H2O was in attendance last week at PyData  in Dallas, Texas. Our CTO, Cliff Click, spoke at PyData about driving H2O from Python to perform feature-engineering, group by, quantiles, and model building with H2O’s GBM, GLM, and Distributed Random Forest .
We met a lot of great people and we are really excited to see the enthusiasm for H2O with Python. We want Python users to be able to use H2O efficiently and smoothly, so it was fantastic to get feedback from the PyData attendees.

H2O is a high-performance modeling engine that makes the transition of models from development to production seamless. H2O supports many popular interfaces, including R , javascript , Spark , and now Python.
The H2O python module  is not intended as a replacement for other popular machine learning frameworks such as scikit-learn, pylearn2, and their ilk, but is intended to bring H2O to a wider audience of data and machine learning devotees who work exclusively with Python.
H2O from Python is a tool for rapidly turning over models, doing data munging, and building applications in a fast, scalable environment without any of the mental anguish about parallelism and distribution of work.
Thanks to everyone for coming out!

Nightly H2O-dev Build:

Download 

[Install H2O in Python](http://www.h2o.ai/download)

Full video of Cliff’s Presentation:
 
Slides from Cliff’s Presentation:
 

Citibike demo script:

https://github.com/h2oai/h2o-dev/blob/master/h2o-py/demos/citi_bike_small.ipynb