odysseus.city_data_manager.city_data_source.trips_data_source package¶
Submodules¶
odysseus.city_data_manager.city_data_source.trips_data_source.austin_scooter_trips module¶
- class AustinScooterTrips¶
Bases:
odysseus.city_data_manager.city_data_source.trips_data_source.trips_data_source.TripsDataSource- load_raw()¶
Method for loading the data to be preprocessed. Since the data format differs in the various datasets, the method is left abstract. Each city has its own implementation. All implementations will read the data through the pandas readcsv method
- Returns
nothing
- normalise(year, month)¶
This method is used to standardize the data format. Again the implementation is highly dependent on the data source and almost all modules override the method.
- Returns
A normalized pandas.DataFrame
odysseus.city_data_manager.city_data_source.trips_data_source.big_data_db_trips module¶
- class BigDataDBTrips(city_name)¶
Bases:
odysseus.city_data_manager.city_data_source.trips_data_source.trips_data_source.TripsDataSource- load_raw()¶
Method for loading the data to be preprocessed. Since the data format differs in the various datasets, the method is left abstract. Each city has its own implementation. All implementations will read the data through the pandas readcsv method
- Returns
nothing
- normalise(year, month)¶
This method is used to standardize the data format. Again the implementation is highly dependent on the data source and almost all modules override the method.
- Returns
A normalized pandas.DataFrame
- save_norm(year, month)¶
It stores normalized data both in a csv file and in a pickle file. The files produced are of the format <year>_<month number>.csv (or .pickle). For example 2017_11.csv.
- Returns
nothing
odysseus.city_data_manager.city_data_source.trips_data_source.calgary_scooter_trips module¶
- class CalgaryScooterTrips¶
Bases:
odysseus.city_data_manager.city_data_source.trips_data_source.trips_data_source.TripsDataSource- load_raw()¶
Method for loading the data to be preprocessed. Since the data format differs in the various datasets, the method is left abstract. Each city has its own implementation. All implementations will read the data through the pandas readcsv method
- Returns
nothing
- normalise(year, month)¶
This method is used to standardize the data format. Again the implementation is highly dependent on the data source and almost all modules override the method.
- Returns
A normalized pandas.DataFrame
odysseus.city_data_manager.city_data_source.trips_data_source.chicago_scooter_trips module¶
- class ChicagoScooterTrips¶
Bases:
odysseus.city_data_manager.city_data_source.trips_data_source.trips_data_source.TripsDataSource- load_raw()¶
Method for loading the data to be preprocessed. Since the data format differs in the various datasets, the method is left abstract. Each city has its own implementation. All implementations will read the data through the pandas readcsv method
- Returns
nothing
- normalise(year, month)¶
This method is used to standardize the data format. Again the implementation is highly dependent on the data source and almost all modules override the method.
- Returns
A normalized pandas.DataFrame
odysseus.city_data_manager.city_data_source.trips_data_source.kansas_city_scooter_trips module¶
- class KansasCityScooterTrips¶
Bases:
odysseus.city_data_manager.city_data_source.trips_data_source.trips_data_source.TripsDataSource- load_raw()¶
Method for loading the data to be preprocessed. Since the data format differs in the various datasets, the method is left abstract. Each city has its own implementation. All implementations will read the data through the pandas readcsv method
- Returns
nothing
- normalise(year, month)¶
This method is used to standardize the data format. Again the implementation is highly dependent on the data source and almost all modules override the method.
- Returns
A normalized pandas.DataFrame
odysseus.city_data_manager.city_data_source.trips_data_source.louisville_scooter_trips module¶
- class LouisvilleScooterTrips¶
Bases:
odysseus.city_data_manager.city_data_source.trips_data_source.trips_data_source.TripsDataSource- load_raw()¶
Method for loading the data to be preprocessed. Since the data format differs in the various datasets, the method is left abstract. Each city has its own implementation. All implementations will read the data through the pandas readcsv method
- Returns
nothing
- normalise(year, month)¶
This method is used to standardize the data format. Again the implementation is highly dependent on the data source and almost all modules override the method.
