📢 Python-Blosc2 4.14 is out!
A common interface to stream, query & slice remote Parquet, #PyTables, #HDF5, #Zarr & #Blosc2 data over HTTP/S3:
⚡️ Zero full downloads
⚡️ Cloud index pushdown
⚡️ DuckDB/Polars streaming!
Read the tour: blosc.org/posts/remote...
Compress Better, Stream Smarter 🚀
A common interface to stream, query & slice remote Parquet, #PyTables, #HDF5, #Zarr & #Blosc2 data over HTTP/S3:
⚡️ Zero full downloads
⚡️ Cloud index pushdown
⚡️ DuckDB/Polars streaming!
Read the tour: blosc.org/posts/remote...
Compress Better, Stream Smarter 🚀
September 30, 2026 at 11:46 AM
📢 Python-Blosc2 4.14 is out!
A common interface to stream, query & slice remote Parquet, #PyTables, #HDF5, #Zarr & #Blosc2 data over HTTP/S3:
⚡️ Zero full downloads
⚡️ Cloud index pushdown
⚡️ DuckDB/Polars streaming!
Read the tour: blosc.org/posts/remote...
Compress Better, Stream Smarter 🚀
A common interface to stream, query & slice remote Parquet, #PyTables, #HDF5, #Zarr & #Blosc2 data over HTTP/S3:
⚡️ Zero full downloads
⚡️ Cloud index pushdown
⚡️ DuckDB/Polars streaming!
Read the tour: blosc.org/posts/remote...
Compress Better, Stream Smarter 🚀
We are making a lot of progress on accessing remote PyTables tables via Blosc2 and fsspec.
Stay tuned!
Stay tuned!
September 22, 2026 at 11:40 AM
We are making a lot of progress on accessing remote PyTables tables via Blosc2 and fsspec.
Stay tuned!
Stay tuned!
After some tinkering, I have confirmed that NumExpr is, in fact, VERY GOOD at optimizing small expressions over n>100k arrays. I did manage to outrun it with a hand-tuned implementation in Rust, but I needed rayon AND chunked hardware-accelerated fns to do it.
Starred
github.com/pydata/numexpr
Starred
github.com/pydata/numexpr
GitHub - pydata/numexpr: Fast numerical array expression evaluator for Python, NumPy, Pandas, PyTables and more
Fast numerical array expression evaluator for Python, NumPy, Pandas, PyTables and more - pydata/numexpr
github.com
February 25, 2026 at 9:47 PM
After some tinkering, I have confirmed that NumExpr is, in fact, VERY GOOD at optimizing small expressions over n>100k arrays. I did manage to outrun it with a hand-tuned implementation in Rust, but I needed rayon AND chunked hardware-accelerated fns to do it.
Starred
github.com/pydata/numexpr
Starred
github.com/pydata/numexpr
Learn more at our blog: www.blosc.org/posts/pytabl...
Optimized Hyper-slicing in PyTables with Blosc2 NDim
The recent and long-awaited PyTables 3.9 release carries many goodies, including a particular one which makes us at the PyTables and Blosc teams very excited: optimized HDF5 hyper-slicing that leverag
www.blosc.org
May 20, 2025 at 8:30 AM
Learn more at our blog: www.blosc.org/posts/pytabl...
💡 Did you know you can supercharge your #HDF5 datasets with #Blosc2? 🚀
Leverage hdf5plugin (hdf5plugin.readthedocs.io) to integrate Blosc2 as a filter within HDF5. Create and read data using popular Python wrappers like h5py or PyTables, while achieving excellent performance! 💨
Compress Better!
Leverage hdf5plugin (hdf5plugin.readthedocs.io) to integrate Blosc2 as a filter within HDF5. Create and read data using popular Python wrappers like h5py or PyTables, while achieving excellent performance! 💨
Compress Better!
May 20, 2025 at 8:25 AM
💡 Did you know you can supercharge your #HDF5 datasets with #Blosc2? 🚀
Leverage hdf5plugin (hdf5plugin.readthedocs.io) to integrate Blosc2 as a filter within HDF5. Create and read data using popular Python wrappers like h5py or PyTables, while achieving excellent performance! 💨
Compress Better!
Leverage hdf5plugin (hdf5plugin.readthedocs.io) to integrate Blosc2 as a filter within HDF5. Create and read data using popular Python wrappers like h5py or PyTables, while achieving excellent performance! 💨
Compress Better!
Just played overseas Python ambassador. Watch for the revelatory moment when you show them pytables.
November 19, 2024 at 12:23 PM
Just played overseas Python ambassador. Watch for the revelatory moment when you show them pytables.
