Package: python3-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: 10518
Depends: python3-numpy (>= 1:1.13.1), python3-numpy-abi9, python3-dbg (<< 3.7), python3-dbg (>= 3.6~), python3-dbg:any (>= 3.2~), libblosc1 (>= 1.9.0), libbz2-1.0, libc6 (>= 2.14), libhdf5-100, liblzo2-2, python3-tables (= 3.4.2-4), python3-tables-lib (= 3.4.2-4), python3-numpy-dbg, python3-numexpr-dbg
Suggests: python-tables-doc, python3-netcdf4
Section: debug
Priority: optional
Homepage: http://www.pytables.org
Description: 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.
Build-Ids: 237737d910ddf40f4bae69717cd7a0d027d76e20 51d75c7f07ba3aaa3da57553968199b45c4af091 5d4257cd3375ec226d87045e12e74f50340f8ddd 71a81317eb3769c53b3ea88f2a8b247d35663882 9bdeba7eb5964e16078db64cde6cc30d830af077 9daa09f1345aba1dad6a8f2a9342219f4c3b3ff3 dca5543b0926e61c916233ba0293c693bcdde18f eb0ec6f66b5b4c6b0485b63a7fa18e6bbf650e93
