vaex

Out of core dataframes for Python, visualize and explore big tabular data at a billion rows per second. ML ready. @maartenbreddels @JovanVaex

[vaex @vaex_io]

Vaex version 4.0 is out! 🚀 with core @ApacheArrow integration

Blog: A Hybrid Apache Arrow/Numpy DataFrame with Vaex version 4.0 https://t.co/OHHn2TGHcp

#Python #vaex #datascience

[Maarten A. Breddels @maartenbreddels]

Introducing Reacton 🥳 - a pure Python port of React for ipywidgets! With the proven success of ReactJS, we've implemented a similar API in Python to help create larger applications using ipywidgets. Check out my article at:
https://t.co/qw80Hulvi8
#React #Python #Jupyter

[vaex @vaex_io]

If you are in a hurry, read this thread, which summarizes @JovanVaex 's latest Vaex article.

If you want to read the full article:
https://t.co/1UndLJ6a2F

[Maarten A. Breddels @maartenbreddels]

Learn 8 powerful Vaex DataFrame features you might not have known about.

Read @JovanVaex 's article
https://t.co/WbVPhfEQ6I

Or get the short version in this thread
👇🏻

[Mehmet Ergene @Cyb3rMonk]

@NickCerny @duckdb @vaex_io I know duckdb but haven't seen Vaex before. Looks pretty interesting!

[Jovan @JovanVaex]

Did you know that @vaex_io 's documentation contains a section with guides?

Guides can help one better understand certain functionalities of Vaex, especially those that are not common in other DataFrame libraries.

https://t.co/6Q8g1OhZ7H

1/4

[vaex @vaex_io]

Vaex: "One of the 13 best libraries for machine learning and data analysis."

[PyQuant News 🐍 @pyquantnews]

Vaex

Vaex is a high-performance Python library for lazy Out-of-Core DataFrames (similar to Pandas), to visualize and explore big tabular datasets.

https://t.co/Vm9sIvG8pm

[PyQuant News 🐍 @pyquantnews]

College completely failed to teach me data analysis.

So I spent over 10,000 hours learning Python.

Then, I picked the 13 best libraries for machine learning and data analysis.

But unlike college, these won't cost you $120,000.

Here they are for free:

[vaex @vaex_io]

Wondering how to make simple and fast @streamlit apps using large datasets? In our latest article we use combine @vaex_io and @streamlit to do just that. We cover:
- caching for efficient computations
- optimized delayed evaluations
- progress bars

https://t.co/xc7ZVcXu8c

[vaex @vaex_io]

Awesome writeup :)

[Orchest @orchestofficial]

📣 New blog post: "Out-of-core processing with Vaex", the second part of our series "The great Python dataframe showdown"

https://t.co/6jHxR4CONT

Ever heard of @vaex_io but haven't tried it yet? Read on 👇

[radis @radis_radiation]

RADIS 0.12.1 was released today ;
- more accurate for very thin lineshapes.
- @vaex_io is the new default HDF5 library for all platforms (faster!)
- use arbitrary @astropy units when rescaling spectra
- and many docs improvements
But mainly : we welcome 5 new contributors !

[MLOps Community @mlopscommunity]

Jovan and Maarten will showcase @vaex_io, an open-source DataFrame library in Python, tailor-made to allow fast, interactive workflows with datasets that are too large to fit in RAM on a single node.

Join us live here https://t.co/5HGLXyBPtB on Wednesday at 9am PST/ 5pm UK!

[vaex @vaex_io]

Is your data in the Cloud? No problem: Vaex can open and stream your data directly from your favorite cloud storage provider. Even better - it will only download the parts of the data that you view or use!

Learn more about reading data from the cloud at: https://t.co/YuDexBTwJD

[Sylvain Corlay @SylvainCorlay]

It would be interesting to add support for the SIMD instruction sets of various POWER architectures in #xsimd, which underlies SIMD acceleration in Scipy (through Pythran), Apache Arrow, Xtensor, and more.

Would folks at @ibm be interested in funding such work?

[vaex @vaex_io]

Vaex 4.8.0 is out! It includes various performance improvements, improved asyncio support, fancy progressbars and more!

See the full changelog at https://t.co/VqxbVLg4wm

[vaex @vaex_io]

Turn caching on to speed up computations in Vaex! Especially useful for dashboard backends and interactive EDA.

In this example we see that the groupby operation of 1 billion rows is sped up significantly by reusing a previous result from the unique calculation.

[vaex @vaex_io]

Did you know that Vaex can do multiple computations with a single pass over the data?

Set “delay=True” to schedule operations to be executed in parallel. The Rich progress bars nicely indicate this with [1] and [2], showing how we aggregate the 1.1 billion rows on a single node!

[vaex @vaex_io]

See our latest talk on Vaex with @maartenbreddels and @JovanVaex

[GOTO @GOTOcon]

Watch this new #GOTOcph talk where Jovan and @maartenbreddels show how you can access and stream your data directly from the #Cloud — perfect for building cloud services.

All of that using just a single machine!
https://t.co/irEOWAAGxb

[Pietro Lesci @pietro_lesci]

Wordify 2.0 is out! 🚀
Now faster, more interactive, and has more languages. These improvements have been possible thanks to great libraries in the ecosystem, respectively: @vaex_io, @streamlit, and @spacy_io.

Thanks to @federicobianchy, @dirk_hovy, and the @MilaNLProc lab!

[Will McGugan @willmcgugan]

Wow. Neato.

Seeing a lot of clever uses of Rich today!

[vaex @vaex_io]

Together with the #Rich library, Vaex can show detailed information about how it executes a computation. Next to the time spent on each step, we also see how many passes over the data it takes (in square brackets). Great for understanding and optimizing your processing pipelines!

[Joe @agileanalytics1]

How to analyse 100s of GBs of data on your laptop with Python https://t.co/Mxg2WVCde7 #Python #vaex #datascience via @vaex_io