Add 'spearman' correlation method for dataframe.corr and series.corr

python/cudf/cudf/core/dataframe.py

@@ -5652,10 +5652,25 @@ def cov(self, **kwargs):
         df._set_column_names_like(self)
         return df
 
-    @_cudf_nvtx_annotate
-    def corr(self):
-        """Compute the correlation matrix of a DataFrame."""
-        corr = cupy.corrcoef(self.values, rowvar=False)
+    def corr(self, method="pearson"):
+        """Compute the correlation matrix of a DataFrame.
+        Parameters
+        ----------
+        method : {'pearson', 'spearman'}, default 'pearson'
+            The correlation method to use, one of 'pearson' or 'spearman'.
+
+        Returns
+        -------
+        DataFrame
+            The requested correlation matrix.
+        """
+        if method == "pearson":
+            values = self.values
+        elif method == "spearman":
+            values = self.rank().values
+        else:
+            raise ValueError("method must be either 'pearson', 'spearman'")
+        corr = cupy.corrcoef(values, rowvar=False)
         cols = self._data.to_pandas_index()
         df = DataFrame(cupy.asfortranarray(corr)).set_index(cols)
         df._set_column_names_like(self)
@@ -5665,7 +5680,6 @@ def corr(self):
     def to_struct(self, name=None):
         """
         Return a struct Series composed of the columns of the DataFrame.
-
         Parameters
         ----------
         name: optional

python/cudf/cudf/core/series.py

@@ -2422,11 +2422,13 @@ def corr(self, other, method="pearson", min_periods=None):
         >>> import cudf
         >>> ser1 = cudf.Series([0.9, 0.13, 0.62])
         >>> ser2 = cudf.Series([0.12, 0.26, 0.51])
-        >>> ser1.corr(ser2)
+        >>> ser1.corr(ser2, method="pearson")
         -0.20454263717316112
+        >>> ser1.corr(ser2, method="spearman")
+        -0.5
         """
 
-        if method not in ("pearson",):
+        if method not in {"pearson", "spearman"}:
             raise ValueError(f"Unknown method {method}")
 
         if min_periods not in (None,):
@@ -2438,6 +2440,9 @@ def corr(self, other, method="pearson", min_periods=None):
         lhs = self.nans_to_nulls().dropna()
         rhs = other.nans_to_nulls().dropna()
         lhs, rhs = _align_indices([lhs, rhs], how="inner")
+        if method == "spearman":
+            lhs = lhs.rank()
+            rhs = rhs.rank()
 
         try:
             return lhs._column.corr(rhs._column)

python/cudf/cudf/tests/test_stats.py

@@ -408,24 +408,26 @@ def test_cov1d(data1, data2):
         cudf.Series([5]),
     ],
 )
-def test_corr1d(data1, data2):
+@pytest.mark.parametrize("method", ["spearman", "pearson"])
+def test_corr1d(data1, data2, method):
     gs1 = cudf.Series(data1)
     gs2 = cudf.Series(data2)
 
     ps1 = gs1.to_pandas()
     ps2 = gs2.to_pandas()
 
-    got = gs1.corr(gs2)
-    expected = ps1.corr(ps2)
+    got = gs1.corr(gs2, method)
+    expected = ps1.corr(ps2, method)
     np.testing.assert_approx_equal(got, expected, significant=8)
 
 
-def test_df_corr():
+@pytest.mark.parametrize("method", ["spearman", "pearson"])
+def test_df_corr(method):
 
     gdf = randomdata(100, {str(x): float for x in range(50)})
     pdf = gdf.to_pandas()
-    got = gdf.corr()
-    expected = pdf.corr()
+    got = gdf.corr(method)
+    expected = pdf.corr(method)
     assert_eq(got, expected)