Aggregate

User interface

General Principles Add Missing Dates Add text column Add Total Rows Aggregate Append Compare Text Columns Argmax Argmin Concatenate columns Convert Convert date to text Convert text to date Cumulated sum Custom step Delete column(s) Geographically dissolve data Duplicate Duration Evolution Extract date information Extract substring Fill null Filter Formula Geographical hierarchy Get unique groups/values Hierarchical rollup ifthenelse Join Keep column(s) Moving Average Percentage Pivot Rank Rename Replace Geographical simplification Sort Split column Column's Statistics To lowercase To uppercase Top N rows Unpivot Waterfall

Technical documentation

Packages Steps Designing a new step Known limitations Variables UI components Custom code editor Python package

Aggregate

You can use this step to perform aggregations on one or several columns. Equivalent to a GROUP BY clause in SQL, or to the $group operator in Mongo aggregation pipeline.

This step is supported by the following backends:

Mongo 5.0
Mongo 4.2
Mongo 4.0
Mongo 3.6
Pandas (python)

Where to find this step?

Widget Aggregate
Search bar

Options reference

Group rows by...: you can select one or several columns that will be used to constitute unique groups (equivalent to the columns that you would specify after the GROUP BY clause in SQL or in the _id field of a $group in Mongo aggregation pipeline)
And aggregate...: in this section of the form you can specify one or more columns to aggregate, with the corresponding aggregation function to be applied (equivalent to SUM(MY_COLUMN) for example in SQL, or to my_column: { $sum: { $my_column } } in Mongo). You can add columns to aggregate by clicking on the button Add aggregation.
- Columns: the columns to be aggregated (you can apply the same aggregation function to several columns at once)
- Function the aggregation function to be applied (sum, average, count, count distinct, min, max, first or last)
Keep original granularity and add aggregation(s) in new column(s): whether to keep the original granularity, in that case computed aggregations will be added in new columns

Example 1: do not keep original granularity

This configuration results in:

Example 2: keep original granularity

This configuration results in: