Pdf Extraction

import pdfplumber
# Open PDFwith pdfplumber.open('document.pdf') as pdf:    # Access pages    first_page = pdf.pages[0]        # Document metadata    print(pdf.metadata)        # Number of pages    print(len(pdf.pages))

with pdfplumber.open('document.pdf') as pdf:    # Single page    text = pdf.pages[0].extract_text()        # All pages    full_text = ''    for page in pdf.pages:        full_text += page.extract_text() or ''

# With layout preservationtext = page.extract_text(    x_tolerance=3,      # Horizontal tolerance for grouping    y_tolerance=3,      # Vertical tolerance    layout=True,        # Preserve layout    x_density=7.25,     # Chars per unit width    y_density=13        # Chars per unit height)
# Extract words with positionswords = page.extract_words(    x_tolerance=3,    y_tolerance=3,    keep_blank_chars=False,    use_text_flow=False)
# Each word includes: text, x0, top, x1, bottom, etc.for word in words:    print(f"{word['text']} at ({word['x0']}, {word['top']})")

# Get all characterschars = page.chars
for char in chars:    print(f"'{char['text']}' at ({char['x0']}, {char['top']})")    print(f"  Font: {char['fontname']}, Size: {char['size']}")

with pdfplumber.open('report.pdf') as pdf:    page = pdf.pages[0]        # Extract all tables    tables = page.extract_tables()        for i, table in enumerate(tables):        print(f"Table {i+1}:")        for row in table:            print(row)

# Custom table detectiontable_settings = {    "vertical_strategy": "lines",      # or "text", "explicit"    "horizontal_strategy": "lines",    "explicit_vertical_lines": [],     # Custom line positions    "explicit_horizontal_lines": [],    "snap_tolerance": 3,    "snap_x_tolerance": 3,    "snap_y_tolerance": 3,    "join_tolerance": 3,    "edge_min_length": 3,    "min_words_vertical": 3,    "min_words_horizontal": 1,    "intersection_tolerance": 3,    "text_tolerance": 3,    "text_x_tolerance": 3,    "text_y_tolerance": 3,}
tables = page.extract_tables(table_settings)

# Find tables (without extracting)table_finder = page.find_tables()
for table in table_finder:    print(f"Table at: {table.bbox}")  # (x0, top, x1, bottom)        # Extract specific table    data = table.extract()

# Create visual debug imageim = page.to_image(resolution=150)
# Draw detected objectsim.draw_rects(page.chars)        # Character bounding boxesim.draw_rects(page.words)        # Word bounding boxesim.draw_lines(page.lines)        # Linesim.draw_rects(page.rects)        # Rectangles
# Save debug imageim.save('debug.png')
# Debug tablesim.reset()im.debug_tablefinder()im.save('table_debug.png')

# Define bounding box (x0, top, x1, bottom)bbox = (0, 0, 300, 200)
# Crop pagecropped = page.crop(bbox)
# Extract from cropped areatext = cropped.extract_text()tables = cropped.extract_tables()

# Filter characters by regiondef within_bbox(obj, bbox):    x0, top, x1, bottom = bbox    return (obj['x0'] >= x0 and obj['x1'] <= x1 and            obj['top'] >= top and obj['bottom'] <= bottom)
bbox = (100, 100, 400, 300)filtered_chars = [c for c in page.chars if within_bbox(c, bbox)]

# Get text by fontdef extract_by_font(page, font_name):    chars = [c for c in page.chars if font_name in c['fontname']]    return ''.join(c['text'] for c in chars)
# Extract bold text (often "Bold" in font name)bold_text = extract_by_font(page, 'Bold')
# Extract by sizelarge_chars = [c for c in page.chars if c['size'] > 14]

with pdfplumber.open('document.pdf') as pdf:    # Document metadata    meta = pdf.metadata    print(f"Title: {meta.get('Title')}")    print(f"Author: {meta.get('Author')}")    print(f"Created: {meta.get('CreationDate')}")        # Page info    for i, page in enumerate(pdf.pages):        print(f"Page {i+1}: {page.width} x {page.height}")        print(f"  Rotation: {page.rotation}")

import pandas as pd
def pdf_tables_to_dataframes(pdf_path):    """Extract all tables from PDF as pandas DataFrames."""    dfs = []        with pdfplumber.open(pdf_path) as pdf:        for i, page in enumerate(pdf.pages):            tables = page.extract_tables()                        for j, table in enumerate(tables):                if table and len(table) > 1:                    # First row as header                    df = pd.DataFrame(table[1:], columns=table[0])                    df['_page'] = i + 1                    df['_table'] = j + 1                    dfs.append(df)        return dfs

