Notes by djb on using Fil-C (2025) I'm impressed with the level of compatibility of the new memory-safe C/C++ compiler Fil-C (filcc, fil++). Many libraries and applications that I've tried work under Fil-C without changes, and the exceptions haven't been hard to get working. I've accumulated miscellaneous notes on this page regarding usage of Fil-C. My selfish objective here is to protect various
How to improve Python packaging, or why fourteen tools are at least tw
There is an area of Python that many developers have problems with. This is an area that has seen many different solutions pop up over the years, with many different opinions, wars, and attempts to solve it. Many have complained about the packaging ecosystem and tools making their lives harder. Many beginners are confused about virtual environments. But does it have to be this way? Are the current
What is HPy?¶ HPy provides a new API for extending Python in C. In other words, you use #include <hpy.h> instead of #include <Python.h>. What are the advantages of HPy?¶ Zero overhead on CPython: extensions written in HPy run at the same speed as "normal" extensions. Much faster on alternative implementations such as PyPy, GraalPy. Universal binaries: extensions built for the HPy Universal ABI can
はじめに この記事は会計系 Advent Calendar 2024 18日目の記事として投稿させていただいています。 「Pythonで会計データの分析をやってみたいけど簡単に使えるデータがない」そんなニーズにお応えして、オープンデータの有価証券報告書の提出データを分析してみます。 しかし、このデータはXBRLと呼ばれる構造化ドキュメントです。XBRLは、その構造が大変複雑で扱いづらい点と、データが散らばっている点で分析が困難です。 そこで、XBRLの複雑な部分をまるっと隠して必要な情報だけ抽出するラッパーをつくりました。 とりあえず、pythonライブラリのpandasで扱えるテーブルにしてしまえば、一般的なテーブルデータと同様に扱えるでしょう。データモデリングでいうところの大福帳（One Big Table）をつくってしまおうという訳です。 「データサイエンスの9割は前処理」と言われま
After needing to do a deep dive on the venv module (which I will explain later in this blog post as to why), I thought I would explain how virtual environments work to help demystify them. Why do virtual environments exist?Back in my the day, there was no concept of environments in Python: all you had was your Python installation and the current directory. That meant when you installed something y
【概要】本ガイドでは、Ubuntu 24.04 LTSにおける各種ソフトウェアのインストール手順を説明する。プログラミング環境（C/C++、Python、Java、R）、NVIDIA GPU環境（ドライバ、CUDA Toolkit、cuDNN）、データベース（PostgreSQL）、人工知能・機械学習ライブラリ、3次元コンピュータグラフィックス・地理情報システム・メディア処理ツール、エディタ・統合開発環境などを扱う。 【この記事の対象読者】Ubuntu 24.04 LTS上で開発・研究を行う大学生や技術者。C/C++やPythonによるプログラミング、人工知能・機械学習、3次元コンピュータグラフィックス制作、データベース管理などの環境を構築したい人を対象としている。Windows環境向けの情報は別ページ »で説明している。 【重要概念】 LTS（Long Term Support）: 長期
Press ← or → to navigate between chapters Press S or / to search in the book Press ? to show this help Press Esc to hide this help The PyO3 user guide Welcome to the PyO3 user guide! This book is a companion to PyO3's API docs. It contains examples and documentation to explain all of PyO3's use cases in detail. The rough order of material in this user guide is as follows: Getting started Wrapping
JupyterLab Changelog — JupyterLab 4.6.0a1 documentation
JupyterLab Changelog# v4.5# JupyterLab 4.5 includes a number of new features (described below), bug fixes, and enhancements. This release is compatible with extensions supporting JupyterLab 4.0. Extension authors are encouraged to consult the Extension Migration Guide which lists deprecations and changes to the public API. Performance and windowing# The default windowing mode is now contentVisibil
I started using uv because the benchmarks seemed too good to be true—10–100x faster than pip, resolves and installs in milliseconds. After reading the source code and the official resolver internals documentation, I understand why, and the answers are more interesting than just "it's written in Rust." This post traces every layer: from the repository structure, through what literally happens when
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Notes by djb on using Fil-C (2025)
HPy - A better C API for Python
99から始める有価証券報告書XBRLデータ分析 - アカデミックと実務をつなぐ
How virtual environments work
Ubuntu 24.04 開発・研究環境構築ガイド｜金子邦彦研究室
Introduction - PyO3 user guide
How uv Works Under the Hood | Noos - Where Thought, Code, and Craft Converge