A project for the 42 Madrid curriculum exploring advanced data structure manipulation, collections efficiency, and memory optimization.

Data Quest: Mastering Python Collections

🎮 Description: A game analytics platform mastering Python collections, generators, and comprehensions under strict flake8 and mypy standards.

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📖 Project Overview

Data Quest is a data engineering module focused on building core analytic components within a simulated gaming ecosystem. The project shifts from basic syntax toward evaluating the performance, memory footprint, and algorithmic efficiency of Python's built-in data types.

By simulating data ingestion pipelines—such as real-time player score streams, 3D coordinate vector mapping, achievement tracking via set theory, and complex player inventory transformations—this module demonstrates how structural choices directly impact execution performance and time complexity ($O(1)$ vs $O(n)$).

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🛠️ Tech Stack & Standards

• Language: Python 3.12.3
• Static Analysis: Strict type hinting verified with mypy
• Linter Compliance: PEP 8 styling enforced via flake8
• Environment Constraints: Pure in-memory processing and deferred execution loops. No external non-standard libraries or unauthorized file I/O operations.

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📂 Project Structure

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├── ex0/
│   └── ft_command_quest.py
├── ex1/
│   └── ft_score_analytics.py
├── ex2/
│   └── ft_coordinate_system.py
├── ex3/
│   └── ft_achievement_tracker.py
├── ex4/
│   └── ft_inventory_system.py
├── ex5/
│   └── ft_data_stream.py
└── ex6/
│   └── ft_data_alchemist.py