The correct answer is D. pyarrow.
pyarrow is a Python library that provides a unified interface for reading and writing data in a variety of formats, including Apache Parquet. It is designed to be efficient and scalable, and it supports a wide range of features, such as schema evolution and partitioning.
Matplotlib is a Python library for plotting data. It is widely used for scientific and engineering applications, and it provides a variety of features for creating high-quality plots. However, it does not support reading or writing data in the Apache Parquet file format.
Seaborn is a Python library for statistical visualization. It is built on top of Matplotlib, and it provides a number of features that make it easier to create effective statistical plots. However, it also does not support reading or writing data in the Apache Parquet file format.
Pandas is a Python library for data analysis. It provides a variety of features for working with structured data, including reading and writing data in a variety of formats. However, it does not support the Apache Parquet file format natively. There are third-party libraries that can be used to read and write Parquet data with Pandas, but pyarrow is a more direct and efficient option.
In conclusion, pyarrow is the best library for working with data stored in the Apache Parquet file format in Python. It is efficient, scalable, and supports a wide range of features.