PyTables as a Computing Kernel and Compressed Container for Arbitrarily Large Datasets <a href="http://pytables.org/moin/ComputingKernel" class="hover:underline text-blue-600 dark:text-sky-400 no-card-link" target="_blank" rel="noopener" data-link="bsky">http://pytables.org/moin/ComputingKernel
Page not found · GitHub Pages
pytables.org
November 19, 2024 at 10:39 AM
PyTables as a Computing Kernel and Compressed Container for Arbitrarily Large Datasets <a href="http://pytables.org/moin/ComputingKernel" class="hover:underline text-blue-600 dark:text-sky-400 no-card-link" target="_blank" rel="noopener" data-link="bsky">http://pytables.org/moin/ComputingKernel
Numexpr: Fast numerical array expression evaluator for Python, NumPy, Pandas
Discussion | hackernews | Author: tosh
Discussion | hackernews | Author: tosh
Numexpr: Fast numerical array expression evaluator for Python, NumPy, Pandas
Fast numerical array expression evaluator for Python, NumPy, Pandas, PyTables and more - pydata/numexpr
github.com
May 21, 2026 at 9:36 AM
Numexpr: Fast numerical array expression evaluator for Python, NumPy, Pandas
Discussion | hackernews | Author: tosh
Discussion | hackernews | Author: tosh
NumFocus houses essential tools in the data science ecosystem like NumPy, Matplotlib, Jupyter, Pandas, etc.
Check out other NumFocus sponsored projects at numfocus.org/sponsored-pr....
Check out other NumFocus sponsored projects at numfocus.org/sponsored-pr....
Sponsored Projects | pandas, NumPy, Matplotlib, Jupyter, + more - NumFOCUS
Explore NumFOCUS Sponsored Projects, including: pandas, NumPy, Matplotlib, Jupyter, rOpenSci, Julia, Bokeh, PyMC, Stan, nteract, SymPy, FEniCS, PyTables...
numfocus.org
March 19, 2025 at 3:11 PM
NumFocus houses essential tools in the data science ecosystem like NumPy, Matplotlib, Jupyter, Pandas, etc.
Check out other NumFocus sponsored projects at numfocus.org/sponsored-pr....
Check out other NumFocus sponsored projects at numfocus.org/sponsored-pr....
Vega (Vega, Vega-Lite, Altair, etc) is now a @numfocus.bsky.social sponsored project. We are excited to continue advancing the visualization and data science ecosystem together on this sustainable foundation.
Plus, you can now sponsor Vega's development: app.hubspot.com/payments/GyP...
Plus, you can now sponsor Vega's development: app.hubspot.com/payments/GyP...
Sponsored Projects | pandas, NumPy, Matplotlib, Jupyter, + more - NumFOCUS
Explore NumFOCUS Sponsored Projects, including: pandas, NumPy, Matplotlib, Jupyter, rOpenSci, Julia, Bokeh, PyMC, Stan, nteract, SymPy, FEniCS, PyTables...
numfocus.org
March 19, 2025 at 3:11 PM
Vega (Vega, Vega-Lite, Altair, etc) is now a @numfocus.bsky.social sponsored project. We are excited to continue advancing the visualization and data science ecosystem together on this sustainable foundation.
Plus, you can now sponsor Vega's development: app.hubspot.com/payments/GyP...
Plus, you can now sponsor Vega's development: app.hubspot.com/payments/GyP...
**pandas Repository Update (December 11, 2024)**
Key Updates:
• Improved CI experience: Smoother build process on GitHub's test-arm
• Enhanced testing framework: New tests for external error handling
• Code quality checks: Fixes to improve expressions and pytables
Key Updates:
• Improved CI experience: Smoother build process on GitHub's test-arm
• Enhanced testing framework: New tests for external error handling
• Code quality checks: Fixes to improve expressions and pytables
December 12, 2024 at 10:51 PM
**pandas Repository Update (December 11, 2024)**
Key Updates:
• Improved CI experience: Smoother build process on GitHub's test-arm
• Enhanced testing framework: New tests for external error handling
• Code quality checks: Fixes to improve expressions and pytables
Key Updates:
• Improved CI experience: Smoother build process on GitHub's test-arm
• Enhanced testing framework: New tests for external error handling
• Code quality checks: Fixes to improve expressions and pytables
**pandas - December 11, 2024**
• Improved testing with `tm.external_error_raised`.
• Refactored code using `ruff checks` for better readability.
• Fixed formatting and best practice issues in expression and pytables modules.
• Improved testing with `tm.external_error_raised`.
• Refactored code using `ruff checks` for better readability.
• Fixed formatting and best practice issues in expression and pytables modules.
December 11, 2024 at 9:55 PM
**pandas - December 11, 2024**
• Improved testing with `tm.external_error_raised`.
• Refactored code using `ruff checks` for better readability.
• Fixed formatting and best practice issues in expression and pytables modules.
• Improved testing with `tm.external_error_raised`.
• Refactored code using `ruff checks` for better readability.
• Fixed formatting and best practice issues in expression and pytables modules.
🎉 Exciting news! Python-Blosc2 is making huge strides towards array-api compliance. 🚀
From its humble beginnings in 2010 as a compressor for PyTables, it has evolved into a powerful n-dimensional container. 💪
There's more to come, but what a fantastic milestone! ✨
From its humble beginnings in 2010 as a compressor for PyTables, it has evolved into a powerful n-dimensional container. 💪
There's more to come, but what a fantastic milestone! ✨
🗣️ Announcing Python-Blosc2 3.8.0 🚀
A step closer to compliance with the array-api standard: data-apis.org/array-api!
