Some of the data structures to consider include:
- Classes/Objects: Use classes to represent different entities such as historic sites, museums, monuments, streets, and squares. Each class will have attributes like name, location, historical period, visiting hours, and entrance fees.
Example:
python class Site: def init(self, name, location, historical_period, visiting_hours, entrance_fee): self.name = name self.location = location self.historical_period = historical_period self.visiting_hours = visiting_hours self.entrance_fee = entrance_fee
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Graphs: To represent the relationships between different sites, such as distance or adjacency, you can use a graph data structure. Nodes in the graph represent sites, while edges represent connections between sites, such as distance or travel time.
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Dictionaries/Hashmaps: These can be used to store and efficiently access information related to sites, itineraries, or user preferences using unique keys. For example, you can use a dictionary to store site objects with their names as keys.
Example:
python sites = { "Eiffel Tower": Site("Eiffel Tower", "Paris", "Late 19th Century", "9AM-12AM", 20), "Louvre Museum": Site("Louvre Museum", "Paris", "Medieval to Contemporary", "9AM-6PM", 15), }
- Priority Queues/Heaps: When generating itineraries based on user preferences, you may need to prioritize certain sites or paths. Priority queues or heaps can be useful for efficiently sorting and accessing elements based on their priorities.
These data structures can be used together to create a robust and efficient system for generating tailored tourist itineraries while considering various constraints and preferences.