Why do we need pandas? We have SQL and PySpark. Why use pandas?

Pandas provides a lightweight, in‑memory data structure (DataFrame) that is ideal for quick exploratory data analysis, prototyping, and small to medium datasets. Unlike SQL, which operates on tables in a database, pandas lets you manipulate data directly in Python, offering intuitive indexing, filtering, and vectorized operations. Compared to PySpark, pandas is simpler to set up and has a richer ecosystem of plotting libraries (Matplotlib, Seaborn) and statistical functions. It excels when the dataset fits in RAM and when you need rapid iteration, interactive notebooks, or integration with machine‑learning libraries like scikit‑learn. In practice, pandas is often used for data cleaning and feature engineering before pushing the processed data to a distributed system for large‑scale training or analytics.