When aggregating data, Py Spark's standard group By() function returns one row per collection of data records. It works for sums over a thousand or a million rows, but it can't also return additional data from the rows that went into the aggregation. That's where Py Spark window functions come into play.

They let you calculate values across related records without collapsing those records into a single result. You keep the transaction details while gaining aggregation information about the wider group. They are useful when you need rankings, running totals, comparisons with previous records, or calculations within groups.

A window defines the set of rows that Py Spark should consider when calculating a value for the current row. Most window specifications contain partition By(), order By(), and rows Between() or range Between(). This divides the data by store: every London transaction belongs to one window partition, every Manchester transaction belongs to another, and every Bristol transaction belongs to a third.

Common use cases include ranking rows within groups using row_number, rank, and dense_rank, selecting the top records per group, calculating running totals, comparing a row with the previous row, calculating a row's share of a group total, and calculating moving averages. Performance tips include filtering early, selecting only required columns, watching for skewed partitions, and reusing calculated results carefully.