pandas merge: Left Join, Inner Join, and the One That Doubled the Revenue

By Michael Nocito , data analyst · Published August 8, 2026 By the end of this page you can attach columns from one DataFrame to another on a shared key, choose the right how for the question, see at a glance which rows failed to match, and catch the failure that quietly inflates every total in the frame. It is about twenty-five minutes, and every output below was produced by running the code. Here is what to do today, on every merge you write. Print the row count immediately before and immediately after it. A left merge must not change the row count, and if it did, the right-hand table has the key more than once and your totals have just gone up. The short version: merge pairs rows from two frames wherever their keys match, and the number of rows that come out depends on how many times each key appears on each side. One key twice on the right is the idea, so it gets the picture. The original carries a diagram here. In words: On the left a single row is drawn as a wide box, holding the key Desk and the value 880. To its right stands a small lookup table with two rows, and both of those rows carry the same key, Desk. Two lines run from the single left-hand row, one to each of the two matching lookup rows, so the one row is paired twice. On the far right the result is drawn as two separate output rows, and both of them contain Desk and 880; the value 880 is ringed in amber in each of them to show that it is the same original figure appearing twice. One row went in and two came out, without anything being added to the left-hand table. Every output on this page is real. Sixteen orders totalling 9,890 and a three-row product table, the same tables used across this whole set of guides, merged in pandas 3.0.2 with the results copied back. If you know SQL joins , this is the same operation with different words, and the two failure modes are identical. 1. merge in one line Two frames, one shared column, one call. orders.merge(products, on="product", how="left") order_id prod