Implement numeric_only=False for GroupBy.corr and ``GroupBy.c…

…ov`` (dask#10264)

dask/dataframe/groupby.py

@@ -25,6 +25,7 @@
     GROUP_KEYS_DEFAULT,
     DataFrame,
     Series,
+    _convert_to_numeric,
     _extract_meta,
     _Frame,
     aca,
@@ -90,8 +91,6 @@
 )
 
 NUMERIC_ONLY_NOT_IMPLEMENTED = [
-    "corr",
-    "cov",
     "cumprod",
     "cumsum",
     "mean",
@@ -667,6 +666,10 @@ def _cov_chunk(df, *by, numeric_only=no_default):
     if is_series_like(df):
         df = df.to_frame()
     df = df.copy()
+    if numeric_only is False:
+        dt_df = df.select_dtypes(include=["datetime", "timedelta"])
+        for col in dt_df.columns:
+            df[col] = _convert_to_numeric(dt_df[col], True)
 
     # mapping columns to str(numerical) values allows us to easily handle
     # arbitrary column names (numbers, string, empty strings)
@@ -2101,7 +2104,6 @@ def std(self, ddof=1, split_every=None, split_out=1, numeric_only=no_default):
         return result
 
     @derived_from(pd.DataFrame)
-    @numeric_only_not_implemented
     def corr(self, ddof=1, split_every=None, split_out=1, numeric_only=no_default):
         """Groupby correlation:
         corr(X, Y) = cov(X, Y) / (std_x * std_y)
@@ -2116,7 +2118,6 @@ def corr(self, ddof=1, split_every=None, split_out=1, numeric_only=no_default):
         )
 
     @derived_from(pd.DataFrame)
-    @numeric_only_not_implemented
     def cov(
         self, ddof=1, split_every=None, split_out=1, std=False, numeric_only=no_default
     ):

dask/dataframe/tests/test_groupby.py

@@ -3634,3 +3634,24 @@ def test_groupby_numeric_only_true(func):
         ddf_result = getattr(ddf.groupby("A"), func)(numeric_only=True)
         pdf_result = getattr(df.groupby("A"), func)(numeric_only=True)
         assert_eq(ddf_result, pdf_result)
+
+
+@pytest.mark.skipif(not PANDAS_GT_150, reason="numeric_only not supported for <1.5")
+@pytest.mark.parametrize("func", ["cov", "corr"])
+def test_groupby_numeric_only_false_cov_corr(func):
+    df = pd.DataFrame(
+        {
+            "float": [1.0, 2.0, 3.0, 4.0, 5, 6.0, 7.0, 8.0],
+            "int": [1, 2, 3, 4, 5, 6, 7, 8],
+            "timedelta": pd.to_timedelta([1, 2, 3, 4, 5, 6, 7, 8]),
+            "A": 1,
+        }
+    )
+    ddf = dd.from_pandas(df, npartitions=2)
+    dd_result = getattr(ddf.groupby("A"), func)(numeric_only=False)
+    pd_result = getattr(df.groupby("A"), func)(numeric_only=False)
+    assert_eq(dd_result, pd_result)
+
+    dd_result = getattr(ddf.groupby("A"), func)(numeric_only=True)
+    pd_result = getattr(df.groupby("A"), func)(numeric_only=True)
+    assert_eq(dd_result, pd_result)