This is an effort across all array-based libraries so that your code works (e.g. for both blosc2 and NumPy) by simply changing the import statement below!
#arrayapi #Blosc2
A step closer to compliance with the array-api standard: data-apis.org/array-api!
This is an effort across all array-based libraries so that your code works (e.g. for both blosc2 and NumPy) by simply changing the import statement below!
#arrayapi #Blosc2
September 12, 2025 at 11:40 AM
🎉 Exciting news! Python-Blosc2 is making huge strides towards array-api compliance. 🚀
From its humble beginnings in 2010 as a compressor for PyTables, it has evolved into a powerful n-dimensional container. 💪
There's more to come, but what a fantastic milestone! ✨
From its humble beginnings in 2010 as a compressor for PyTables, it has evolved into a powerful n-dimensional container. 💪
There's more to come, but what a fantastic milestone! ✨
So if you’re someone who cares about Python and SQL workflows and handoffs, want to make them more streamlined, I’d love to chat or hear your feedback on PyTables!
June 7, 2023 at 8:34 PM
So if you’re someone who cares about Python and SQL workflows and handoffs, want to make them more streamlined, I’d love to chat or hear your feedback on PyTables!
Last, DuckDB has a Python client with a lot of great Python/SQL interop features. However, they’re features of the client. There’s a big gap to cross if you want to hand Python work to someone who wants to fire up their favorite SQL IDE. PyTables works with any DuckDB client.
June 7, 2023 at 8:33 PM
Last, DuckDB has a Python client with a lot of great Python/SQL interop features. However, they’re features of the client. There’s a big gap to cross if you want to hand Python work to someone who wants to fire up their favorite SQL IDE. PyTables works with any DuckDB client.
Thing I’d love for you to check out: I built a DuckDB extension that lets you run SQL on the result of a Python function:
https://github.com/MarkRoddy/duckdb-pytables
If you’ve ever wanted to run:
> SELECT * FROM hit_the_rest_api()
This will be up your alley. Some more details 👇
https://github.com/MarkRoddy/duckdb-pytables
If you’ve ever wanted to run:
> SELECT * FROM hit_the_rest_api()
This will be up your alley. Some more details 👇
June 7, 2023 at 8:33 PM
Thing I’d love for you to check out: I built a DuckDB extension that lets you run SQL on the result of a Python function:
https://github.com/MarkRoddy/duckdb-pytables
If you’ve ever wanted to run:
> SELECT * FROM hit_the_rest_api()
This will be up your alley. Some more details 👇
https://github.com/MarkRoddy/duckdb-pytables
If you’ve ever wanted to run:
> SELECT * FROM hit_the_rest_api()
This will be up your alley. Some more details 👇
March 6, 2026 at 2:27 PM
Found some wizardry:
webapps.stackexchange.com/a/44927/116555
TL;DR: trick r-devel into sending both digests and individual messages to your inbox.
Filter out the individual messages by default, but easily retrieve them to reply in-thread if needed 🤯
webapps.stackexchange.com/a/44927/116555
TL;DR: trick r-devel into sending both digests and individual messages to your inbox.
Filter out the individual messages by default, but easily retrieve them to reply in-thread if needed 🤯
Replying to a Mailman list using Gmail from digest
I'd like to reply to a thread from a mailman list (pytables-users) from Gmail, but I'm not sure exactly how. Do I just hit reply the manually edit the subject line and copy/paste anything relevant ...
webapps.stackexchange.com
February 6, 2025 at 5:15 PM
Found some wizardry:
webapps.stackexchange.com/a/44927/116555
TL;DR: trick r-devel into sending both digests and individual messages to your inbox.
Filter out the individual messages by default, but easily retrieve them to reply in-thread if needed 🤯
webapps.stackexchange.com/a/44927/116555
TL;DR: trick r-devel into sending both digests and individual messages to your inbox.
Filter out the individual messages by default, but easily retrieve them to reply in-thread if needed 🤯
A new AI review! PyTables/PyTables ⭐3.8/5.0
PyTables is a mature, production-grade library for storing and querying hierarchical, large-scale datasets in HDF5 from Python.
https://gitrated.com/PyTables/PyTables
PyTables is a mature, production-grade library for storing and querying hierarchical, large-scale datasets in HDF5 from Python.
https://gitrated.com/PyTables/PyTables
June 3, 2026 at 1:25 AM
A new AI review! PyTables/PyTables ⭐3.8/5.0
PyTables is a mature, production-grade library for storing and querying hierarchical, large-scale datasets in HDF5 from Python.
https://gitrated.com/PyTables/PyTables
PyTables is a mature, production-grade library for storing and querying hierarchical, large-scale datasets in HDF5 from Python.
https://gitrated.com/PyTables/PyTables