Welcome to the Pokémon Exploratory Data Analysis project repository! This project is an in-depth exploration of Pokémon attributes using data analysis techniques. Through Python scripts and Jupyter Notebook, we delve into the intricate world of Pokémon, uncovering insights, correlations, and patterns within their attributes.
In this project, we aim to answer key questions and gain insights into various aspects of Pokémon attributes:
- What is the distribution of Pokémon types based on their primary Type?
- How are different stats of Pokémon correlated with each other?
- How does the distribution of Attack stats vary among different Pokémon types?
- Are there any trends in the Average BMI across different Pokémon generations?
- What impact does Mega Evolution have on a Pokémon's Base Stat Total (BST)?
- How have the stats of Pokémon evolved across different generations?
script.py: Python script containing data analysis and visualization code.colors.py: Python script defining custom colors for plots.pokemon_data.csv: Dataset containing attributes of over 1000 Pokémon species.notebook.ipynb: Jupyter Notebook containing the complete data analysis process.
- Python 3.10
- Libraries: Numpy, Pandas, Matplotlib, and Scipy
- Prepare Data: Loading and cleaning the dataset.
- Exploring Data: Visualizations and insights about Pokémon types, correlations, and more.
- Insights and Visualizations: In-depth analysis and visualizations addressing specific questions.
- Conclusion: Summarizing the project's findings and insights.
- Clone this repository:
git clone https://github.com/yourusername/pokemon-insights.git - Install requirements:
pip install numpy pandas matplotlib scipy - Navigate to the project directory:
cd pokemon-insights - Run the
script.pyor explore thenotebook.ipynbto see the complete analysis.
Feel free to contribute, share your insights, or use this project as a reference for your own data analysis projects!