back out KDE

Author: nicolaskruchten

packages/python/plotly/plotly/express/__init__.py

@@ -35,7 +35,6 @@
     strip,
     histogram,
     ecdf,
-    kde,
     scatter_matrix,
     parallel_coordinates,
     parallel_categories,
@@ -92,7 +91,6 @@
     "strip",
     "histogram",
     "ecdf",
-    "kde",
     "choropleth",
     "choropleth_mapbox",
     "pie",

packages/python/plotly/plotly/express/_chart_types.py

@@ -555,59 +555,6 @@ def ecdf(
 )
 
 
-def kde(
-    data_frame=None,
-    x=None,
-    y=None,
-    color=None,
-    line_dash=None,
-    facet_row=None,
-    facet_col=None,
-    facet_col_wrap=0,
-    facet_row_spacing=None,
-    facet_col_spacing=None,
-    hover_name=None,
-    hover_data=None,
-    animation_frame=None,
-    animation_group=None,
-    category_orders=None,
-    labels=None,
-    color_discrete_sequence=None,
-    color_discrete_map=None,
-    line_dash_sequence=None,
-    line_dash_map=None,
-    marginal=None,
-    opacity=None,
-    orientation=None,
-    bw_method=None,
-    render_mode="auto",
-    log_x=False,
-    log_y=False,
-    range_x=None,
-    range_y=None,
-    title=None,
-    template=None,
-    width=None,
-    height=None,
-):
-    """
-    In a Kernel Density Estimation (KDE) plot, rows of `data_frame` are used as inputs
-    to a KDE smoothing function and a line is drawn with one point pre row of input.
-    """
-    return make_figure(args=locals(), constructor=go.Scatter)
-
-
-kde.__doc__ = make_docstring(
-    kde,
-    append_dict=dict(
-        x=["If `orientation` is `'h'`, this argument is used as KDE weights."]
-        + _wide_mode_xy_append,
-        y=["If `orientation` is `'v'`, this argument is used as KDE weights."]
-        + _wide_mode_xy_append,
-    ),
-)
-
-
 def violin(
     data_frame=None,
     x=None,

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

@@ -1318,9 +1318,7 @@ def build_dataframe(args, constructor):
     wide_cross_name = None  # will likely be "index" in wide_mode
     value_name = None  # will likely be "value" in wide_mode
     hist2d_types = [go.Histogram2d, go.Histogram2dContour]
-    hist1d_orientation = (
-        constructor == go.Histogram or "ecdfmode" in args or "bw_method" in args
-    )
+    hist1d_orientation = constructor == go.Histogram or "ecdfmode" in args
     if constructor in cartesians:
         if wide_x and wide_y:
             raise ValueError(
@@ -1807,7 +1805,7 @@ def infer_config(args, constructor, trace_patch, layout_patch):
             trace_patch["marker"] = dict(opacity=args["opacity"])
     if (
         "line_group" in args or "line_dash" in args
-    ):  # px.line, px.line_*, px.area, px.ecdf, px, kde
+    ):  # px.line, px.line_*, px.area, px.ecdf
         modes = set()
         if args.get("lines", True):
             modes.add("lines")
@@ -1878,9 +1876,6 @@ def infer_config(args, constructor, trace_patch, layout_patch):
             )
         args["histnorm"] = args["ecdfnorm"]
 
-    if "bw_method" in args:
-        args["histnorm"] = "density"
-
     # Compute applicable grouping attributes
     for k in group_attrables:
         if k in args:
@@ -2113,19 +2108,6 @@ def make_figure(args, constructor, trace_patch=None, layout_patch=None):
                 elif args["ecdfnorm"] == "percent":
                     group[var] = 100.0 * group[var] / group_sum
 
-            if "bw_method" in args:
-                from scipy.stats import gaussian_kde
-
-                base = args["x"] if args["orientation"] == "v" else args["y"]
-                var = args["x"] if args["orientation"] == "h" else args["y"]
-                bw = args.get("bw_method")
-                group = group.sort_values(by=base)
-
-                kernel = gaussian_kde(
-                    dataset=group[base], weights=group[var], bw_method=bw
-                )
-                group[var] = kernel.evaluate(group[base])
-
             patch, fit_results = make_trace_kwargs(
                 args, trace_spec, group, mapping_labels.copy(), sizeref
             )

packages/python/plotly/plotly/express/_doc.py

@@ -591,11 +591,6 @@
         "If `'complementary'`, the CCDF is plotted such that values represent data above the point.",
         "If `'reversed'`, a variant of the CCDF is plotted such that values represent data at or above the point.",
     ],
-    bw_method=[
-        "str, scalar or callable (default `'scott'`)",
-        "If str, must be one of `'scott'` or `'silverman'`.",
-        "Passed to `scipy.stats.gaussian_kde`.",
-    ],
 )
 
 

packages/python/plotly/plotly/tests/test_optional/test_px/test_marginals.py

@@ -14,7 +14,7 @@ def test_xy_marginals(px_fn, marginal_x, marginal_y):
     assert len(fig.data) == 1 + (marginal_x is not None) + (marginal_y is not None)
 
 
-@pytest.mark.parametrize("px_fn", [px.histogram, px.ecdf, px.kde])
+@pytest.mark.parametrize("px_fn", [px.histogram, px.ecdf])
 @pytest.mark.parametrize("marginal", [None, "rug", "histogram", "box", "violin"])
 @pytest.mark.parametrize("orientation", ["h", "v"])
 def test_single_marginals(px_fn, marginal, orientation):