Package: python-tables-dbg
Source: pytables
Version: 3.4.2-4
Architecture: i386
Maintainer: Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
Original-Maintainer: Debian Science Maintainers <debian-science-maintainers@lists.alioth.debian.org>
Installed-Size: 7093
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 (>= 2.4), 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: 3f2b7aca75db168b8edc60a7e0863f544eaec39a 499c9dd1371886a4d802d5032214e0ca1c64f6ab 59a58e511c8f640ec3103a459b9053eaa72ee621 72972c8a71ba54165e0aa952c7203579fa794f57 8fdb537664ee888691fab2c04d34fa9f138f8fa0 a546637a0dc57c4cbc658678e99b647c07686ad5 ad51657cc7b20ee35307a28aad8b446b618b0d25 c9ffe97cb51d51eab106a39997700aa6268412d3
