Fix bug caused by groupby.agg on column with many unique values (#174)

* update export tutorial to add explanation for standalone argument

* minor fixes and remove cell output in notebooks

* added contributing doc

* fix bugs and uncomment some tests

* remove raise warning

* remove unnecessary import

* split up rename test into two parts

* fix setting warning, fix data_type bugs and add relevant tests

* remove ordinal data type

* add test for small dataframe resetting index

* add loc and iloc tests

* fix attribute access directly to dataframe

* add small changes to code

* added test for qcut and cut

* add check if dtype is Interval

* added qcut test

* fix Record KeyError

* add tests

* take care of reset_index case

* small edits

* add data_model to column_group Clause

* small edits for row_group

* fixes to row group

Co-authored-by: Doris Lee <dorisjunglinlee@gmail.com>

lux/action/column_group.py

@@ -46,12 +46,17 @@ def column_group(ldf):
                 vis = Vis(
                     [
                         lux.Clause(
-                            index_column_name,
+                            attribute=index_column_name,
                             data_type="nominal",
                             data_model="dimension",
                             aggregation=None,
                         ),
-                        lux.Clause(str(attribute), data_type="quantitative", aggregation=None),
+                        lux.Clause(
+                            attribute=str(attribute),
+                            data_type="quantitative",
+                            data_model="measure",
+                            aggregation=None,
+                        ),
                     ]
                 )
                 collection.append(vis)

lux/action/row_group.py

@@ -45,7 +45,7 @@ def row_group(ldf):
             # 	rowdf.cardinality["index"]=len(rowdf)
             # if isinstance(ldf.columns,pd.DatetimeIndex):
             # 	rowdf.data_type_lookup[dim_name]="temporal"
-            vis = Vis([dim_name, lux.Clause(row.name, aggregation=None)], rowdf)
+            vis = Vis([dim_name, lux.Clause(row.name, data_model="measure", aggregation=None)], rowdf)
             collection.append(vis)
     vlst = VisList(collection)
     # Note that we are not computing interestingness score here because we want to preserve the arrangement of the aggregated data

lux/core/series.py

@@ -102,7 +102,7 @@ def __repr__(self):
                 ldf._widget.observe(ldf.remove_deleted_recs, names="deletedIndices")
                 ldf._widget.observe(ldf.set_intent_on_click, names="selectedIntentIndex")
 
-                if len(ldf._recommendation) > 0:
+                if len(ldf.recommendation) > 0:
                     # box = widgets.Box(layout=widgets.Layout(display='inline'))
                     button = widgets.Button(
                         description="Toggle Pandas/Lux",

lux/executor/PandasExecutor.py

@@ -152,11 +152,11 @@ def execute_aggregate(vis: Vis, isFiltered=True):
             has_color = True
         else:
             color_cardinality = 1
-
         if measure_attr != "":
             if measure_attr.attribute == "Record":
                 vis._vis_data = vis.data.reset_index()
                 # if color is specified, need to group by groupby_attr and color_attr
+
                 if has_color:
                     vis._vis_data = (
                         vis.data.groupby([groupby_attr.attribute, color_attr.attribute])

tests/test_pandas_coverage.py

@@ -145,6 +145,28 @@ def test_groupby_agg(global_var):
     assert len(new_df.cardinality) == 7
 
 
+def test_groupby_agg_big(global_var):
+    df = pd.read_csv("lux/data/car.csv")
+    new_df = df.groupby("Brand").agg(sum)
+    new_df._repr_html_()
+    assert list(new_df.recommendation.keys()) == ["Column Groups"]
+    assert len(new_df.cardinality) == 8
+    year_vis = list(
+        filter(
+            lambda vis: vis.get_attr_by_attr_name("Year") != [], new_df.recommendation["Column Groups"]
+        )
+    )[0]
+    assert year_vis.mark == "bar"
+    assert year_vis.get_attr_by_channel("x")[0].attribute == "Year"
+    new_df = new_df.T
+    new_df._repr_html_()
+    year_vis = list(
+        filter(lambda vis: vis.get_attr_by_attr_name("Year") != [], new_df.recommendation["Row Groups"])
+    )[0]
+    assert year_vis.mark == "bar"
+    assert year_vis.get_attr_by_channel("x")[0].attribute == "Year"
+
+
 def test_qcut(global_var):
     df = pd.read_csv("lux/data/car.csv")
     df["Year"] = pd.to_datetime(df["Year"], format="%Y")

tests/test_parser.py

@@ -121,6 +121,7 @@ def test_validator_invalid_attribute(global_var):
     df = pytest.college_df
     with pytest.raises(KeyError, match="'blah'"):
         with pytest.warns(
-            UserWarning, match="The input attribute 'blah' does not exist in the DataFrame."
+            UserWarning,
+            match="The input attribute 'blah' does not exist in the DataFrame.",
         ):
             df.intent = ["blah"]