[FIX] Group by: compute mode when all values in group nan

Orange/widgets/data/owgroupby.py

@@ -5,6 +5,7 @@
 from typing import Any, Dict, List, Optional, Set
 
 import pandas as pd
+from numpy import nan
 from AnyQt.QtCore import (
     QAbstractTableModel,
     QEvent,
@@ -58,7 +59,7 @@ def concatenate(x):
     "Mean": Aggregation("mean", {ContinuousVariable, TimeVariable}),
     "Median": Aggregation("median", {ContinuousVariable, TimeVariable}),
     "Mode": Aggregation(
-        lambda x: pd.Series.mode(x)[0], {ContinuousVariable, TimeVariable}
+        lambda x: pd.Series.mode(x).get(0, nan), {ContinuousVariable, TimeVariable}
     ),
     "Standard deviation": Aggregation("std", {ContinuousVariable, TimeVariable}),
     "Variance": Aggregation("var", {ContinuousVariable, TimeVariable}),
@@ -405,7 +406,7 @@ def __gb_changed(self) -> None:
         self.gb_attrs = [values[row.row()] for row in sorted(rows)]
         # everything cached in result should be recomputed on gb change
         self.result = Result()
-        self.commit()
+        self.commit.deferred()
 
     def __aggregation_changed(self, agg: str) -> None:
         """
@@ -421,7 +422,7 @@ def __aggregation_changed(self, agg: str) -> None:
             else:
                 self.aggregations[attr].discard(agg)
             self.agg_table_model.update_aggregation(attr)
-        self.commit()
+        self.commit.deferred()
 
     @Inputs.data
     def set_data(self, data: Table) -> None:
@@ -449,11 +450,12 @@ def set_data(self, data: Table) -> None:
         self.agg_table_model.set_domain(data.domain if data else None)
         self._set_gb_selection()
 
-        self.commit()
+        self.commit.now()
 
     #########################
     # Task connected methods
 
+    @gui.deferred
     def commit(self) -> None:
         self.Error.clear()
         self.Warning.clear()

Orange/widgets/data/tests/test_owgroupby.py

@@ -12,6 +12,8 @@
 from Orange.data import (
     Table,
     table_to_frame,
+    Domain,
+    ContinuousVariable,
 )
 from Orange.data.tests.test_aggregate import create_sample_data
 from Orange.widgets.data.owgroupby import OWGroupBy
@@ -689,6 +691,65 @@ def test_time_variable(self):
         output = self.get_output(self.widget.Outputs.data)
         self.assertEqual(2, len(output))
 
+    def test_only_nan_in_group(self):
+        data = Table(
+            Domain([ContinuousVariable("A"), ContinuousVariable("B")]),
+            np.array([[1, np.nan], [2, 1], [1, np.nan], [2, 1]]),
+        )
+        self.send_signal(self.widget.Inputs.data, data)
+
+        # select feature A as group-by
+        self._set_selection(self.widget.gb_attrs_view, [0])
+        # select all aggregations for feature B
+        self.select_table_rows(self.widget.agg_table_view, [1])
+        for cb in self.widget.agg_checkboxes.values():
+            while not cb.isChecked():
+                cb.click()
+
+        # unselect all aggregations for attr A
+        self.select_table_rows(self.widget.agg_table_view, [0])
+        for cb in self.widget.agg_checkboxes.values():
+            while cb.isChecked():
+                cb.click()
+
+        expected_columns = [
+            "B - Mean",
+            "B - Median",
+            "B - Mode",
+            "B - Standard deviation",
+            "B - Variance",
+            "B - Sum",
+            "B - Min. value",
+            "B - Max. value",
+            "B - Span",
+            "B - First value",
+            "B - Last value",
+            "B - Random value",
+            "B - Count defined",
+            "B - Count",
+            "B - Proportion defined",
+            "B - Concatenate",
+            "A",
+        ]
+        n = np.nan
+        expected_df = pd.DataFrame(
+            [
+                [n, n, n, n, n, 0, n, n, n, n, n, n, 0, 2, 0, "", 1],
+                [1, 1, 1, 0, 0, 2, 1, 1, 0, 1, 1, 1, 2, 2, 1, "1.0 1.0", 2],
+            ],
+            columns=expected_columns,
+        )
+        output_df = table_to_frame(
+            self.get_output(self.widget.Outputs.data), include_metas=True
+        )
+        pd.testing.assert_frame_equal(
+            output_df,
+            expected_df,
+            check_dtype=False,
+            check_column_type=False,
+            check_categorical=False,
+        )
+
 
 if __name__ == "__main__":
     unittest.main()