AI Agents · Lesson

PDF Parsing with PyMuPDF and pdfplumber

Extracting text, tables, and metadata from PDFs programmatically.

PDF Parsing with PyMuPDF and pdfplumber is a free AI Agents lesson on CoddyKit — lesson 1 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the AI Agents learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

Why PDF Parsing Is Non-Trivial

PDFs are a presentation format, not a data format. Text is stored as positioned glyphs, not logical paragraphs. Extracting meaningful text requires understanding layout, reading order, font properties, and handling edge cases like multi-column layouts, headers/footers, and embedded images.

Two libraries dominate: PyMuPDF (speed) and pdfplumber (table extraction).

PyMuPDF Basics

PyMuPDF (imported as fitz) is the fastest Python PDF library. It handles text extraction, metadata, images, and rendering. Install with pip install pymupdf.

import fitz  # PyMuPDF

# Open a PDF
doc = fitz.open('document.pdf')

print(f'Pages: {len(doc)}')
print(f'Metadata: {doc.metadata}')

# Extract text from all pages
full_text = ''
for page_num in range(len(doc)):
    page = doc[page_num]
    text = page.get_text()  # plain text extraction
    full_text += f'--- Page {page_num + 1} ---\n{text}\n'

doc.close()
print(full_text[:500])

PyMuPDF Text Extraction Modes

page.get_text() supports different output formats. Use 'text' for plain text, 'blocks' for text blocks with bounding boxes, and 'dict' for rich structured output including font names and sizes.

import fitz

doc = fitz.open('report.pdf')
page = doc[0]

# Mode 1: plain text
plain = page.get_text('text')

# Mode 2: blocks (each block has bbox + text)
blocks = page.get_text('blocks')
for block in blocks:
    x0, y0, x1, y1, text, block_no, block_type = block
    if block_type == 0:  # text block (1 = image)
        print(f'Block at ({x0:.0f},{y0:.0f}): {text[:80]}')

# Mode 3: dict (full detail including font info)
page_dict = page.get_text('dict')
for block in page_dict['blocks']:
    if block.get('type') == 0:  # text
        for line in block['lines']:
            for span in line['spans']:
                print(f"Font: {span['font']}, Size: {span['size']:.1f}, Text: {span['text']}")

doc.close()

Extracting Page Metadata

Page metadata helps agents understand document structure: page count, document title, author, creation date, and page dimensions. PyMuPDF exposes all of this.

import fitz

def extract_pdf_metadata(filepath):
    doc = fitz.open(filepath)
    meta = doc.metadata
    info = {
        'title':    meta.get('title', 'Unknown'),
        'author':   meta.get('author', 'Unknown'),
        'subject':  meta.get('subject', ''),
        'creator':  meta.get('creator', ''),
        'created':  meta.get('creationDate', ''),
        'modified': meta.get('modDate', ''),
        'pages':    len(doc),
        'page_size': {
            'width':  doc[0].rect.width,
            'height': doc[0].rect.height
        }
    }
    doc.close()
    return info

meta = extract_pdf_metadata('contract.pdf')
print(f"Title: {meta['title']}, Pages: {meta['pages']}")

pdfplumber for Table Extraction

pdfplumber excels at extracting tables from PDFs. It uses geometric analysis to detect cell boundaries, even in PDFs without explicit table markup.

Install with pip install pdfplumber.

import pdfplumber

with pdfplumber.open('financial_report.pdf') as pdf:
    for page_num, page in enumerate(pdf.pages):
        tables = page.extract_tables()
        for table_idx, table in enumerate(tables):
            print(f'Page {page_num+1}, Table {table_idx+1}:')
            # table is a list of rows; each row is a list of cell strings
            headers = table[0]
            for row in table[1:]:
                row_dict = dict(zip(headers, row))
                print(row_dict)

pdfplumber Table Settings

pdfplumber's table extraction can be tuned with settings to handle different table styles: explicit lines, space-separated columns, or mixed layouts.

import pdfplumber

# Custom table settings for borderless tables
table_settings = {
    'vertical_strategy':   'text',   # 'lines', 'lines_strict', 'text', 'explicit'
    'horizontal_strategy': 'text',
    'snap_tolerance':       5,
    'join_tolerance':       3,
    'edge_min_length':     50,
    'min_words_vertical':   3,
    'min_words_horizontal': 1
}

with pdfplumber.open('nolines_table.pdf') as pdf:
    page = pdf.pages[0]
    table = page.extract_table(table_settings)
    if table:
        import csv
        import io
        output = io.StringIO()
        writer = csv.writer(output)
        writer.writerows(table)
        csv_string = output.getvalue()
        print(csv_string[:300])

Handling Multi-Column Layouts

Academic papers and newspapers use multi-column layouts. Naive text extraction reads across columns left-to-right, producing garbled text. Fix this by sorting text blocks by column first.

import fitz

def extract_multicolumn_text(page, n_columns=2):
    page_width = page.rect.width
    col_width = page_width / n_columns

    blocks = page.get_text('blocks')
    # Filter text blocks only
    text_blocks = [b for b in blocks if b[6] == 0]

    # Assign each block to a column based on x position
    columns = [[] for _ in range(n_columns)]
    for block in text_blocks:
        x0 = block[0]
        col_idx = min(int(x0 / col_width), n_columns - 1)
        columns[col_idx].append(block)

