gtfspy/gtfs.py
def get_trip_counts_per_day(self):
        """
        Get trip counts per day between the start and end day of the feed.

        Returns
        -------
        trip_counts : pandas.DataFrame
            Has columns "date_str" (dtype str) "trip_counts" (dtype int)
        """
        query = "SELECT date, count(*) AS number_of_trips FROM day_trips GROUP BY date"
        # this yields the actual data
        trip_counts_per_day = pd.read_sql_query(query, self.conn, index_col="date")
        # the rest is simply code for filling out "gaps" in the time span
        # (necessary for some visualizations)
        max_day = trip_counts_per_day.index.max()
        min_day = trip_counts_per_day.index.min()
        min_date = datetime.datetime.strptime(min_day, '%Y-%m-%d')
        max_date = datetime.datetime.strptime(max_day, '%Y-%m-%d')
        num_days = (max_date - min_date).days
        dates = [min_date + datetime.timedelta(days=x) for x in range(num_days + 1)]
        trip_counts = []
        date_strings = []
        for date in dates:
            date_string = date.strftime("%Y-%m-%d")
            date_strings.append(date_string)
            try:
                value = trip_counts_per_day.loc[date_string, 'number_of_trips']
            except KeyError:
                # set value to 0 if dsut is not present, i.e. when no trips
                # take place on that day
                value = 0
            trip_counts.append(value)
        # check that all date_strings are included (move this to tests?)
        for date_string in trip_counts_per_day.index:
            assert date_string in date_strings
        data = {"date": dates, "date_str": date_strings, "trip_counts": trip_counts}
        return pd.DataFrame(data)
def get_trip_counts_per_day(self):
        """
        Get trip counts per day between the start and end day of the feed.

        Returns
        -------
        trip_counts : pandas.DataFrame
            Has columns "date_str" (dtype str) "trip_counts" (dtype int)
        """
        query = "SELECT date, count(*) AS number_of_trips FROM day_trips GROUP BY date"
        # this yields the actual data
        trip_counts_per_day = pd.read_sql_query(query, self.conn, index_col="date")
        # the rest is simply code for filling out "gaps" in the time span
        # (necessary for some visualizations)
        max_day = trip_counts_per_day.index.max()
        min_day = trip_counts_per_day.index.min()
        min_date = datetime.datetime.strptime(min_day, '%Y-%m-%d')
        max_date = datetime.datetime.strptime(max_day, '%Y-%m-%d')
        num_days = (max_date - min_date).days
        dates = [min_date + datetime.timedelta(days=x) for x in range(num_days + 1)]
        trip_counts = []
        date_strings = []
        for date in dates:
            date_string = date.strftime("%Y-%m-%d")
            date_strings.append(date_string)
            try:
                value = trip_counts_per_day.loc[date_string, 'number_of_trips']
            except KeyError:
                # set value to 0 if dsut is not present, i.e. when no trips
                # take place on that day
                value = 0
            trip_counts.append(value)
        # check that all date_strings are included (move this to tests?)
        for date_string in trip_counts_per_day.index:
            assert date_string in date_strings
        data = {"date": dates, "date_str": date_strings, "trip_counts": trip_counts}
        return pd.DataFrame(data)
def get_trip_counts_per_day ( self ) : query = "SELECT date, count(*) AS number_of_trips FROM day_trips GROUP BY date" # this yields the actual data trip_counts_per_day = pd . read_sql_query ( query , self . conn , index_col = "date" ) # the rest is simply code for filling out "gaps" in the time span # (necessary for some visualizations) max_day = trip_counts_per_day . index . max ( ) min_day = trip_counts_per_day . index . min ( ) min_date = datetime . datetime . strptime ( min_day , '%Y-%m-%d' ) max_date = datetime . datetime . strptime ( max_day , '%Y-%m-%d' ) num_days = ( max_date - min_date ) . days dates = [ min_date + datetime . timedelta ( days = x ) for x in range ( num_days + 1 ) ] trip_counts = [ ] date_strings = [ ] for date in dates : date_string = date . strftime ( "%Y-%m-%d" ) date_strings . append ( date_string ) try : value = trip_counts_per_day . loc [ date_string , 'number_of_trips' ] except KeyError : # set value to 0 if dsut is not present, i.e. when no trips # take place on that day value = 0 trip_counts . append ( value ) # check that all date_strings are included (move this to tests?) for date_string in trip_counts_per_day . index : assert date_string in date_strings data = { "date" : dates , "date_str" : date_strings , "trip_counts" : trip_counts } return pd . DataFrame ( data )
Get trip counts per day between the start and end day of the feed.

        Returns
        -------
        trip_counts : pandas.DataFrame
            Has columns "date_str" (dtype str) "trip_counts" (dtype int)
Get trip counts per day between the start and end day of the feed .