old_contents: from setuptools import setup
  #import versioneer
  
  
  def get_version():
      # http://stackoverflow.com/questions/2058802/how-can-i-get-the-version-defined-in-setup-py-setuptools-in-my-package
      from ast import parse
      with open('pybroom.py') as f:
          version = parse(next(filter(
              lambda line: line.startswith('__version__'), f))).body[0].value.s
      return version
  
  
  long_description = r"""
  pybroom
  =======
  
  **Pybroom** is a small python 3+ library for converting collections of
  fit results (curve fitting or other optimizations)
  to `Pandas <http://pandas.pydata.org/>`__
  `DataFrame <http://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe>`__
  in tidy format (or long-form)
  `(Wickham 2014) <http://dx.doi.org/10.18637/jss.v059.i10>`__.
  Once fit results are in tidy DataFrames, it is possible to leverage
  `common patterns <http://tomaugspurger.github.io/modern-5-tidy.html>`__
  for tidy data analysis. Furthermore powerful visual
  explorations using multi-facet plots becomes easy thanks to libraries
  like `seaborn <https://pypi.python.org/pypi/seaborn/>`__ natively
  supporting tidy DataFrames.
  
  See the `pybroom homepage <http://pybroom.readthedocs.io/>`__ for more info.
  """
  
  setup(
      name='pybroom',
      version=get_version(),
      py_modules=['pybroom'],
      #version=versioneer.get_version(),
      #cmdclass=versioneer.get_cmdclass(),
      author='Antonino Ingargiola',
      author_email='tritemio@gmail.com',
      url='http://pybroom.readthedocs.io/',
      download_url='https://github.com/tritemio/pybroom',
      install_requires=['pandas', 'lmfit', 'setuptools'],
      include_package_data=True,
      license='MIT',
      description=("Make tidy DataFrames from messy fit/model results."),
      long_description=long_description,
      platforms=('Windows', 'Linux', 'Mac OS X'),
      classifiers=['Intended Audience :: Science/Research',
                   'Operating System :: OS Independent',
                   'Programming Language :: Python',
                   'Programming Language :: Python :: 3.4',
                   'Programming Language :: Python :: 3.5',
                   'Topic :: Scientific/Engineering',
                   'License :: OSI Approved :: MIT License'],
      keywords=('dataframe tidy-data long-form model fitting tidyverse'))
  
new_contents: from setuptools import setup
  #import versioneer
  
  
  def get_version():
      # http://stackoverflow.com/questions/2058802/how-can-i-get-the-version-defined-in-setup-py-setuptools-in-my-package
      from ast import parse
      with open('pybroom.py') as f:
          version = parse(next(filter(
              lambda line: line.startswith('__version__'), f))).body[0].value.s
      return version
  
  
  long_description = r"""
  pybroom
  =======
  
  **Pybroom** is a small python 3+ library for converting collections of
  fit results (curve fitting or other optimizations)
  to `Pandas <http://pandas.pydata.org/>`__
  `DataFrame <http://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe>`__
  in tidy format (or long-form)
  `(Wickham 2014) <http://dx.doi.org/10.18637/jss.v059.i10>`__.
  Once fit results are in tidy DataFrames, it is possible to leverage
  `common patterns <http://tomaugspurger.github.io/modern-5-tidy.html>`__
  for tidy data analysis. Furthermore powerful visual
  explorations using multi-facet plots becomes easy thanks to libraries
  like `seaborn <https://pypi.python.org/pypi/seaborn/>`__ natively
  supporting tidy DataFrames.
  
  See the `pybroom homepage <http://pybroom.readthedocs.io/>`__ for more info.
  """
  
  setup(
      name='pybroom',
      version=get_version(),
      py_modules=['pybroom'],
      #version=versioneer.get_version(),
      #cmdclass=versioneer.get_cmdclass(),
      author='Antonino Ingargiola',
      author_email='tritemio@gmail.com',
      url='http://pybroom.readthedocs.io/',
      download_url='https://github.com/tritemio/pybroom',
      install_requires=['pandas', 'lmfit'],
      include_package_data=True,
      license='MIT',
      description=("Make tidy DataFrames from messy fit/model results."),
      long_description=long_description,
      platforms=('Windows', 'Linux', 'Mac OS X'),
      classifiers=['Intended Audience :: Science/Research',
                   'Operating System :: OS Independent',
                   'Programming Language :: Python',
                   'Programming Language :: Python :: 3.4',
                   'Programming Language :: Python :: 3.5',
                   'Topic :: Scientific/Engineering',
                   'License :: OSI Approved :: MIT License'],
      keywords=('dataframe tidy-data long-form model fitting tidyverse'))
  
subject: Remove setuptools from install_requires