Regular expression for address field validation

Regular expression for simple address validation

^[#.0-9a-zA-Z\s,-]+$

E.g. for Address match case

#1, North Street, Chennai - 11 

E.g. for Address not match case

$1, North Street, Chennai @ 11

This one worked for me:

\d+[ ](?:[A-Za-z0-9.-]+[ ]?)+(?:Avenue|Lane|Road|Boulevard|Drive|Street|Ave|Dr|Rd|Blvd|Ln|St)\.?

The source: https://www.codeproject.com/Tips/989012/Validate-and-Find-Addresses-with-RegEx

As a simple one line expression recommend this,

^([a-zA-z0-9/\\''(),-\s]{2,255})$

Here is the approach I have taken to finding addresses using regular expressions:

A set of patterns is useful to find many forms that we might expect from an address starting with simply a number followed by set of strings (ex. 1 Basic Road) and then getting more specific such as looking for "P.O. Box", "c/o", "attn:", etc.

Below is a simple test in python. The test will find all the addresses but not the last 4 items which are company names. This example is not comprehensive, but can be altered to suit your needs and catch examples you find in your data.

import re
strings = [
    '701 FIFTH AVE',
    '2157 Henderson Highway',
    'Attn: Patent Docketing',
    'HOLLYWOOD, FL 33022-2480',
    '1940 DUKE STREET',
    '111 MONUMENT CIRCLE, SUITE 3700',
    'c/o Armstrong Teasdale LLP',
    '1 Almaden Boulevard',
    '999 Peachtree Street NE',
    'P.O. BOX 2903',
    '2040 MAIN STREET',
    '300 North Meridian Street',
    '465 Columbus Avenue',
    '1441 SEAMIST DR.',
    '2000 PENNSYLVANIA AVENUE, N.W.',
    '465 Columbus Avenue',
    '28 STATE STREET',
    'P.O, Drawer 800889.',
    '2200 CLARENDON BLVD.',
    '840 NORTH PLANKINTON AVENUE',
    '1025 Connecticut Avenue, NW',
    '340 Commercial Street',
    '799 Ninth Street, NW',
    '11318 Lazarro Ln',
    'P.O, Box 65745',
    'c/o Ballard Spahr LLP',
    '8210 SOUTHPARK TERRACE',
    '1130 Connecticut Ave., NW, Suite 420',
    '465 Columbus Avenue',
    "BANNER & WITCOFF , LTD",
    "CHIP LAW GROUP",
    "HAMMER & ASSOCIATES, P.C.",
    "MH2 TECHNOLOGY LAW GROUP, LLP",
]

patterns = [
    "c\/o [\w ]{2,}",
    "C\/O [\w ]{2,}",
    "P.O\. [\w ]{2,}",
    "P.O\, [\w ]{2,}",
    "[\w\.]{2,5} BOX [\d]{2,8}",
    "^[#\d]{1,7} [\w ]{2,}",
    "[A-Z]{2,2} [\d]{5,5}",
    "Attn: [\w]{2,}",
    "ATTN: [\w]{2,}",
    "Attention: [\w]{2,}",
    "ATTENTION: [\w]{2,}"
]
contact_list = []
total_count = len(strings)
found_count = 0
for string in strings:
    pat_no = 1
    for pattern in patterns:
        match = re.search(pattern, string.strip())
        if match:
            print("Item found: " + match.group(0) + " | Pattern no: " + str(pat_no))
            found_count += 1
        pat_no += 1

print("-- Total: " + str(total_count) + " Found: " + str(found_count)) 

UiPath Academy training video lists this RegEx for US addresses (and it works fine for me):

\b\d{1,8}(-)?[a-z]?\W[a-z|\W|\.]{1,}\W(road|drive|avenue|boulevard|circle|street|lane|waylrd\.|st\.|dr\.|ave\.|blvd\.|cir\.|In\.|rd|dr|ave|blvd|cir|ln)

