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Address cleanse

address_cleanse matches a freeform UK address to the closest address on file and scores the match. Use it to clean addresses you already hold, such as a CRM export.

address_cleanse.pypython
from ideal_postcodes import IdealPostcodes, types

with IdealPostcodes("ak_test") as client:
    result = client.address_cleanse(query="9 Malyons Rd, Swanley, BR8 7RE")
    if isinstance(result, types.GbrCleanseMatch) and result.confidence > 0:
        print(result.match.line_1, result.match.postcode, result.confidence)
    else:
        print("No match")

Match or no match​

Check the type and the score. A match is a GbrCleanseMatch with a confidence above 0. A no-match is a GbrCleanseNoMatch, with every score at 0 and every match level at NO_MATCH.

Separate postcode and post town​

If your records keep the postcode or post town in their own fields, pass them as postcode and post_town alongside query rather than joining them into it.

Confidence threshold​

It depends on your data. Each wrong, missing or misspelt element lowers the score, and one source tends to repeat the same mistakes. Cleanse a sample, sort it by confidence and set your threshold where the matches start to go wrong.

The result also scores each element separately, in organisation_match, premise_match, thoroughfare_match, locality_match, post_town_match and postcode_match. The API reference explains each level.

Cost​

One lookup for a match. A no-match costs nothing.