How do I select rows from a pandas DataFrame based on column values?

I have a DataFrame of orders and want only the rows where status is "shipped" and total is above 100. In SQL:

SELECT * FROM orders WHERE status = 'shipped' AND total > 100;

What is the idiomatic pandas equivalent?

Use a boolean mask. Parenthesise each condition — & binds tighter than ==:

shipped = orders[(orders["status"] == "shipped") & (orders["total"] > 100)]

Or with query, which reads closer to SQL:

shipped = orders.query("status == 'shipped' and total > 100")

For membership use isin:

orders[orders["status"].isin(["shipped", "delivered"])]

The parentheses were my bug — I had orders.status == 'shipped' & orders.total > 100.

query also accepts local variables with @: orders.query('total > @threshold').

Is status part of a composite index, or is it a separate single-column one?

Also check pg_stats.n_distinct for that column — the planner may think a seq scan is cheaper.