GoldenMatch

Find the rows in your data that are the same person, company or product. No rules to write, no labelled pairs, no model to train.

Zero-config entity resolution for Python. Point it at a CSV or a table: it chooses how to match, shows you what it chose, and merges each group of duplicates into one golden record.

v3.22.0: Zero-config person dedupe is 18-30x faster, and your records come back as you entered them. 250k rows went from 520 s to 18 s with identical clusters (upgrade pulls goldenmatch-native 0.2.3). The unique, duplicate and deduplicated tables now carry your original values instead of standardized ones; keep_original_values=False or --standardized-values restores the old output.

v3.21.0: No false alarms on small files, and no LLM calls you didn't ask for. A correct run on a small file no longer logs RED "output may be low-precision", and auto-config now turns on the (paid) LLM scorer only when you pass llm_auto=True -- it used to do so whenever an OPENAI_API_KEY or ANTHROPIC_API_KEY happened to be set. If you relied on that, pass llm_auto=True or set llm_scorer in your config.

v3.20.0: Integer ID columns no longer crash zero-config. A CSV whose phone or account column is plain digits (read as int64) made gm.dedupe(path) and goldenmatch dedupe die with ArrowNotImplementedError in auto-config's blocking; it now runs, and negative evidence on such a column scores instead of silently switching itself off. The CLI summary now says what it counts -- 12 records -> 7 entities (4 duplicate groups) -- and result.total_entities matches it.

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Try it

pip install goldenmatch
goldenmatch dedupe customers.csv

Take a customers.csv where the same people were entered more than once:

id	name	email	phone	address	zip
1	Maria Gonzalez	maria.gonzalez@example.com	555-201-3344	14 Oak Street	62704
2	Maria Gonzales	mgonzalez@example.com	(555) 201-3344	14 Oak St	62704
3	María González	maria.gonzalez@example.com	5552013344	14 Oak St.	62704
4	James O'Neil	jim.oneil@example.com	555-883-1200	902 Pine Avenue	84065
5	Jim O'Neill	jim.oneil@example.com	555.883.1200	902 Pine Ave	84065
8	Thomas Becker	tbecker@example.com	555-667-0192	77 Elm Court	97024
12	Thomas Baker	thomas.baker@example.com	555-120-4455	41 Walnut Way	97024

GoldenMatch puts rows 1-3 together as one person and rows 4-5 as another. Thomas Becker and Thomas Baker share a zip code and stay separate. Each group becomes one golden record, built from the best value of each field. The run writes <timestamp>_deduplicated.csv, one row per person (the four golden records plus the three people who had no duplicate), and <timestamp>_golden.csv with the merged records alone.

From Python:

import goldenmatch as gm

result = gm.dedupe("customers.csv")
print(result)       # DedupeResult(records=12, clusters=4, match_rate=75.0%)

result.deduplicated # one row per person: 7 here
result.golden       # one merged record per duplicate group
result.unique       # the records that had no duplicate
result.clusters     # which input rows belong together
result.config       # the matching config it chose

Already have the data in memory? gm.dedupe_df(table) takes a pyarrow Table or a polars DataFrame.

What it chose, and how to change it