---
name: pdf
description: "ALWAYS use this skill instead of the Read tool for PDF files. The Read tool cannot extract PDF tables properly. Use this skill when: (1) Reading ANY PDF file, (2) Extracting tables from PDFs, (3) Converting PDF tables to pandas DataFrames, (4) Processing multi-page PDFs"
---

# PDF Processing Guide

## IMPORTANT: Do NOT Use the Read Tool for PDFs

**The Read tool cannot properly extract tabular data from PDFs.** It will only show you a limited preview of the first page's text content, missing most of the data.

For PDF files, especially multi-page PDFs with tables:
- **DO**: Use `pdfplumber` as shown in this skill
- **DO NOT**: Use the Read tool to view PDF contents

If you need to extract data from a PDF, write Python code using pdfplumber. This is the only reliable way to get complete table data from all pages.

## Overview

Extract text and tables from PDF documents using Python libraries.

## Table Extraction with pdfplumber

pdfplumber is the recommended library for extracting tables from PDFs.

### Basic Table Extraction

```python
import pdfplumber

with pdfplumber.open("document.pdf") as pdf:
    for page in pdf.pages:
        tables = page.extract_tables()
        for table in tables:
            print(table)  # List of lists
```

### Convert to pandas DataFrame

```python
import pdfplumber
import pandas as pd

with pdfplumber.open("document.pdf") as pdf:
    page = pdf.pages[0]  # First page
    tables = page.extract_tables()

    if tables:
        # First row is usually headers
        table = tables[0]
        df = pd.DataFrame(table[1:], columns=table[0])
        print(df)
```

### Extract All Tables from Multi-Page PDF

```python
import pdfplumber
import pandas as pd

all_tables = []

with pdfplumber.open("document.pdf") as pdf:
    for page in pdf.pages:
        tables = page.extract_tables()
        for table in tables:
            if table and len(table) > 1:  # Has data
                df = pd.DataFrame(table[1:], columns=table[0])
                all_tables.append(df)

# Combine if same structure
if all_tables:
    combined = pd.concat(all_tables, ignore_index=True)
```

## Text Extraction

### Full Page Text

```python
import pdfplumber

with pdfplumber.open("document.pdf") as pdf:
    text = ""
    for page in pdf.pages:
        text += page.extract_text() + "\n"
```

### Text with Layout Preserved

```python
with pdfplumber.open("document.pdf") as pdf:
    page = pdf.pages[0]
    text = page.extract_text(layout=True)
```

## Common Patterns

### Extract Multiple Tables and Create Mappings

When a PDF contains multiple related tables (possibly spanning multiple pages), extract from ALL pages and build lookup dictionaries:

```python
import pdfplumber
import pandas as pd

category_map = {}      # CategoryID -> CategoryName
product_map = {}       # ProductID -> (ProductName, CategoryID)

with pdfplumber.open("catalog.pdf") as pdf:
    for page_num, page in enumerate(pdf.pages):
        tables = page.extract_tables()

        for table in tables:
            if not table or len(table) < 2:
                continue

            # Check first row to identify table type
            header = [str(cell).strip() if cell else '' for cell in table[0]]

            # Determine if first row is header or data (continuation page)
            if 'CategoryID' in header and 'CategoryName' in header:
                # Categories table with header
                for row in table[1:]:
                    if row and len(row) >= 2 and row[0]:
                        cat_id = int(row[0].strip())
                        cat_name = row[1].strip()
                        category_map[cat_id] = cat_name

            elif 'ProductID' in header and 'ProductName' in header:
                # Products table with header
                for row in table[1:]:
                    if row and len(row) >= 3 and row[0]:
                        try:
                            prod_id = int(row[0].strip())
                            prod_name = row[1].strip()
                            cat_id = int(row[2].strip())
                            product_map[prod_id] = (prod_name, cat_id)
                        except (ValueError, AttributeError):
                            continue
            else:
                # Continuation page (no header) - check if it's product data
                for row in table:
                    if row and len(row) >= 3 and row[0]:
                        try:
                            prod_id = int(row[0].strip())
                            prod_name = row[1].strip()
                            cat_id = int(row[2].strip())
                            product_map[prod_id] = (prod_name, cat_id)
                        except (ValueError, AttributeError):
                            continue

# Build final mapping: ProductID -> CategoryName
product_to_category_name = {
    pid: category_map[cat_id]
    for pid, (name, cat_id) in product_map.items()
}
# {1: 'Beverages', 2: 'Beverages', 3: 'Condiments', ...}
```

**Important:** Always iterate over ALL pages (`for page in pdf.pages`) - tables often span multiple pages, and continuation pages may not have headers.

### Clean Extracted Table Data

```python
import pdfplumber
import pandas as pd

with pdfplumber.open("document.pdf") as pdf:
    table = pdf.pages[0].extract_tables()[0]
    df = pd.DataFrame(table[1:], columns=table[0])

    # Clean whitespace
    df = df.apply(lambda x: x.str.strip() if x.dtype == "object" else x)

    # Remove empty rows
    df = df.dropna(how='all')

    # Rename columns if needed
    df.columns = df.columns.str.strip()
```

### Handle Missing Headers

```python
# If table has no header row
table = pdf.pages[0].extract_tables()[0]
df = pd.DataFrame(table)
df.columns = ['Col1', 'Col2', 'Col3', 'Col4']  # Assign manually
```

## Quick Reference

| Task | Code |
|------|------|
| Open PDF | `pdfplumber.open("file.pdf")` |
| Get pages | `pdf.pages` |
| Extract tables | `page.extract_tables()` |
| Extract text | `page.extract_text()` |
| Table to DataFrame | `pd.DataFrame(table[1:], columns=table[0])` |

## Troubleshooting

- **Empty table**: Try `page.extract_tables(table_settings={...})` with custom settings
- **Merged cells**: Tables with merged cells may extract incorrectly
- **Scanned PDFs**: Use OCR (pytesseract) for image-based PDFs
- **No tables found**: Check if content is actually a table or styled text