Ubuntu
pytables package

pytables 3.6.1-2build1 source package in Ubuntu

Changelog

pytables (3.6.1-2build1) focal; urgency=medium

  * No-change rebuild to drop python3.7.

 -- Matthias Klose <email address hidden>  Tue, 18 Feb 2020 10:45:29 +0100

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

See full publishing history Publishing

Series Pocket Published Component Section
Focal release universe python

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File Size SHA-256 Checksum
pytables_3.6.1.orig.tar.gz 4.2 MiB 4cea86bab5bcb5423a07c7951b8c65e24b674e0dcec0e448d434829eff5f18d0
pytables_3.6.1-2build1.debian.tar.xz 19.4 KiB 2b304aaade93cc9a4c01c659fa34369166036de33f57cbd2b3f8006c3b8bb36c
pytables_3.6.1-2build1.dsc 2.8 KiB e8561e4bd723104c44fbafff6989577116fbdd0f5fc6509887efe28009e34589

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Binary packages built by this source

python-tables-data: No summary available for python-tables-data in ubuntu groovy.

No description available for python-tables-data in ubuntu groovy.

python-tables-doc: No summary available for python-tables-doc in ubuntu groovy.

No description available for python-tables-doc in ubuntu groovy.

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.
 .
 This is the Python 3 version of the package.

python3-tables-dbg: hierarchical database for Python 3 based on HDF5 (debug 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 debug interpreter.

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.