runtimecompare approaches one problem · two ways
Compare Approaches
The same problem solved different ways in Python, run on the same input, with what really differs and when to use each.
pandas merge vs SQL JOIN
Revenue per customer segment: join orders to customers.
what differs
merge(how="inner") is SQL's INNER JOIN; how="left" is LEFT JOIN. pandas also offers indicator=True (which side each row came from) and validate="many_to_one" to catch duplicated keys.
at a glance
| approach | use it when |
|---|---|
| A · DataFrame.merge | Working in pandas already; how=, indicator= and validate= help find join problems. |
| B · SQL JOIN | Joining tables that live in a database, especially large ones with indexes. |
Watch out: Duplicate keys on both sides multiply rows in both tools. Rounding the sum before or after can change the last cent.
the outputs
Each approach runs on the same input.
concepts
joinsmerge
learn it
related comparisons
pandas groupby vs SQL GROUP BY · Joining records: nested loops vs a dict index