If you work with data in Python, Pandas is a real workhorse: it provides robust data structures (like DataFrame and Series) and numerous utilities for reading, organizing, filtering, joining, and analyzing data. The library is built on NumPy and has become a cornerstone of the Python data ecosystem.

The name Pandas comes from the econometrics term “panel data” and was originally developed by Wes McKinney for working with financial time series. The main idea is that you can work with tables similar to Excel or SQL tables (but in Python), with rows and columns, indexing, time series handling, and convenient support for missing values.

With Pandas you can, for example, read CSV and Excel files or import database tables, create group-by or pivot tables, perform time series analysis, merge different datasets, and much more. This makes the library very useful for data analysts, researchers, and developers who want to process and understand data before moving on to visualization or machine learning. One caveat: when datasets grow very large, Pandas typically requires the data to fit in memory, which can limit scalability-so for very large datasets you may need to complement it with other tools.