old_contents: # -*- coding: utf-8 -*-
  # Copyright (c) 2015-2016, Exa Analytics Development Team
  # Distributed under the terms of the Apache License 2.0
  '''
  Exa
  #########
  This package creates a systematic infrastructure for an ecosystem of packages,
  tailored to specific industry or academic displines, for organizing, processing,
  analyzing, and visualizing data. It is built with minimal dependencies, leverages
  established open-source packages, is itself extensible, and is targeted at both
  industry and academic applications.
  
  At a high level, data objects such as series or dataframes (i.e. `pandas`_
  like objects) are organized into containers which track relationships between
  these objects and provide methods for computation, conversion to other formats,
  analysis, and visualization within the `Jupyter notebook`_ environment.
  
  .. _pandas: http://pandas.pydata.org/pandas-docs/stable/index.html
  .. _Jupyter notebook: http://jupyter.org/
  '''
  __exa_version__ = (0, 2, 3)
  __version__ = '.'.join((str(v) for v in __exa_version__))
  
  
  from exa import _config
  from exa import log
  from exa import test
  from exa import relational
  from exa import widget
  from exa import math
  from exa import distributed
  from exa import mpl, tex
  from exa import error
  
  # User API
  from exa.numerical import Series, DataFrame, Field3D, SparseSeries, SparseDataFrame
  from exa.container import Container
  from exa.editor import Editor
  from exa.filetypes import CSV
  
new_contents: # -*- coding: utf-8 -*-
  # Copyright (c) 2015-2016, Exa Analytics Development Team
  # Distributed under the terms of the Apache License 2.0
  '''
  Exa
  #########
  This package creates a systematic infrastructure for an ecosystem of packages,
  tailored to specific industry or academic displines, for organizing, processing,
  analyzing, and visualizing data. It is built with minimal dependencies, leverages
  established open-source packages, is itself extensible, and is targeted at both
  industry and academic applications.
  
  At a high level, data objects such as series or dataframes (i.e. `pandas`_
  like objects) are organized into containers which track relationships between
  these objects and provide methods for computation, conversion to other formats,
  analysis, and visualization within the `Jupyter notebook`_ environment.
  
  .. _pandas: http://pandas.pydata.org/pandas-docs/stable/index.html
  .. _Jupyter notebook: http://jupyter.org/
  '''
  __exa_version__ = (0, 2, 4)
  __version__ = '.'.join((str(v) for v in __exa_version__))
  
  
  from exa import _config
  from exa import log
  from exa import test
  from exa import relational
  from exa import widget
  from exa import math
  from exa import distributed
  from exa import mpl, tex
  from exa import error
  
  # User API
  from exa.numerical import Series, DataFrame, Field3D, SparseSeries, SparseDataFrame
  from exa.container import Container
  from exa.editor import Editor
  from exa.filetypes import CSV
  
subject: bump version