old_contents: from setuptools import setup, find_packages
  import os
  import versioneer
  import pypandoc
  
  here = os.path.abspath(os.path.dirname(__file__))
  
  long_description = pypandoc.convert('README.md', 'rst')
  
  setup(
      name='alphatwirl',
      version=versioneer.get_version(),
      cmdclass=versioneer.get_cmdclass(),
      description='A Python library for summarizing event data',
      long_description=long_description,
      author='Tai Sakuma',
      author_email='tai.sakuma@gmail.com',
      url='https://github.com/alphatwirl/alphatwirl',
      classifiers=[
          'Development Status :: 4 - Beta',
          'Intended Audience :: Science/Research',
          'License :: OSI Approved :: BSD License',
          'Programming Language :: Python :: 2.7',
          'Programming Language :: Python :: 3.6',
      ],
      packages=find_packages(exclude=['docs', 'images', 'tests']),
  )
  
new_contents: from setuptools import setup, find_packages
  import os
  import versioneer
  
  long_description = """
  *AlphaTwirl* is a python library that loops over event data and
  summarizes them into multi-dimensional categorical data as data
  frames. Event data, input to AlphaTwirl, are data with one entry (or
  row) for one event: for example, data in `ROOT
  <https://root.cern.ch/>`__ `TTrees
  <https://root.cern.ch/doc/master/classTTree.html>`__ with one entry
  per collision event of an `LHC
  <https://home.cern/topics/large-hadron-collider>`__ experiment at
  `CERN <http://home.cern/>`__. Event data are often large---too large
  to be loaded in memory---because they have as many entries as events.
  Multi-dimensional categorical data, the output of AlphaTwirl, have one
  row for one category. They are usually small---small enough to be
  loaded in memory---because they only have as many rows as categories.
  Users can, for example, import them as data frames into `R
  <https://www.r-project.org/>`__ and `pandas
  <http://pandas.pydata.org/>`__, which usually load all data in memory,
  and can perform categorical data analyses with a rich set of data
  operations available in R and pandas.
  """
  
  setup(
      name='alphatwirl',
      version=versioneer.get_version(),
      cmdclass=versioneer.get_cmdclass(),
      description='A Python library for summarizing event data',
      long_description=long_description,
      author='Tai Sakuma',
      author_email='tai.sakuma@gmail.com',
      url='https://github.com/alphatwirl/alphatwirl',
      classifiers=[
          'Development Status :: 4 - Beta',
          'Intended Audience :: Science/Research',
          'License :: OSI Approved :: BSD License',
          'Programming Language :: Python :: 2.7',
          'Programming Language :: Python :: 3.6',
      ],
      packages=find_packages(exclude=['docs', 'images', 'tests']),
  )
  
subject: update setup.py - write long_description in RST