By Any Other Name

Comparing names with Python/Pandas

Like a teacher taking role, his finger slid down a list of names looking for a match. Every list had to be audited name by name. A list of terminated employees was cross-checked: nope, not there. A much longer list of current staff — called “The Census” — was reviewed: and there it was. The name was on the list, access granted, noted, and checked off. Next.

Ugh — SOX audits. Every company application carrying financial data — be it Tableau, Cubes, or the DataLake — has to be secured against even internal employees. Until these apps use central sign-on, they each have a different directory of users, where nicknames or misspellings lurk under human fingers.

Let’s save this guy’s (who was me, by the way) sanity, and automate the name checks using Python/Pandas. A name-for-name comparison tool should be simple enough. But we’ll also need to catch exception cases where name punctuation (O’Hara) and spacing (de Boers) could throw off the matching. And when names don’t find a match on either list, we should provide hints as to possible matches (could the userlist have ‘Alex’ while the census has ‘Alexandra’?). And output the whole thing into a clean Excel sheet to help the auditor AND provide documentation for outside audits.

We already have lists of Last,First names: Census contains our current employees (or players, in the World Cup example), Terms lists our former employees or players. Our whole purpose is to compare an unverified list of Users to determine if they match the Census or Terms.

Let’s write a simple Python function using the Pandas module to ingest these 3 files, which can be in either .csv or .xlsx format.

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