# Calculating the MODE in BigQuery

How do you compute the MODE (most frequent value) in BigQuery?

For the other measures of central tendency like MEAN and MEDIAN, there are straightforward ways to compute results - functions AVG and PERCENTILE\_CONT/PERCENTILE\_DIST respectively, but there's no dedicated function for MODE.

By the way, if you have a huge dataset and can bear some lack of precision, take a look at APPROX\_TOP\_COUNT.

Say we have the following input data:

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1708553490924/574e8efa-e260-4dc0-b8b1-8e41d4a16dd8.png align="center")

Now here's how we can compute them otherwise:  
\- filter out NULLS (if we want to ignore them) or do nothing if we want to keep them  
\- compute value counts for our desired grain  
\- take the most frequent one per our grain using QUALIFY + RANK

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1708553539348/88be7fdd-e864-4fa4-924f-cfec2d47b464.png align="center")

Here's how the output would look with NULLS excluded.

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1708553571790/e0ede638-19e6-4140-8a75-d49ebea22e92.png align="center")

And with them included:

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1708553662143/5cedea71-1150-4646-874a-eb941cec5ff7.png align="center")

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