{"id":13502234,"url":"https://github.com/PyTables/PyTables","last_synced_at":"2025-03-29T10:32:55.126Z","repository":{"id":1539590,"uuid":"1844194","full_name":"PyTables/PyTables","owner":"PyTables","description":"A Python package to manage extremely large amounts of data","archived":false,"fork":false,"pushed_at":"2025-02-26T19:14:32.000Z","size":41210,"stargazers_count":1323,"open_issues_count":156,"forks_count":275,"subscribers_count":63,"default_branch":"master","last_synced_at":"2025-02-26T20:24:54.169Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"http://www.pytables.org","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":"er-mo/Android-Battery-Widget","license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/PyTables.png","metadata":{"files":{"readme":"README.rst","changelog":null,"contributing":"CONTRIBUTING.md","funding":"FUNDING.yml","license":"LICENSE.txt","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":"CITATION.bib","codeowners":null,"security":"SECURITY.md","support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null},"funding":{"github":["numfocus"],"custom":["https://numfocus.org/donate-to-pytables"]}},"created_at":"2011-06-03T19:44:46.000Z","updated_at":"2025-02-26T19:14:36.000Z","dependencies_parsed_at":"2023-07-05T20:17:05.099Z","dependency_job_id":"ced56fb8-171a-4c13-9eb4-b293b17dcd32","html_url":"https://github.com/PyTables/PyTables","commit_stats":null,"previous_names":[],"tags_count":57,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PyTables%2FPyTables","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PyTables%2FPyTables/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PyTables%2FPyTables/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PyTables%2FPyTables/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/PyTables","download_url":"https://codeload.github.com/PyTables/PyTables/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246174207,"owners_count":20735406,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-07-31T22:02:07.165Z","updated_at":"2025-03-29T10:32:55.091Z","avatar_url":"https://github.com/PyTables.png","language":"Python","readme":"===========================================\n PyTables: hierarchical datasets in Python\n===========================================\n\n.. image:: https://badges.gitter.im/Join%20Chat.svg\n   :alt: Join the chat at https://gitter.im/PyTables/PyTables\n   :target: https://gitter.im/PyTables/PyTables\n\n.. image:: https://github.com/PyTables/PyTables/workflows/CI/badge.svg\n   :target: https://github.com/PyTables/PyTables/actions?query=workflow%3ACI\n\n.. image:: https://img.shields.io/pypi/v/tables.svg\n  :target: https://pypi.org/project/tables/\n\n.. image:: https://img.shields.io/pypi/pyversions/tables.svg\n  :target: https://pypi.org/project/tables/\n\n.. image:: https://img.shields.io/pypi/l/tables\n  :target: https://github.com/PyTables/PyTables/\n\n\n:URL: http://www.pytables.org/\n\n\nPyTables is a package for managing hierarchical datasets, designed\nto efficiently cope with extremely large amounts of data.\n\nIt is built on top of the HDF5 library and the NumPy package. It\nfeatures an object-oriented interface that, combined with C extensions\nfor the performance-critical parts of the code (generated using\nCython), makes it a fast, yet extremely easy to use tool for\ninteractively saving and retrieving very large amounts of data. One\nimportant feature of PyTables is that it optimizes memory and disk\nresources so that they take much less space (between 3 to 5 times\nand more if the data is compressible) than other solutions, like for\nexample, relational or object-oriented databases.\n\nState-of-the-art compression\n----------------------------\n\nPyTables supports the `Blosc compressor \u003chttp://www.blosc.org\u003e`_ out of the box.\nThis allows for extremely high compression speed, while keeping decent\ncompression ratios. By doing so, I/O can be accelerated by a large extent, and\nyou may end up achieving higher performance than the bandwidth provided by your\nI/O subsystem. See the\n`Tuning The Chunksize section of the Optimization Tips chapter\n\u003chttp://www.pytables.org/usersguide/optimization.html#fine-tuning-the-chunksize\u003e`_\nof the user documentation for some benchmarks.\n\nNot a RDBMS replacement\n-----------------------\n\nPyTables is not designed to work as a relational database replacement,\nbut rather as a teammate. If you want to work with large datasets of\nmultidimensional data (for example, for multidimensional analysis), or\njust provide a categorized structure for some portions of your\ncluttered RDBS, then give PyTables a try. It works well for storing\ndata from data acquisition systems, simulation software, network\ndata monitoring systems (for example, traffic measurements of IP\npackets on routers), or as a centralized repository for system logs,\nto name only a few possible use cases.\n\nTables\n------\n\nA table is defined as a collection of records whose values are stored\nin fixed-length fields. All records have the same structure, and all\nvalues in each field have the same data type. The terms \"fixed-length\"\nand strict \"data types\" seem to be a strange requirement for an\ninterpreted language like Python, but they serve a useful function if\nthe goal is to save very large quantities of data (such as\ngenerated by many scientific applications, for example) in an\nefficient manner that reduces demand on CPU time and I/O.\n\nArrays\n------\n\nThere are other useful objects like arrays, enlargeable arrays, or\nvariable-length arrays that can cope with different use cases on your\nproject.\n\nEasy to use\n-----------\n\nOne of the principal objectives of PyTables is to be user-friendly.\nIn addition, many different iterators have been implemented to\nmake interactive work as productive as possible.\n\nPlatforms\n---------\n\nWe use Linux on top of Intel32 and Intel64 boxes as the main\ndevelopment platforms, but PyTables should be easy to compile/install\non other UNIX (including macOS) or Windows machines.\n\nCompiling\n---------\n\nTo compile PyTables, you will need a recent version of the HDF5\n(C flavor) library, the Zlib compression library, and the NumPy and\nNumexpr packages. Besides, PyTables comes with support for the Blosc, LZO,\nand bzip2 compressor libraries. Blosc is mandatory, but PyTables comes\nwith Blosc sources so, although it is recommended to have Blosc\ninstalled in your system, you don't absolutely need to install it\nseparately. LZO and bzip2 compression libraries are, however,\noptional.\n\nMake sure you have HDF5 version 1.10.5 or above. On Debian-based Linux\ndistributions, you can install it with::\n\n   $ sudo apt install libhdf5-serial-dev\n\nInstallation\n------------\n\n1. Install with `pip \u003chttps://pip.pypa.io/en/stable/\u003e`_:\n\n       $ python3 -m pip install tables\n\n2. To run the test suite::\n\n       $ python3 -m tables.tests.test_all\n\n   If there is some test that does not pass, please send us the\n   complete output using the\n   `GitHub Issue Tracker \u003chttps://github.com/PyTables/PyTables/issues/new\u003e`_.\n\n\n**Enjoy data!** -- The PyTables Team\n\n.. 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