text: # noinspection PyUnresolvedReferences
  import pandas
  
  # noinspection PyUnresolvedReferences
  import numpy
  
  # noinspection PyUnresolvedReferences
  import scipy
  
  from vtreat.vtreat_api import *
  
  __docformat__ = "restructuredtext"
  __version__ = "0.3.1"
  
  __doc__ = """
  This<https://github.com/WinVector/pyvtreat> is the Python version of the vtreat data preparation system
  (also available as an R package<http://winvector.github.io/vtreat/>.
  
  vtreat is a DataFrame processor/conditioner that prepares
  real-world data for supervised machine learning or predictive modeling
  in a statistically sound manner.
  
  vtreat takes an input DataFrame
  that has a specified column called "the outcome variable" (or "y")
  that is the quantity to be predicted (and must not have missing
  values).  Other input columns are possible explanatory variables
  (typically numeric or categorical/string-valued, these columns may
  have missing values) that the user later wants to use to predict "y".
  In practice such an input DataFrame may not be immediately suitable
  for machine learning procedures that often expect only numeric
  explanatory variables, and may not tolerate missing values.
  
  To solve this, vtreat builds a transformed DataFrame where all
  explanatory variable columns have been transformed into a number of
  numeric explanatory variable columns, without missing values.  The
  vtreat implementation produces derived numeric columns that capture
  most of the information relating the explanatory columns to the
  specified "y" or dependent/outcome column through a number of numeric
  transforms (indicator variables, impact codes, prevalence codes, and
  more).  This transformed DataFrame is suitable for a wide range of
  supervised learning methods from linear regression, through gradient
  boosted machines.
  
  The idea is: you can take a DataFrame of messy real world data and
  easily, faithfully, reliably, and repeatably prepare it for machine
  learning using documented methods using vtreat.  Incorporating
  vtreat into your machine learning workflow lets you quickly work
  with very diverse structured data.
  
  Worked examples can be found `here`<https://github.com/WinVector/pyvtreat/tree/master/Examples>.
  
  For more detail please see here: `arXiv:1611.09477
  stat.AP`<https://arxiv.org/abs/1611.09477> (the documentation describes the R version,
  however all of the examples can be found worked in Python 
  `here`<https://github.com/WinVector/pyvtreat/tree/master/Examples/vtreat_paper1>).
  
  vtreat is available
  as a `Python/Pandas package`<https://github.com/WinVector/vtreat>,
  and also as an `R package`<https://github.com/WinVector/vtreat>.
  """
  