Solution

The pandas library in Python is essential for data manipulation and analysis, offering powerful functions to handle data structures like DataFrames and Series. It allows users to perform operations such as filtering, reshaping, merging, and aggregating data, making it indispensable for data analysts. With pandas, data can be cleaned, organized, and prepared efficiently, allowing for transformations necessary for deeper analysis. Its versatile functionalities support complex data workflows, making it one of the most widely used libraries in data science and analytics. The other options are incorrect because: • Option 1 (NumPy) is primarily for numerical operations and lacks high-level data manipulation tools. • Option 3 (Matplotlib) is for data visualization, not data manipulation. • Option 4 (Seaborn) extends Matplotlib’s capabilities but focuses on visualization. • Option 5 (scikit-learn) is used for machine learning, not data manipulation.