pandas merge: How the wrong join type can silently inflate your revenue totals

Data analyst Michael Nocito published a tutorial on August 8, 2026, explaining how to use pandas merge with left, inner, and other join types in Python. The guide demonstrates that choosing the wrong join — particularly when a lookup table contains duplicate keys — can cause row counts to multiply and revenue totals to inflate without any obvious error. Using a sample dataset of 16 orders totalling 9,890, Nocito shows how different 'how' arguments produce different row counts and revenue figures, with inner and right joins silently dropping 1,600 in lamp-related revenue. He recommends printing row counts and a key total immediately before and after every merge as a quick sanity check. The tutorial targets analysts familiar with SQL joins, noting the logic and failure modes are effectively identical across both tools.