I had a different use case - find any addresses in logs and scold application developers (favourite part of a devops job). I had the advantage of having the word "address" in the pattern but should work without that if you have specific field to scan

\baddress.[0-9\\\/# ,a-zA-Z]+[ ,]+[0-9\\\/#, a-zA-Z]{1,}

1. Look for the word "address" - skip this if not applicable
2. Look for first part numbers, letters, #, space - Unit Number / street number/suite number/door number
3. Separated by a space or comma
4. Look for one or more of rest of address numbers, letters, #, space

Tested against :

• 1 Sleepy Boulevard PO, Box 65745
• Suite #100 /98,North St,Snoozepura
• Ave., New Jersey,
• Suite 420 1130 Connect Ave., NW,
• Suite 420 19 / 21 Old Avenue,
• Suite 12, Springfield, VIC 3001
• Suite#100/98 North St Snoozepura

This worked for me when there were street addresses with unit/suite numbers, zip codes, only street. It also didn't match IP addresses or mac addresses. Worked with extra spaces. This assumes users are normal people separate elements of a street address with a comma, hash sign, or space and not psychopaths who use characters like "|" or ":"!

For French address and some international address too, I use it.

[\\D+ || \\d]+\\d+[ ||,||[A-Za-z0-9.-]]+(?:[Rue|Avenue|Lane|... etcd|Ln|St]+[ ]?)+(?:[A-Za-z0-9.-](.*)]?)

I was inspired from the responses given here and came with those 2 solutions

• support optional uppercase
• support french also

regex structure

1. numbers (required)
2. letters, chars and spaces
3. at least one common address keyword (required)
4. as many chars you want before the line break

definitions:

accuracy

capacity of detecting addresses and not something that looks like an address which is not.

range

capacity to detect uncommon addresses.

Regex 1:

• high accuracy
• low range

/[0-9]+[ |[a-zà-ú.,-]* ((highway)|(autoroute)|(north)|(nord)|(south)|(sud)|(east)|(est)|(west)|(ouest)|(avenue)|(lane)|(voie)|(ruelle)|(road)|(rue)|(route)|(drive)|(boulevard)|(circle)|(cercle)|(street)|(cer\.)|(cir\.)|(blvd\.)|(hway\.)|(st\.)|(aut\.)|(ave\.)|(ln\.)|(rd\.)|(hw\.)|(dr\.)|(a\.))([ .,-]*[a-zà-ú0-9]*)*/i

regex 2:

• low accuracy
• high range

/[0-9]*[ |[a-zà-ú.,-]* ((highway)|(autoroute)|(north)|(nord)|(south)|(sud)|(east)|(est)|(west)|(ouest)|(avenue)|(lane)|(voie)|(ruelle)|(road)|(rue)|(route)|(drive)|(boulevard)|(circle)|(cercle)|(street)|(cer\.?)|(cir\.?)|(blvd\.?)|(hway\.?)|(st\.?)|(aut\.?)|(ave\.?)|(ln\.?)|(rd\.?)|(hw\.?)|(dr\.?)|(a\.))([ .,-]*[a-zà-ú0-9]*)*/i

This one works well for me

^(\d+) ?([A-Za-z](?= ))? (.*?) ([^ ]+?) ?((?<= )APT)? ?((?<= )\d*)?$

Source : https://community.alteryx.com/t5/Alteryx-Designer-Discussions/RegEx-Addresses-different-formats-and-headaches/td-p/360147

I needed

STREET # | STREET | CITY | STATE | ZIP

So I wrote the following regex

[0-9]{1,5}( [a-zA-Z.]*){1,4},?( [a-zA-Z]*){1,3},? [a-zA-Z]{2},? [0-9]{5}

This allows

• 1-5 Street #s
• 1-4 Street description words
• 1-3 City words
• 2 Char State
• 5 Char Zip code

I also added option , for separating street, city, state, zip