diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
index 85dcc041d089..53d046f7fa30 100644
--- a/lib/matplotlib/axes/_axes.py
+++ b/lib/matplotlib/axes/_axes.py
@@ -7374,7 +7374,7 @@ def matshow(self, Z, **kwargs):
@_preprocess_data(replace_names=["dataset"], label_namer=None)
def violinplot(self, dataset, positions=None, vert=True, widths=0.5,
showmeans=False, showextrema=True, showmedians=False,
- points=100, bw_method=None):
+ percentiles=[], points=100, bw_method=None):
"""
Make a violin plot.
@@ -7410,6 +7410,10 @@ def violinplot(self, dataset, positions=None, vert=True, widths=0.5,
showmedians : bool, default = False
If `True`, will toggle rendering of the medians.
+ percentiles : array-like, default = []
+ Displays percentiles in the plot for all the given percentiles
+ in the set
+
points : scalar, default = 100
Defines the number of points to evaluate each of the
gaussian kernel density estimations at.
@@ -7457,6 +7461,11 @@ def violinplot(self, dataset, positions=None, vert=True, widths=0.5,
:class:`matplotlib.collections.LineCollection` instance
created to identify the median values of each of the
violin's distribution.
+
+ - ``cpercentiles`` : A
+ :class:`matplotlib.collections.LineCollection` instance
+ created to identify the percentile values of each of the
+ violins' distributions
"""
def _kde_method(X, coords):
@@ -7466,7 +7475,8 @@ def _kde_method(X, coords):
kde = mlab.GaussianKDE(X, bw_method)
return kde.evaluate(coords)
- vpstats = cbook.violin_stats(dataset, _kde_method, points=points)
+ vpstats = cbook.violin_stats(dataset, _kde_method, points=points,
+ percentiles=percentiles)
return self.violin(vpstats, positions=positions, vert=vert,
widths=widths, showmeans=showmeans,
showextrema=showextrema, showmedians=showmedians)
@@ -7501,6 +7511,8 @@ def violin(self, vpstats, positions=None, vert=True, widths=0.5,
- ``max``: The maximum value for this violin's dataset.
+ - ``percentiles``: The percentiles for this violin's dataset.
+
positions : array-like, default = [1, 2, ..., n]
Sets the positions of the violins. The ticks and limits are
automatically set to match the positions.
@@ -7523,6 +7535,10 @@ def violin(self, vpstats, positions=None, vert=True, widths=0.5,
showmedians : bool, default = False
If true, will toggle rendering of the medians.
+ percentiles : array-like, default = []
+ Displays percentiles in the plot for all the given percentiles in the set.
+
+
Returns
-------
result : dict
@@ -7558,6 +7574,11 @@ def violin(self, vpstats, positions=None, vert=True, widths=0.5,
:class:`matplotlib.collections.LineCollection` instance
created to identify the median values of each of the
violin's distribution.
+
+ - ``cpercentiles``: A
+ :class:`matplotlib.collections.LineCollection` instance
+ created to identify the percentiles values of each of the
+ violin's distribution.
"""
@@ -7566,6 +7587,7 @@ def violin(self, vpstats, positions=None, vert=True, widths=0.5,
mins = []
maxes = []
medians = []
+ percentiles = []
# Collections to be returned
artists = {}
@@ -7622,6 +7644,7 @@ def violin(self, vpstats, positions=None, vert=True, widths=0.5,
mins.append(stats['min'])
maxes.append(stats['max'])
medians.append(stats['median'])
+ percentiles.append(stats['percentiles'])
artists['bodies'] = bodies
# Render means
@@ -7640,11 +7663,18 @@ def violin(self, vpstats, positions=None, vert=True, widths=0.5,
# Render medians
if showmedians:
- artists['cmedians'] = perp_lines(medians,
- pmins,
- pmaxes,
+ artists['cmedians'] = perp_lines(medians, pmins, pmaxes,
colors=edgecolor)
+ # Render percentiles
+ artists['cpercentiles'] = []
+ for pcs, pmin, pmax in zip(percentiles, pmins, pmaxes):
+ linelen = pmax - pmin
+ artists['cpercentiles'].append(perp_lines(pcs,
+ pmin + linelen * 0.17,
+ pmax - linelen * 0.17,
+ colors=edgecolor))
+
return artists
def tricontour(self, *args, **kwargs):
diff --git a/lib/matplotlib/cbook/__init__.py b/lib/matplotlib/cbook/__init__.py
index 0b6a4968b113..794c233c2f2e 100644
--- a/lib/matplotlib/cbook/__init__.py
+++ b/lib/matplotlib/cbook/__init__.py
@@ -2047,7 +2047,7 @@ def _reshape_2D(X, name):
raise ValueError("{} must have 2 or fewer dimensions".format(name))
-def violin_stats(X, method, points=100):
+def violin_stats(X, method, points=100, percentiles=[]):
"""
Returns a list of dictionaries of data which can be used to draw a series
of violin plots. See the `Returns` section below to view the required keys
@@ -2071,6 +2071,9 @@ def violin_stats(X, method, points=100):
Defines the number of points to evaluate each of the gaussian kernel
density estimates at.
+ percentiles : array-like, default = []
+ Defines a set of percentiles, if any, that will be displayed per plotted point
+
Returns
-------
@@ -2085,6 +2088,7 @@ def violin_stats(X, method, points=100):
- median: The median value for this column of data.
- min: The minimum value for this column of data.
- max: The maximum value for this column of data.
+ - percentiles: The set of percentiles that will be rendered for this data.
"""
# List of dictionaries describing each of the violins.
@@ -2093,7 +2097,13 @@ def violin_stats(X, method, points=100):
# Want X to be a list of data sequences
X = _reshape_2D(X, "X")
- for x in X:
+ if percentiles is None:
+ percentiles = []
+ percentiles = _reshape_2D(percentiles, "percentiles")
+ while len(percentiles) < len(X):
+ percentiles.append([])
+
+ for x, pcs in zip(X, percentiles):
# Dictionary of results for this distribution
stats = {}
@@ -2111,6 +2121,7 @@ def violin_stats(X, method, points=100):
stats['median'] = np.median(x)
stats['min'] = min_val
stats['max'] = max_val
+ stats['percentiles'] = np.percentile(x, pcs)
# Append to output
vpstats.append(stats)
diff --git a/lib/matplotlib/pyplot.py b/lib/matplotlib/pyplot.py
index 668124817326..6c0c9bf3a892 100644
--- a/lib/matplotlib/pyplot.py
+++ b/lib/matplotlib/pyplot.py
@@ -3652,8 +3652,8 @@ def triplot(*args, **kwargs):
# changes will be lost
@_autogen_docstring(Axes.violinplot)
def violinplot(dataset, positions=None, vert=True, widths=0.5, showmeans=False,
- showextrema=True, showmedians=False, points=100, bw_method=None,
- hold=None, data=None):
+ showextrema=True, showmedians=False, percentiles=[], points=100,
+ bw_method=None, hold=None, data=None):
ax = gca()
# Deprecated: allow callers to override the hold state
# by passing hold=True|False
@@ -3668,7 +3668,8 @@ def violinplot(dataset, positions=None, vert=True, widths=0.5, showmeans=False,
ret = ax.violinplot(dataset, positions=positions, vert=vert,
widths=widths, showmeans=showmeans,
showextrema=showextrema, showmedians=showmedians,
- points=points, bw_method=bw_method, data=data)
+ points=points, bw_method=bw_method, data=data,
+ percentiles=percentiles)
finally:
ax._hold = washold
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