Ubuntu
pytables package

pytables 3.7.0-8 source package in Ubuntu

Changelog

pytables (3.7.0-8) unstable; urgency=medium

  * debian/patches:
    - Update d/patches/0007-Compatibility-with-numexpr-v2.8.5.patch
      to make it comatible again with numexpr < v2.8.5.

 -- Antonio Valentino <email address hidden>  Fri, 18 Aug 2023 13:16:16 +0000

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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_3.7.0-8.dsc 3.9 KiB 290b266c3272a0479b2837c306b37de6df7fd1b00efb5e2ae416f6a1736ffa8f
pytables_3.7.0.orig.tar.gz 3.4 MiB 41065fc11b958dde09bd5b9c069d88e40ca07ad10687dd597835fcc8199e81ea
pytables_3.7.0-8.debian.tar.xz 19.2 KiB a471f5b7096e471910861bb2e0a2e61315bb15388fda79165e7854d5db1ace78

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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