In Tableau, window functions are a special category of table calculations that operate across a range of rows (a "window") in your visualization. Instead of calculating values for just one row at a time, they look across multiple rows and return a single aggregated result for that defined scope.
Questions that we can answer with window functions:
What is the maximum sales values in this region?
How does each player's performance compare to the lowest score on their team? Where does this value stand in relation to peers?
Window functions are very useful for comparison and benchmarking. They are used for:
Finding relative values such as maximum, minimum or average within a group
Compare across partitions ( e.g., one region, one player's team, one product category)
We can also provide context. Instead of just showing raw values, we can highlight how a value performs compared to the group it belongs to.
Most common window functions:
WINDOW_MAX()
WINDOW_MIN()
WINDOW_AVG()
WINDOW_SUM()
How would we use WINDOW_MAX() in analyzing NBA player scoring data. Suppose each row in the data set is a player with their Points Per Game (PPG). Using WINDOW_MAX([Points Per game]), Tableau will calculate the highest PPG in the entire view or partition
If we partition by the Team, the calculation find the top scorer on each team. This will give us an opportunity to create a following field
[Points Per Game] / WINDOW_MAX([Points Per Game])
This tells us how close each player is to their team’s highest scorer. For example:
Steph Curry might be at 1.0 (the benchmark for Golden State).
Another player on the same team might show 0.65, meaning they score at 65% of Curry’s average.
This is a powerful way to normalize values within a group.
Let's flip it to WINDOW_MIN([Points Per Game]) by partitioning by team, this function finds the lowest score in each group. Now we can compare players to the baseline:
[Points Per Game] - WINDOW_MIN([Points Per Game])
This shows the scoring gap between each player and their team’s least productive scorer.
If a team’s bench player averages 2 PPG, while a star averages 28, the gap is 26 points. This context highlights not just the star’s performance, but the spread of contributions across the roster.
Window functions in Tableau elevate our analysis from individual rows to group-level. By using Window Max and Window Min we can:
Benchmark each row against the best or worst performers.
Highlight outliers in the data.
Add richer storytelling to dashboards by giving values context.
Whether we're working with NBA stats, Superstore sales, or Customer data, window functions are a very useful tool.
Resources:
https://help.tableau.com/current/pro/desktop/en-us/functions_functions_tablecalculation.htm