Скачать или смотреть How to Apply Conditional Formatting in Pandas for Supply Chain Data

🎨 Conditional Formatting in Pandas — Supply Chain Lead Time Edition
Welcome to this hands-on tutorial where we use Python to spot supply chain delays. We go beyond just coloring a table: we analyze lead times, count urgent orders, and identify which suppliers are underperforming.

Code and data available at github.com/Savila-Education/tutorial_1

🎯 What you’ll learn
In this tutorial, we tackle a common supply chain challenge: spotting delivery issues fast using Python Pandas.

We’ll:

Use conditional formatting to color the lead_time column in a DataFrame
→ 🟢 Green for optimal, 🟡 yellow for standard, 🔴 red for urgent
Set custom thresholds to define what “urgent” really means in our context
Count how many orders fall into each lead time category
Find out which suppliers are consistently delivering late by building a pivot table to rank suppliers by their total urgent lead time
This project uses a fictional supply chain dataset and runs 100% in Google Colab.

Have a tutorial request for supply chain analytics? Drop it in the comments — we're building this channel with you 🙌

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LinkedIn → Stephania Kossman and Luis Fernando Pérez Armas