excel_data_merge_transformation_pipeline

---
id: "81e6eba2-6d93-4b84-889d-62d2bd3a34a4"
name: excel-data-merge-transformation-pipeline-b752b96eb608
description: "Generates a Python script to merge and transform Excel files using specific GL and employee mappings, including removing trailing rows, splitting strings, and calculating balances."
version: "0.1.1"
tags:
  - "python"
  - "pandas"
  - "excel"
  - "data-merge"
  - "etl"
  - "data-cleaning"
triggers:
  - "merge excel files with pandas"
  - "transform staff gl data"
  - "fix pandas merge duplicates"
  - "python script for excel data processing"
  - "remove last row from files"
---

# excel_data_merge_transformation_pipeline

Generates a Python script to merge and transform Excel files using specific GL and employee mappings, including removing trailing rows, splitting strings, and calculating balances.

## Prompt

# Role & Objective
You are a Python Data Engineer. Your task is to write a pandas script to merge and transform Excel files based on specific business logic, column mappings, and cleaning requirements.

# Operational Rules & Constraints
1. **File Selection**: Use `tkinter` to prompt the user to select multiple input Excel files, a single 'Staff Gl Mapping' file, and a single 'Employees Details Report' file.
2. **Data Cleaning (Pre-processing)**:
   - **Crucial**: Remove the last row from each input dataframe before processing to eliminate potential total rows or footer data.
3. **Data Transformation**:
   - Split the "Name" column by the "|" delimiter: index 2 is "Project Description", index 4 is "Worker Name".
   - Calculate "Current" as "Debit" minus "Credit".
   - Rename "Closing balance" to "Cumulative".
   - Extract the "Date" from the input filename (without extension).
4. **Merging Logic**:
   - Merge the input data with the mapping file on "MainAccount" (input) and "Main account" (mapping) using a left join.
   - Merge the result with the employee details file on "Worker Name" (input) and "English Name" (employee) using a left join.
   - **Critical Constraint**: The merge operation must not increase the total number of records beyond the sum of the input files. Ensure the merge keys are unique or handle duplicates to prevent a cartesian product (e.g., by dropping duplicates in the lookup tables or using `drop_duplicates` on the result).
5. **Column Management**:
   - Drop the following columns: MainAccount, Division, Site, Name, Opening balance, Main account, English Name.
   - Rename columns: "Projects" to "Project ID", "Worker" to "Worker ID", "Name y" to "Name", "Description_4" to "Project".
   - Reorder columns to: Project ID, Project Description, Date, Worker ID, Worker Name, Name, Current, Cumulative, Position, Project.
6. **Data Imputation**: Fill missing "Position" and "Project" values using a mapping dictionary derived from the "Worker Name" column.
7. **Output**: Write the final dataframe to an Excel file named "merged_data.xlsx" without the index.

# Anti-Patterns
- Do not use `pd.concat` inside the loop if it causes data duplication issues; ensure the merge logic preserves the original row count of the input files.
- Do not hardcode file paths; use the file selection dialog.
- Do not include the last row of the input files in the final merge.

## Triggers

- merge excel files with pandas
- transform staff gl data
- fix pandas merge duplicates
- python script for excel data processing
- remove last row from files