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Automate Excel with Python

▲ 53 points 23 comments by teleforce 4d ago HN discussion ↗

Pangram verdict · v3.3

We believe that this document is a mix of AI-generated, and human-written content

52 %

AI likelihood · overall

Mixed
41% human-written 59% AI-generated
SEGMENTS · HUMAN 1 of 2
SEGMENTS · AI 1 of 2
WORD COUNT 394
PEAK AI % 77% · §1
Analyzed
Jul 8
backend: pangram/v3.3
Segments scanned
2 windows
avg 197 words each
Distribution
41 / 59%
human / AI fraction
Verdict
Mixed
Pangram v3.3

Article text · 394 words · 2 segments analyzed

Human AI-generated
§1 AI · 77%

From Manual Grind to One-Click Workflowby John WenglerMay 2026, 272 pp.ISBN-13: 9781718504646ContentsDownload 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:

Read existing Excel files you already use into Python, even the messy ones Replace daily copy/paste routines with reusable scripts Merge and match data across sources with auditable results Handle dates, times, and the edge cases that break formulas Export from Python to Excel with column widths, number formats, and frozen panes intact Build workflows that run daily without babysitting

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.

§2 Human · 14%

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