Computing “average revenue per customer per region” correctly requires:

Which regex matches one or more digits?

You merge customers (1,000 rows) with orders (5,000 rows) using how=”left”. The result has 6,200 rows. What is most likely?

A left merge of customers (1,000 rows) with orders (3,000 rows) returns 4,500 rows. The most likely explanation:

After cleaning a column, what’s the most useful verification step before continuing?

A regex with capture groups in str.extract returns:

When loading a deeply nested JSON structure into a flat DataFrame, the recommended function is:

pd.concat is more appropriate than pd.merge when you want to:

The cleanest pandas type for a column with values “Low”, “Medium”, “High” that should sort in that order:

The Streamlit widget that returns a LIST of selected values: