How to Convert PDF to CSV: The Complete Guide

Converting a bank statement?

Bank statements need special care with dates, debit and credit signs, and running balances. See our bank statement PDF to CSV guide, or try the bank statement converter for a balance-checked ledger.

Why Convert PDF to CSV?

CSV (Comma-Separated Values) is the universal format for data exchange. Converting PDF to CSV unlocks your data for:

• Database imports - MySQL, PostgreSQL, MongoDB
• Spreadsheet analysis - Excel, Google Sheets, Numbers
• Business intelligence - Tableau, Power BI, Looker
• Programming - Python pandas, R, JavaScript

PDF to CSV vs PDF to Excel: Which to Choose?

Choose CSV when:

• Importing to databases
• Processing with scripts
• Maximum compatibility needed
• File size matters

Choose Excel when:

• Preserving formatting matters
• Multiple related tables
• Further spreadsheet editing needed

Step-by-Step: Convert PDF to CSV

Method 1: Using PDFDrill (Recommended)

1. Upload your PDF to PDFDrill dashboard
2. Select CSV as output format
3. Click Convert - processing time depends on the document
4. Download your clean CSV file

Method 2: Via API

import os
import time
import requests

def convert_pdf_to_csv(path):
    base = 'https://pdfdrill.com/api/api'
    headers = {'Authorization': 'Bearer ' + os.environ['PDFDRILL_API_KEY']}
    with open(path, 'rb') as source:
        upload = requests.post(base + '/upload', headers=headers,
                               files={'file': source}, timeout=60)
    upload.raise_for_status()
    job_id = upload.json()['id']
    for _ in range(60):
        status = requests.get(base + '/jobs/' + job_id, headers=headers, timeout=30)
        status.raise_for_status()
        state = status.json()['status']
        if state == 'done':
            break
        if state == 'error':
            raise RuntimeError('PDF processing failed; inspect job status')
        time.sleep(2)
    else:
        raise TimeoutError('Job is not ready; inspect its status before retrying')
    output = requests.get(base + '/jobs/' + job_id + '/download',
                          headers=headers, params={'output_type': 'csv'}, timeout=60)
    output.raise_for_status()
    return output.content

with open('output.csv', 'wb') as result:
    result.write(convert_pdf_to_csv('document.pdf'))

Handling Complex PDF Tables

Multi-Column Data

PDFDrill's AI detects column boundaries even without visible borders:

Name       | Email              | Amount
John Smith | john@email.com     | $1,234.56
Jane Doe   | jane@email.com     | $2,567.89

Converts to:

Name,Email,Amount
John Smith,john@email.com,$1234.56
Jane Doe,jane@email.com,$2567.89

Handling Special Characters

• Commas in data are automatically quoted
• Unicode characters preserved
• Line breaks within cells handled correctly

Common Challenges and Solutions

Challenge 1: Merged Header Cells

Problem: Financial reports often have multi-row headers Solution: PDFDrill flattens hierarchical headers intelligently

Challenge 2: Multiple Tables Per Page

Problem: A single page contains several distinct tables Solution: Each table exports to a separate CSV file or combined with clear separators

Challenge 3: Scanned PDFs

Problem: The PDF is an image, not selectable text Solution: Enable OCR mode before conversion

CSV Formatting Options

Delimiter Choice

While CSV traditionally uses commas, some regions prefer:

• Semicolon (;) - European standard
• Tab (\t) - TSV format
• Pipe (|) - Legacy systems

Encoding Options

• UTF-8 - Recommended for international data
• UTF-8 with BOM - Better Excel compatibility
• Latin-1 - Legacy Western European

Automating PDF to CSV Conversion

Batch Processing Script

from pathlib import Path

# Reuse convert_pdf_to_csv above; each upload uses the account's page allowance.
for source in Path('pdfs').glob('*.pdf'):
    source.with_suffix('.csv').write_bytes(convert_pdf_to_csv(source))

Quality Validation

After conversion, validate your CSV:

import pandas as pd

# Load and check
df = pd.read_csv('output.csv')
print(f"Rows: {len(df)}")
print(f"Columns: {list(df.columns)}")
print(df.head())

# Check for issues
print(f"Null values: {df.isnull().sum().sum()}")

Conclusion

Converting PDF tables to CSV can make data easier to review and reuse. PDFDrill supports table extraction to Excel and CSV; check exported rows against the source before relying on them. Try PDFDrill free: 10 pages a month.