tut01-lesson5-create_tw_pol_table.py

def create_tw_politican_table(self, apidef):
  # Panda Data Frame - Table of all Parliament Members
  panda_data = { 'ScreenName' : self.__col_screen_name,
                 'Name': self.__col_name,
                 'Description': self.__col_description,
                 "FollowersCount": self.__col_followers_count,
                 "FriendsCount": self.__col_friends_count,
                 "Party": self.__col_party
                 }
  df = DataFrame(panda_data, index=self.__labels)
  df = df.apply(self.__calculate_name_matching, axis=1)
  # Sort by Followers Count
  df = df.sort_values(['FollowersCount'], ascending=[0])
  # Create a Plotly Table
  trace = go.Table(
      header=dict(values=list(df.columns)),
      cells=dict(values=[df.ScreenName, df.Name, df.Description, df.FollowersCount, 
                         df.FriendsCount, df.Party, df.col_match1, df.col_match2, 
                         df.col_match3]))
  data = [trace]
  py.plot(data, filename=self.__country_code+'-tw-politician-list')

  merge_df = pd.merge(left=df, right=apidef, how='left', left_on='col_match2', 
                      right_on='col_match2')

  trace = go.Table(
      header=dict(values=['ScreenName', 'Name', 'FollowersCount', 'FriendsCount', 'Party', 
                          'party', 'electedDate','gender', 'maritalStatus', 'birthDate', 
                          'normalizedName']),
      cells=dict(values=[merge_df.ScreenName, merge_df.Name, merge_df.FollowersCount, 
                         merge_df.FriendsCount, merge_df.Party, merge_df.party, 
                         merge_df.electedDate, merge_df.gender, merge_df.maritalStatus, 
                         merge_df.birthDate, merge_df.col_match1_x]))
  data = [trace]
  py.plot(data, filename=self.__country_code+'-tw-politician-merged-list')
  return merge_df