Imported from johnalbertini14-glitch/openclaw-skills (skills/lijie420461340/table-extractor/SKILL.md). Install upstream with npx skills add johnalbertini14-glitch/openclaw-skills --skill table-extractor. Copyright stays with the author.

Table Extractor Skill

Overview

This skill enables precise extraction of tables from PDF documents using camelot - the gold standard for PDF table extraction. Handle complex tables with merged cells, borderless tables, and multi-page layouts with high accuracy.

How to Use

1. Provide the PDF containing tables
2. Optionally specify pages or table detection method
3. I'll extract tables as pandas DataFrames

Example prompts:

• "Extract all tables from this PDF"
• "Get the table on page 5 of this report"
• "Extract borderless tables from this document"
• "Convert PDF tables to Excel format"

Domain Knowledge

camelot Fundamentals

import camelot

# Extract tables from PDF
tables = camelot.read_pdf('document.pdf')

# Access results
print(f"Found {len(tables)} tables")

# Get first table as DataFrame
df = tables[0].df
print(df)

Extraction Methods

# Lattice method (default) - for tables with visible borders
tables = camelot.read_pdf('document.pdf', flavor='lattice')

# Stream method - for borderless tables
tables = camelot.read_pdf('document.pdf', flavor='stream')

Page Selection

# Single page
tables = camelot.read_pdf('document.pdf', pages='1')

# Multiple pages
tables = camelot.read_pdf('document.pdf', pages='1,3,5')

# Page range
tables = camelot.read_pdf('document.pdf', pages='1-5')

# All pages
tables = camelot.read_pdf('document.pdf', pages='all')

Advanced Options

Lattice Options

tables = camelot.read_pdf(
    'document.pdf',
    flavor='lattice',
    line_scale=40,              # Line detection sensitivity
    copy_text=['h', 'v'],       # Copy text across merged cells
    shift_text=['l', 't'],      # Shift text alignment
    split_text=True,            # Split text at newlines
    flag_size=True,             # Flag super/subscripts
    strip_text='\n',            # Characters to strip
    process_background=False,   # Process background lines
)

Stream Options

tables = camelot.read_pdf(
    'document.pdf',
    flavor='stream',
    edge_tol=500,               # Edge tolerance
    row_tol=10,                 # Row tolerance
    column_tol=0,               # Column tolerance
    strip_text='\n',            # Characters to strip
)

Table Area Specification

# Extract from specific area (x1, y1, x2, y2)
# Coordinates from bottom-left, in PDF points (72 points = 1 inch)
tables = camelot.read_pdf(
    'document.pdf',
    table_areas=['72,720,540,400'],  # One area
)

# Multiple areas
tables = camelot.read_pdf(
    'document.pdf',
    table_areas=['72,720,540,400', '72,380,540,200'],
)

Column Specification

# Manually specify column positions (for stream method)
tables = camelot.read_pdf(
    'document.pdf',
    flavor='stream',
    columns=['100,200,300,400'],  # X positions of column separators
)

Working with Results

import camelot

tables = camelot.read_pdf('document.pdf')

for i, table in enumerate(tables):
    # Access DataFrame
    df = table.df
    
    # Table metadata
    print(f"Table {i+1}:")
    print(f"  Page: {table.page}")
    print(f"  Accuracy: {table.accuracy}")
    print(f"  Whitespace: {table.whitespace}")
    print(f"  Order: {table.order}")
    print(f"  Shape: {df.shape}")
    
    # Parsing report
    report = table.parsing_report
    print(f"  Report: {report}")

Export Options

import camelot

tables = camelot.read_pdf('document.pdf')

# Export to CSV
tables[0].to_csv('table.csv')

# Export to Excel
tables[0].to_excel('table.xlsx')

# Export to JSON
tables[0].to_json('table.json')

# Export to HTML
tables[0].to_html('table.html')

# Export all tables
for i, table in enumerate(tables):
    table.to_excel(f'table_{i+1}.xlsx')

Visual Debugging

import camelot

# Enable visual debugging
tables = camelot.read_pdf('document.pdf')

# Plot detected table areas
camelot.plot(tables[0], kind='contour').show()

# Plot text on table
camelot.plot(tables[0], kind='text').show()

# Plot detected lines (lattice only)
camelot.plot(tables[0], kind='joint').show()
camelot.plot(tables[0], kind='line').show()

