Python Data Analysis and Visualization in Java GUI Applications Through TCP Socket Programming

Python is popular in artificial intelligence (AI) and machine learning (ML) due to its versatility, adaptability, rich libraries, and active community. The existing Python interoperability in Java was investigated using socket programming on a non-graphical user interface (GUI). Python's data analysis library modules such as numpy, pandas, and scipy, together with visualization library modules such as Matplotlib and Seaborn, and Scikit-learn for machine-learning, aim to integrate into Java graphical user interface (GUI) applications such as Java applets, Java Swing, and Java FX. The substantial method used in the integration process is TCP socket programming, which makes instruction and data transfers to provide interoperability between Python and Java GUIs. This empirical research integrates Python data analysis and visualization graphs into Java applications and does not require any additional libraries or third-party libraries. The experimentation confirmed the advantages and challenges of this integration with a concrete solution. The intended audience for this research extends to software developers, data analysts, and scientists, recognizing Python's broad applicability to artificial intelligence (AI) and machine learning (ML). The integration of data analysis and visualization and machine-learning functionalities within the Java GUI. It emphasizes the self-sufficiency of the integration process and suggests future research directions, including comparative analysis with Java's native capabilities, interactive data visualization using libraries like Altair, Bokeh, Plotly, and Pygal, performance and security considerations, and no-code and low-code implementations.