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@vaex_io

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

Katılım Haziran 2018
174 Takip Edilen1.7K Takipçiler
vaex
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: @jovan.veljanoski/8-powerful-vaex-dataframe-features-you-might-have-not-known-about-b2e2d3d3ee9" target="_blank" rel="nofollow noopener">medium.com/@jovan.veljano…
Maarten A. Breddels@maartenbreddels

Learn 8 powerful Vaex DataFrame features you might not have known about. Read @JovanVaex 's article @jovan.veljanoski/8-powerful-vaex-dataframe-features-you-might-have-not-known-about-b2e2d3d3ee9" target="_blank" rel="nofollow noopener">medium.com/@jovan.veljano… Or get the short version in this thread 👇🏻

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Jovan
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. vaex.io/docs/guides.ht… 1/4
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PyQuant News 🐍
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:
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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 vaex.io/blog/vaex-plus…
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radis
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 !
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MLOps Community
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 go.mlops.community/register on Wednesday at 9am PST/ 5pm UK!
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vaex
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: vaex.io/docs/example_i…
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Sylvain Corlay
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?
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vaex
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 github.com/vaexio/vaex/bl…
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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.
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vaex
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!
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Will McGugan
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!

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vaex
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!
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Keşfet

@JovanVaex @NickCerny @duckdb @streamlit @astropy @ibm @maartenbreddels @spacy_io