I’m working with the Google Contacts API and running into an issue where all the address information comes back as one long text string instead of being broken down into individual parts. I need to take this single address string and convert it into an organized address object that has separate fields for each component. Specifically, I’m looking for a way to extract and separate the first street address line, second street address line (like apartment or suite number), city name, state or province, and postal code. Has anyone found a reliable library or method that can handle this kind of address parsing? I’ve tried doing it manually with string splitting but it gets messy with different address formats.
Had this exact issue migrating legacy contact data last year. What saved me tons of time was a two-step validation after parsing. I’d run basic regex patterns to catch ZIP codes and state abbreviations, then cross-check everything against USPS address validation. The game-changer was treating apartment numbers and suite info as optional instead of forcing every address into the same format. International addresses were a nightmare since Google Contacts pulls from everywhere. Preprocessing the strings to normalize common abbreviations before parsing helped a lot. The validation step caught about 20% of parsing errors that would’ve gotten through otherwise.
yep, regex can be a pain! Libpostal is super helpful for that kinda thing. Just keep an eye on the prcing if ur dealing with a lot of adds, but honestly, it’s def worth it for the results.
Had this exact issue 6 months back while building a customer management system. Here’s what worked: I used Google Maps Geocoding API first to reverse-parse addresses, then added pattern matching as backup when it failed. The API’s solid at breaking down components since Google’s got massive address datasets - just structure your request to get parsed pieces back. For fallback, I started with postal codes (they follow predictable patterns) then worked backwards to split city/state. Edge cases like PO boxes or international addresses are tricky, but this combo handled 85% of cases automatically without paying for a service.