  AsaPy is a custom-made Python library designed to simplify and optimize the
analysis of aerospace simulation data. Instead of introducing new
methodologies, it excels in combining various established techniques, creating
a unified, specialized platform. It offers a range of features, including the
design of experiment methods, statistical analysis techniques, machine learning
algorithms, and data visualization tools. AsaPy's flexibility and
customizability make it a viable solution for engineers and researchers who
need to quickly gain insights into aerospace simulations. AsaPy is built on top
of popular scientific computing libraries, ensuring high performance and
scalability. In this work, we provide an overview of the key features and
capabilities of AsaPy, followed by an exposition of its architecture and
demonstrations of its effectiveness through some use cases applied in military
operational simulations. We also evaluate how other simulation tools deal with
data science, highlighting AsaPy's strengths and advantages. Finally, we
discuss potential use cases and applications of AsaPy and outline future
directions for the development and improvement of the library.
