In Python, which library is commonly used for data manipulation and analysis?

Pandas is the go-to library for data manipulation and analysis in Python. It provides powerful tools for handling structured data, such as data frames, which can easily be modified and analyzed. With its intuitive data structures like Series and DataFrame, Pandas allows for seamless data loading, preparation, and cleaning operations. It supports numerous functions that facilitate tasks such as merging, reshaping, and aggregating data. This makes it particularly effective for working with large datasets, where such capabilities are essential for analysis and insight extraction.

Other libraries mentioned, while very useful, serve different primary purposes. Numpy mainly focuses on numerical computing and provides support for large, multi-dimensional arrays and matrices, alongside a collection of mathematical functions to operate on these arrays. Matplotlib, on the other hand, is primarily a plotting library used for visualizing data rather than manipulating it. Scikit-learn is designed for machine learning tasks and offers tools for building and evaluating models rather than directly manipulating data sets. Each library complements the data analysis process, but Pandas stands out for its dedicated functionality in data manipulation and analysis.