align bar and histogram behaviours in wide mode with categorical values

Author: nicolaskruchten

packages/python/plotly/plotly/express/_core.py

@@ -1114,13 +1114,13 @@ def build_dataframe(args, constructor):
 
     if missing_bar_dim and constructor == go.Bar:
         # now that we've populated df_output, we check to see if the non-missing
-        # dimensio is categorical: if so, then setting the missing dimension to a
+        # dimension is categorical: if so, then setting the missing dimension to a
         # constant 1 is a less-insane thing to do than setting it to the index by
         # default and we let the normal auto-orientation-code do its thing later
         other_dim = "x" if missing_bar_dim == "y" else "y"
         if not _is_continuous(df_output, args[other_dim]):
-            args[missing_bar_dim] = missing_bar_dim
-            constants[missing_bar_dim] = 1
+            args[missing_bar_dim] = "_count_"
+            constants["_count_"] = 1
         else:
             # on the other hand, if the non-missing dimension is continuous, then we
             # can use this information to override the normal auto-orientation code
@@ -1146,14 +1146,24 @@ def build_dataframe(args, constructor):
         df_output[var_name] = df_output[var_name].astype(str)
         args["orientation"] = args.get("orientation", None) or "v"
         orient_v = args["orientation"] == "v"
-        if constructor in [go.Scatter, go.Bar]:
+        if constructor == go.Scatter:
             args["x" if orient_v else "y"] = index_name
             args["y" if orient_v else "x"] = "_value_"
             args["color"] = args["color"] or var_name
+        if constructor == go.Bar:
+            if _is_continuous(df_output, "_value_"):
+                args["x" if orient_v else "y"] = index_name
+                args["y" if orient_v else "x"] = "_value_"
+                args["color"] = args["color"] or var_name
+            else:
+                args["x" if orient_v else "y"] = "_value_"
+                args["y" if orient_v else "x"] = "_count_"
+                df_output["_count_"] = 1
+                args["color"] = args["color"] or var_name
         if constructor in [go.Violin, go.Box]:
             args["x" if orient_v else "y"] = var_name
             args["y" if orient_v else "x"] = "_value_"
-        if constructor in [go.Histogram]:
+        if constructor == go.Histogram:
             args["x" if orient_v else "y"] = "_value_"
             args["color"] = args["color"] or var_name
 

packages/python/plotly/plotly/tests/test_core/test_px/test_px_wide.py

@@ -120,6 +120,35 @@ def test_wide_mode_internal(trace_type, x, y, color, orientation):
         assert args_out == dict(x=y, y=x, color=color, orientation="h")
 
 
+@pytest.mark.parametrize(
+    "orientation", [None, "v", "h"],
+)
+def test_wide_mode_internal_bar_exception(orientation):
+    df_in = pd.DataFrame(dict(a=["q", "r", "s"], b=["t", "u", "v"]), index=[11, 12, 13])
+    args_in = dict(data_frame=df_in, color=None, orientation=orientation)
+    args_out = build_dataframe(args_in, go.Bar)
+    df_out = args_out.pop("data_frame")
+    assert_frame_equal(
+        df_out.sort_index(axis=1),
+        pd.DataFrame(
+            dict(
+                index=[11, 12, 13, 11, 12, 13],
+                _column_=["a", "a", "a", "b", "b", "b"],
+                _value_=["q", "r", "s", "t", "u", "v"],
+                _count_=[1, 1, 1, 1, 1, 1],
+            )
+        ).sort_index(axis=1),
+    )
+    if orientation is None or orientation == "v":
+        assert args_out == dict(
+            x="_value_", y="_count_", color="_column_", orientation="v"
+        )
+    else:
+        assert args_out == dict(
+            x="_count_", y="_value_", color="_column_", orientation="h"
+        )
+
+
 def test_wide_mode_internal_special_cases():
     # given all of the above tests, and given that the melt() code is not sensitive
     # to the trace type, we can do all sorts of special-case testing just by focusing