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Pandas is a Python library used for data analysis and manipulation. It provides powerful functions for importing and processing data from various data sources, including CSV files, Excel files, databases and web APIs.
The core components of Pandas are two data structures: Series and DataFrames. Series is a one-dimensional data structure, similar to a list or array, while DataFrames are a tabular data structure consisting of columns and rows, similar to a table in a database.
Pandas allows you to filter, sort, group, merge, transform and clean data. It also supports the creation of pivot tables and time series analysis. Pandas also allows users to handle missing values and interpolate missing data.
Pandas is often used in conjunction with other libraries such as NumPy, Matplotlib and Scikit-learn to perform complex data analysis. Due to its powerful features and ease of use, Pandas has become one of the most popular libraries for data analysis in Python.