ptrepack
hierarchical database for Python3 based on HDF5
Install
- All systems
-
curl cmd.cat/ptrepack.sh
- Debian
-
apt-get install python3-tables - Ubuntu
-
apt-get install python3-tables - Arch Linux
-
pacman -S python-pytables - Kali Linux
-
apt-get install python3-tables - Fedora
-
dnf install python3-tables - Windows (WSL2)
-
sudo apt-get updatesudo apt-get install python3-tables - Raspbian
-
apt-get install python-tables - Dockerfile
- dockerfile.run/ptrepack
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.
python-tables
hierarchical database for Python 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 2 version of the package.