e3f2s.city_data_manager.city_data_source.trips_data_source package¶
Submodules¶
e3f2s.city_data_manager.city_data_source.trips_data_source.austin_scooter_trips module¶
-
class
AustinScooterTrips
¶ Bases:
e3f2s.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
-
e3f2s.city_data_manager.city_data_source.trips_data_source.big_data_db_trips module¶
-
class
BigDataDBTrips
(city_name)¶ Bases:
e3f2s.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
-
e3f2s.city_data_manager.city_data_source.trips_data_source.calgary_scooter_trips module¶
-
class
CalgaryScooterTrips
¶ Bases:
e3f2s.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
-
e3f2s.city_data_manager.city_data_source.trips_data_source.chicago_scooter_trips module¶
-
class
ChicagoScooterTrips
¶ Bases:
e3f2s.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
-
e3f2s.city_data_manager.city_data_source.trips_data_source.kansas_city_scooter_trips module¶
e3f2s.city_data_manager.city_data_source.trips_data_source.louisville_scooter_trips module¶
-
class
LouisvilleScooterTrips
¶ Bases:
e3f2s.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
-
e3f2s.city_data_manager.city_data_source.trips_data_source.minneapolis_scooter_trips module¶
-
class
MinneapolisScooterTrips
¶ Bases:
e3f2s.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
-
e3f2s.city_data_manager.city_data_source.trips_data_source.new_york_city_bikes_trips module¶
e3f2s.city_data_manager.city_data_source.trips_data_source.new_york_city_taxi_trips module¶
e3f2s.city_data_manager.city_data_source.trips_data_source.norfolk_scooter_trips module¶
city_data_source.trips_data_source.trips_data_source module¶
-
class
TripsDataSource
(city_name, data_source_id, vehicles_type_id)¶ Bases:
object
TripsDataSource 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