Ubuntu
pytables package

pytables 3.9.2-1 source package in Ubuntu

Changelog

pytables (3.9.2-1) unstable; urgency=medium

  * New upstream release.
  * debian/patches:
    - Drop pathces applied upstream:
      0005-Fix-compatibility-with-numpy-1.22.patch,
      0006-Compatibility-with-sphinx-7.1.patch,
      0007-Compatibility-with-numexpr-v2.8.5.patch,
      0008-Python-3.12-compat.patch,
      and 0009-Numpy-v1.25-compat.patch.
    - Refresh remaining patches.
  * debian/control:
    - Build-depend on c-blosc2-dev.
    - Add dependency on python3-cpuinfo.
    - Versioned dependency on numexpt (>= 2.8.7).
  * Update d/copyright.
  * Update d/python3-tables.links.

 -- Antonio Valentino <email address hidden>  Sat, 30 Dec 2023 18:26:59 +0000

Upload details

Uploaded by:
Debian Science Team
Uploaded to:
Sid
Original maintainer:
Debian Science Team
Architectures:
any all
Section:
python
Urgency:
Medium Urgency

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File Size SHA-256 Checksum
pytables_3.9.2-1.dsc 3.9 KiB 5c6906641e908c396928ee021cecf7fa8135c0cf82ecf1f0b4f1cca0eeef4b9e
pytables_3.9.2.orig.tar.gz 3.6 MiB 4d7f2fc77fc63c95aaed2f8b8bf6cfbbdc7d52607b2112a80bf330c53b6c9838
pytables_3.9.2-1.debian.tar.xz 18.6 KiB cb69e1410d411ccbc74ae8bc38b4b89d47a5f6ef6454d5707d605ee65847cc27

Available diffs

No changes file available.

Binary packages built by this source

python-tables-data: Hierarchical database for Python3 based on HDF5 (test data)

 PyTables is a package for managing hierarchical datasets and designed
 to efficiently cope with extremely large amounts of data.
 .
 It is built on top of the HDF5 library and the NumPy package. It
 features an object-oriented interface that, combined with C extensions
 for the performance-critical parts of the code (generated using
 Cython), makes it a fast, yet extremely easy to use tool for
 interactively save and retrieve very large amounts of data. One
 important feature of PyTables is that it optimizes memory and disk
 resources so that they take much less space (between a factor 3 to 5,
 and more if the data is compressible) than other solutions, like for
 example, relational or object oriented databases.
 .
  - Compound types (records) can be used entirely from Python (i.e. it
    is not necessary to use C for taking advantage of them).
  - The tables are both enlargeable and compressible.
  - I/O is buffered, so you can get very fast I/O, specially with
    large tables.
  - Very easy to select data through the use of iterators over the
    rows in tables. Extended slicing is supported as well.
  - It supports the complete set of NumPy objects.
 .
 This package includes data files used for unit testing.

python-tables-doc: Hierarchical database for Python3 based on HDF5 (documentation)

 PyTables is a package for managing hierarchical datasets and designed
 to efficiently cope with extremely large amounts of data.
 .
 It is built on top of the HDF5 library and the NumPy package. It
 features an object-oriented interface that, combined with C extensions
 for the performance-critical parts of the code (generated using
 Cython), makes it a fast, yet extremely easy to use tool for
 interactively save and retrieve very large amounts of data. One
 important feature of PyTables is that it optimizes memory and disk
 resources so that they take much less space (between a factor 3 to 5,
 and more if the data is compressible) than other solutions, like for
 example, relational or object oriented databases.
 .
  - Compound types (records) can be used entirely from Python (i.e. it
    is not necessary to use C for taking advantage of them).
  - The tables are both enlargeable and compressible.
  - I/O is buffered, so you can get very fast I/O, specially with
    large tables.
  - Very easy to select data through the use of iterators over the
    rows in tables. Extended slicing is supported as well.
  - It supports the complete set of NumPy objects.
 .
 This package includes the manual in HTML formats.

python3-tables: Hierarchical database for Python3 based on HDF5

 PyTables is a package for managing hierarchical datasets and designed
 to efficiently cope with extremely large amounts of data.
 .
 It is built on top of the HDF5 library and the NumPy package. It
 features an object-oriented interface that, combined with C extensions
 for the performance-critical parts of the code (generated using
 Cython), makes it a fast, yet extremely easy to use tool for
 interactively save and retrieve very large amounts of data. One
 important feature of PyTables is that it optimizes memory and disk
 resources so that they take much less space (between a factor 3 to 5,
 and more if the data is compressible) than other solutions, like for
 example, relational or object oriented databases.
 .
  - Compound types (records) can be used entirely from Python (i.e. it
    is not necessary to use C for taking advantage of them).
  - The tables are both enlargeable and compressible.
  - I/O is buffered, so you can get very fast I/O, specially with
    large tables.
  - Very easy to select data through the use of iterators over the
    rows in tables. Extended slicing is supported as well.
  - It supports the complete set of NumPy objects.

python3-tables-lib: Hierarchical database for Python3 based on HDF5 (extension)

 PyTables is a package for managing hierarchical datasets and designed
 to efficiently cope with extremely large amounts of data.
 .
 It is built on top of the HDF5 library and the NumPy package. It
 features an object-oriented interface that, combined with C extensions
 for the performance-critical parts of the code (generated using
 Cython), makes it a fast, yet extremely easy to use tool for
 interactively save and retrieve very large amounts of data. One
 important feature of PyTables is that it optimizes memory and disk
 resources so that they take much less space (between a factor 3 to 5,
 and more if the data is compressible) than other solutions, like for
 example, relational or object oriented databases.
 .
  - Compound types (records) can be used entirely from Python (i.e. it
    is not necessary to use C for taking advantage of them).
  - The tables are both enlargeable and compressible.
  - I/O is buffered, so you can get very fast I/O, specially with
    large tables.
  - Very easy to select data through the use of iterators over the
    rows in tables. Extended slicing is supported as well.
  - It supports the complete set of NumPy objects.
 .
 This package contains the extension built for the Python 3 interpreter.

python3-tables-lib-dbgsym: debug symbols for python3-tables-lib