DOC ensures sklearn.utils.shuffle passes numpydoc validation (#24367)

zadilkhwaja · glemaitre · web-flow · commit 49279c3267c0 · 2022-09-07T13:37:16.000+02:00
Co-authored-by: Guillaume Lemaitre &lt;g.lemaitre58@gmail.com&gt;
diff --git a/sklearn/tests/test_docstrings.py b/sklearn/tests/test_docstrings.py
@@ -46,7 +46,6 @@
     "sklearn.utils.multiclass.unique_labels",
     "sklearn.utils.safe_mask",
     "sklearn.utils.safe_sqr",
-    "sklearn.utils.shuffle",
     "sklearn.utils.sparsefuncs.count_nonzero",
     "sklearn.utils.sparsefuncs.csc_median_axis_0",
     "sklearn.utils.sparsefuncs.incr_mean_variance_axis",
diff --git a/sklearn/utils/__init__.py b/sklearn/utils/__init__.py
@@ -611,6 +611,10 @@ def shuffle(*arrays, random_state=None, n_samples=None):
         Sequence of shuffled copies of the collections. The original arrays
         are not impacted.
 
+    See Also
+    --------
+    resample : Resample arrays or sparse matrices in a consistent way.
+
     Examples
     --------
     It is possible to mix sparse and dense arrays in the same run::
@@ -643,10 +647,6 @@ def shuffle(*arrays, random_state=None, n_samples=None):
 
       >>> shuffle(y, n_samples=2, random_state=0)
       array([0, 1])
-
-    See Also
-    --------
-    resample
     """
     return resample(
         *arrays, replace=False, n_samples=n_samples, random_state=random_state
