old_contents: #!/usr/bin/env python3
  
  import glob
  import os
  import pandas
  import pytest
  
  
  columns = ['candidate', 'county', 'district', 'office', 'votes']
  
  
  @pytest.mark.parametrize("year,date,election_type", [
      ("2014", "0603", "primary"),
      ("2014", "1104", "general"),
      ("2015", "0317", "special__primary"),
      ("2015", "0519", "special__general"),
      ("2016", "0405", "special__primary"),
      ("2016", "0607", "primary"),
      ("2016", "1108", "general"),
      ("2017", "0404", "special__primary"),
      ("2017", "0606", "special__general"),
      ("2017", "1003", "special__primary"),
  ])
  def test_data(subtests, year, date, election_type):
      state_file = '{0}/{0}{1}__ca__{2}.csv'.format(
          year, date, election_type)
      state_data = pandas.read_csv(state_file, na_values='').fillna('')
      state_data = state_data[state_data.votes != 0]
  
      county_file_pattern = os.path.join(year, "**", f"{year}{date}__ca__{election_type}__*__precinct.csv")
      for county_file in sorted(glob.glob(county_file_pattern)):
          with subtests.test(msg=f"{county_file}"):
              county_data = pandas.read_csv(county_file, na_values='').fillna('')
  
              # Each county file should only contain a single county.
              assert len(county_data.drop_duplicates(
                  ['county']).county.values) == 1
  
              county = county_data.drop_duplicates(['county']).county.values[0]
              county_data = county_data[columns].groupby(
                  columns[:-1]).sum()
              state_cmp = state_data[state_data.county == county][
                  columns].groupby(columns[: -1]).sum()
  
              assert state_cmp.to_dict()['votes'] == county_data.to_dict()[
                  'votes'], '%s failed' % county
  
new_contents: #!/usr/bin/env python3
  
  import glob
  import os
  import pandas
  import pytest
  
  
  columns = ['candidate', 'county', 'district', 'office', 'votes']
  
  
  @pytest.mark.parametrize("year,date,election_type", [
      ("2014", "0603", "primary"),
      ("2014", "1104", "general"),
      ("2015", "0317", "special__primary"),
      ("2015", "0519", "special__general"),
      ("2016", "0405", "special__primary"),
      ("2016", "0607", "primary"),
      ("2016", "1108", "general"),
      ("2017", "0404", "special__primary"),
      ("2017", "0606", "special__general"),
      ("2017", "1003", "special__primary"),
  ])
  def test_data(subtests, year, date, election_type):
      state_file = '{0}/{0}{1}__ca__{2}.csv'.format(
          year, date, election_type)
      state_data = pandas.read_csv(state_file, na_values='').fillna('')
      state_data = state_data[state_data.votes != 0]
  
      county_file_pattern = os.path.join(year, "**", f"{year}{date}__ca__{election_type}__*__precinct.csv")
      for county_file in sorted(glob.glob(county_file_pattern, recursive=True)):
          with subtests.test(msg=f"{county_file}"):
              county_data = pandas.read_csv(county_file, na_values='').fillna('')
  
              # Each county file should only contain a single county.
              assert len(county_data.drop_duplicates(
                  ['county']).county.values) == 1
  
              county = county_data.drop_duplicates(['county']).county.values[0]
              county_data = county_data[columns].groupby(
                  columns[:-1]).sum()
              state_cmp = state_data[state_data.county == county][
                  columns].groupby(columns[: -1]).sum()
  
              assert state_cmp.to_dict()['votes'] == county_data.to_dict()[
                  'votes'], '%s failed' % county
  
subject: Use recursive globbing to search for county files.