# Save plot
fig = camelot.plot(tables[0])
fig.savefig('debug.png')

Handling Multi-page Tables

import camelot
import pandas as pd

def extract_multipage_table(pdf_path, pages='all'):
    """Extract and combine tables that span multiple pages."""
    
    tables = camelot.read_pdf(pdf_path, pages=pages)
    
    # Group tables by similar structure (columns)
    table_groups = {}
    
    for table in tables:
        cols = tuple(table.df.columns)
        if cols not in table_groups:
            table_groups[cols] = []
        table_groups[cols].append(table.df)
    
    # Combine similar tables
    combined = []
    for cols, dfs in table_groups.items():
        if len(dfs) > 1:
            # Combine and deduplicate header rows
            combined_df = pd.concat(dfs, ignore_index=True)
            combined.append(combined_df)
        else:
            combined.append(dfs[0])
    
    return combined

Best Practices

1. Try Both Methods: Lattice for bordered, stream for borderless
2. Check Accuracy Score: Above 90% is usually good
3. Use Visual Debugging: Understand extraction results
4. Specify Areas: For PDFs with multiple table types
5. Handle Headers: First row often needs special treatment

Common Patterns

Batch Table Extraction

import camelot
from pathlib import Path
import pandas as pd

def batch_extract_tables(input_dir, output_dir):
    """Extract tables from all PDFs in directory."""
    
    input_path = Path(input_dir)
    output_path = Path(output_dir)
    output_path.mkdir(exist_ok=True)
    
    results = []
    
    for pdf_file in input_path.glob('*.pdf'):
        try:
            tables = camelot.read_pdf(str(pdf_file), pages='all')
            
            for i, table in enumerate(tables):
                # Skip low accuracy tables
                if table.accuracy < 80:
                    continue
                
                output_file = output_path / f"{pdf_file.stem}_table_{i+1}.xlsx"
                table.to_excel(str(output_file))
                
                results.append({
                    'source': str(pdf_file),
                    'table': i + 1,
                    'page': table.page,
                    'accuracy': table.accuracy,
                    'output': str(output_file)
                })
        
        except Exception as e:
            results.append({
                'source': str(pdf_file),
                'error': str(e)
            })
    
    return results

Auto-detect Table Method

import camelot

def smart_extract_tables(pdf_path, pages='1'):
    """Try both methods and return best results."""
    
    # Try lattice first
    lattice_tables = camelot.read_pdf(pdf_path, pages=pages, flavor='lattice')
    
    # Try stream
    stream_tables = camelot.read_pdf(pdf_path, pages=pages, flavor='stream')
    
    # Compare and return best
    results = []
    
    if lattice_tables and lattice_tables[0].accuracy > 70:
        results.extend(lattice_tables)
    elif stream_tables:
        results.extend(stream_tables)
    
    return results

Examples

Example 1: Financial Statement Extraction

import camelot
import pandas as pd

def extract_financial_tables(pdf_path):
    """Extract financial tables from annual report."""
    
    # Extract all tables
    tables = camelot.read_pdf(pdf_path, pages='all', flavor='lattice')
    
    financial_data = {
        'income_statement': None,
        'balance_sheet': None,
        'cash_flow': None,
        'other_tables': []
    }
    
    for table in tables:
        df = table.df
        text = df.to_string().lower()
        
        # Identify table type
        if 'revenue' in text or 'sales' in text:
            if 'operating income' in text or 'net income' in text:
                financial_data['income_statement'] = df
        elif 'asset' in text and 'liabilities' in text:
            financial_data['balance_sheet'] = df
        elif 'cash flow' in text or 'operating activities' in text:
            financial_data['cash_flow'] = df
        else:
            financial_data['other_tables'].append({
                'page': table.page,
                'data': df,
                'accuracy': table.accuracy
            })
    
    return financial_data

financials = extract_financial_tables('annual_report.pdf')
if financials['income_statement'] is not None:
    print("Income Statement found:")
    print(financials['income_statement'])

Example 2: Scientific Data Extraction

import camelot
import pandas as pd

def extract_research_data(pdf_path, pages='all'):
    """Extract data tables from research paper."""
    
