ENH: Allow to plot weighted KDEs.

pandas/plotting/_core.py

@@ -1449,6 +1449,7 @@ def hist(
     def kde(
         self,
         bw_method: Literal["scott", "silverman"] | float | Callable | None = None,
+        weights: np.ndarray | None = None,
         ind: np.ndarray | int | None = None,
         **kwargs,
     ) -> PlotAccessor:
@@ -1470,6 +1471,9 @@ def kde(
             'scott', 'silverman', a scalar constant or a callable.
             If None (default), 'scott' is used.
             See :class:`scipy.stats.gaussian_kde` for more information.
+        weights : NumPy array, optional
+            Weights of datapoints. This must be the same shape as dataset.
+            If None (default), the samples are assumed to be equally weighted.
         ind : NumPy array or int, optional
             Evaluation points for the estimated PDF. If None (default),
             1000 equally spaced points are used. If `ind` is a NumPy array, the
@@ -1560,7 +1564,7 @@ def kde(
 
             >>> ax = df.plot.kde(ind=[1, 2, 3, 4, 5, 6])
         """
-        return self(kind="kde", bw_method=bw_method, ind=ind, **kwargs)
+        return self(kind="kde", bw_method=bw_method, weights=weights, ind=ind, **kwargs)
 
     density = kde
 

pandas/plotting/_matplotlib/hist.py

@@ -269,6 +269,7 @@ def _plot(  #  type: ignore[override]
         y: np.ndarray,
         style=None,
         bw_method=None,
+        weights=None,
         ind=None,
         column_num=None,
         stacking_id: int | None = None,
@@ -277,7 +278,7 @@ def _plot(  #  type: ignore[override]
         from scipy.stats import gaussian_kde
 
         y = remove_na_arraylike(y)
-        gkde = gaussian_kde(y, bw_method=bw_method)
+        gkde = gaussian_kde(y, bw_method=bw_method, weights=weights)
 
         y = gkde.evaluate(ind)
         lines = MPLPlot._plot(ax, ind, y, style=style, **kwds)