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Cleaning Text and Categories

by LucidPlexus
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Find every spelling, give it one agreed name, and prove nothing else moved. Two A4 pages on cleaning text and category columns in SQL, written on the tables of Can I Trust This Data?, where USA arrives in three spellings and soybean…
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Find every spelling, give it one agreed name, and prove nothing else moved.

Two A4 pages on cleaning text and category columns in SQL, written on the tables of Can I Trust This Data?, where USA arrives in three spellings and soybean in three. Every query was run, and its real result is printed under it.

It covers listing every spelling with its count, what trim, lower, replace and regexp_replace do and do not do, one match key written once as a macro, near-duplicate spellings and fuzzy scores, a mapping table instead of a CASE, the three tests a mapping table must pass, joining to master data, and standardizing inside a view that keeps the raw value beside the clean one.

It ends with the proof: rows and totals unchanged, spellings down to the master list's count, and the duplicate count that should go up.

PDF to print, and an HTML version to read on screen. The SQL is DuckDB, as in the LucidPlexus workbench, and it marks the DuckDB-only parts (the macro, arg_max, the similarity function).

#cheat-sheet #data-cleaning #data-quality #duckdb #sql
Type Cheat Sheet
Pages 2
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