tables
Hierarchical datasets for Python
Decision gist · record as of 2026-08-14
Yes, if you work with large multidimensional datasets or time-series data and need efficient compression and hierarchical storage. The active maintenance, permissive license, and broad platform support make it a solid choice. Install friction is moderate due to HDF5 dependency, but pre-built wheels mitigate this for common platforms. Not suitable if you need relational query capabilities or are working with small datasets where compression overhead outweighs benefits.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires HDF5 C library (libhdf5-serial-dev on Debian/Ubuntu) and Python 3.11 or later; blosc2 compression library is mandatory.
- Medium install friction due to compiled C extensions and HDF5 library dependency.
- Active maintenance with recent releases; requires HDF5 1.10.5 or above and system libraries like libhdf5-serial-dev on Linux.
License · maintenance · safety
BSD 3-Clause License (permissive) — BSD 3-Clause License (permissive) allows commercial and private use with minimal restrictions; attribution required but no copyleft obligations.
last release 2026-03-01 (166 days) · last repo commit 2026-08-09 · 1,371 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 6,451,690 downloads/mo, #1,908 on PyPI
Alternatives
Verify before relying
pip install tables
import tables
with tables.open_file('data.h5', mode='w') as h5file:
h5file.create_table('/', 'mytable', description={'col1': tables.Int32Col()})- Whether the stated 3–5x compression ratio applies to typical user workloads or only to highly compressible data.
- Performance characteristics compared to alternatives like Parquet or NetCDF for specific use cases.
- Whether all optional compressors (LZO, bzip2) are available in pre-built wheels or require separate installation.
What it is and what it does
PyTables is a Python library built on HDF5 that stores and retrieves hierarchical datasets with built-in compression and an object-oriented API. It optimizes memory and disk usage by compressing data on write and decompressing on read, supporting state-of-the-art Blosc compression out of the box. The library is designed for scientific and data-intensive applications that work with large multidimensional arrays, tables of fixed-length records, and enlargeable arrays—not as a relational database replacement, but as a specialized tool for efficient bulk storage and retrieval.
The package depends on NumPy, NumExpr, packaging, py-cpuinfo, and blosc2, and requires a system HDF5 library to compile. It is actively maintained, supports Python 3.11 through 3.14, and offers pre-built wheels for macOS (Intel and ARM), Linux (x86_64 and aarch64), and Windows, reducing installation friction on common platforms.
Use it for
- Store and query large time-series datasets from data acquisition systems or network monitoring without the overhead of a relational database.
- Archive multidimensional scientific simulation outputs (e.g., climate models, physics simulations) with efficient compression and hierarchical organization.
- Centralize system logs or structured event data from distributed systems in a single compressed repository.
- Perform interactive analysis on datasets too large to fit in memory by using PyTables' iterators and lazy-loading capabilities.
- Replace inefficient flat-file or database storage for columnar scientific data where compression and I/O speed are critical.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you work with large multidimensional datasets or time-series data and need efficient compression and hierarchical storage.
The active maintenance, permissive license, and broad platform support make it a solid choice. Install friction is moderate due to HDF5 dependency, but pre-built wheels mitigate this for common platforms. Not suitable if you need relational query capabilities or are working with small datasets where compression overhead outweighs benefits.
Install
tables on PyPI
Before you install
Medium install friction due to compiled C extensions and HDF5 library dependency. Active maintenance with recent releases; requires HDF5 1.10.5 or above and system libraries like libhdf5-serial-dev on Linux. Pre-built wheels available for common platforms reduce friction.
Requires HDF5 C library (libhdf5-serial-dev on Debian/Ubuntu) and Python 3.11 or later; blosc2 compression library is mandatory.
License in practice
BSD 3-Clause License (permissive) allows commercial and private use with minimal restrictions; attribution required but no copyleft obligations.
Quickstart
pip install tables
import tables
with tables.open_file('data.h5', mode='w') as h5file:
h5file.create_table('/', 'mytable', description={'col1': tables.Int32Col()})Verify before relying
- Whether the stated 3–5x compression ratio applies to typical user workloads or only to highly compressible data.
- Performance characteristics compared to alternatives like Parquet or NetCDF for specific use cases.
- Whether all optional compressors (LZO, bzip2) are available in pre-built wheels or require separate installation.
Package facts
| License | BSD 3-Clause License permissive |
| Python support | Supports the current Python release >=3.11 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 5 packagesnumpynumexprpackagingpy-cpuinfoblosc2 |
| Maintenance | Actively maintained 166 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 6,451,690 / month, #1,908 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: Information TechnologyIntended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseOperating System :: Microsoft :: WindowsOperating System :: UnixProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: DatabaseTopic :: Software Development :: Libraries :: Python Modules |
Evidence: tables-3.11.1-cp311-abi3-macosx_10_9_x86_64.whl; tables-3.11.1-cp311-abi3-macosx_11_0_arm64.whl; tables-3.11.1-cp311-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl; tables-3.11.1-cp311-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl; tables-3.11.1-cp311-abi3-win_amd64.whl; tables-3.11.1-cp314-cp314t-macosx_10_15_x86_64.whl; tables-3.11.1-cp314-cp314t-macosx_11_0_arm64.whl; tables-3.11.1-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl; tables-3.11.1-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl; tables-3.11.1-cp314-cp314t-win_amd64.whl
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See also hdf5plugin · hdmf · h5py · hickle · blosc · h5grove · mudata · netCDF4 · h5netcdf · tiledb