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- DataFrame: a data structure for representing and manipulating tabular data, similar to a spreadsheet or a database table
- Series: a data structure for representing and manipulating one-dimensional data
- Index: a data structure for representing and manipulating axis labels for data
- Input/Output: tools for reading and writing data from/to a variety of formats and sources, such as CSV, Excel, and SQL databases
- Data cleaning: tools for dealing with missing or duplicate values, and transforming or normalizing data
- Data manipulation: tools for sorting, filtering, aggregating, and joining data
- Data visualization: tools for creating charts and plots to visualize data
- Statistics and machine learning: tools for running statistical tests, fitting models, and performing machine learning algorithms on data
- Optimization and performance: tools for optimizing the performance and memory usage of data manipulation and analysis operations
Overall, Pandas is a powerful and versatile library for working with data in Python. It provides a range of tools and features that make it easy to load, clean, manipulate, and analyze data. It is widely used in a variety of applications, including data science, finance, and statistics.