A DataFrame full of thousands of rows is only useful if you can get data into it, make sense of what's inside, and reshape it into something you can actually analyze. In this module, you'll load data from Excel into pandas, inspect and summarize what you find, transform rows and columns, and work with dates and times the way pandas handles them best.

A pandas DataFrame can hold your data, but it has no opinion about fonts, column widths, or how a spreadsheet should actually look to someone opening it in Excel. In this module, you'll use openpyxl to build and modify workbooks directly, select specific cells and ranges, apply formatting, and define named ranges and tables.

A polished Excel report rarely comes from one tool alone; it's pandas doing the heavy lifting on the data and openpyxl doing the finishing touches. In this module, you'll combine data from multiple sheets, tame a large dataset, apply conditional formatting, and build both native Excel charts and Python-generated plots inside a workbook.

You've now got a working toolkit of pandas and openpyxl skills that most Excel users never develop, but the real payoff only shows up once you start using them on your own reports. In this module, you'll wrap up the course and see where to go next to keep building on what you've learned.