Automating Excel Data Replacement with Python: A Step‑by‑Step Guide

1. Introduction

Hello, I'm PiPi. Recently a question about Python automation was raised in a group chat. The problem: when both inbound and outbound records contain semi‑finished products from factories 1, 2, and 3 with identical material codes, how to replace the inbound entries with the corresponding IONP information.

2. Implementation Process

Initially people tried using Excel, but the logic was unclear. The clarified business logic is:

Group the Excel rows by material code.

Filter rows where the Inbound column contains a semi‑finished product from any factory and the corresponding Outbound column also contains a semi‑finished product from any factory.

Replace the Inbound value in the filtered rows with the Inbound value from the first row of each group (the one containing IONP).

The final code (shown in the image below) resets the index of a DataFrame, merges two DataFrames on the Inbound and Outbound columns, and then updates the original DataFrame with the merged values.

Explanation of the key lines: t2.reset_index() resets the index of t2.

merge(t1, left_on="Inbound", right_on="Outbound", suffixes=("", "_y"))

performs a left join.

Iterating over t.itertuples() and assigning df.loc[row.Index, "Inbound"] = row.Inbound_y updates the original DataFrame.

3. Conclusion

The article reviews the Python automation problem, presents the clarified requirements, and provides a concrete implementation that works for the supplied demo data.

Readers are encouraged to share clear problem statements and demo data when seeking help.