{"id":18614017,"url":"https://github.com/enthought/pytables","last_synced_at":"2026-02-14T08:31:11.502Z","repository":{"id":20341249,"uuid":"23615864","full_name":"enthought/PyTables","owner":"enthought","description":"Default Repo description from terraform module","archived":false,"fork":false,"pushed_at":"2014-09-03T10:28:17.000Z","size":30698,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":21,"default_branch":"develop","last_synced_at":"2025-10-08T05:44:09.888Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/enthought.png","metadata":{"files":{"readme":"README.rst","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.txt","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2014-09-03T10:16:53.000Z","updated_at":"2021-06-01T16:01:08.000Z","dependencies_parsed_at":"2022-07-25T12:33:53.097Z","dependency_job_id":null,"html_url":"https://github.com/enthought/PyTables","commit_stats":null,"previous_names":[],"tags_count":28,"template":false,"template_full_name":null,"purl":"pkg:github/enthought/PyTables","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/enthought%2FPyTables","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/enthought%2FPyTables/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/enthought%2FPyTables/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/enthought%2FPyTables/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/enthought","download_url":"https://codeload.github.com/enthought/PyTables/tar.gz/refs/heads/develop","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/enthought%2FPyTables/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29440297,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-14T07:24:13.446Z","status":"ssl_error","status_checked_at":"2026-02-14T07:23:58.969Z","response_time":53,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":"2024-11-07T03:24:42.923Z","updated_at":"2026-02-14T08:31:11.487Z","avatar_url":"https://github.com/enthought.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"===========================================\n PyTables: hierarchical datasets in Python\n===========================================\n\n:URL: http://www.pytables.org/\n\n\nPyTables is a package for managing hierarchical datasets and 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 save and retrieve 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 a factor 3 to 5,\nand more if the data is compressible) than other solutions, like for\nexample, relational or object oriented databases.\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 (DAS), 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 uses.\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\" seems to be quite 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 is\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 missions on your\nproject. Also, quite a bit of effort has been invested to make\nbrowsing the hierarchical data structure a pleasant\nexperience. PyTables implements a few easy-to-use methods for\nbrowsing. See the documentation (located in the ``doc/`` directory)\nfor more details.\n\nEasy to use\n-----------\n\nOne of the principal objectives of PyTables is to be user-friendly.\nTo that end, special Python features like generators, slots and\nmetaclasses in new-brand classes have been used. In addition,\niterators has been implemented were context was appropriate so as to\nenable the interactive work to be as productive as possible. For these\nreasons, you will need to use Python 2.6 or higher to take advantage of\nPyTables.\n\nPlatforms\n---------\n\nWe are using Linux on top of Intel32 and Intel64 boxes as the main\ndevelopment platforms, but PyTables should be easy to compile/install\non other UNIX or Windows machines.  Nonetheless, caveat emptor: more\ntesting is needed to achieve complete portability, we'd appreciate\ninput on how it compiles and installs on your platform.\n\nCompiling\n---------\n\nTo compile PyTables you will need, at least, a recent version of HDF5\n(C flavor) library, the Zlib compression library and the NumPy and\nNumexpr packages. Besides, if you want to take advantage of the LZO\nand bzip2 compression libraries support you will also need recent\nversions of them. LZO and bzip2 compression libraries are, however,\noptional.\n\nWe've tested this PyTables version with HDF5 1.8.11/1.8.12, NumPy 1.7.1/1.8.0\nand Numexpr 2.2.2, and you *need* to use these versions, or higher, to\nmake use of PyTables.\n\nInstallation\n------------\n\nThe Python Distutils are used to build and install PyTables, so it is\nfairly simple to get things ready to go. Following are very simple\ninstructions on how to proceed. However, more detailed instructions,\nincluding a section on binary installation for Windows users, is\navailable in Chapter 2 of the User's Manual (``doc/usersguide.pdf`` or\nhttp://www.pytables.org/moin/HowToUse).\n\n1. First, make sure that you have HDF5, NumPy and Numexpr installed\n   (you will need at least HDF5 1.8.4, HDF5 \u003e= 1.8.7 is strongly recommended,\n   NumPy 1.4.1 and Numexpr 2.0).\n   If don't, get them from http://www.hdfgroup.org/HDF5/,\n   http://www.numpy.org and http://code.google.com/p/numexpr.\n   Compile/install them.\n\n   Optionally, consider to install the excellent LZO compression\n   library from http://www.oberhumer.com/opensource/.  You can also\n   install the high-performance bzip2 compression library, available\n   at http://www.bzip.org/.\n\n2. From the main PyTables distribution directory run this command,\n   (plus any extra flags needed as discussed above)::\n\n    $ python setup.py build_ext --inplace\n\n3. To run the test suite, set the PYTHONPATH environment variable to\n   include the ``.`` directory, enter the Python interpreter and issue\n   the commands::\n\n    \u003e\u003e\u003e import tables\n    \u003e\u003e\u003e tables.test()\n\n   If there is some test that does not pass, please send the\n   complete output for tests back to us.\n\n4. To install the entire PyTables Python package, run this command as\n   the root user (remember to add any extra flags needed)::\n\n    $ python setup.py install\n\n\nThat's it!  Good luck, and let us know of any bugs, suggestions,\ngripes, kudos, etc. you may have.\n\n----\n\n  **Enjoy data!**\n\n  -- The PyTables Team\n\n.. Local Variables:\n.. mode: text\n.. coding: utf-8\n.. fill-column: 70\n.. 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