DOC: Clarify data inputs for boxplot() and violinplot()

Author: timhoffm

lib/matplotlib/axes/_axes.py

@@ -4325,10 +4325,12 @@ def boxplot(self, x, notch=None, sym=None, vert=None,
 
         Parameters
         ----------
-        x : Array or a sequence of vectors.
-            The input data.  If a 2D array, a boxplot is drawn for each column
-            in *x*.  If a sequence of 1D arrays, a boxplot is drawn for each
-            array in *x*.
+        x : 1D array or sequence of 1D arrays or 2D array
+            The input data.
+
+            - If a 1D array, a single box is drawn.
+            - If a sequence of 1D arrays, a box is drawn for each array in the sequence.
+            - If a 2D array, a box is drawn for each column in the array.
 
         notch : bool, default: :rc:`boxplot.notch`
             Whether to draw a notched boxplot (`True`), or a rectangular
@@ -8862,9 +8864,13 @@ def violinplot(self, dataset, positions=None, vert=None,
 
         Parameters
         ----------
-        dataset : Array or a sequence of vectors.
+        dataset : 1D array or sequence of 1D arrays or 2D array
             The input data.
 
+            - If a 1D array, a single box is drawn.
+            - If a sequence of 1D arrays, a box is drawn for each array in the sequence.
+            - If a 2D array, a box is drawn for each column in the array.
+
         positions : array-like, default: [1, 2, ..., n]
             The positions of the violins; i.e. coordinates on the x-axis for
             vertical violins (or y-axis for horizontal violins).

lib/matplotlib/cbook.py

@@ -1463,7 +1463,14 @@ def violin_stats(X, method=("GaussianKDE", "scott"), points=100, quantiles=None)
 
     Parameters
     ----------
-    X : array-like
+    X : 1D array or sequence of 1D arrays or 2D array
+        Sample data that will be used to produce the gaussian kernel density
+        estimates. Must have 2 or fewer dimensions.
+
+        - If a 1D array, statistics are computed for that array.
+        - If a sequence of 1D arrays, statistics are computer for array in the sequence.
+        - If a 2D array, statistics are computed for each column in the array.
+
         Sample data that will be used to produce the gaussian kernel density
         estimates. Must have 2 or fewer dimensions.