# PyTables/PyTables

A Python package to manage extremely large amounts of data

Repository: https://github.com/PyTables/PyTables
Canonical: https://ross.abutalabs.com/products/pytables
Homepage: http://www.pytables.org
Language: Python
License: BSD-3-Clause
License Family: permissive
Last push: 2026-08-23T15:01:55+00:00

## Health v2 (maintenance only)
Score: 79/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 42, longevity 100
- inputs: {"age_days": 5570, "days_push": 10, "days_rel": 179, "gap_med": 212.5, "n_releases_24m": 3}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1372, forks 282 (observed 2026-08-28T04:04:32.221695+00:00)

## What it is
PyTables is a Python package for managing hierarchical datasets built on top of the HDF5 library and NumPy. It provides a fast, object-oriented interface for storing, browsing, and querying extremely large amounts of data with efficient compression and memory/disk optimization.

## Use cases
- store very large numerical datasets in Python
- save and retrieve large arrays and tables efficiently
- compress large scientific datasets on disk
- organize simulation or data acquisition output hierarchically
- query large tables without loading everything into memory
- replace cluttered relational storage for multidimensional data

## When to choose
- you need to persist huge NumPy arrays or structured tables in HDF5 format
- you want fast I/O with state-of-the-art compression like Blosc
- you work with scientific or simulation data that fits a hierarchical layout
- you need interactive browsing and searching of large datasets from Python

## When to avoid
- you need a full relational database with joins, transactions, and SQL
- your data is small and fits comfortably in memory or plain files
- you need concurrent multi-user write access
- you require a document or key-value store rather than typed tabular/array data

## Facets
- artifact type: library
- maturity: stable
- function: database, serialization, compression, data-science
- domain: data-science, big-data, databases, files
- platform: python, cross-platform
- tags: hdf5, numpy, large-datasets, scientific-computing, blosc, columnar-storage

## Member repositories
- PyTables/PyTables (main) score 79

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:32.221695+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:40:44.996257+00:00, confidence not recorded.
  - readme: https://github.com/PyTables/PyTables (fetched 2026-08-28T04:04:32.221695+00:00, sha 7659ee6cb65e)
  - homepage: http://www.pytables.org (fetched 2026-08-29T11:57:21.481970+00:00, sha b8ba135f5b55)
  - site_page: http://www.pytables.org/FAQ.html (fetched 2026-08-29T11:57:21.490881+00:00, sha d7297dccbef7)
- Data as of 2026-08-30T08:39:29.467469+00:00.