- Returns
A normalized pandas.DataFrame
odysseus.city_data_manager.city_data_source.trips_data_source.minneapolis_scooter_trips module¶
- class MinneapolisScooterTrips¶
Bases:
odysseus.city_data_manager.city_data_source.trips_data_source.trips_data_source.TripsDataSource- load_raw(year, month)¶
Method for loading the data to be preprocessed. Since the data format differs in the various datasets, the method is left abstract. Each city has its own implementation. All implementations will read the data through the pandas readcsv method
- Returns
nothing
- normalise(year, month)¶
This method is used to standardize the data format. Again the implementation is highly dependent on the data source and almost all modules override the method.
- Returns
A normalized pandas.DataFrame
odysseus.city_data_manager.city_data_source.trips_data_source.new_york_city_bikes_trips module¶
- class NewYorkCityBikeTrips(city_name)¶
Bases:
odysseus.city_data_manager.city_data_source.trips_data_source.trips_data_source.TripsDataSource- load_raw(year, month)¶
Method for loading the data to be preprocessed. Since the data format differs in the various datasets, the method is left abstract. Each city has its own implementation. All implementations will read the data through the pandas readcsv method
- Returns
nothing
- normalise(year, month)¶
This method is used to standardize the data format. Again the implementation is highly dependent on the data source and almost all modules override the method.
- Returns
A normalized pandas.DataFrame
odysseus.city_data_manager.city_data_source.trips_data_source.new_york_city_taxi_trips module¶
- class NewYorkCityTaxiTrips(city_name)¶
Bases:
odysseus.city_data_manager.city_data_source.trips_data_source.trips_data_source.TripsDataSource- load_raw(year, month)¶
Method for loading the data to be preprocessed. Since the data format differs in the various datasets, the method is left abstract. Each city has its own implementation. All implementations will read the data through the pandas readcsv method
- Returns
nothing
odysseus.city_data_manager.city_data_source.trips_data_source.norfolk_scooter_trips module¶
- class NorfolkScooterTrips¶
Bases:
odysseus.city_data_manager.city_data_source.trips_data_source.trips_data_source.TripsDataSource- load_raw()¶
Method for loading the data to be preprocessed. Since the data format differs in the various datasets, the method is left abstract. Each city has its own implementation. All implementations will read the data through the pandas readcsv method
- Returns
nothing
- normalise(year, month)¶
This method is used to standardize the data format. Again the implementation is highly dependent on the data source and almost all modules override the method.
- Returns
A normalized pandas.DataFrame
city_data_source.trips_data_source.trips_data_source module¶
- class TripsDataSource(city_name, data_source_id, vehicles_type_id)¶
Bases:
objectTripsDataSource is an abstract class that contains the information needed to describe a trip. This class is implemented by the other classes of this module. The constructor method takes as parameters:
- Parameters
city_name (str) – City name. The name also serves to determine the timezone to which the city belongs
data_source_id (str) – Data source from which the information is taken. This allows us to have multiple data sources associated with the same city (for example from different operators)
vehicles_type_id (str) – Type of service represented by the data source (e.g. car sharing or e-scooter)
- load_norm(year, month)¶
Load a previously created normalized file from memory. It requests month and year as parameters, and checks if the file for that period exists in memory (looking for it with the same format as save_norm in the city folder). If it exists, it returns a pandas.DataFrame containing the data read, otherwise it returns an empty DataFrame
- Parameters
year (int) – year expressed as a four-digit number (e.g. 1999)
month (int) – month expressed as a number (e.g. for November the method expects to receive 11)
- Returns
If the file exists, it returns a pandas.DataFrame containing the data read, otherwise it returns an empty DataFrame
- load_raw()¶
Method for loading the data to be preprocessed. Since the data format differs in the various datasets, the method is left abstract. Each city has its own implementation. All implementations will read the data through the pandas readcsv method
- Returns
nothing
- normalise()¶
This method is used to standardize the data format. Again the implementation is highly dependent on the data source and almost all modules override the method.
- Returns
A normalized pandas.DataFrame
- save_norm()¶
It stores normalized data both in a csv file and in a pickle file. The files produced are of the format <year>_<month number>.csv (or .pickle). For example 2017_11.csv.
- Returns
nothing