def extract_invoice_amount(pdf_path):    """Extract amount from typical invoice layout."""    with pdfplumber.open(pdf_path) as pdf:        page = pdf.pages[0]                # Search for "Total" and get nearby numbers        words = page.extract_words()                for i, word in enumerate(words):            if 'total' in word['text'].lower():                # Look at next few words                for next_word in words[i+1:i+5]:                    text = next_word['text'].replace(',', '').replace('$', '')                    try:                        return float(text)                    except ValueError:                        continue        return None

def extract_columns(page, num_columns=2):    """Extract text from multi-column layout."""    width = page.width    col_width = width / num_columns        columns = []    for i in range(num_columns):        x0 = i * col_width        x1 = (i + 1) * col_width                cropped = page.crop((x0, 0, x1, page.height))        columns.append(cropped.extract_text())        return columns

import pdfplumberimport pandas as pd
def extract_financial_tables(pdf_path):    """Extract tables from financial report and save to Excel."""        with pdfplumber.open(pdf_path) as pdf:        all_tables = []                for page_num, page in enumerate(pdf.pages):            # Debug: save table visualization            im = page.to_image()            im.debug_tablefinder()            im.save(f'debug_page_{page_num+1}.png')                        # Extract tables            tables = page.extract_tables({                "vertical_strategy": "lines",                "horizontal_strategy": "lines",                "snap_tolerance": 5,            })                        for table in tables:                if table and len(table) > 1:                    # Clean data                    clean_table = []                    for row in table:                        clean_row = [cell.strip() if cell else '' for cell in row]                        clean_table.append(clean_row)                                        df = pd.DataFrame(clean_table[1:], columns=clean_table[0])                    df['Source Page'] = page_num + 1                    all_tables.append(df)                # Save to Excel with multiple sheets        with pd.ExcelWriter('extracted_tables.xlsx') as writer:            for i, df in enumerate(all_tables):                df.to_excel(writer, sheet_name=f'Table_{i+1}', index=False)                return all_tables
tables = extract_financial_tables('annual_report.pdf')print(f"Extracted {len(tables)} tables")

import pdfplumberimport refrom datetime import datetime
def extract_invoice_data(pdf_path):    """Extract structured data from invoice PDF."""        data = {        'invoice_number': None,        'date': None,        'total': None,        'line_items': []    }        with pdfplumber.open(pdf_path) as pdf:        page = pdf.pages[0]        text = page.extract_text()                # Extract invoice number        inv_match = re.search(r'Invoice\s*#?\s*:?\s*(\w+)', text, re.IGNORECASE)        if inv_match:            data['invoice_number'] = inv_match.group(1)                # Extract date        date_match = re.search(r'Date\s*:?\s*(\d{1,2}[/-]\d{1,2}[/-]\d{2,4})', text)        if date_match:            data['date'] = date_match.group(1)                # Extract total        total_match = re.search(r'Total\s*:?\s*\$?([\d,]+\.?\d*)', text, re.IGNORECASE)        if total_match:            data['total'] = float(total_match.group(1).replace(',', ''))                # Extract line items from table        tables = page.extract_tables()        for table in tables:            if table and any('description' in str(row).lower() for row in table[:2]):                # Found line items table                for row in table[1:]:  # Skip header                    if row and len(row) >= 3:                        data['line_items'].append({                            'description': row[0],                            'quantity': row[1] if len(row) > 1 else None,                            'amount': row[-1]                        })        return data
invoice = extract_invoice_data('invoice.pdf')print(f"Invoice #{invoice['invoice_number']}")print(f"Total: ${invoice['total']}")

import pdfplumber
def parse_resume(pdf_path):    """Extract structured sections from resume."""        with pdfplumber.open(pdf_path) as pdf:        full_text = ''        for page in pdf.pages:            full_text += (page.extract_text() or '') + '\n'                # Common resume sections        sections = {            'contact': '',            'summary': '',            'experience': '',            'education': '',            'skills': ''        }                # Split by common headers        import re        section_patterns = {            'summary': r'(summary|objective|profile)',            'experience': r'(experience|employment|work history)',            'education': r'(education|academic)',            'skills': r'(skills|competencies|technical)'        }                lines = full_text.split('\n')        current_section = 'contact'                for line in lines:            line_lower = line.lower().strip()                        # Check if line is a section header            for section, pattern in section_patterns.items():                if re.match(pattern, line_lower):                    current_section = section                    break                        sections[current_section] += line + '\n'                return sections
resume = parse_resume('resume.pdf')print("Skills:", resume['skills'])