content: from __future__ import absolute_import
  import pandas as pd
  from os.path import join
  import sys
  from . import _data_dir
  
  
  '''
  This module provides a pandas DataFrame instance of four
  of the datasets from gapminder.org.
  
  These are read in from csvs that have been downloaded from Bokeh's
  sample data on S3. But the original code that generated the csvs from the
  raw gapminder data is available at the bottom of this file.
  '''
  
  data_dir = _data_dir()
  
  datasets = [
      'fertility',
      'life_expectancy',
      'population',
      'regions',
  ]
  
  for dataset in datasets:
      filename = join(data_dir, 'gapminder_%s.csv' % dataset)
      try:
          setattr(
              sys.modules[__name__],
              dataset,
              pd.read_csv(filename, index_col='Country')
          )
      except (IOError, OSError):
          raise RuntimeError('Could not load gapminder data file "%s". Please execute bokeh.sampledata.download()' % filename)
  
  __all__ = datasets
  
  
  # ====================================================
  
  # Original data is from Gapminder - www.gapminder.org.
  # The google docs links are maintained by gapminder
  
  # The following script was used to get the data from gapminder
  # and process it into the csvs stored in bokeh's sampledata.
  
  """
  population_url = "http://spreadsheets.google.com/pub?key=phAwcNAVuyj0XOoBL_n5tAQ&output=xls"
  fertility_url = "http://spreadsheets.google.com/pub?key=phAwcNAVuyj0TAlJeCEzcGQ&output=xls"
  life_expectancy_url = "http://spreadsheets.google.com/pub?key=tiAiXcrneZrUnnJ9dBU-PAw&output=xls"
  regions_url = "https://docs.google.com/spreadsheets/d/1OxmGUNWeADbPJkQxVPupSOK5MbAECdqThnvyPrwG5Os/pub?gid=1&output=xls"
  
  def _get_data(url):
      # Get the data from the url and return only 1962 - 2013
      df = pd.read_excel(url, index_col=0)
      df = df.unstack().unstack()
      df = df[(df.index >= 1964) & (df.index <= 2013)]
      df = df.unstack().unstack()
      return df
  
  fertility_df = _get_data(fertility_url)
  life_expectancy_df = _get_data(life_expectancy_url)
  population_df = _get_data(population_url)
  regions_df = pd.read_excel(regions_url, index_col=0)
  
  # have common countries across all data
  fertility_df = fertility_df.drop(fertility_df.index.difference(life_expectancy_df.index))
  population_df = population_df.drop(population_df.index.difference(life_expectancy_df.index))
  regions_df = regions_df.drop(regions_df.index.difference(life_expectancy_df.index))
  
  fertility_df.to_csv('gapminder_fertility.csv')
  population_df.to_csv('gapminder_population.csv')
  life_expectancy_df.to_csv('gapminder_life_expectancy.csv')
  regions_df.to_csv('gapminder_regions.csv')
  """
  
  # ======================================================
  