Merging Two Messy CSVs: The Key Problems Excel Formulas Hide From You

Merging two messy CSV files often leads to quiet data disasters that stem from hidden assumptions, according to a report on merging CSV files. The first mistake is assuming the ID column is a reliable key, as it may have different names or formatting in each file. To avoid this, count distinct values and compare the sets before merging.

The second issue arises from invisible keys, such as trailing spaces or different number formats, which can be fixed by trimming, casting, and casefolding both sides before joining. The third problem is that uniqueness is not guaranteed; a single left table may expand from 1,000 rows to 1,400 after merging, causing inflated sums.

To address this, run `df[ id ].is_unique` on both sides and check for duplicates. The fourth issue is the choice of merge type, which depends on the deliverable's requirements. Inner merges drop unmatched rows, left merges keep original rows and nulls from the other file, and outer merges keep everything. Explicitly state the merge type and log the dropped rows.

Lastly, dates can be represented in multiple formats, so parse them with explicit formats to avoid confusion. The recommended workflow is to load files raw, normalize keys, assert uniqueness, merge with explicit instructions and indicators, and count before, after, matched, and dropped rows. This process ensures accurate reconciliation and clear communication with clients when totals do not match.