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When both tables share a column with the same name, on is all you need to merge them together.

Sometimes one column is not enough. Pass a list to on and pandas matches rows on every key at once.

When key columns are named differently, use left_on and right_on to tell pandas which column maps to which.

With left_on and right_on, both key columns survive in the result. You often drop the duplicate afterward.

If your key lives in the index instead of a column, set left_index or right_index to True to join on it.

For two index-aligned tables, the join method is shorter. It merges on the index by default.

When both tables have a column with the same name, pandas adds suffixes so the two versions stay distinct.

A common surprise: a key stored as a string on one side and an integer on the other will simply never match.

If a key value repeats on both sides, merge produces every matching pair. Rows can multiply unexpectedly.

Unlike merge, the join method uses a left join by default. Pass how to change it when you need to.

Use merge for column keys with full control, and join when both tables are already aligned on their index. 🔧

Your key columns have different names in each table. What do you use?

You can merge on shared columns, mismatched names, or the index, and use suffixes to keep overlapping columns clear. Nicely done!