Merge branch 'master' into add-new-traces-to-templategen

Author: archmoj

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

@@ -652,9 +652,6 @@ def set_cartesian_axis_opts(args, axis, letter, orders):
 
 
 def configure_cartesian_marginal_axes(args, fig, orders):
-    if "histogram" in [args["marginal_x"], args["marginal_y"]]:
-        fig.layout["barmode"] = "overlay"
-
     nrows = len(fig._grid_ref)
     ncols = len(fig._grid_ref[0])
 
@@ -1489,25 +1486,21 @@ def build_dataframe(args, constructor):
     # PySpark to pandas.
     is_pd_like = False
 
-    # Flag that indicates if data_frame requires to be converted to arrow via the
-    # dataframe interchange protocol.
-    # True if Ibis, DuckDB, Vaex or implements __dataframe__
+    # Flag that indicates if data_frame needs to be converted to PyArrow.
+    # True if Ibis, DuckDB, Vaex, or implements __dataframe__
     needs_interchanging = False
 
     # If data_frame is provided, we parse it into a narwhals DataFrame, while accounting
     # for compatibility with pandas specific paths (e.g. Index/MultiIndex case).
     if df_provided:
-
         # data_frame is pandas-like DataFrame (pandas, modin.pandas, cudf)
         if nw.dependencies.is_pandas_like_dataframe(args["data_frame"]):
-
             columns = args["data_frame"].columns  # This can be multi index
             args["data_frame"] = nw.from_native(args["data_frame"], eager_only=True)
             is_pd_like = True
 
         # data_frame is pandas-like Series (pandas, modin.pandas, cudf)
         elif nw.dependencies.is_pandas_like_series(args["data_frame"]):
-
             args["data_frame"] = nw.from_native(
                 args["data_frame"], series_only=True
             ).to_frame()
@@ -1861,7 +1854,6 @@ def _check_dataframe_all_leaves(df: nw.DataFrame) -> None:
     for row_idx, row in zip(
         null_indices_mask, null_mask.filter(null_indices_mask).iter_rows()
     ):
-
         i = row.index(True)
 
         if not all(row[i:]):
@@ -1990,7 +1982,6 @@ def process_dataframe_hierarchy(args):
 
     if args["color"]:
         if discrete_color:
-
             discrete_aggs.append(args["color"])
             agg_f[args["color"]] = nw.col(args["color"]).max()
             agg_f[f'{args["color"]}{n_unique_token}'] = (
@@ -2045,7 +2036,6 @@ def post_agg(dframe: nw.LazyFrame, continuous_aggs, discrete_aggs) -> nw.LazyFra
         ).drop([f"{col}{n_unique_token}" for col in discrete_aggs])
 
     for i, level in enumerate(path):
-
         dfg = (
             df.group_by(path[i:], drop_null_keys=True)
             .agg(**agg_f)
@@ -2487,6 +2477,10 @@ def make_figure(args, constructor, trace_patch=None, layout_patch=None):
         constructor = go.Bar
         args = process_dataframe_timeline(args)
 
+    # If we have marginal histograms, set barmode to "overlay"
+    if "histogram" in [args.get("marginal_x"), args.get("marginal_y")]:
+        layout_patch["barmode"] = "overlay"
+
     trace_specs, grouped_mappings, sizeref, show_colorbar = infer_config(
         args, constructor, trace_patch, layout_patch
     )
@@ -2558,7 +2552,12 @@ def make_figure(args, constructor, trace_patch=None, layout_patch=None):
                     legendgroup=trace_name,
                     showlegend=(trace_name != "" and trace_name not in trace_names),
                 )
-            if trace_spec.constructor in [go.Bar, go.Violin, go.Box, go.Histogram]:
+
+            # Set 'offsetgroup' only in group barmode (or if no barmode is set)
+            barmode = layout_patch.get("barmode")
+            if trace_spec.constructor in [go.Bar, go.Box, go.Violin, go.Histogram] and (
+                barmode == "group" or barmode is None
+            ):
                 trace.update(alignmentgroup=True, offsetgroup=trace_name)
             trace_names.add(trace_name)
 

packages/python/plotly/plotly/tests/test_optional/test_utils/test_utils.py

@@ -95,7 +95,7 @@ def np_inf():
     columns=["col 1"], data=[1, 2, 3, dt(2014, 1, 5), pd.NaT, np_nan(), np_inf()]
 )
 
-rng = pd.date_range("1/1/2011", periods=2, freq="H")
+rng = pd.date_range("1/1/2011", periods=2, freq="h")
 ts = pd.Series([1.5, 2.5], index=rng)