fix: Make dx histogram behavior consistent with px

plugins/plotly-express/docs/histogram.md

@@ -47,6 +47,27 @@ hist_3_bins = dx.histogram(setosa, x="SepalLength", nbins=3)
 hist_8_bins = dx.histogram(setosa, x="SepalLength", nbins=8)
 ```
 
+### Bin and aggregate on different columns
+
+If both `x` and `y` are specified, the histogram will be binned across one column and aggregated on the other.
+
+```python order=hist_v,hist_h,hist_avg,iris
+import deephaven.plot.express as dx
+iris = dx.data.iris()
+
+# subset to get specific species
+setosa = iris.where("Species == `setosa`")
+
+# The default orientation is "v" (vertical) and the default aggregation function is "sum"
+hist_v = dx.histogram(setosa, x="SepalLength", y="SepalWidth")
+
+# Control the plot orientation using orientation
+hist_h = dx.histogram(setosa, x="SepalLength", y="SepalWidth", orientation="h")
+
+# Control the aggregation function using histfunc
+hist_avg = dx.histogram(setosa, x="SepalLength", y="SepalWidth", histfunc="avg")
+```
+
 ### Distributions of several groups
 
 Histograms can also be used to compare the distributional properties of different groups of data, though they may be a little harder to read than [box plots](box.md) or [violin plots](violin.md). Pass the name of the grouping column(s) to the `by` argument.

plugins/plotly-express/src/deephaven/plot/express/deephaven_figure/generate.py

@@ -111,7 +111,8 @@
     "current_col",
     "current_var",
     "labels",
-    "hist_val_name",
+    "hist_agg_label_h",
+    "hist_agg_label_v",
     "pivot_vars",
     "current_partition",
     "colors",
@@ -824,7 +825,8 @@ def hover_text_generator(
 
 def compute_labels(
     hover_mapping: list[dict[str, str]],
-    hist_val_name: str | None,
+    hist_agg_label_h: str | None,
+    hist_agg_label_v: str | None,
     heatmap_agg_label: str | None,
     # hover_data - todo, dependent on arrays supported in data mappings
     types: set[str],
@@ -837,7 +839,8 @@ def compute_labels(
 
     Args:
       hover_mapping: The mapping of variables to columns
-      hist_val_name: The histogram name for the value axis, generally histfunc
+      hist_agg_label_h: The histogram agg label when oriented horizontally
+      hist_agg_label_v: The histogram agg label when oriented vertically
       heatmap_agg_label: The aggregate density heatmap column title
       types: Any types of this chart that require special processing
       labels: A dictionary of old column name to new column name mappings
@@ -847,7 +850,7 @@ def compute_labels(
         the renamed current_col
     """
 
-    calculate_hist_labels(hist_val_name, hover_mapping[0])
+    calculate_hist_labels(hist_agg_label_h, hist_agg_label_v, hover_mapping[0])
 
     calculate_density_heatmap_labels(heatmap_agg_label, hover_mapping[0], labels)
 
@@ -880,27 +883,31 @@ def calculate_density_heatmap_labels(
 
 
 def calculate_hist_labels(
-    hist_val_name: str | None, current_mapping: dict[str, str]
+    hist_agg_label_h: str | None,
+    hist_agg_label_v: str | None,
+    hover_mapping: dict[str, str],
 ) -> None:
     """Calculate the histogram labels
 
     Args:
-      hist_val_name: The histogram name for the value axis, generally histfunc
-      current_mapping: The mapping of variables to columns
+      hist_agg_label_h: The histogram agg label when oriented horizontally
+      hist_agg_label_v: The histogram agg label when oriented vertically
+      hover_mapping: The mapping of variables to columns
 
     """
-    if hist_val_name:
-        # swap the names
-        current_mapping["x"], current_mapping["y"] = (
-            current_mapping["y"],
-            current_mapping["x"],
-        )
+    # only one should be set
+    if hist_agg_label_h:
+        # a bar chart oriented horizontally has the histfunc on the x-axis
+        hover_mapping["x"] = hist_agg_label_h
+    elif hist_agg_label_v:
+        hover_mapping["y"] = hist_agg_label_v
 
 
 def add_axis_titles(
     custom_call_args: dict[str, Any],
     hover_mapping: list[dict[str, str]],
-    hist_val_name: str | None,
+    hist_agg_label_h: str | None,
+    hist_agg_label_v: str | None,
     heatmap_agg_label: str | None,
 ) -> None:
     """Add axis titles. Generally, this only applies when there is a list variable
@@ -909,7 +916,8 @@ def add_axis_titles(
       custom_call_args: The custom_call_args that are used to
         create hover and axis titles
       hover_mapping: The mapping of variables to columns
-      hist_val_name: The histogram name for the value axis, generally histfunc
+      hist_agg_label_h: The histogram agg label when oriented horizontally
+      hist_agg_label_v: The histogram agg label when oriented vertically
       heatmap_agg_label: The aggregate density heatmap column title
 
     """
@@ -919,8 +927,8 @@ def add_axis_titles(
     new_xaxis_titles = None
     new_yaxis_titles = None
 
-    if hist_val_name:
-        # hist names are already set up in the mapping
+    if hist_agg_label_h or hist_agg_label_v:
+        # hist labels are already set up in the mapping
         new_xaxis_titles = [hover_mapping[0].get("x", None)]
         new_yaxis_titles = [hover_mapping[0].get("y", None)]
 
@@ -978,14 +986,16 @@ def create_hover_and_axis_titles(
     types = get_list_var_info(data_cols)
 
     labels = custom_call_args.get("labels", None)
-    hist_val_name = custom_call_args.get("hist_val_name", None)
+    hist_agg_label_h = custom_call_args.get("hist_agg_label_h", None)
+    hist_agg_label_v = custom_call_args.get("hist_agg_label_v", None)
     heatmap_agg_label = custom_call_args.get("heatmap_agg_label", None)
 
     current_partition = custom_call_args.get("current_partition", {})
 
     compute_labels(
         hover_mapping,
-        hist_val_name,
+        hist_agg_label_h,
+        hist_agg_label_v,
         heatmap_agg_label,
         types,
         labels,
@@ -998,7 +1008,13 @@ def create_hover_and_axis_titles(
         # it's possible that heatmap_agg_label was relabeled, so grab the new label
         heatmap_agg_label = hover_mapping[0]["z"]
 
-    add_axis_titles(custom_call_args, hover_mapping, hist_val_name, heatmap_agg_label)
+    add_axis_titles(
+        custom_call_args,
+        hover_mapping,
+        hist_agg_label_h,
+        hist_agg_label_v,
+        heatmap_agg_label,
+    )
 
     return hover_text
 

plugins/plotly-express/src/deephaven/plot/express/plots/PartitionManager.py

@@ -418,7 +418,7 @@ def process_partitions(self) -> Table | PartitionedTable:
 
         # preprocessor needs to be initialized after the always attached arguments are found
         self.preprocessor = Preprocessor(
-            args, self.groups, self.always_attached, self.pivot_vars
+            args, self.groups, self.always_attached, self.pivot_vars, self.list_var
         )
 
         if partition_cols:

plugins/plotly-express/src/deephaven/plot/express/plots/_private_utils.py

@@ -489,7 +489,6 @@ def shared_histogram(is_marginal: bool = True, **args: Any) -> DeephavenFigure:
     set_all(args, HISTOGRAM_DEFAULTS)
 
     args["bargap"] = 0
-    args["hist_val_name"] = args.get("histfunc", "count")
 
     func = px.bar
     groups = {"bar", "preprocess_hist", "supports_lists"}

plugins/plotly-express/src/deephaven/plot/express/plots/bar.py

@@ -10,7 +10,7 @@
 from ._private_utils import validate_common_args, process_args
 from ..shared import default_callback
 from ..deephaven_figure import generate_figure, DeephavenFigure
-from ..types import PartitionableTableLike
+from ..types import PartitionableTableLike, Orientation
 
 
 def bar(
@@ -42,6 +42,7 @@ def bar(
     range_color: list[float] | None = None,
     color_continuous_midpoint: float | None = None,
     opacity: float | None = None,
+    orientation: Orientation | None = None,
     barmode: str = "relative",
     log_x: bool = False,
     log_y: bool = False,
@@ -114,6 +115,12 @@ def bar(
       color_continuous_midpoint: A number that is the midpoint of the color axis
       opacity: Opacity to apply to all markers. 0 is completely transparent
         and 1 is completely opaque.
