A SURVEY ON PYTHON-BASED DATA VISUALIZATION TECHNIQUES AND EDUCATIONAL APPROACHES

Data visualization has become a fundamental component of modern data analysis, enabling users to interpret complex datasets efficiently and make informed decisions. With the rapid growth of big data and digital information, there is an increasing need for effective tools that can transform raw data into meaningful visual insights. Python has emerged as a dominant programming language in this domain due to its simplicity, flexibility, and extensive ecosystem of libraries such as Matplotlib, Seaborn, Plotly, and Bokeh. This paper presents a comprehensive study of Python-based data visualization techniques, focusing on both static and interactive approaches. It examines how different libraries support various types of visualizations, including line charts, bar graphs, and pie charts, and evaluates their usability, performance, and application areas. The study also highlights the integration of data processing tools such as Pandas and NumPy with visualization frameworks to enhance analytical capabilities. Finally, the paper identifies key challenges, including scalability, real-time processing, and accessibility, and outlines future directions such as AI-driven visualization and cloud-based analytics. Overall, this study emphasizes the importance of Python visualization tools in bridging the gap between data analysis and decision-making, making them essential in both academic and industrial environments.