Python is a popular programming language for financial data analysis due to its extensive libraries and tools. Libraries such as Pandas for data manipulation, NumPy for numerical calculations, provide efficient data structures and functions for handling and manipulating financial data. In addition, libraries like Matplotlib and Seaborn allow for visualization of financial data. Analyzing financial timeseries data involves steps like data cleaning, visualization, and statistical analysis to understand the historical performance of investments and predict future trends.
