"All-column" vis when only few columns in dataframe #199

lux/core/frame.py

@@ -498,9 +498,15 @@ def maintain_recs(self, is_series="DataFrame"):
                 if len(vlist) > 0:
                     rec_df._recommendation[action_type] = vlist
             rec_df._rec_info = rec_infolist
+            quantitative_columns = [i for i in self.dtypes if i != "O" and i != "str"]
+            if len(quantitative_columns) == 2 or len(quantitative_columns) == 3:
+                self.current_vis = VisList([i for i in self.columns], self)
             self._widget = rec_df.render_widget()
         # re-render widget for the current dataframe if previous rec is not recomputed
         elif show_prev:
+            quantitative_columns = [i for i in self.dtypes if i != "O" and i != "str"]
+            if len(quantitative_columns) == 2 or len(quantitative_columns) == 3:
+                self.current_vis = VisList([i for i in self.columns], self)
             self._widget = rec_df.render_widget()
         self._recs_fresh = True
 

tests/test_action.py

@@ -274,3 +274,132 @@ def test_intent_retained():
 
     df._ipython_display_()
     assert list(df.recommendation.keys()) == ["Enhance", "Filter"]
+
+
+def test_all_column_current_vis():
+    df = pd.read_csv(
+        "https://raw.githubusercontent.com/koldunovn/python_for_geosciences/master/DelhiTmax.txt",
+        delimiter=r"\s+",
+        parse_dates=[[0, 1, 2]],
+        header=None,
+    )
+    df.columns = ["Date", "Temp"]
+    df._ipython_display_()
+    assert df.current_vis != None
+
+    df = pd.DataFrame(
+        {
+            "nPts": {
+                0: 49999,
+                1: 71174,
+                2: 101317,
+                3: 144224,
+                4: 205303,
+                5: 292249,
+                6: 416016,
+                7: 592198,
+                8: 842993,
+                9: 1200000,
+            },
+            "t_heatmap": {
+                0: 0.15121674537658691,
+                1: 0.1811518669128418,
+                2: 0.2179429531097412,
+                3: 0.2787730693817139,
+                4: 0.3973350524902344,
+                5: 0.4233138561248779,
+                6: 0.580251932144165,
+                7: 0.7928099632263184,
+                8: 1.0876789093017578,
+                9: 1.6242818832397459,
+            },
+            "t_color_heatmap": {
+                0: 0.14242982864379886,
+                1: 0.18866705894470212,
+                2: 0.1566781997680664,
+                3: 0.16737699508666992,
+                4: 0.19900894165039065,
+                5: 0.2701129913330078,
+                6: 0.2533812522888184,
+                7: 0.37183785438537603,
+                8: 0.3830866813659668,
+                9: 0.39321017265319824,
+            },
+            "t_bar": {
+                0: 0.01940608024597168,
+                1: 0.02618718147277832,
+                2: 0.024693727493286133,
+                3: 0.029685020446777344,
+                4: 0.03471803665161133,
+                5: 0.04173588752746582,
+                6: 0.04706382751464844,
+                7: 0.0667569637298584,
+                8: 0.08260798454284668,
+                9: 0.1006929874420166,
+            },
+            "t_cbar": {
+                0: 0.04185795783996582,
+                1: 0.050965070724487305,
+                2: 0.052091121673583984,
+                3: 0.0610501766204834,
+                4: 0.07504606246948242,
+                5: 0.09924101829528807,
+                6: 0.10392117500305176,
+                7: 0.13044309616088867,
+                8: 0.18137121200561526,
+                9: 0.2069528102874756,
+            },
+            "t_hist": {
+                0: 0.01846003532409668,
+                1: 0.018136978149414062,
+                2: 0.018748998641967773,
+                3: 0.01876473426818848,
+                4: 0.02434706687927246,
+                5: 0.025368213653564453,
+                6: 0.02530217170715332,
+                7: 0.02823114395141602,
+                8: 0.029034852981567383,
+                9: 0.034781217575073235,
+            },
+            "t_scatter": {
+                0: 0.8179378509521484,
+                1: 1.0982017517089844,
+                2: 1.4875690937042236,
+                3: 2.146117925643921,
+                4: 3.1123709678649902,
+                5: 3.92416787147522,
+                6: 6.097048044204713,
+                7: 8.803220987319945,
+                8: 12.054464101791382,
+                9: 17.740845680236816,
+            },
+            "t_color_scatter": {
+                0: 1.3474130630493164,
+                1: 1.7953579425811768,
+                2: 2.6268541812896733,
+                3: 3.646371126174927,
+                4: 4.901940107345581,
+                5: 6.974237203598023,
+                6: 10.197483777999876,
+                7: 14.134275913238525,
+                8: 21.13951110839844,
+                9: 27.127063989639282,
+            },
+        }
+    )
+    df = df.melt(
+        id_vars=["nPts"],
+        value_vars=[
+            "t_heatmap",
+            "t_color_heatmap",
+            "t_bar",
+            "t_cbar",
+            "t_hist",
+            "t_scatter",
+            "t_color_scatter",
+        ],
+        var_name="type",
+        value_name="t",
+    )
+    df._ipython_display_()
+    assert df.current_vis != None