📊 Data Automation Toolkit

A beginner-friendly data cleaning and automation toolkit built with Python.
This project uses real-world datasets (Titanic, Netflix, and Superstore) to demonstrate how to clean, transform, and export data in multiple formats (CSV, JSON, Excel).

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🚀 Features

• Handle missing/null values (drop, fill, interpolate).
• Normalize text (case formatting, remove duplicates).
• Convert column types (dates, numbers, categorical).
• Perform filtering, sorting, and aggregations.
• Create derived columns (e.g., profit margin, categories).
• Import/export datasets to CSV, JSON, Excel.
• Command-line utility to clean datasets with a single command.

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📊 Datasets Used

• Titanic Dataset → survival analysis & missing data.
• Netflix Movies & TV Shows → text cleaning & JSON export.
• Superstore Sales Dataset → Excel operations.
• Messy CSVs (self-generated) → practice handling nulls & inconsistencies.

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⚡ Installation

Clone this repo and install dependencies:

git clone https://github.com/your-username/data-automation-toolkit.git
cd data-automation-toolkit
pip install -r requirements.txt
🛠 Usage
1️⃣ Run from CLI

python clean_data.py data/titanic.csv --export json
Options:

--export csv → save as CSV

--export json → save as JSON

--export excel → save as Excel

2️⃣ Use in Jupyter Notebook

import pandas as pd
from utils.clean_utils import clean_dataframe
from utils.file_utils import export_data

df = pd.read_csv("data/netflix.csv")
df_clean = clean_dataframe(df)
export_data(df_clean, "output/netflix_clean.json", "json")
📦 Requirements
Python 3.9+

Pandas

OpenPyXL

Jupyter Notebook

Install via:


pip install -r requirements.txt
📸 Example Output
Name	Age	Survived
Jack Dawson	20	0
Rose DeWitt	19	1
Thomas Andrews	39	0

Cleaned Titanic dataset sample (missing ages handled, casing normalized).

🔮 Roadmap
Add automated data profiling report (using pandas-profiling).

Extend CLI to support multiple datasets at once.

Add visualization utilities for summary statistics.

🤝 Contributing
Contributions are welcome!

Fork the repo

Create a feature branch (git checkout -b feature-new)

Commit changes (git commit -m 'Add new feature')

Push to branch & open a Pull Request

📜 License
This project is licensed under the MIT License.