Project 1 – Book Management Application (Python, SOLID Architecture)

Overview

Project 1 is a Python Book Management Application designed using SOLID design principles and a layered architecture. The goal of this project is to demonstrate clean separation of concerns, maintainable code structure, and integration of both traditional application logic and data science tooling.

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Architectural Requirements

The project must follow SOLID design principles:

• S – Single Responsibility Principle
  Each class or module should have one clear responsibility.
• O – Open/Closed Principle
  Code should be open for extension but closed for modification.
• L – Liskov Substitution Principle
  Subtypes must be usable in place of their base types.
• I – Interface Segregation Principle
  Avoid forcing classes to depend on methods they do not use.
• D – Dependency Inversion Principle
  Depend on abstractions, not concrete implementations.

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Layered Structure

The application will be organized into the following layers:

1. Application Layer

• Contains a single file: repl.py
• Responsible for:
  • User interaction
  • Command parsing
  • Displaying output
  • Delegating business logic to services
• Should not contain business logic or data access code.

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2. Domain Layer

• Contains core business entities and rules and the Book object.
• Responsibilities:
  • Data validation
  • Pure business logic
• No direct database or UI dependencies.

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3. Repository Layer

• Handles data persistence and retrieval.
• JSON for P1, SQL for P2
• Responsibilities:
  • CRUD operations at the storage level
  • Abstract interfaces for swapping implementations
• Should not contain business rules.

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4. Service Layer

• Contains application/business logic.
• Responsibilities:
  • Coordinating workflows
  • Enforcing policies
  • Calling repository methods
  • Returning processed results

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Core Functional Features

CRUD Operations on Books

• Create a book
• Read / list books
• Update book information
• Delete a book

Book Fields

• title: str
• author: str
• genre: Optional[int]
• publication_year: Optional[int]
• page_count: Optional[int]
• average_rating: Optional[float]
• ratings_count: Optional[int]
• price_usd: Optional[float]
• publisher: Optional[str]
• language: Optional[str]
• format: Optional[str]
• in_print: Optional[bool]
• sales_millions: Optional[float]
• last_checkout: Optional[str]
• available: Optional[bool]
• book_id: str

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Library Workflow Features

• Check-Out Book
  • Mark a book as unavailable.
• Check-In Book
  • Mark a book as available.
• Checkout History
  • Track when books are checked in/out
• Due Dates (Stretch Goal)
  • Track who last checked out a book by their email
  • Notify them 7 days before their due date

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Analytics Features

The application will also include a data science component focused on analyzing the book dataset.

Required Libraries

• NumPy – numerical operations and array manipulation
• Pandas – data cleaning, transformation, and tabular analysis
• Matplotlib – visualizations and charts
• Jupyter Notebooks – interactive analysis and reporting

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Analytics Requirements

• Use pandas DataFrames as the data source
• Clean data
• Create a bar chart that shows which genres are most common
• Create a bar chart that shows which genres tend to be rated highest
  • Use a Bayesion Average and sort columns in descending order:

   weighted_rating = (median_ratings_count / (median_ratings_count + m)) * mean_average_rating
   + (m / (median_ratings_count + m)) * mean_average_rating_all_books
   
   m = minimum ratings threshold (50-100 recommended)
  

• Create a scatter plot that shows if higher priced books have better ratings
• Create a line chart that shows books released by year
• Create a pie chart that shows our checked in vs available books

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