Inner, left, right i outer

Real data rarely lives in one table. A join combines two tables into one by matching rows on a shared column.

Every join needs a key: the column whose values link rows across both tables, like a shared customer id.

In pandas you join with merge. You pass the two frames and tell it which key column to match on.

An inner join keeps only rows whose key exists in both tables. Anything unmatched simply disappears.

A left join keeps every row from the left table, filling missing right-side columns with NaN when there is no match.

A right join keeps every row from the right table instead. It is just a left join with the tables swapped.

An outer join keeps every row from both tables, pairing what it can and leaving NaN where a match is missing.

The how argument is the whole decision. It controls which unmatched rows survive, so pick it on purpose every time.

Be careful: an inner join silently drops rows with no partner, so your result can be much smaller than you expected.

When you want to enrich a main table without losing any of its rows, reach for a left join first. 👍

After a left, right, or outer join, fresh NaN values mark the rows that had no match on the other side.

You want every row of your main table kept, even unmatched ones. Which join?

You learned the four join types: inner keeps matches, left and right keep one side, and outer keeps everything. Choose with care!