uk_address_matcher 1.2.3

A package for matching UK addresses using a pretrained Splink model

High performance UK addresses matcher (geocoder)

Fast, simple address matching (geocoding) in Python.

For full documentation, see our main documentation site.

Why use this library

• Simple. Setup in seconds, runs on a laptop. No separate infrastructure of services needed.
• Fast. Match 100,000 addresses in ~30 seconds.
• Proven accuracy. We use public, labelled datasets to measure and document accuracy.
• Support for Ordnance Survey data. We provide a automated build pipeline for users wishing to match to Ordnance Survey data. Matching to any other canonical dataset is also supported.

The end-to-end process of matching 100,000 addresses to Ordnance Survey data, including all software downloads and data processing takes:

• Less than a minute if you are matching to a small area such as a local council region.
• If matching to the whole UK, there's a one-time preprocessing step that takes around 10 minutes. Subsequent matching of 100k records takes less than a minute.

Installation

pip install uk_address_matcher

What does it do?

Given the following data:

• a "messy" dataset of addresses that you want to match
• a "canonical" dataset of known addresses, often an Ordnance Survey dataset such as AddressBase or NGD.

this package will find the best matching canonical address for each messy address.

Example:

Your address files need, at minimum, two columns: unique_id and address_concat.

postcode is optional by recommended. If not provided an attempt is made to parse them out of address_concat

Given the following data:

Messy data

unique_id	address_concat	postcode
m_1	Flat A Example Court, 10 Demo Road, Townton	AB1 2BC
...more rows

Canonical data

unique_id	address_concat	postcode
c_1	Flat A, 10 Demo Road, Townton	AB1 2BC
c_2	Flat B, 10 Demo Road, Townton	AB1 2BC
c_3	Basement Flat, 10 Demo Road, Townton	AB1 2BC
...more rows

You can match it as follows:

import duckdb
from uk_address_matcher import AddressMatcher

con = duckdb.connect()
messy = con.read_csv("example_data/messy_example.csv")
canonical = con.read_csv("example_data/canonical_example.csv")

matcher = AddressMatcher(
    canonical_addresses=canonical,
    addresses_to_match=messy,
    con=con,
)
result = matcher.match()
result.matches().show(max_width=10000)

Example output:

unique_id	resolved_canonical_id	original_address_concat	original_address_concat_canonical	match_reason	match_weight	distinguishability
m_1	c_2	Flat A Example Court, 10 Demo Road, Townton	Flat A, 10 Demo Road, Townton	splink: probabilistic match	13.5885	11.5033

Development

The scripts and tests will run better if you create .vscode/settings.json with the following:

{
    "jupyter.notebookFileRoot": "${workspaceFolder}",
    "python.analysis.extraPaths": [
        "${workspaceFolder}"
    ],
    "python.testing.pytestEnabled": true,
    "python.testing.unittestEnabled": false,
    "python.testing.pytestArgs": [
        "-v",
        "--capture=tee-sys"
    ]
}