SQL and Python pandas both handle data efficiently, but they differ in usage and flexibility. SQL is ideal for querying structured data from relational databases, using commands like SELECT, JOIN, and GROUP BY. It’s highly optimized for large-scale data retrieval and manipulation within databases. On the other hand, pandas offers more versatility within Python programs, allowing in-memory data manipulation using DataFrames. It supports diverse data formats like CSV, Excel, and JSON, and is excellent for complex data transformations, analysis, and visualization.
For those looking to deepen their data handling skills, enrolling in a Python certification course is a smart step.