In my previous articles, I have discussed how to use pandas as a replacement for Excel when it comes to data wrangling. In many cases, a python + pandas solution is superior to the highly manual processes many people use for manipulating data in Excel. However, Excel is used for many scenarios in a business environment - not just data wrangling. This specific post will discuss how to do financial modeling in pandas instead of Excel. For this example, I will build a simple amortization table in pandas and show how to model various outcomes.
In some ways, building the model is easier in Excel (there are many examples just a google search away). However, as an exercise in learning about pandas, it is useful because it forces you to think about how to use pandas strengths to solve a problem in a way different from the Excel solution. In my opinion the solution is more powerful because you can build on it to run multiple scenarios, easily chart various outcomes and focus on aggregating the data in a way most useful for your needs.