
Install openpyxl and create an in-memory workbook, write values to cells, save as example.xls, and prepare a function to convert yield curve data into a table.
learn how to build and use pandas multi indexes from tuples, including year and quarter, and apply them to real estate and CPI data for flexible indexing, grouping, and joining.
Adjust the real estate index for inflation using the consumer price index, compare it to the inflation-adjusted index, and compute quarterly returns with a pivot, then save to csv.
Develop a data creation workflow that generates year-and-quarter reports by end-of-quarter date. Use modulo to advance quarters and build yield curve and inflation-adjusted real estate data frames.
Welcome to the Excel Report Automation with Python course! This course is meant to be a more advanced course taken after some of the basic FinanceAndPython courses are complete. Within this course, you will learn exactly how to build scripts to automatically parse data into excel reports. Before beginning, please down the course files through github. The notebooks are also there as well if you want to follow along through them.
Throughout the course, you will work on a real world project of producing an economic report on the real estate and treasury markets automatically. This project will give you hands-on experience with all the concepts and tools covered in the course, and help you develop a better understanding of how to apply them in practice. There are many industries with which this kind of skill can be applied, but especially so in the world of finance.
By the end of this course, you will have a strong understanding of how to automate Excel report generation with Python, and be able to apply this knowledge to your own projects and workflows. So let's get started and dive into the world of Excel report automation with Python! You'll be amazed at what you can build!