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Pykemon: Data Analysis & Python Fundamentals 🐉🐍

This project was developed for the Information Systems Fundamentals course as part of the Computer Science Engineering degree. The primary goal is to demonstrate proficiency in Python for data processing, storage, and visualization using a real-world Pokémon dataset.

📊 About the Dataset

The project is based on the 15_pokemon.csv file, which includes detailed statistics from the first 6 generations:

  • Identifiers: ID, Name, and Types (Primary & Secondary).
  • Combat Stats: HP, Attack, Defense, Sp. Atk, Sp. Def, and Speed.
  • Metadata: Generation and Legendary status.

📁 Project Structure (P1 - P6)

Following academic requirements, the source code is organized into six packages within the /Experimentos directory. Each package addresses a specific stage of the data pipeline:

Package Description Key Files
P1 Python fundamentals, logic gates, and basic structures. P1.py
P2 Object-Oriented Programming (OOP) and data modeling. P2.py, P2_UML.png
P3 Data persistence using SQLite and database management. P3.py, pokemon.db
P4 Advanced data manipulation and business logic. P4.py
P5 Exploratory Data Analysis (EDA) with Pandas. p5.ipynb
P6 Data visualization with Seaborn and Matplotlib. P6.ipynb, *.png

📈 Insights & Visualizations

The P6 module automates the generation of visual insights from the raw dataset. Some of the featured analyses include:

  • HP Distribution by Type: A comparative study of base health across elemental types.
  • Type Correlations: Analysis of the most common type combinations and their average stats.
  • Type Frequency: Bar charts displaying type predominance across all 6 generations.

Analysis Example

📝 Additional Documentation

  • Assignment: The Trabajo de Python.pdf file contains the official guidelines and grading criteria.
  • Modeling: The P2 folder includes a UML diagram describing the class architecture used in the project.
  • Environment: This repository includes PyCharm configuration files (folder .idea) to ensure technical consistency during review.
  • Examples: An Ejemplo folder is provided with alternative datasets (e.g., VinoBlanco.csv) used during the initial testing phase.

👤 Author


This project was created for academic purposes for the Computer Science Engineering Department.

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A comprehensive Python data analysis project using the Pokémon dataset, featuring custom logic, OOP, SQLite database integration, and advanced visualization with Pandas and Seaborn.

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