hypertools is a well‑engineered Python library for high‑dimensional data analysis and visualization, with a strong focus on animated 3D trajectories, multi‑subject alignment (Haxby hyperalignment, SRM, Procrustes), cluster analysis, time‑series forecasting, and missing‑data imputation. The package exposes a scikit‑learn–compatible Pipeline: 'manip → normalize → reduce → align → plot', plus standalone dispatchers ('hyp.plot', 'hyp.align', 'hyp.cluster', 'hyp.predict', 'hyp.impute') that share a consistent model‑spec grammar (string, class, instance, dict) and support `return_model` reuse. Two plotting backends (matplotlib and plotly) produce parity for kaleidoscopic animations (serialitary, chemo, bullet, morph) and static charts. The codebase is fortified by an audit‑driven test suite of >160 test files, many using real instead of mocked data, and delivery‑orchestrated through a coherent CI pipeline. Vendored code (PPCA, brainiak SRM, Noto Sans fonts) and a lazy‑import mechanism that auto‑installs optional extras on demand keep the core lean while enabling rich functionality.
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.
This is the official repository for the TidyTuesday project, a weekly social data project that provides curated datasets for practicing data wrangling and visualization, primarily in R. The repository contains hundreds of datasets organized by year and week (from 2018 to 2026), each with accompanying metadata, cleaning scripts, and documentation. It also includes GitHub Actions workflows for automated validation and deployment, as well as a set of R scripts for dataset curation and quality control. The repo serves as a community resource for learning data science skills through hands-on practice with real-world data. While not a software application, it demonstrates strong data engineering practices in its curation pipeline and validation infrastructure.
Zui (formerly Brim) is an Electron-based desktop application for network security monitoring and data exploration. It provides a graphical interface to query and visualize data stored in a Zed/SuperDB lake, with a focus on Zeek and Suricata log analysis. The application supports importing packet captures (pcap), structured logs (Zeek TSV, NDJSON, CSV, Parquet), and offers a rich query editor (Monaco), histogram, table/inspector views, and export workflows. The codebase is a monorepo (Yarn workspaces, Nx) with packages for type definitions (`superdb-types`), a Node.js client (`superdb-node-client`), sample data, and an e2e test framework (`app-player`). The architecture follows a domain-driven pattern with handlers and operations, a Redux store with versioned state migrations, and a plugin system for extensibility. While the application is actively maintained and functionally rich, it exhibits some technical drift due to a mix of legacy and modern patterns, a large migration history, and tight coupling between core and plugins.
The repository is the official code bundle for the book 'Pandas Cookbook - Second Edition' by Packt Publishing. It contains Jupyter notebooks for each chapter, demonstrating pandas functionality for data manipulation, analysis, and visualization. The notebooks use a variety of real-world datasets to illustrate concepts such as data cleaning, filtering, grouping, reshaping, and time series analysis. Additionally, there are two demo directories that provide a reusable module for loading and cleaning Kaggle survey data, along with tests. The repository serves as an educational resource for learning pandas through practical examples.