Pandas is a popular open-source data analysis and manipulation library for Python. It provides data structures for efficiently storing and manipulating large and complex datasets, as well as tools for cleaning, transforming, and processing data.

The primary data structures in Pandas are Series and DataFrame. A Series is a one-dimensional array-like object that can hold any data type, while a DataFrame is a two-dimensional table-like structure that can hold multiple Series objects. Pandas also provides many functions for handling missing data, combining and merging datasets, reshaping and pivoting data, and performing statistical analysis.

Pandas is widely used in data science and machine learning applications, as it enables users to quickly and easily explore and analyze datasets, and prepare data for modeling and prediction. It is also commonly used for data wrangling and preprocessing tasks in data pipelines and ETL (extract, transform, load) processes.

Pandas is a powerful tool for working with data in Python, and its popularity and versatility have made it an essential library for anyone working with data in Python.

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