pytables 3.5.2-4 source package in Ubuntu
Changelog
pytables (3.5.2-4) unstable; urgency=medium
* Update standards version to 4.4.1, no changes needed.
* Remove obsolete fields Name from debian/upstream/metadata.
* Fix day-of-week for changelog entries 2.1.2-3, 0.7.2-2.
* debian/patches:
- new 0007-Compat-with-Python-3.8.patch
* debian/control:
- explicitly set Rules-Requires-Root: no
* Replace deprecated ADTTMP with AUTOPKGTEST_TMP in debian/tests
scripts.
-- Antonio Valentino <email address hidden> Thu, 21 Nov 2019 20:06:22 +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 | Published | Component | Section |
|---|
Downloads
| File | Size | SHA-256 Checksum |
|---|---|---|
| pytables_3.5.2-4.dsc | 3.2 KiB | 29158d7c6504ee6bba2d9614762a73f46dc34416639a3d1ac4c799f6302ad628 |
| pytables_3.5.2.orig.tar.gz | 4.2 MiB | e4fc6f1194f02a8b10ff923e77364fb70710592f620d7de35f4d4e064dc70e91 |
| pytables_3.5.2-4.debian.tar.xz | 19.6 KiB | db1079c96081df38c7ea6194d1c652bc52bfecd9cb2b7707aac0f9e9b3028f8d |
Available diffs
No changes file available.
Binary packages built by this source
- python-tables: No summary available for python-tables in ubuntu focal.
No description available for python-tables in ubuntu focal.
- 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: No summary available for python-tables-dbg in ubuntu focal.
No description available for python-tables-dbg in ubuntu focal.
- python-tables-doc: hierarchical database for Python based on HDF5 - documentation
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 the manual in PDF and HTML formats.
- python-tables-lib: No summary available for python-tables-lib in ubuntu focal.
No description available for python-tables-lib in ubuntu focal.
- 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.
