The correct answer is: A. NumPy
NumPy is a Python library that provides fast, efficient numerical computation. It is the foundation of many other Python libraries for scientific computing, including Pandas.
NumPy arrays are multidimensional, homogeneous data structures that can be used to store and manipulate data efficiently. They are similar to lists, but they have several advantages, including:
- They are more efficient for storing and manipulating large amounts of data.
- They can be used to perform vectorized operations, which can be much faster than performing the same operations on individual elements.
- They can be used to create and manipulate matrices, which are useful for many scientific and engineering applications.
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.
Pandas is built on top of NumPy, and it uses NumPy arrays to store data. However, Pandas provides a higher-level interface to NumPy arrays, which makes it easier to work with data. Pandas also provides a number of features that are not available in NumPy, such as the ability to create and manipulate DataFrames, which are multidimensional data structures that can be used to store and analyze data.
In conclusion, NumPy is a Python library that provides fast, efficient numerical computation. It is the foundation of many other Python libraries for scientific computing, including Pandas. 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 is built on top of NumPy, and it uses NumPy arrays to store data. However, Pandas provides a higher-level interface to NumPy arrays, which makes it easier to work with data. Pandas also provides a number of features that are not available in NumPy, such as the ability to create and manipulate DataFrames, which are multidimensional data structures that can be used to store and analyze data.