Ubuntu
pytables package

pytables 3.4.2-4 source package in Ubuntu

Changelog

pytables (3.4.2-4) unstable; urgency=medium

  * debian/control
    - python-tables-doc (>= 3.4.2-3) tries to overwrite files in older
      python-tables.
      Set the proper package dependency relations (Closes: #891023).

 -- Antonio Valentino <email address hidden>  Sun, 25 Feb 2018 12:07:35 +0000

Upload details

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

See full publishing history Publishing

Series Pocket Published Component Section
Bionic release universe python

Downloads

File Size SHA-256 Checksum
pytables_3.4.2-4.dsc 3.2 KiB 1063f356fa93bf6b593390367b08afff0a30bafd48ce2f1d949efe3c912470cd
pytables_3.4.2.orig.tar.gz 4.0 MiB 629a0227bb2b315c5d97629073696609eaee41d0fc89e03f997412613cd46d3a
pytables_3.4.2-4.debian.tar.xz 18.1 KiB 4d5113196f807efc4a5608cd8808e7ee8f41ee7f8553a08a85c9358780e18f80

Available diffs

No changes file available.

Binary packages built by this source

python-tables: No summary available for python-tables in ubuntu cosmic.

No description available for python-tables in ubuntu cosmic.

python-tables-data: hierarchical database for Python 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 daya fils used for unit testing.

python-tables-dbg: hierarchical database for Python 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 2 debug interpreter.

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

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

python-tables-lib: No summary available for python-tables-lib in ubuntu cosmic.

No description available for python-tables-lib in ubuntu cosmic.

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: No summary available for python3-tables-dbg in ubuntu cosmic.

No description available for python3-tables-dbg in ubuntu cosmic.

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.