Package: python-tables-dbg
Source: pytables
Version: 3.4.2-4
Architecture: amd64
Maintainer: Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
Original-Maintainer: Debian Science Maintainers <debian-science-maintainers@lists.alioth.debian.org>
Installed-Size: 8908
Depends: python-numpy (>= 1:1.13.1), python-numpy-abi9, python-dbg (<< 2.8), python-dbg (>= 2.7~), python-dbg:any (<< 2.8), python-dbg:any (>= 2.7~), libblosc1 (>= 1.9.0), libbz2-1.0, libc6 (>= 2.14), libhdf5-100, liblzo2-2, python-tables (= 3.4.2-4), python-tables-lib (= 3.4.2-4), python-numpy-dbg, python-numexpr-dbg
Suggests: python-tables-doc, python-netcdf4
Section: debug
Priority: optional
Homepage: http://www.pytables.org
Description: 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.
Build-Ids: 0b2614252d75dde7066dc2f0fe2bb952065605b2 1a0e59a4224de6daacd9e556cbdcc16282525087 3f3afa4978e0a5c22c00b3f0f93bc6dbe5c959bc 5fce3a8cd39af6491ba8d20a6e61b3b70d23e266 bc10eb48f928f66caed11c0e886c408d88c3eb1e c2b96b09d79e88249b2e29882b52cf1f098d2c9b cdc34f1ad7b048e0dc06bda2b4e44ab22cae64d7 dab4d0ab1971448e2a5642621d62243a0e7e55eb
