SimpleDataProcessing

Three self-contained Jupyter Notebooks that clean messy CSV/Excel files and produce 5 clear charts per case. Each notebook saves a cleaned dataset, chart images (PNGs), and keeps the steps reproducible in code.

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What’s inside (short)

SimpleDataProcessing/
├─ case1_ecommerce_sales/      # notebook + charts + sample raw/clean CSV
├─ case2_saas_churn/           # notebook + charts + sample raw/clean CSV
├─ case3_marketing_perf/       # notebook + charts + sample raw/clean CSV
└─ .gitignore

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Quick start

1) Clone and open

git clone https://github.com/STProgrammer/SimpleDataProcessing.git
cd SimpleDataProcessing

Open in JupyterLab or classic Jupyter Notebook (VS Code works too).

2) Install minimal dependencies

python -m venv .venv
# Windows: .venv\Scripts\activate
# macOS/Linux:
source .venv/bin/activate

pip install --upgrade pip
pip install jupyter pandas numpy matplotlib
# optional: pip install seaborn

3) Run any notebook

• Open case*/*_template.ipynb.
• Adjust the input file path (CSV/XLSX) in the first cell if needed.
• Run cells top-to-bottom.

Each notebook will:

• load the raw file
• standardize text/casing, parse dates, fix types, remove duplicates, handle nulls
• produce 5 charts (saved under charts/)
• export a cleaned dataset (*clean.csv)

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Per-case notes

• case1_ecommerce_sales — Ecommerce sales data, 10 charts, a Jupyter Notebook
• case2_saas_churn — SaaS sales and churt data, 10 charts, a Jupyter Notebook
• case3_marketing_perf — Marketing performance data, 5 charts, a Jupyter Notebook

(Column names vary by dataset; tweak early cells to map your columns.)

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Outputs

• charts/1_*.png … charts/5_*.png (publication-ready)
• *clean.csv (cleaned dataset)
• Executed notebook.ipynb documenting every step

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Environment

• Python 3.10+ recommended
• Packages: jupyter, pandas, numpy, matplotlib (optional: seaborn)

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License

Add a LICENSE file (e.g., MIT/BSD/Apache) if you plan to reuse.