ST_Aggr_Intersection operates on a grouped DataFrame and returns the intersection of geometries in each group. You can group your DataFrame using DataFrame.groupBy() or with a GROUP BY clause in a SQL statement. An empty geometry is returned when no geometries intersect.
When there are coincident points, the first input geometry is used.
To find the intersection of geometries in each record, use ST_Intersection.
| Function | Syntax |
|---|---|
| Python | aggr |
| SQL | ST |
| Scala | aggr |
For more details, go to the GeoAnalytics for Microsoft Fabric API reference for aggr_intersection.
Examples
from geoanalytics_fabric.sql import functions as ST, Polygon
data = [
(1, Polygon([[[5,5],[12,5],[12,10],[5,10],[5,5]]])),
(1, Polygon([[[10,8],[14,8],[14,15],[10,15],[10,8]]])),
(2, Polygon([[[6,8],[20,8],[20,20],[6,20],[6,8]]]))
]
df = spark.createDataFrame(data, ["id", "polygon"])
df.groupBy("id")\
.agg(ST.aggr_intersection("polygon").alias("aggr_intersection"))\
.show(truncate=False)Result
+---+--------------------------------------------------+
|id |aggr_intersection |
+---+--------------------------------------------------+
|1 |{"rings":[[[10,8],[10,10],[12,10],[12,8],[10,8]]]}|
|2 |{"rings":[[[6,8],[20,8],[20,20],[6,20],[6,8]]]} |
+---+--------------------------------------------------+Version table
| Release | Notes |
|---|---|
1.0.0-beta | Python, SQL, and Scala functions introduced |