VisiData is a terminal-based interactive spreadsheet application for exploring, cleaning, restructuring, and analyzing tabular data. It provides a real-time curses interface for loading data from diverse sources (CSV, JSON, Excel, SQL databases, APIs, etc.), applying transformations via hundreds of keyboard-driven commands (sort, filter, pivot, melt, unfurl, etc.), and saving results. The architecture centers around a Sheet/Column abstraction with a plugin system for loaders, features, and specialized applications (vgit for Git, vdsql for SQL). Key strengths include its extensible command system, dynamic type inference, and reversible operations via undo/redo. While the core is cohesive, the extensive optional dependencies and experimental features introduce some maintenance complexity.
Repository of structured JSON datasets containing football match fixtures and results across major European leagues (English Premier League, Bundesliga, Serie A, La Liga, UEFA Champions League) spanning 2011-2027. Files are auto-generated from upstream Football.TXT text sources via a GitHub Actions workflow that refreshes data daily. Each league/season file follows a consistent schema: a top-level object with league metadata and a `matches` array containing per-match objects with round, date, teams, and score objects (half-time, full-time, optionally extra time and penalties). The repository is purely a data source - no application code, scripts, tests, or configuration beyond the JSON files. Raw GitHub links serve as a free public API for web applications, mobile apps, prediction models, or analytics pipelines. The data is clean and machine-readable but lacks schema validation, versioning, and formal documentation. Known issues include schema drift across seasons (missing half-time scores, awarded matches with `status: 'awarded'`, team name variations) that downstream consumers must handle defensively. The primary value is the breadth and consistency of match data across 15+ leagues and 15+ seasons, making it suitable for football statistics applications, prediction models, or league visualization tools. The auto-update pipeline keeps current seasons fresh, but upstream Football.TXT sources may not be maintained for older seasons, and the lack of schema enforcement risks silent data drift. This is a data_research repository - a data source rather than an application - with no executable code or processing pipeline present.
This repository is a curated list of public datasets, organized by topic (Agric Climate, etc.). It contains no source code, data pipelines, or software components. The entire repository is a README.rst file with links to external datasets, a license file, and a single zipped CSV (Datasets/titanic.csv.zip). The README states it is automatically generated from a separate project (apd-core), and direct modifications are discouraged. Many entries are marked with FIXME icons, indicating broken or outdated links. The repository serves as a reference index rather than a data engineering or analysis project.