Angielski
Reactive Publishing
Python in Excel for Data Pipelines shows how to turn Excel from a static spreadsheet into a living, breathing data hub.
This book is for analysts, operators, and builders who already live in Excel but need real pipelines not copy-paste workflows. You'll learn how to embed Python directly into Excel to pull data from APIs, automate transformations, schedule refreshes, and keep models continuously up to date without leaving the spreadsheet environment.
Rather than treating Excel as the final stop, this book reframes it as the control layer. Python becomes the engine underneath handling ingestion, validation, enrichment, and orchestration while Excel remains the interface decision-makers actually use.
Inside, you'll learn how to:
Connect Excel to live data sources using REST APIs, webhooks, and authenticated endpoints
Build repeatable ingestion pipelines that clean, normalize, and version data automatically
Schedule refresh cycles so spreadsheets update themselves on defined intervals
Design resilient workflows that handle failures, stale data, and API limits
Combine Python logic with Excel formulas, tables, and Power Query for hybrid pipelines
Turn Excel into a lightweight data product for finance, ops, analytics, and reporting teams
The focus is practical and production-minded. No toy examples. No abstract data engineering theory. Every pattern is designed for real-world constraints where Excel is non-negotiable but manual work is unacceptable.
If you want Excel dashboards that stay current, models that refresh themselves, and spreadsheets that behave like systems instead of documents, this book gives you the architecture and tooling to make it happen.
