In recent years Python has become the most popular programing language for data analysis and visualization thanks to its ease of use with libraries like Pandas, Matplotlib, Seaborn and ScyPi.
In this project I was contracted to write an article analyzing the usability of the Four Percent Rule, a well-known rule of thumb in the personal finance industry. To test the rule, I gathered historic S&P500 data from 1914 to 2015 and uploaded it into Jupyter Notebooks using Pandas. From here I was able to iterate through every 30 year period in the data simulating the change in the value of a theoretical stock portfolio at every starting year from 1915 to 1985 accounting for dividends, inflation and likely sell offs when the portfolio value shrunk too low.
Using Jupyter Notebooks cell-based structure I was able to efficiently create several visualizations of the data all in the same file. These visualizations included histograms with bimodal best fit lines, bar charts and line plots, created using Matplotlib and Seaborn libraries. The article is to be published in a large real estate magazine.