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.
Vaex is a high-performance Python library for lazy out-of-core DataFrames designed to handle datasets larger than memory. It achieves efficiency through memory mapping, zero-copy policies, lazy evaluation, and virtual columns that avoid materializing intermediate results. The system supports HDF5, Apache Arrow, Parquet, CSV, and FITS formats, with accelerated operations via C++ extensions for critical paths like hash-based aggregations and string processing. The architecture is modular, consisting of a core package (vaex-core) and specialized extensions: vaex-viz (visualization), vaex-hdf5 (HDF5 enhancements), vaex-astro (astronomy tools), vaex-ml (machine learning integrations), vaex-jupyter (notebook support), vaex-server (remote access via WebSocket/REST), and vaex-graphql (GraphQL API). Key entry points include the `vaex` CLI tool, `vaex.open()` for data loading, and programmatic APIs for DataFrame operations, expressions, and visualizations. Data flows prioritize out-of-core processing: data remains on disk until needed, computations are lazily executed and cached via fingerprinting, and results are streamed to minimize memory footprint. The codebase demonstrates strong engineering practices with a plugin system for extensibility (expression/dataframe accessors), centralized settings configuration, and a task-based execution engine supporting parallelism and async operations. While primarily a general-purpose data analysis library, it is widely used in data research due to its efficiency with large datasets and domain-specific integrations (e.g., astronomy via vaex-astro). Recent commits indicate active maintenance, low technical drift through adoption of modern dependencies (Apache Arrow, FastAPI), and a comprehensive test suite covering core functionality and extensions.
This repository provides the SPICE dataset, a collection of molecular structures and properties for machine learning and molecular simulation. It includes precomputed HDF5 files for amino acid ligands, the DES370K set of small organic molecules, dipeptides, ions, PubChem compounds, solvated amino acids, water clusters, and more. The accompanying Python scripts generate these datasets from raw SDF and PDB files, performing tasks such as molecular filtering, energy computation, and solvation. The repository is primarily a data resource rather than a software application, with scripts that are tightly coupled to the specific dataset generation pipeline. It lacks dependency pinning and formal testing, but the data files are directly usable for training and benchmarking molecular models.