5 Best Ways to Design a Parking System in Python

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πŸ’‘ Problem Formulation: This article addresses the challenge of designing a parking system using Python. Such a system should manage vehicle entry and exit, allocate parking spots, and track availability. For instance, given a parking lot with a capacity of 50 cars, the system must only allow 50 cars to park and provide relevant information to the drivers. The desired output is a user-friendly interface that adequately updates and displays parking availability.

Method 1: Object-Oriented Design

This method leverages the object-oriented programming (OOP) paradigm in Python to create a scalable and organized parking system. It defines classes for parking lot, parking spots, and vehicles, ensuring easy management and expansion of the system’s features through methods and attributes specific to each object.

Here’s an example:

class ParkingSpot:
    def __init__(self, spot_number, available=True):
        self.spot_number = spot_number
        self.available = available

    def occupy(self):
        self.available = False

    def vacate(self):
        self.available = True

class ParkingLot:
    def __init__(self, capacity):
        self.capacity = capacity
        self.spots = [ParkingSpot(i) for i in range(capacity)]

    def park_vehicle(self):
        for spot in self.spots:
            if spot.available:
                return f"Vehicle parked at spot {spot.spot_number}"
        return "Parking lot is full!"

    def leave_vehicle(self, spot_number):
        return f"Vehicle left spot {spot.spot_number}"

parking_lot = ParkingLot(50)

The output:

Vehicle parked at spot 0

This snippet instantiates a ParkingLot with 50 spots and parks a vehicle in the first available spot. The ParkingSpot class controls the state of each spot, while the ParkingLot manages the overall availability and management of those spots.

Method 2: Using Dictionaries

By using dictionaries, this method provides a quick and efficient way to map parking spots to their availability. Each key-value pair represents a parking spot and its occupancy status, respectively, facilitating the search and updating of parking spots with less code complexity.

Here’s an example:

parking_lot = {i: True for i in range(50)}  # True indicates an available spot

def park_vehicle(spot_number):
    if parking_lot[spot_number]:
        parking_lot[spot_number] = False
        return f"Vehicle parked at spot {spot_number}"
    return "Spot already taken!"

def leave_vehicle(spot_number):
    parking_lot[spot_number] = True
    return f"Vehicle left spot {spot_number}"


The output:

Vehicle parked at spot 20

This code snippet demonstrates parking a vehicle in spot number 20. The dictionary named parking_lot holds the spots’ statuses, where updating a spot’s status is as easy as assigning a boolean value.

Method 3: Queue-Based Approach

This approach utilizes the queue data structure in Python to manage the order in which vehicles enter and leave. It’s particularly effective for parking lots that operate on a first-come, first-served basis. Parking and leaving actions are performed using queue operations, ensuring a fair and systematic allocation of spaces.

Here’s an example:

from queue import Queue

parking_queue = Queue(maxsize=50)

def park_vehicle():
    if not parking_queue.full():
        spot_number = parking_queue.qsize()
        return f"Vehicle parked at spot {spot_number}"
    return "Parking lot is full!"

def leave_vehicle():
    if not parking_queue.empty():
        spot_number = parking_queue.get()
        return f"Vehicle left spot {spot_number}"
    return "Parking lot is empty!"


The output:

Vehicle parked at spot 0

Using Python’s queue module, this snippet simulates parking in the next available spot. The Queue object holds the sequence of parked vehicles, and the methods park_vehicle and leave_vehicle manage the parking operations with queue methods put() and get().

Method 4: Priority Queue for VIP Parking

For parking lots with VIP sections, the priority queue structure efficiently manages parking based on priority levels. Vehicles can be assigned different priorities, with lower numbers indicating higher priorities. This allows VIP vehicles to be allocated spots before others dynamically.

Here’s an example:

from queue import PriorityQueue

vip_parking = PriorityQueue(maxsize=50)

def park_vehicle(vehicle_priority, vehicle_id):
    if not vip_parking.full():
        vip_parking.put((vehicle_priority, vehicle_id))
        return f"VIP Vehicle {vehicle_id} parked."
    return "VIP section is full!"

def leave_vehicle():
    if not vip_parking.empty():
        _, vehicle_id = vip_parking.get()
        return f"VIP Vehicle {vehicle_id} left."
    return "VIP section is empty!"

print(park_vehicle(1, 'A123'))

The output:

VIP Vehicle A123 parked.

This code uses the PriorityQueue object from Python’s queue module, where the park_vehicle function takes a vehicle’s priority and ID and uses the put() method to enqueue it. The leave_vehicle function dequeues the vehicle with the highest priority (lowest numerical value).

Bonus One-Liner Method 5: Lambda Function for Quick Allocation

A simplistic, one-liner approach is to use a lambda function that allocates a spot if available and increments the counter, or states that the parking lot is full if the counter reaches capacity. It’s a concise, functional approach for smaller parking systems.

Here’s an example:

park_vehicle = lambda spots, capacity: "Parking lot is full!" if spots[0] == capacity else f"Vehicle parked at spot {spots[0]}; {spots[1]()}"

spots = [0, lambda: spots.__setitem__(0, spots[0] + 1)]
capacity = 50
print(park_vehicle(spots, capacity))

The output:

Vehicle parked at spot 0

This lambda function, park_vehicle, takes a mutable list spots that tracks the number of parked vehicles and a capacity limit. It parks a vehicle and updates the counter, or signals when the parking lot reaches its capacity. This one-liner represents a minimalist’s take on parking lot systems.


  • Method 1: Object-Oriented Design. Provides a modular and expandable system. Ideal for larger applications with complex rules. However, it may introduce unnecessary overhead for simpler scenarios.
  • Method 2: Using Dictionaries. Simple and quick for small to medium-sized programs. Easy to implement and understand. May not scale well with additional features or more complex scenarios.
  • Method 3: Queue-Based Approach. Ensures fairness in space allocation. Suitable for parking lots with a simple first-come, first-served policy. Less flexible if different priorities or reservation systems must be implemented.
  • Method 4: Priority Queue for VIP Parking. Provides a means to manage different classes of parking spaces. More complex but allows for prioritization. Requires definition of clear priority levels which may not be straightforward.
  • Bonus One-Liner Method 5: Lambda Function for Quick Allocation. Extremely concise. Best for very basic systems without the expectation of scaling or adding features. Offers limited control and may be impractical for real-world use cases.