+      orientation: The orientation of the bars.
+        If 'v', the bars are vertical.
+        If 'h', the bars are horizontal.
+        Defaults to 'v' if only `x` is specified.
+        Defaults to 'h' if only `y` is specified.
+        Defaults to 'v' if both `x` and `y` are specified unless `x` is passed only numeric columns and `y` is not.
       barmode: If 'relative', bars are stacked. If 'overlay', bars are drawn on top
         of each other. If 'group', bars are drawn next to each other.
       log_x: A boolean or list of booleans that specify if

plugins/plotly-express/src/deephaven/plot/express/plots/distribution.py

@@ -18,7 +18,7 @@
     HISTOGRAM_DEFAULTS,
     default_callback,
 )
-from ..types import PartitionableTableLike
+from ..types import PartitionableTableLike, Orientation
 
 
 def violin(
@@ -321,6 +321,7 @@ def histogram(
     pattern_shape_map: dict[str | tuple[str], str] | None = None,
     marginal: str | None = None,
     opacity: float | None = None,
+    orientation: Orientation | None = None,
     barmode: str = HISTOGRAM_DEFAULTS["barmode"],
     barnorm: str = HISTOGRAM_DEFAULTS["barnorm"],
     histnorm: str = HISTOGRAM_DEFAULTS["histnorm"],
@@ -342,11 +343,11 @@ def histogram(
     Args:
       table: A table to pull data from.
       x: A column name or list of columns that contain x-axis values.
-        Only one of x or y can be specified. If x is specified,
-        the bars are drawn horizontally.
+        Column values must be numeric. If x is specified,
+        the bars are drawn vertically by default.
       y: A column name or list of columns that contain y-axis values.
-        Only one of x or y can be specified. If y is specified, the
-        bars are drawn vertically.
+        Column values must be numeric. If only y is specified,
+        the bars are drawn horizontally by default.
       by: A column or list of columns that contain values to plot the figure traces by.
         All values or combination of values map to a unique design. The variable
         by_vars specifies which design elements are used.
@@ -375,6 +376,11 @@ def histogram(
       marginal: The type of marginal; histogram, violin, rug, box
       opacity: Opacity to apply to all markers. 0 is completely transparent
         and 1 is completely opaque.
+      orientation: The orientation of the bars.
+        If 'v', the bars are vertical.
+        If 'h', the bars are horizontal.
+        Defaults to 'v' if `x` is specified.
+        Defaults to 'h' if only `y` is specified.
       barmode: If 'relative', bars are stacked. If
         'overlay', bars are drawn on top of each other. If 'group', bars are
         drawn next to each other.
@@ -396,6 +402,7 @@ def histogram(
       histfunc: The function to use when aggregating within bins. One of
         'abs_sum', 'avg', 'count', 'count_distinct', 'max', 'median', 'min', 'std',
         'sum', or 'var'
+        Defaults to 'count' if only one of x or y is specified and 'sum' if both are.
       cumulative: If True, values are cumulative.
       nbins: The number of bins to use.
       text_auto: If True, display the value at each bar.

plugins/plotly-express/src/deephaven/plot/express/preprocess/FreqPreprocessor.py

@@ -4,11 +4,11 @@
 
 from deephaven.table import Table
 
-from .UnivariatePreprocessor import UnivariatePreprocessor
+from .UnivariateAwarePreprocessor import UnivariateAwarePreprocessor
 from ..shared import get_unique_names
 
 
-class FreqPreprocessor(UnivariatePreprocessor):
+class FreqPreprocessor(UnivariateAwarePreprocessor):
     """
     A type of univariate preprocessor for frequency bar plots
 
@@ -33,14 +33,14 @@ def preprocess_partitioned_tables(
           A tuple containing (the new table, an update to make to the args)
 
         """
-        column = self.col_val if not column else column
+        column = self.agg_col if not column else column
 
         names = get_unique_names(self.table, ["count"])
 
-        self.args[self.other_var] = names["count"]
+        self.args[self.agg_var] = names["count"]
 
         for table in tables:
             yield table.view([column]).count_by(names["count"], by=column), {
-                self.var: column,
-                self.other_var: names["count"],
+                self.bin_var: column,
+                self.agg_var: names["count"],
             }