pdf-to-structured

PDF to Structured Data Conversion

Overview

Based on DDC methodology (Chapter 2.4), this skill transforms unstructured PDF documents into structured formats suitable for analysis and integration. Construction projects generate vast amounts of PDF documentation - specifications, BOMs, schedules, and reports - that need to be extracted and processed.

Book Reference: "Преобразование данных в структурированную форму" / "Data Transformation to Structured Form"

"Преобразование данных из неструктурированной в структурированную форму — это и искусство, и наука. Этот процесс часто занимает значительную часть работы инженера по обработке данных." — DDC Book, Chapter 2.4

ETL Process Overview

The conversion follows the ETL pattern:

1. Extract: Load the PDF document
2. Transform: Parse and structure the content
3. Load: Save to CSV, Excel, or JSON

Quick Start

import pdfplumber
import pandas as pd

# Extract table from PDF
with pdfplumber.open("construction_spec.pdf") as pdf:
    page = pdf.pages[0]
    table = page.extract_table()
    df = pd.DataFrame(table[1:], columns=table[0])
    df.to_excel("extracted_data.xlsx", index=False)

Installation

# Core libraries
pip install pdfplumber pandas openpyxl

# For scanned PDFs (OCR)
pip install pytesseract pdf2image
# Also install Tesseract OCR: https://github.com/tesseract-ocr/tesseract

# For advanced PDF operations
pip install pypdf

Native PDF Extraction (pdfplumber)

Extract All Tables from PDF

import pdfplumber
import pandas as pd

def extract_tables_from_pdf(pdf_path):
    """Extract all tables from a PDF file"""
    all_tables = []

    with pdfplumber.open(pdf_path) as pdf:
        for page_num, page in enumerate(pdf.pages):
            tables = page.extract_tables()
            for table_num, table in enumerate(tables):
                if table and len(table) > 1:
                    # First row as header
                    df = pd.DataFrame(table[1:], columns=table[0])
                    df['_page'] = page_num + 1
                    df['_table'] = table_num + 1
                    all_tables.append(df)

    if all_tables:
        return pd.concat(all_tables, ignore_index=True)
    return pd.DataFrame()

# Usage
df = extract_tables_from_pdf("material_specification.pdf")
df.to_excel("materials.xlsx", index=False)