pytables 3.7.0-8ubuntu1 source package in Ubuntu
Changelog
pytables (3.7.0-8ubuntu1) mantic; urgency=medium * Filter out 'nocheck' to avoid FTBFS on riscv64, see #1050176 -- Graham Inggs <email address hidden> Mon, 21 Aug 2023 14:33:40 +0000
Upload details
- Uploaded by:
- Graham Inggs
- Uploaded to:
- Mantic
- Original maintainer:
- Ubuntu Developers
- Architectures:
- any all
- Section:
- python
- Urgency:
- Medium Urgency
See full publishing history Publishing
| Series | Published | Component | Section |
|---|
Downloads
| File | Size | SHA-256 Checksum |
|---|---|---|
| pytables_3.7.0.orig.tar.gz | 3.4 MiB | 41065fc11b958dde09bd5b9c069d88e40ca07ad10687dd597835fcc8199e81ea |
| pytables_3.7.0-8ubuntu1.debian.tar.xz | 19.3 KiB | cee8f18d714f211adf0743e25b07ac6ea0389bca1372de12af621fdabf520827 |
| pytables_3.7.0-8ubuntu1.dsc | 3.9 KiB | bde75fb8cd1f6d2091e1601aad45ffb8f343b8fbc411c1718c513cb97eab5fce |
Available diffs
Binary packages built by this source
- python-tables-data: Hierarchical database for Python3 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 data files used for unit testing.
- python-tables-doc: Hierarchical database for Python3 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 HTML formats.
- 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.
- 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.
- python3-tables-lib-dbgsym: debug symbols for python3-tables-lib
