PDF

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

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)

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)

import pdfplumber

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

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

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', ...}

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()

# 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