Parse online booking confirmation details

To parse online booking confirmation details effectively, you typically need to extract structured data such as:

• Booking ID / Reference Number

• Customer Name

• Date of Booking

• Check-in / Check-out Dates (for hotels) or Departure/Return Dates (for flights)

• Location / Destination

• Service Type (e.g., flight, hotel, car rental)

• Total Cost

• Payment Status

• Contact Information

• Cancellation Policy

Here’s how you can approach parsing this data depending on your source:

1. From Text (Emails, PDFs, HTML):

Use regular expressions or natural language processing to extract key-value pairs. For example:

python
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import re

text = """Booking Confirmation
Reference: ABC12345
Name: John Doe
Hotel: Grand Bay Resort
Check-in: July 15, 2025
Check-out: July 20, 2025
Total: $1,200.00
Status: Confirmed
"""

booking_info = {
    "reference": re.search(r"Reference:s*(S+)", text).group(1),
    "name": re.search(r"Name:s*(.+)", text).group(1),
    "hotel": re.search(r"Hotel:s*(.+)", text).group(1),
    "check_in": re.search(r"Check-in:s*(.+)", text).group(1),
    "check_out": re.search(r"Check-out:s*(.+)", text).group(1),
    "total": re.search(r"Total:s*(.+)", text).group(1),
    "status": re.search(r"Status:s*(.+)", text).group(1)
}

print(booking_info)

2. From Structured JSON or API Response:

If you’re working with an API like Booking.com, Expedia, or airline APIs, you’ll usually get a JSON response:

json
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{
  "booking_id": "XYZ98765",
  "customer_name": "Alice Smith",
  "service": "Flight",
  "departure_date": "2025-08-10",
  "return_date": "2025-08-20",
  "origin": "LAX",
  "destination": "JFK",
  "price": 650.00,
  "currency": "USD",
  "status": "Confirmed"
}

You can directly parse this in Python or JavaScript using standard parsing libraries like json.loads().

3. Email Parsing Tools (Optional for Automation):

Use tools like:

• IMAP + Python (e.g., imaplib, email.parser) for parsing booking emails.

• Zapier or Make to auto-parse and push data to a CRM or Google Sheet.

• Cloud-based services like Mailparser.io or Parsio.io for non-coders.

4. From HTML Pages (Web Scraping):

Use BeautifulSoup (Python):

python
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from bs4 import BeautifulSoup

html = '''<div><span id="booking-id">Booking ID: ABC123</span></div>'''
soup = BeautifulSoup(html, 'html.parser')
booking_id = soup.find('span', id='booking-id').text.split(":")[1].strip()

Tips:

• Normalize date formats to YYYY-MM-DD.

• Convert prices to a consistent currency if needed.

• Validate extracted fields (e.g., is the reference number alphanumeric?).

• Handle edge cases like missing data or split confirmation sections.

If you have a sample confirmation (text, HTML, JSON, etc.), I can help build a custom parser.