SOLVING COMPLEX ECONOMIC PROBLEMS WITH PYTHON

Bunyodbek Anvarjonov

Keywords

Economic Problems, Python, Data Analysis, Econometrics, GDP, Pandas, Statsmodels, ARIMA, Visualization.

Abstract

This study explores the use of Python for solving complex economic problems, focusing on data analysis, econometric modeling, and simulation scenarios. By leveraging libraries such as Pandas, Statsmodels, Matplotlib, and Seaborn, we demonstrate practical applications in GDP analysis, policy impact simulations, and predictive modeling.

pdf (Русский)

References

McKinney, W. (2017). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. O'Reilly Media, Inc.

VanderPlas, J. (2016). Python Data Science Handbook: Essential Tools for Working with Data. O'Reilly Media, Inc.

Sheppard, K. Introductory Econometrics with Python.

Brownlee, J. (2017). "ARIMA Model - Complete Guide to Time Series Forecasting". Machine Learning Mastery.

Mallayev O., Anvarjonov B., Aziz M. Cache Problems in Parallel Computational Processes //Annals of the Romanian Society for Cell Biology. – 2021. – С. 8924-8934.

Bunyodbek A. Solving examples of the distance between two straight lines in Python //Innovations in exact science. – 2024. – Т. 1. – №. 3. – С. 1-7.