Built on top of HDF5
PyTables utilizes the power of HDF5, which is capable of storing exabytes of data!
PyTables is a package for managing hierarchical datasets and designed to efficiently and easily cope with extremely large amounts of data.
PyTables utilizes the power of HDF5, which is capable of storing exabytes of data!
Use the tools you already know. PyTables relies heavily on Python and Numpy to present you with a pythonic interface to it high-performance libraries.
The wildly popular Pandas library uses PyTables as its HDF store.
We implement SQL-esque indexing and querying functionality on top HDF5 to help you squeeze the most out of your data.
PyTables contains custom Python C extensions that improve all aspects of dealing. Many operations are automatically parallelized at a very low level.
PyTables packages are distributed with many package managers, such as conda, pypi, apt, and more!
$ pip install tables
Creating a PyTables file from scratch.
import tables
h5 = tables.open_file('detector.h5', mode = 'w', title = 'Test file')
Crate a new group
group = h5file.create_group("/", 'detector', 'Detector information')
Do other amazing things!
If your documentation is very long you can host the full docs page (including FAQ etc) on GitHub and provide a Call to Action button below to direct users there.
These are the videos of a series dedicated to introduce the main features of PyTables in a visual and easy to grasp manner. More videos will be made available with the time:
Here are the slides of some presentations about PyTables that you may find useful:
I hope you find this Bootstrap template useful.
Feel free to get in touch if you have any questions or suggestions.