Build a Python Data Quality ETL Starter for Messy CSV, Excel, JSON, and API-Style Data

1. 1Import necessary libraries like pandas and requests to handle data from different sources
2. 2Build a function to read and clean CSV data using pandas
3. 3Create a function to read and clean Excel data using pandas
4. 4Develop a function to fetch and clean JSON data from APIs using requests and json libraries
5. 5Apply data quality checks and transformations to handle missing values and inconsistent data formats

Data engineers, data analysts, and data scientists can benefit from this tutorial to improve data quality and streamline their ETL processes

💡 Use Python libraries like pandas and requests to build a flexible ETL pipeline for handling diverse data sources and formats

🚀 Improve data quality with Python! Build an ETL starter to handle messy CSV, Excel, JSON, and API data 📊