    # Sort each column by vertical position
    for col in columns:
        col.sort(key=lambda b: b[1])  # sort by y0

    # Read columns left to right
    full_text = ''
    for col in columns:
        for block in col:
            full_text += block[4] + '\n'
    return full_text

Filtering Headers and Footers

PDF headers and footers repeat on every page and pollute extracted text. Identify them by their vertical position (top or bottom 10% of the page) and exclude them from content extraction.

import fitz

def extract_without_headers_footers(page, margin_ratio=0.08):
    page_height = page.rect.height
    top_margin = page_height * margin_ratio
    bottom_margin = page_height * (1 - margin_ratio)

    blocks = page.get_text('blocks')
    content_blocks = []

    for block in blocks:
        x0, y0, x1, y1, text, block_no, block_type = block
        if block_type != 0:
            continue  # skip image blocks
        # Skip blocks in header or footer zone
        if y0 < top_margin or y1 > bottom_margin:
            continue
        content_blocks.append(text)

    return '\n'.join(content_blocks)

Chunking PDF Text for Agents

Long PDFs must be split into chunks for embedding and retrieval. Chunk at natural boundaries: paragraphs, sections, or pages. Include overlap between chunks to avoid cutting context mid-sentence.

import fitz

def pdf_to_chunks(filepath, chunk_size=1000, overlap=200):
    doc = fitz.open(filepath)
    chunks = []

    for page_num in range(len(doc)):
        page = doc[page_num]
        page_text = page.get_text()

        # Split into chunks with overlap
        start = 0
        while start < len(page_text):
            end = start + chunk_size
            chunk = page_text[start:end]
            chunks.append({
                'text': chunk,
                'page': page_num + 1,
                'char_start': start,
                'source': filepath
            })
            start += chunk_size - overlap  # overlap

    doc.close()
    return chunks

chunks = pdf_to_chunks('research_paper.pdf')
print(f'Total chunks: {len(chunks)}')
print(f'Sample: {chunks[0]["text"][:200]}')

Combining PyMuPDF and pdfplumber

Use PyMuPDF for text extraction (faster) and pdfplumber for table detection (more accurate). A combined extractor runs both on each page and returns structured content with text and tables separated.

import fitz
import pdfplumber

def full_pdf_extract(filepath):
    result = {'text_by_page': [], 'tables': []}

    # Text extraction with PyMuPDF
    doc = fitz.open(filepath)
    for i in range(len(doc)):
        text = extract_without_headers_footers(doc[i])
        result['text_by_page'].append({'page': i + 1, 'text': text})
    doc.close()

    # Table extraction with pdfplumber
    with pdfplumber.open(filepath) as pdf:
        for page_num, page in enumerate(pdf.pages):
            tables = page.extract_tables()
            for t in tables:
                result['tables'].append({
                    'page': page_num + 1,
                    'headers': t[0] if t else [],
                    'rows': t[1:] if t and len(t) > 1 else []
                })

    return result

Saving Extracted Text to Files

After extracting text from PDFs, save it in a format that downstream agents can consume. Plain text files work well for embedding pipelines; JSON preserves page structure for citation tracking.

import json, os

class FakePage:
    def __init__(self, text): self.text = text
    def get_text(self): return self.text

class FakeDoc(list):
    def close(self): pass

def fitz_open(path):
    return FakeDoc([FakePage('Page 1 text'), FakePage('Page 2 text')])

def pdf_to_text_file(pdf_path, output_dir):
    doc = fitz_open(pdf_path)
    base = os.path.splitext(os.path.basename(pdf_path))[0]
    txt_path = os.path.join(output_dir, base + '.txt')
    with open(txt_path, 'w', encoding='utf-8') as f:
        for i, page in enumerate(doc):
            f.write(f'--- Page {i+1} ---\n{page.get_text()}\n')
    json_path = os.path.join(output_dir, base + '.json')
    pages = [{'page': i+1, 'text': p.get_text()} for i, p in enumerate(doc)]
    with open(json_path, 'w') as f:
        json.dump({'source': pdf_path, 'pages': pages}, f, indent=2)
    doc.close()
    print(f'Saved: {txt_path} and {json_path}')
    return txt_path, json_path

pdf_to_text_file('sample.pdf', '.')

Knowledge Check

Which Python library is best suited for extracting tables from PDF files?

Recap: PDF Parsing with PyMuPDF and pdfplumber

PyMuPDF (fitz) is the fast choice for text extraction, metadata, and multi-column layout handling. pdfplumber excels at table extraction with configurable geometric strategies.

Key techniques: use get_text('blocks') for position-aware extraction, filter headers/footers by vertical position, handle multi-column layouts by sorting blocks by column, and chunk text with overlap for agent retrieval systems. Combine both libraries for the best results.

Frequently asked questions

Is the “PDF Parsing with PyMuPDF and pdfplumber” lesson free?

Yes — the full text of “PDF Parsing with PyMuPDF and pdfplumber” is free to read here on the web, and the AI Agents course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the AI Agents course, upgrade to CoddyKit PRO.

What will I learn in “PDF Parsing with PyMuPDF and pdfplumber”?

Extracting text, tables, and metadata from PDFs programmatically. You practise AI Agents with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start AI Agents?

No prior experience is required. AI Agents on CoddyKit is structured for beginners through advanced learners; this is — lesson 1 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “PDF Parsing with PyMuPDF and pdfplumber” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

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