The correct answer is C. Dask.
Dask is a Python library that enables distributed computing and parallel processing. It is commonly used for big data analysis, as it can handle large datasets efficiently. Dask can be used to parallelize tasks such as data loading, cleaning, and analysis. It can also be used to create distributed data structures, such as DataFrames and Series.
Pandas is a Python library that provides high-performance, easy-to-use data structures and data analysis tools for working with structured (tabular, multidimensional, potentially heterogeneous) and time series data. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language.
Seaborn is a Python data visualization library based on Matplotlib. It provides a high-level interface for drawing attractive statistical graphics. Seaborn makes it easy to create complex visualizations, such as line plots, bar charts, and scatter plots. It also provides a number of features that make it easy to customize the appearance of your visualizations.
Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell, web application servers, and various graphical user interface toolkits.
Dask is a better choice than Pandas for distributed data processing and parallel computing, especially for big data analysis. This is because Dask is specifically designed for these tasks, while Pandas is not. Dask can handle large datasets more efficiently than Pandas, and it can also be used to create distributed data structures.