Python

Python

With PyCharm 2026.1, users can leverage a comprehensive set of editing and formatting tools for modern web languages, including basic React support with code completion and navigation.

Data classes in Python allow developers to define classes with less boilerplate code, focusing on the essential attributes and behaviors of the data being modeled.

febelfin-coda is a library to parse CODA files, which specify the layout for electronic files by banks to customers regarding account transactions.

Version 0.2.0 of the aeb43 library introduces support for parsing AEB43 files, a standard format for Spanish banks, with significant improvements.

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.

You'll explore how I/O-bound programs face latency, which concurrency patterns to use, the differences between threading, asyncio, and multiprocessing, and how the Global Interpreter Lock (GIL) affects Python programs.

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.