HDF5 is a file format that supports fast and space efficient analysis of large datasets. PyTables is a project that wraps and expands on the capabilities of HDF5 to make it easy to integrate with the larger Python data ecosystem. Francesc Alted explains how the project got started, how it works, and how it can be used for creating sharable and archivable data sets.
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- Your host as usual is Tobias Macey and today I’m interviewing Francesc Alted about PyTables
- How did you get introduced to Python?
- To start with, what is HDF5 and what was the problem that motivated you to wrap Python around it to create PyTables?
- Which are the most relevant contributors for PyTables? How you interacted?
- How is the project architected and what are some of the design decisions that you are most proud of?
- What are some of the typical use cases for PyTables and how does it tie into the broader Python data ecosystem?
- How common is it to use an HDF5 file as a data interchange format to be shared between researchers or between languages?
- Given the ability to create custom node types, does that inhibit the ability to interact with the stored data using other libraries?
- What are some of the capabilities of HDF5 and PyTables that can’t be reasonably replicated in other data storage systems?
- One of the more intriguing capabilities that I noticed while reading the documentation is the ability to perform undo and redo operations on the data. How might that be leveraged in a real-world use case?
- What are some of the most interesting or unexpected uses of PyTables that you are aware of?
Keep In Touch
- PyTables – Optimization
- Presentations and Videos about PyTables
- Part of the story behind PyTables