Revert "PERF: faster corrwith method for pearson and spearman correlation when other is a Series and axis = 0 (column-wise) (#46174)"

doc/source/whatsnew/v1.5.1.rst

@@ -88,6 +88,7 @@ Fixed regressions
 - 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 :meth:`DataFrame.to_parquet` raising when file name was specified as ``bytes`` (:issue:`48944`)
 - 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

@@ -10577,40 +10577,8 @@ def corrwith(
         if numeric_only is lib.no_default and len(this.columns) < len(self.columns):
             com.deprecate_numeric_only_default(type(self), "corrwith")
 
-        # GH46174: when other is a Series object and axis=0, we achieve a speedup over
-        # passing .corr() to .apply() by taking the columns as ndarrays and iterating
-        # over the transposition row-wise. Then we delegate the correlation coefficient
-        # computation and null-masking to np.corrcoef and np.isnan respectively,
-        # which are much faster. We exploit the fact that the Spearman correlation
-        # of two vectors is equal to the Pearson correlation of their ranks to use
-        # substantially the same method for Pearson and Spearman,
-        # just with intermediate argsorts on the latter.
         if isinstance(other, Series):
-            if axis == 0 and method in ["pearson", "spearman"]:
-                corrs = {}
-                if numeric_only:
-                    cols = self.select_dtypes(include=np.number).columns
-                    ndf = self[cols].values.transpose()
-                else:
-                    cols = self.columns
-                    ndf = self.values.transpose()
-                k = other.values
-                if method == "pearson":
-                    for i, r in enumerate(ndf):
-                        nonnull_mask = ~np.isnan(r) & ~np.isnan(k)
-                        corrs[cols[i]] = np.corrcoef(r[nonnull_mask], k[nonnull_mask])[
-                            0, 1
-                        ]
-                else:
-                    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(),
-                        )[0, 1]
-                return Series(corrs)
-            else:
-                return this.apply(lambda x: other.corr(x, method=method), axis=axis)
+            return this.apply(lambda x: other.corr(x, method=method), axis=axis)
 
         if numeric_only_bool:
             other = other._get_numeric_data()

pandas/tests/frame/methods/test_cov_corr.py

@@ -355,7 +355,10 @@ def test_corrwith_mixed_dtypes(self, numeric_only):
             expected = Series(data=corrs, index=["a", "b"])
             tm.assert_series_equal(result, expected)
         else:
-            with pytest.raises(TypeError, match="not supported for the input types"):
+            with pytest.raises(
+                TypeError,
+                match=r"unsupported operand type\(s\) for /: 'str' and 'int'",
+            ):
                 df.corrwith(s, numeric_only=numeric_only)
 
     def test_corrwith_index_intersection(self):
@@ -406,3 +409,26 @@ def test_corrwith_kendall(self):
         result = df.corrwith(df**2, method="kendall")
         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
+        df1 = DataFrame(
+            {
+                "A": [1, np.nan, 7, 8],
+                "B": [False, True, True, False],
+                "C": [10, 4, 9, 3],
+            }
+        )
+        df2 = df1[["B", "C"]]
+        result = (df1 + 1).corrwith(df2.B, method="spearman")
+        expected = Series([0.0, 1.0, 0.0], index=["A", "B", "C"])
+        tm.assert_series_equal(result, expected)
+
+        df_bool = DataFrame(
+            {"A": [True, True, False, False], "B": [True, False, False, True]}
+        )
+        ser_bool = Series([True, True, False, True])
+        result = df_bool.corrwith(ser_bool)
+        expected = Series([0.57735, 0.57735], index=["A", "B"])
+        tm.assert_series_equal(result, expected)