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Python Record Linkage Toolkit

As explained in The Well-Grounded Data Analyst

The Python Record Linkage Toolkit, also known as recordlinkage, is a Python library for comparing, deduplicating, and linking records.

Use the Python Record Linkage Toolkit, imported as recordlinkage, when customer records may represent the same entity but differ across fields. It supports entity resolution by comparing candidate record pairs instead of requiring every pair to be inspected manually. section 3.4.3

A practical workflow starts by choosing candidate pairs, defining comparison rules, computing comparison vectors, and retaining records that satisfy the matching criteria. Blocking on postcode reduces comparisons, but assumes postcodes contain no relevant typos and therefore trades recall for performance. section 3.4.3

A minimal linkage configuration can:

  • Index records with the same postcode as candidate pairs. section 3.4.3
  • Compare first names and surnames with Damerau–Levenshtein similarity using an 0.85 threshold. section 3.4.3
  • Compare postcodes exactly and select pairs whose comparison vector contains three matching features. section 3.4.3
  • Merge linked identifiers back into the customer data model for downstream deduplication. section 3.4.3

Resolving nearly identical customer records

Suppose two customer rows share a postcode and nearly match on first name and surname. Configure recordlinkage to test that pair, use fuzzy comparisons for the names, and retain it when all three comparison features match. Compare the resulting links with exact duplicate detection: disagreements indicate cases requiring investigation or a later iteration, such as fuzzy matching or purchasing-pattern analysis. section 3.4.3

indexer = recordlinkage.Index()     #1
indexer.block('postcode')                #2
candidate_links
 = indexer.index(all_customers.set_index("customer_id"))      #3

Inspect the compact indexing pattern used to block records by postcode before comparison. section 3.4.3

compare = recordlinkage.Compare()              #1
compare.string('first_name', 'first_name', method='damerau_levenshtein',
 threshold=0.85, label="first_name")                                #2
compare.string('surname', 'surname', method='damerau_levenshtein',
 threshold=0.85, label="surname")
compare.exact('postcode', 'postcode', label="postcode")     #3

Inspect the comparison configuration for fuzzy names and exact postcodes. section 3.4.3

How The Well-Grounded Data Analyst covers it

How deep it goes
Explains indexing, comparison rules, matching, and data-model integration, but defers underlying algorithm implementations.
Examples
Uses customer accounts, postcode blocking, fuzzy names, exact postcodes, linked identifiers, and comparisons with exact duplicate detection.
What it assumes
Assumes tabular datasets and basic loading, column manipulation, joining, sorting, grouping, and reshaping skills.
What you can do after
Apply it via Python to generate candidate links, compare records, identify duplicates, and merge linkage results into tabular data.
How it connects
Connects record linkage with entity resolution, deduplication, fuzzy string matching, blocking, and data modeling.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of The Well-Grounded Data Analyst. The section links point to the parts of the book it draws on.