Build a Real-World Data Cleaning Pipeline in Python & Pandas | Multi-Dataset Project with Streamlit

Real-world data is messy. In this video, I walk you through a complete, modular Data Cleaning & Data Quality Pipeline built with Python and Pandas, so you learn how to organize your code like a real project instead of writing everything in one file. 🔗 GitHub Repository: https://github.com/vanitha-spec/Multi... 📌 What you'll learn in this video: ✅ How to read CSV and Excel files with Pandas ✅ Profiling data before cleaning (missing values, duplicates, data types) ✅ Cleaning strings: whitespace, names, inconsistent categories (Male/male/M) ✅ Cleaning numbers and currency (₹50,000, Rs. 60,000, $1,200) without losing currency information ✅ Handling multiple date formats and invalid dates ✅ Removing duplicate records ✅ Validating data: age ranges, email format, unique IDs, required fields ✅ Generating before/after data quality reports ✅ Exporting cleaned data to CSV and Excel ✅ Using a YAML config file to separate rules from code ✅ Structuring a project with modules and one pipeline for multiple datasets 📂 Datasets used (intentionally messy): • Customers • Orders • Products • Employees 🧠 Key concepts explained: • Cleaning vs Validation: what's the difference? • Why you should never modify raw data (use df.copy()) • Separation of concerns and modular project design • Common pipeline + dataset-specific rules 🛠 Tech stack: Python, Pandas, openpyxl, PyYAML ▶️ How to run: 1. Clone the repo 2. pip install -r requirements.txt 3. python main.py 🚧 Coming next: rejected-records handling, advanced validation, pytest testing, logging, CLI and groupby analytics. 👍 If this helped you, please Like, Share and Subscribe for more Python, Pandas and Data Engineering projects! 💬 Questions? Drop them in the comments and I'll reply. #Python #Pandas #DataCleaning #DataEngineering #DataAnalytics