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

pandas/core/frame.py

@@ -9838,7 +9838,27 @@ def corrwith(self, other, axis: Axis = 0, drop=False, method="pearson") -> Serie
         this = self._get_numeric_data()
 
         if isinstance(other, Series):
-            return this.apply(lambda x: other.corr(x, method=method), axis=axis)
+            if axis == 0 and method in ["pearson", "spearman"]:
+                corrs = {}
+                numeric_cols = self.select_dtypes(include=np.number).columns
+                ndf = self[numeric_cols].values.transpose()
+                k = other.values
+                if method == "pearson":
+                    for i, r in enumerate(ndf):
+                        nonnull_mask = ~np.isnan(r) & ~np.isnan(k)
+                        corrs[numeric_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[numeric_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)
 
         other = other._get_numeric_data()
         left, right = this.align(other, join="inner", copy=False)