Re-implement `03.boxplot.ipynb` in Python as a `.py` script

### 🐍 Convert `03.boxplot.ipynb` to a Python script

This notebook currently uses **R** for data analysis and visualization. Your task is to **convert the logic into Python** and save it as a standalone `.py` script in the same folder.

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### 📁 Target file path:

```

projects/03.boxplot.py

```

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### ✅ Guidelines:

- Use `pandas` for data manipulation
- Use `matplotlib` / `seaborn` for plots
- Use `read_csv()` on datasets from the `datasets/` folder
- Match the logic of the original R notebook as closely as possible
- Add meaningful comments and structure your code clearly
- You may use `argparse` if needed to allow parameterization

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### 💡 Bonus Tips:

- You can preview the R logic inside `03.boxplot.ipynb` before translating it.
- If any logic seems inefficient in R, feel free to write cleaner Python alternatives.
- You may wrap sections of the script into functions or classes for clarity.

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Once done, open a pull request referencing this issue.
Happy Hacking! 🎉

Start by inspecting projects/03.boxplot.ipynb to understand the existing R analysis, then review the relevant datasets in datasets/. Create projects/03.boxplot.py using pandas and matplotlib or seaborn, preserving the notebook's logic and visualizations. Done means the standalone script runs and reproduces the original analysis without relying on the notebook.

Written by the indexing model from the issue text.