    # Try lattice for bordered tables
    tables = camelot.read_pdf(pdf_path, pages=pages, flavor='lattice')
    
    if not tables or all(t.accuracy < 70 for t in tables):
        # Fall back to stream for borderless
        tables = camelot.read_pdf(pdf_path, pages=pages, flavor='stream')
    
    extracted_data = []
    
    for table in tables:
        df = table.df
        
        # Clean up the DataFrame
        # Set first row as header if it looks like one
        if not df.iloc[0].str.contains(r'\d').any():
            df.columns = df.iloc[0]
            df = df[1:]
            df = df.reset_index(drop=True)
        
        extracted_data.append({
            'page': table.page,
            'accuracy': table.accuracy,
            'data': df
        })
    
    return extracted_data

data = extract_research_data('research_paper.pdf')
for i, item in enumerate(data):
    print(f"Table {i+1} (Page {item['page']}, Accuracy: {item['accuracy']}%):")
    print(item['data'].head())

Example 3: Invoice Line Items

import camelot

def extract_invoice_items(pdf_path):
    """Extract line items from invoice."""
    
    # Usually invoices have bordered tables
    tables = camelot.read_pdf(pdf_path, flavor='lattice')
    
    line_items = []
    
    for table in tables:
        df = table.df
        
        # Look for table with typical invoice columns
        header_text = ' '.join(df.iloc[0].astype(str)).lower()
        
        if any(term in header_text for term in ['quantity', 'qty', 'amount', 'price', 'description']):
            # This looks like a line items table
            df.columns = df.iloc[0]
            df = df[1:]
            
            for _, row in df.iterrows():
                item = {}
                for col in df.columns:
                    col_lower = str(col).lower()
                    value = row[col]
                    
                    if 'desc' in col_lower or 'item' in col_lower:
                        item['description'] = value
                    elif 'qty' in col_lower or 'quantity' in col_lower:
                        item['quantity'] = value
                    elif 'price' in col_lower or 'rate' in col_lower:
                        item['unit_price'] = value
                    elif 'amount' in col_lower or 'total' in col_lower:
                        item['amount'] = value
                
                if item:
                    line_items.append(item)
    
    return line_items

items = extract_invoice_items('invoice.pdf')
for item in items:
    print(item)

Example 4: Table Comparison

import camelot
import pandas as pd

def compare_pdf_tables(pdf1_path, pdf2_path):
    """Compare tables between two PDF versions."""
    
    tables1 = camelot.read_pdf(pdf1_path)
    tables2 = camelot.read_pdf(pdf2_path)
    
    comparisons = []
    
    # Match tables by shape and position
    for t1 in tables1:
        best_match = None
        best_score = 0
        
        for t2 in tables2:
            if t1.df.shape == t2.df.shape:
                # Calculate similarity
                try:
                    similarity = (t1.df == t2.df).mean().mean()
                    if similarity > best_score:
                        best_score = similarity
                        best_match = t2
                except:
                    pass
        
        if best_match:
            comparisons.append({
                'page1': t1.page,
                'page2': best_match.page,
                'similarity': best_score,
                'identical': best_score == 1.0,
                'diff': pd.DataFrame(t1.df != best_match.df)
            })
    
    return comparisons

comparison = compare_pdf_tables('report_v1.pdf', 'report_v2.pdf')

Limitations

• Encrypted PDFs not supported
• Image-based PDFs need OCR preprocessing
• Very complex merged cells may need tuning
• Rotated tables require preprocessing
• Large PDFs may need page-by-page processing

Installation

pip install camelot-py[cv]

# Additional dependencies
# macOS
brew install ghostscript tcl-tk

# Ubuntu
apt-get install ghostscript python3-tk

Resources

• camelot Documentation
• GitHub Repository
• Comparison with Other Tools