Which library is used for visualizing data with graphs?

Matplotlib is a widely used library in Python for creating static, interactive, and animated visualizations in a variety of formats. It provides a comprehensive set of tools to create different types of plots and visualizations such as line charts, bar charts, histograms, scatter plots, and more, allowing users to effectively present and interpret their data.

The library is known for its flexibility and control, enabling users to customize almost every aspect of their visualizations, which is particularly useful for data scientists and analysts when trying to convey insights from complex data sets. While Seaborn, another powerful visualization library, is built on top of Matplotlib and is designed for statistical data visualization, it is Matplotlib that serves as the foundational library for creating a wide array of plots.

Scikit-learn is primarily focused on machine learning and data processing, and Keras is specifically designed for building and training deep learning models; thus, neither of these libraries is specialized for data visualization tasks.