📝 Message Matching Tool

Match messages from Column A (platform messages) against Column B (internal messages) in an Excel file.

This script cleans and normalizes both columns and then checks:

Does each message in Column A exist somewhere in Column B (after cleaning)?

If yes → Column C will contain the matching B value If no → Column C will say "no match"

The script outputs a new Excel file with the results.

---

🚀 What This Script Does

• Reads an .xlsx file

• Cleans/normalizes the text in both columns

  • lowercasing
  • remove punctuation
  • remove emojis
  • remove weird characters
  • collapse extra spaces

• Creates a fast lookup of normalized Column B values

• For every row in Column A:

  • If normalized A matches normalized B → copy the original B into Column C
  • Otherwise → "no match"

• Saves a new .xlsx with the results

---

📦 Requirements

You need Python installed.

Install dependencies:

pip install pandas openpyxl

That’s it.

---

📁 Folder Structure

your-folder/
│
├── match_messages.py
├── input.xlsx
└── (results will be saved here as output.xlsx)

---

▶️ How To Run It (Step-By-Step)

1. Put your input Excel file in the same folder (example: input.xlsx).

2. Open a terminal / command prompt in that folder.

3. Run the script:

python match_messages.py input.xlsx output.xlsx

4. A new file will appear:

output.xlsx

5. Open output.xlsx and look at Column C:

   • If a matching message was found → it shows the matching B message
   • If not → "no match"

---

🔍 Example

Input columns:

Column A (platform msg)	Column B (internal msg)
"Please refund me!!!"	"please refund me"
"Driver stole my order"	no similar message

Output column:

Column C
please refund me
no match

---

⚙️ Script Settings (Optional)

The script assumes:

• Column A = first column in Excel
• Column B = second column
• It writes results to a new Column C

If your file is different, you can easily adjust the code.

---

🧠 How It Works (Simple Explanation)

• It cleans both A and B using the same rules
• It then compares the cleaned text, not the raw text
• If the cleaned A text equals cleaned B text → that’s a match
• Exact message deduping, not fuzzy or semantic matching

This avoids mistakes and runs fast.