talfco

    def create_party_table(self):
        # Panda Data Frame - Table of Tweeter Users per Party
        # https://stackoverflow.com/questions/48909110/python-pandas-mean-and-sum-groupby-on-different-columns-at-the-same-time
        panda_data = { "Party": self.__col_party,
                       "PartyCount": self.__col_party, # Duplicate Party column for the counting
                       "FollowersCount": self.__col_followers_count,
                       "FriendsCount": self.__col_friends_count
                       }
        df = DataFrame(panda_data , index=self.__labels)
        # We use the as_index parameter, so that Party will also be a column of the data frame

talfco

twitterListAccount: "SoMePolis"
twitterListName: "BundesparlamentarierInnen"
# https://www.parlament.ch/de/organe/fraktionen/im-parlament-vertretene-parteien
parties:
    - twitter: "spschweiz"
      abbrs: ["SP","PS"]
      keywords: ["socialist", "sozialist"]
    - twitter: "FDP_Liberalen"
      abbrs: ["FDP","PLR"]
      keywords: ["fdp","plr"]

talfco

   def __check_for_party(self, user_list):
        party_column = []
        # iterate through each user account
        for user in user_list:
            # iterate trough all parties
            for party in self.__cfg.get('parties'):
                res = "unknown"
                # Check first all abbreviations
                # We don't do lower here because that could result to false positive
                for abbr in party['abbrs']:

talfco

   def create_politican_table(self):
        # 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)

talfco

def main():
    analyzer_ch = GovernmentSocialMediaAnalyzer("CH")
    analyzer_ch.create_politican_table()
    analyzer_ch.create_party_table()

talfco

    def __init__(self):
        res = {}
        # Reading the secret values from a local disk
        with open("../../../secret/twitter.yaml", 'r') as stream:
            try:
                res= yaml.load(stream)
            except yaml.YAMLError as exc:
                print(exc)
                exit(1)

talfco

    def get_government_members(self):
        lists = self.__api.lists_all('SoMePolis')

        for list in lists:
            if list.name == 'BundesparlamentarierInnen':
                result = []
                for item in tweepy.Cursor(self.__api.list_members, list_id=list.id).items():
                    result.append(item)
                return result
        return None

talfco

    def create_plotly_table(self):
        lists = self.get_government_members()
        labels = range(0,len(lists))
        col1 = [list.screen_name for list in lists]
        col2 = [list.name for list in lists]
        col3 = [list.description for list in lists]
        col4 = [list.followers_count for list in lists]
        col5 = [list.friends_count for list in lists]

        # Panda Data Set

talfco

        # attempt authentication
        try:
            # create OAuthHandler object
            auth = OAuthHandler(consumer_key, consumer_secret)
            # set access token and secret
            auth.set_access_token(access_token, access_token_secret)
            # create tweepy API object to fetch tweets
            self.__api = tweepy.API(auth)
        except:
            print("Error: Authentication Failed")

talfco

 def create_party_friends_count_bar_chart(self, df):
        data = [
            go.Bar(
                x=df.Party, # assign x as the dataframe column 'x'
                y=df.FriendsCount
            )
        ]
        py.plot(data, filename=self.__country_code+'-tw-party_politicans_count')

    def create_party_politicans_count_pie_chart(self, df):