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Automate Your Data Processing With Python: 10 Templates That Save Hours

#python #productivity #datascience

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'))
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2. Data Cleaning

Remove duplicates, handle missing values, trim whitespace, and standardize column names.

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

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

DataForge().load('data.csv').save('data.xlsx')
DataForge().load('data.xlsx').save('data.json')
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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'))
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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'))
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6. File Comparison

Find differences between two datasets.

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

Process multiple files at once.

df = (DataForge()
      .batch_load('data_folder/*.csv')
      .remove_duplicates()
      .save('combined_output.xlsx'))
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8-10. Multi-Sheet Excel, CLI Mode, Extension Guide

Work with multiple sheets, use command-line interface, and learn to create custom transformations.

Key Features

  • Chainable API — Clean, readable code
  • Multiple Formats — CSV, Excel, JSON
  • Well Documented — Clear docstrings and examples
  • Zero Extra Dependencies — Only pandas, openpyxl, xlrd

Requirements

  • Python 3.8+
  • pandas, openpyxl, xlrd

Get DataForge Pro

Stop rewriting the same data processing code. Get 10 ready-to-use templates.

👉 Get DataForge Pro

Also available on Gumroad.

Questions? Message me anytime. Happy coding!

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