REG: fix regression in df.corrwith on tied data when method is spearman

doc/source/whatsnew/v1.5.1.rst

@@ -86,6 +86,7 @@ Fixed regressions
 - Fixed regression in :meth:`DataFrame.apply` when passing non-zero ``axis`` via keyword argument (:issue:`48656`)
 - Fixed regression in :meth:`Series.groupby` and :meth:`DataFrame.groupby` when the grouper is a nullable data type (e.g. :class:`Int64`) or a PyArrow-backed string array, contains null values, and ``dropna=False`` (:issue:`48794`)
 - Fixed regression in :class:`ExcelWriter` where the ``book`` attribute could no longer be set; however setting this attribute is now deprecated and this ability will be removed in a future version of pandas (:issue:`48780`)
+- Fixed regression in :meth:`DataFrame.corrwith` when computing correlation on tied data with ``method="spearman"`` (:issue:`48826`)
 
 .. ---------------------------------------------------------------------------
 

pandas/core/frame.py

@@ -10605,11 +10605,12 @@ def corrwith(
                             0, 1
                         ]
                 else:
+                    from scipy.stats import rankdata
                     for i, r in enumerate(ndf):
                         nonnull_mask = ~np.isnan(r) & ~np.isnan(k)
                         corrs[cols[i]] = np.corrcoef(
-                            r[nonnull_mask].argsort().argsort(),
-                            k[nonnull_mask].argsort().argsort(),
+                            rankdata(r[nonnull_mask]),
+                            rankdata(k[nonnull_mask]),
                         )[0, 1]
                 return Series(corrs)
             else:

pandas/tests/frame/methods/test_cov_corr.py

@@ -399,6 +399,15 @@ def test_corrwith_spearman(self):
         expected = Series(np.ones(len(result)))
         tm.assert_series_equal(result, expected)
 
+    @td.skip_if_no_scipy
+    def test_corrwith_spearman_with_tied_data(self):
+        # GH#48826
+        df = DataFrame({"A": [2, np.nan, 8, 9], "B": [0, 1, 1, 0]})
+        s = Series([0, 1, 1, 0])
+        result = df.corrwith(s, method="spearman")
+        expected = Series([0.0, 1.0], index=["A", "B"])
+        tm.assert_series_equal(result, expected)
+
     @td.skip_if_no_scipy
     def test_corrwith_kendall(self):
         # GH#21925