This paper examines the use of the Python programming language for statistical processing of economic data, with a particular focus on its role in financial analysis and education. In the context of increasing data availability and the growing importance of data-driven decision-making, Python has emerged as a practical and flexible environment for performing statistical analysis. The study highlights the use of key Python libraries — including NumPy, Pandas, SciPy, and Matplotlib — for data collection, preprocessing, analysis, and visualization. The empirical part of the paper illustrates how Python can be used to retrieve real-world financial data from online sources and perform both descriptive and inferential statistical analyses. Using daily closing stock price data for selected companies over the period 2019–2025, the paper demonstrates the application of basic statistical measures, hypothesis testing methods (paired t-test and chi-square test), and correlation analysis (Pearson correlation matrix). The results indicate the practical utility of Python in handling economic datasets and supporting the interpretation of quantitative findings. The paper also emphasizes the importance of integrating programming skills into economics education in order to bridge the gap between theoretical statistical knowledge and practical data analysis. By enabling economists to conduct the full analytical workflow independently, Python can support more efficient and timely data-driven decision-making.

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