptions is to use pytest.raises as a context manager, and have separate tests for the cases that succeed and those that fail. Instead, this approach lets you unify them. I tweaked it to this, which I think reads nicely: One parameterized test that covers both good and bad outcomes. Nice.