The correct answer is D. Pandas.
Pandas is a Python library that provides fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. 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.
Dask is a parallel computing library for Python built on top of NumPy and Numba. It provides high-performance, scalable, and flexible parallel data structures and operations. Dask can be used to parallelize NumPy arrays, Pandas DataFrames, and other Python data structures.
Seaborn is a Python data visualization library based on Matplotlib. It provides a high-level interface for drawing attractive statistical graphics. Seaborn is designed to work with the pandas data structures, and provides a number of features that make it well-suited for data visualization.
xarray is a Python package for working with multidimensional arrays and data arrays with coordinate information. It is built on top of NumPy and Pandas, and provides a number of features that make it well-suited for working with scientific data.
In conclusion, the correct answer is D. Pandas. Pandas is a Python library that provides fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. 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.