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It is a lazy Out-of-Core DataFrames (similar to Pandas), to visualize and explore big tabular datasets.

It calculates statistics such as mean, sum, count, standard deviation etc., on an N-dimensional grid for more than a billion (10^9) samples/rows per second.

Visualization is done using histograms, density plots and 3D volume rendering, allowing interactive exploration of big data.

Vaex uses memory mapping, zero memory copy policy and lazy computations for best performance (no memory wasted).

The data pipeline on your laptop will be super fast.


$ 𝚙𝚒𝚙 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 𝚟𝚊𝚎𝚡

GitHub: https://github.com/vaexio/vaex

GitHub

GitHub - vaexio/vaex: Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular…

Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second 🚀 - vaexio/vaex

www.tgoop.com/PyLab/512

608 viewsMay 30, 2020 at 07:06

It is a lazy Out-of-Core DataFrames (similar to Pandas), to visualize and explore big tabular datasets.

It calculates statistics such as mean, sum, count, standard deviation etc., on an N-dimensional grid for more than a billion (10^9) samples/rows per second.

Visualization is done using histograms, density plots and 3D volume rendering, allowing interactive exploration of big data.

Vaex uses memory mapping, zero memory copy policy and lazy computations for best performance (no memory wasted).

The data pipeline on your laptop will be super fast.


$ 𝚙𝚒𝚙 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 𝚟𝚊𝚎𝚡

GitHub: https://github.com/vaexio/vaex

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