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Modern Data Science with Vaex: A New Approach to DataFrames and Pipelines

Posted Apr 15
# Pipelines
# DataFrames
# Vaex
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SPEAKERS
Maarten Breddels
Maarten Breddels
Maarten Breddels
Founder @ Vaex.io

Maarten is an entrepreneur and freelance developer/consultant/data scientist working mostly with Python, C++, and Javascript in the Jupyter ecosystem. Creator of ipyvolume and vaex, founder of Vaex.io. His expertise ranges from fast numerical computation, API design, to 3d visualization. He has a Bachelor's in ICT, a Master's and Ph.D. in Astronomy, likes to code and solve problems.

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Maarten is an entrepreneur and freelance developer/consultant/data scientist working mostly with Python, C++, and Javascript in the Jupyter ecosystem. Creator of ipyvolume and vaex, founder of Vaex.io. His expertise ranges from fast numerical computation, API design, to 3d visualization. He has a Bachelor's in ICT, a Master's and Ph.D. in Astronomy, likes to code and solve problems.

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Jovan Veljanoski
Jovan Veljanoski
Jovan Veljanoski
Senior Data Scientist @ Tiqets

Jovan is a senior data scientist at Tiqets, where he creates predictive models and recommender systems centered around the e-commerce domain. Working mostly with Python in the Jupyter/PyData ecosystem, he has considerable experience in creating dashboards, clustering analysis, and predictive modeling. Jovan has a Ph.D. in Astrophysics, is a co-founder of vaex.io, and is interested in novel machine learning technologies and applications.

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Jovan is a senior data scientist at Tiqets, where he creates predictive models and recommender systems centered around the e-commerce domain. Working mostly with Python in the Jupyter/PyData ecosystem, he has considerable experience in creating dashboards, clustering analysis, and predictive modeling. Jovan has a Ph.D. in Astrophysics, is a co-founder of vaex.io, and is interested in novel machine learning technologies and applications.

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SUMMARY

Jovan and Maarten showcase Vaex, 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. Vaex makes this possible by leveraging lazy evaluations, efficient out-of-core algorithms, memory mapping, and computational graphs, all mostly behind the scenes and out of the user's way.

Using data from the New York City YellowCab taxi service comprising 1.1 billion samples and taking up over 100 GB on disk, Jovan and Maarten show how one can conduct an exploratory data analysis, complete with filtering, grouping, calculations of statistics, and interactive visualizations on a single laptop in real-time. Jovan and Maarten also demonstrate how one can automatically build a machine learning pipeline as a by-product of the exploratory data analysis using the computational graphs in Vaex.

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