Every data professional knows the feeling. You open a new project, and the first thing you need to do is clean, transform, or merge some data. And every time, you end up writing the same boilerplate code.

Load CSV. Drop duplicates. Handle missing values. Merge sheets. Save to Excel.

Sound familiar?

I got tired of rewriting these patterns across projects, so I built a set of reusable Python templates that handle the 10 most common data processing tasks.

What Is DataForge Pro?

DataForge Pro is a collection of 10 production-ready Python templates for data processing. Each template is a standalone script that you can copy, customize, and integrate into your workflow.

The 10 Templates

1. Quick Start — Load, Preview, and Save

The foundation template. Load any file (CSV, Excel, JSON), preview its structure, and save it in a different format.

from core import DataForge

df = (DataForge()
      .load('data.csv')
      .preview()
      .save('output.xlsx'))

2. Data Cleaning

Remove duplicates, handle missing values, trim whitespace, and standardize column names — all in a chainable API.

df = (DataForge()
      .load('messy_data.csv')
      .remove_duplicates()
      .drop_empty_rows()
      .trim_whitespace()
      .standardize_columns()
      .save('clean_data.xlsx'))

3. Format Conversion

Convert between CSV, Excel (.xlsx/.xls), and JSON with a single line.

# CSV to Excel
DataForge().load('data.csv').save('data.xlsx')

# Excel to JSON
DataForge().load('data.xlsx').save('data.json')

4. VLOOKUP — Data Matching

The Excel VLOOKUP equivalent in Python. Match and merge data from two files using a common key column.

df = (DataForge()
      .load('orders.csv')
      .vlookup('customers.xlsx', 'CustomerID', ['Name', 'Email', 'City'])
      .save('enriched_orders.xlsx'))

5. Pivot Tables

Create Excel-style pivot tables with group-by and aggregation functions.

df = (DataForge()
      .load('sales.csv')
      .pivot(group_by=['Region', 'Product'], 
             agg={'Revenue': 'sum', 'Quantity': 'count'})
      .save('pivot_report.xlsx'))

6. File Comparison

Find differences between two datasets — added rows, removed rows, and changed values.

diff = DataForge().compare('old_data.csv', 'new_data.csv')
diff.save_report('changes.xlsx')

7. Batch Processing

Process multiple files at once — apply the same transformation to an entire folder.

df = (DataForge()
      .batch_load('data_folder/*.csv')
      .remove_duplicates()
      .save('combined_output.xlsx'))

8. Multi-Sheet Excel

Work with multiple sheets in a single Excel file — read, write, and transform across sheets.

9. CLI Mode

Command-line interface for quick operations without writing Python code.

python core.py load data.csv drop_duplicates save output.xlsx

10. Extension Guide

A template showing how to create your own custom transformations and add them to the chain.

Key Features

• Chainable API — Clean, readable code with method chaining
• Multiple Formats — CSV, Excel (.xlsx/.xls), JSON
• Well Documented — Clear docstrings and example data
• Zero Dependencies — Only pandas, openpyxl, and xlrd
• Easy to Extend — Add your own transformations

Requirements

• Python 3.8+
• pandas (pip install pandas)
• openpyxl (pip install openpyxl)
• xlrd (pip install xlrd)

Who Is This For?

• Data analysts who work with CSV and Excel files daily
• Python developers building data pipelines
• Excel power users who want to automate repetitive tasks
• Anyone who processes files in bulk

Get DataForge Pro

Stop rewriting the same data processing code. Get 10 ready-to-use templates and start saving hours every week.

👉 Get DataForge Pro

Also available on Gumroad and SellAnyCode.

Questions? Message me anytime. Happy coding!