In practice I’ve found the big corporates try hard to keep their excel files with financial data and their Python environments with pip & all those associated risks far apart. That’s if pip works at all & isn’t caught by a firewall
There’s probably some ideal blend of locking regions, in-excel validation, in-python validation, and clean separation of human inputs and machine outputs. Has anyone figured out what that is already?
I've got csv, txt, xlsx in all different shapes and sizes and with just a few settings I can go through them quite easily and very fast as well.
But perhaps this might be good if you know no programming and want to make your life easier.
However in Financial companies, Python and Excel have always been used together by devs and also quants.
And they tend to use Anaconda, and also like all their other package managers, they would host an in-house package repository and block the public one. That way only approved packages are used, and they only update packages as needed.
Many though have a policy of minimising Excel and rolling out formal platforms whether in-house or off the shelf, as Excel is regarded as a ongoing risk of in-accuracy as full editable at all times, lack of git/version control and so on.
https://nostarch.com/download/samples/automate-excel-with-py...
The word "tedious" totally jumped out at me! The vast majority of corporate desk jockeys (like myself) use Excel as part of a tedious daily workflow. Python is the perfect de-tedious-ification tool. And easy to learn to boot.
Can confirm this practice in place at fortune 50 financial institutions. One in particular calls it "End-User Computing System Risk", meaning the end user created a business-critical "system" (i.e., a complex Excel file or Access database) on their own computer.
From Manual Grind to One-Click Workflow
by John Wengler
May 2026, 272 pp.
ISBN-13:
9781718504646
Download Chapter 3: Creating and Manipulating Dataframes and Lists
You’re already good at Excel. But you’re tired of the copy/paste, the helper columns, and the brittle formulas that break when someone adds a row. You want automation, and whether you write the code yourself or let AI generate it, you need to understand what’s actually running your business processes.
Automate Excel with Python teaches you to build real workflows, step-by-step. You’ll read messy workbooks into pandas dataframes, filter and reshape data without helper columns, merge sources without silent VLOOKUP failures, and export polished results with formatting intact. A capstone chapter ties it together: import a multi-tab workbook, generate exception reports, and email the results—all from one script you run with a single click.
You’ll learn how to:
AI can write the code. This book makes sure you’re the one in control of it.
Author Bio
John Wengler taught himself Python to automate a spreadsheet process and solve a “million-dollar problem” at work. He is the author of Managing Energy Risk and has taught at the Illinois Institute of Technology and Tulane University.
Table of contents
Introduction
Part I: From Spreadsheets to Dataframes
Chapter 1: Getting Started with Python
Chapter 2: Displaying Data and Understanding Data Types
Chapter 3: Creating and Manipulating Dataframes and Lists
Chapter 4: Adding, Modifying, and Calculating Column Data
Chapter 5: Accessing and Transforming Individual Cell Values
Chapter 6: Filtering and Displaying Dataframes
Part II: Tools to Replicate Excel Functionality
Chapter 7: Counting and Summing Values
Chapter 8: Combining Dataframes
Chapter 9: Formatting and Calculating Dates and Times
Part III: Workflow Techniques
Chapter 10: Reading Excel Files into Dataframes
Chapter 11: Saving Dataframes to Excel
Chapter 12: There and Back Again: An Excel–Python–Excel Workflow
Appendix A: Working with Folders, Files, and Pathnames
Appendix B: Cleaning Up a Messy Spreadsheet
Appendix C: The Ducks Module
Python Quick Reference
Index
View the Copyright page
View the detailed Table of Contents
View the Index