matplotlib
diff --git a/‎galleries/examples/statistics/bxp.py
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diff --git a/‎lib/matplotlib/axes/_axes.py
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diff --git a/‎lib/matplotlib/cbook.py
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+7-13Lines changed: 7 additions & 13 deletions b/‎lib/matplotlib/cbook.py
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+7-13Lines changed: 7 additions & 13 deletions
diff --git a/‎lib/matplotlib/cbook.pyi
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diff --git a/‎lib/matplotlib/tests/test_cbook.py
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+2-2Lines changed: 2 additions & 2 deletions b/‎lib/matplotlib/tests/test_cbook.py
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+2-2Lines changed: 2 additions & 2 deletions
@@ -25,7 +25,7 @@
 labels = list('ABCD')
 
 # compute the boxplot stats
-stats = cbook.boxplot_stats(data, tick_labels=labels, bootstrap=10000)
+stats = cbook.boxplot_stats(data, labels=labels, bootstrap=10000)
 
 # %%
 # After we've computed the stats, we can go through and change anything.
 
@@ -4001,7 +4001,7 @@ def boxplot(self, x, notch=None, sym=None, vert=None, whis=None,
             bootstrap = mpl.rcParams['boxplot.bootstrap']
 
         bxpstats = cbook.boxplot_stats(x, whis=whis, bootstrap=bootstrap,
-                                       tick_labels=tick_labels, autorange=autorange)
+                                       labels=tick_labels, autorange=autorange)
         if notch is None:
             notch = mpl.rcParams['boxplot.notch']
         if vert is None:
 
@@ -1126,9 +1126,7 @@ def _broadcast_with_masks(*args, compress=False):
     return inputs
 
 
-@_api.rename_parameter("3.9", "labels", "tick_labels")
-def boxplot_stats(X, whis=1.5, bootstrap=None, tick_labels=None,
-                  autorange=False):
+def boxplot_stats(X, whis=1.5, bootstrap=None, labels=None, autorange=False):
     r"""
     Return a list of dictionaries of statistics used to draw a series of box
     and whisker plots using `~.Axes.bxp`.
@@ -1162,14 +1160,10 @@ def boxplot_stats(X, whis=1.5, bootstrap=None, tick_labels=None,
         Number of times the confidence intervals around the median
         should be bootstrapped (percentile method).
 
-    tick_labels : array-like, optional
+    labels : list of str, optional
         Labels for each dataset. Length must be compatible with
         dimensions of *X*.
 
-        .. versionchanged:: 3.9
-            Renamed from *labels*, which is deprecated since 3.9
-            and will be removed in 3.11.
-
     autorange : bool, optional (False)
         When `True` and the data are distributed such that the 25th and 75th
         percentiles are equal, ``whis`` is set to (0, 100) such that the
@@ -1245,13 +1239,13 @@ def _compute_conf_interval(data, med, iqr, bootstrap):
     X = _reshape_2D(X, "X")
 
     ncols = len(X)
-    if tick_labels is None:
-        tick_labels = itertools.repeat(None)
-    elif len(tick_labels) != ncols:
-        raise ValueError("Dimensions of tick_labels and X must be compatible")
+    if labels is None:
+        labels = itertools.repeat(None)
+    elif len(labels) != ncols:
+        raise ValueError("Dimensions of labels and X must be compatible")
 
     input_whis = whis
-    for ii, (x, label) in enumerate(zip(X, tick_labels)):
+    for ii, (x, label) in enumerate(zip(X, labels)):
 
         # empty dict
         stats = {}
 
@@ -135,7 +135,7 @@ def boxplot_stats(
     X: ArrayLike,
     whis: float | tuple[float, float] = ...,
     bootstrap: int | None = ...,
-    tick_labels: ArrayLike | None = ...,
+    labels: ArrayLike | None = ...,
     autorange: bool = ...,
 ) -> list[dict[str, Any]]: ...
 
 
@@ -147,7 +147,7 @@ def test_results_whiskers_percentiles(self):
 
     def test_results_withlabels(self):
         labels = ['Test1', 2, 'Aardvark', 4]
-        results = cbook.boxplot_stats(self.data, tick_labels=labels)
+        results = cbook.boxplot_stats(self.data, labels=labels)
         for lab, res in zip(labels, results):
             assert res['label'] == lab
 
@@ -158,7 +158,7 @@ def test_results_withlabels(self):
     def test_label_error(self):
         labels = [1, 2]
         with pytest.raises(ValueError):
-            cbook.boxplot_stats(self.data, tick_labels=labels)
+            cbook.boxplot_stats(self.data, labels=labels)
 
     def test_bad_dims(self):
         data = np.random.normal(size=(34, 34, 34))