PDF/Text Parser for Unstructured Company Reports

What

A Python script that parses multi-line text reports with inconsistent formatting and extracts structured data (company name, address, phone, contact, city/state/zip, permits, expiration dates).

Why

Demonstrates ability to:

• Extract data from unstructured text
• Handle irregular formatting (inconsistent spacing, page headers, line breaks)
• Convert messy reports into clean CSV for analysis
• Apply pattern matching and edge case handling

How It Works

The script:

1. Reads a text file (exported from PDF or other source)
2. Identifies line patterns based on indentation and content
3. Splits fields using multi-space delimiters
4. Handles edge cases like missing data or varying field order
5. Outputs a structured CSV file

Key Techniques

• Regex pattern matching (re.split(r' {2,}', line))
• Line-by-line state tracking
• Indentation-based parsing (space detection)
• Date pattern detection

Files

• parser.py — Main extraction script
• sample_input.txt — Example of the expected text format (synthetic data)
• sample_output.csv — Example of the structured output

Sample Input Format

Company Name                123 Main St, City           (555) 123-4567
  Contact Person            City, State 12345          PERMIT123        04/15/2026

Sample Output

Company Name	Address	Phone	Contact	City/State/Zip	Permits	Exp Date
Example Co	123 Main St	(555) 123-4567	Contact Person	City, State 12345	PERMIT123	04/15/2026

How to Run

python parser.py

Limitations

• Assumes consistent delimiter (2+ spaces)
• Requires text extraction from PDF before parsing
• Designed for specific report structure

Real-World Application

This script was developed for a real-world data extraction task. The original data cannot be shared due to privacy and ethical considerations. The methodology is demonstrated using synthetic data.