reduce columns of ee.FeatureCollection by groups

geetools/ee_feature_collection.py

@@ -1,6 +1,8 @@
 """Toolbox for the `ee.FeatureCollection` class."""
 from __future__ import annotations
 
+from typing import Optional
+
 import ee
 import geopandas as gpd
 from matplotlib import pyplot as plt
@@ -245,6 +247,59 @@ def byFeatures(
 
         return ee.Dictionary.fromLists(features, values)
 
+    def reduceColumnsGroup(
+        self, reducer: ee.Reducer, groupBy: str = "system:index", selectors: Optional[list] = None
+    ) -> ee.Dictionary:
+        """Reduce a FeatureColletion column/s grouped by a key column.
+
+        Parameters:
+            reducer: The reducer.
+            groupBy: The column that will be used to group the output.
+            selectors: An optional list of columns to reduce. If None it will use all columns.
+
+        Returns:
+            a ee.Dictionary with the following structure:
+
+            {
+              'group1': {
+                'selector1': {
+                  'reducer1_output': value1,
+                  'reducer2_output': value2,
+                },
+                'selector2': {
+                  'reducer1_output': value1,
+                  'reducer2_output': value2,
+                },
+              },
+              'group2': {
+                'selector1': {
+                  'reducer1_output': value1,
+                  'reducer2_output': value2,
+                },
+                'selector2': {
+                  'reducer1_output': value1,
+                  'reducer2_output': value2,
+                },
+              },
+            }
+        """
+        if selectors is None:
+            selectors = self._obj.first().propertyNames()
+        selectors = ee.List(selectors)
+        keys = self._obj.aggregate_array(groupBy).distinct()
+
+        def reduce_key(key):
+            filtered = self._obj.filter(ee.Filter.eq(groupBy, key))
+
+            def reduce_selector(sel):
+                return filtered.reduceColumns(reducer, [sel])
+
+            outputs = selectors.map(reduce_selector)
+            return ee.Dictionary.fromLists(selectors, outputs)
+
+        values = keys.map(reduce_key)
+        return ee.Dictionary.fromLists(keys, values)
+
     def plot_by_features(
         self,
         type: str = "bar",

tests/test_FeatureCollection.py

@@ -33,6 +33,43 @@ def vatican(self):
         return ee.Geometry.Point([12.453386, 41.903282]).buffer(1)
 
 
+class TestReduceColumnsGroup:
+    """Test the ``reduceColumnsGroup`` method."""
+
+    def test_reduce_columns_group(self, data_regression):
+        red = ee.Reducer.mean().combine(ee.Reducer.sum(), sharedInputs=True)
+        output = self.table.geetools.reduceColumnsGroup(
+            reducer=red, groupBy="ADM0_NAME", selectors=["Shape_Area"]  # group by country
+        )
+        data_regression.check(output.getInfo())
+
+    def test_reduce_columns_group_custom_output(self, data_regression):
+        red = ee.Reducer.mean().combine(ee.Reducer.sum(), sharedInputs=True)
+        red = red.setOutputs(["average_km2", "sum_km2"])
+        output = self.table.geetools.reduceColumnsGroup(
+            reducer=red, groupBy="ADM0_NAME", selectors=["Shape_Area"]  # group by country
+        )
+        data_regression.check(output.getInfo())
+
+    def test_reduce_columns_group_fail_column_type(self):
+        red = ee.Reducer.mean()
+        output = self.table.geetools.reduceColumnsGroup(
+            reducer=red, groupBy="ADM0_NAME", selectors=["ADM1_NAME"]  # group by country
+        )
+        with pytest.raises(ee.ee_exception.EEException) as excinfo:
+            output.getInfo()
+        assert (
+            str(excinfo.value)
+            == "Collection.reduceColumns: Can't set input 0 of MEAN to Aragatsotn.: Input must be a scalar number."
+        )
+
+    @property
+    def table(self):
+        return ee.FeatureCollection("FAO/GAUL/2015/level1").filter(
+            ee.Filter.stringStartsWith(leftField="ADM0_NAME", rightValue="Ar")
+        )
+
+
 class TestAddId:
     """Test the ``addId`` method."""
 

tests/test_FeatureCollection/test_reduce_columns_group.yml

@@ -0,0 +1,16 @@
+Argentina:
+  Shape_Area:
+    mean: 11.59538319268763
+    sum: 278.2891966245031
+Armenia:
+  Shape_Area:
+    mean: 0.2852573993497455
+    sum: 3.1378313928472004
+Aruba:
+  Shape_Area:
+    mean: 0.0149496718126
+    sum: 0.0149496718126
+Arunachal Pradesh:
+  Shape_Area:
+    mean: 3.1213382042817
+    sum: 6.2426764085634

tests/test_FeatureCollection/test_reduce_columns_group_custom_output.yml

@@ -0,0 +1,16 @@
+Argentina:
+  Shape_Area:
+    average_km2: 11.59538319268763
+    sum_km2: 278.2891966245031
+Armenia:
+  Shape_Area:
+    average_km2: 0.2852573993497455
+    sum_km2: 3.1378313928472004
+Aruba:
+  Shape_Area:
+    average_km2: 0.0149496718126
+    sum_km2: 0.0149496718126
+Arunachal Pradesh:
+  Shape_Area:
+    average_km2: 3.1213382042817
+    sum_km2: 6.2426764085634