def read_data_as_dataframe(self,
                               travel_impedance_measure,
                               from_stop_I=None,
                               to_stop_I=None,
                               statistic=None):
        """
        Recover pre-computed travel_impedance between od-pairs from the database.

        Returns
        -------
        values: number | Pandas DataFrame
        """
        to_select = []
        where_clauses = []
        to_select.append("from_stop_I")
        to_select.append("to_stop_I")
        if from_stop_I is not None:
            where_clauses.append("from_stop_I=" + str(int(from_stop_I)))
        if to_stop_I is not None:
            where_clauses.append("to_stop_I=" + str(int(to_stop_I)))
        where_clause = ""
        if len(where_clauses) > 0:
            where_clause = " WHERE " + " AND ".join(where_clauses)
        if not statistic:
            to_select.extend(["min", "mean", "median", "max"])
        else:
            to_select.append(statistic)
        to_select_clause = ",".join(to_select)
        if not to_select_clause:
            to_select_clause = "*"
        sql = "SELECT " + to_select_clause + " FROM " + travel_impedance_measure + where_clause + ";"
        df = pd.read_sql(sql, self.conn)
        return df
def read_data_as_dataframe(self,
                               travel_impedance_measure,
                               from_stop_I=None,
                               to_stop_I=None,
                               statistic=None):
        """
        Recover pre-computed travel_impedance between od-pairs from the database.

        Returns
        -------
        values: number | Pandas DataFrame
        """
        to_select = []
        where_clauses = []
        to_select.append("from_stop_I")
        to_select.append("to_stop_I")
        if from_stop_I is not None:
            where_clauses.append("from_stop_I=" + str(int(from_stop_I)))
        if to_stop_I is not None:
            where_clauses.append("to_stop_I=" + str(int(to_stop_I)))
        where_clause = ""
        if len(where_clauses) > 0:
            where_clause = " WHERE " + " AND ".join(where_clauses)
        if not statistic:
            to_select.extend(["min", "mean", "median", "max"])
        else:
            to_select.append(statistic)
        to_select_clause = ",".join(to_select)
        if not to_select_clause:
            to_select_clause = "*"
        sql = "SELECT " + to_select_clause + " FROM " + travel_impedance_measure + where_clause + ";"
        df = pd.read_sql(sql, self.conn)
        return df
def read_data_as_dataframe ( self , travel_impedance_measure , from_stop_I = None , to_stop_I = None , statistic = None ) : to_select = [ ] where_clauses = [ ] to_select . append ( "from_stop_I" ) to_select . append ( "to_stop_I" ) if from_stop_I is not None : where_clauses . append ( "from_stop_I=" + str ( int ( from_stop_I ) ) ) if to_stop_I is not None : where_clauses . append ( "to_stop_I=" + str ( int ( to_stop_I ) ) ) where_clause = "" if len ( where_clauses ) > 0 : where_clause = " WHERE " + " AND " . join ( where_clauses ) if not statistic : to_select . extend ( [ "min" , "mean" , "median" , "max" ] ) else : to_select . append ( statistic ) to_select_clause = "," . join ( to_select ) if not to_select_clause : to_select_clause = "*" sql = "SELECT " + to_select_clause + " FROM " + travel_impedance_measure + where_clause + ";" df = pd . read_sql ( sql , self . conn ) return df
Recover pre-computed travel_impedance between od-pairs from the database.

        Returns
        -------
        values: number | Pandas DataFrame
Recover pre - computed travel_impedance between od - pairs from the database .