Final answer:
The correct Python package for linear algebra, optimization, mathematical integration, and statistics is A) NumPy. NumPy is a foundational package for scientific computing in Python, enabling a range of mathematical and numerical computations.
Step-by-step explanation:
The Python package that includes support for linear algebra, optimization, mathematical integration, and statistics is A) NumPy. NumPy, which stands for Numerical Python, is a fundamental package for scientific computing in Python. It provides multi-dimensional array objects and a collection of routines for processing those arrays. With NumPy, you can perform a variety of mathematical tasks ranging from simple mathematical operations to complex numerical computations such as Fourier transforms, linear algebra, and random number generation.
While other packages mentioned in the options also provide support in specialized areas (Matplotlib for plotting, Pandas for data manipulation and analysis, Seaborn for statistical data visualization), they do not offer the comprehensive set of tools for mathematical computations that NumPy does. Therefore, for tasks associated with linear algebra, optimization, and the like, NumPy would be the correct choice.