title: conversion of pandas dataframe to h2o frame efficiently
body: <p>I have a Pandas dataframe which has <code>Encoding: latin-1</code> and is delimited by <code>;</code>. The dataframe is very large almost of <code>size: 350000 x 3800</code>. I wanted to use sklearn initially but my dataframe has missing values (<code>NAN values</code>) so i could not use sklearn's random forests or GBM. So i had to use <code>H2O's</code> Distributed random forests for the Training of the dataset. The main Problem is the dataframe is not efficiently converted when i do <code>h2o.H2OFrame(data)</code>. I checked for the possibility for providing the Encoding Options but there is nothing in the documentation. </p>
  
  <p>Do anyone have an idea about this? Any leads could help me. I also want to know if there are any other libraries like H2O which can handle NAN values very efficiently? I know that we can impute the columns but i should not do that in my dataset because my columns are values from different sensors, if the values are not there implies that the sensor is not present. I can use only Python  </p>