BUG: Series.corr/cov raising with masked dtype

doc/source/whatsnew/v2.0.0.rst

@@ -1230,6 +1230,7 @@ Numeric
 - Bug in arithmetic operations on :class:`Series` not propagating mask when combining masked dtypes and numpy dtypes (:issue:`45810`, :issue:`42630`)
 - Bug in :meth:`DataFrame.sem` and :meth:`Series.sem` where an erroneous ``TypeError`` would always raise when using data backed by an :class:`ArrowDtype` (:issue:`49759`)
 - Bug in :meth:`Series.__add__` casting to object for list and masked :class:`Series` (:issue:`22962`)
+- Bug in :meth:`Series.corr` and :meth:`Series.cov` raising ``AttributeError`` for masked dtypes (:issue:`51422`)
 - Bug in :meth:`~arrays.ArrowExtensionArray.mode` where ``dropna=False`` was not respected when there was ``NA`` values (:issue:`50982`)
 - Bug in :meth:`DataFrame.query` with ``engine="numexpr"`` and column names are ``min`` or ``max`` would raise a ``TypeError`` (:issue:`50937`)
 - Bug in :meth:`DataFrame.min` and :meth:`DataFrame.max` with tz-aware data containing ``pd.NaT`` and ``axis=1`` would return incorrect results (:issue:`51242`)

pandas/core/nanops.py

@@ -1610,6 +1610,9 @@ def nancorr(
     if len(a) < min_periods:
         return np.nan
 
+    a = _ensure_numeric(a)
+    b = _ensure_numeric(b)
+
     f = get_corr_func(method)
     return f(a, b)
 
@@ -1668,6 +1671,9 @@ def nancov(
     if len(a) < min_periods:
         return np.nan
 
+    a = _ensure_numeric(a)
+    b = _ensure_numeric(b)
+
     return np.cov(a, b, ddof=ddof)[0, 1]
 
 

pandas/core/series.py

@@ -2647,9 +2647,12 @@ def corr(
         if len(this) == 0:
             return np.nan
 
+        this_values = np.asarray(this.values)
+        other_values = np.asarray(other.values)
+
         if method in ["pearson", "spearman", "kendall"] or callable(method):
             return nanops.nancorr(
-                this.values, other.values, method=method, min_periods=min_periods
+                this_values, other_values, method=method, min_periods=min_periods
             )
 
         raise ValueError(
@@ -2702,8 +2705,10 @@ def cov(
         this, other = self.align(other, join="inner", copy=False)
         if len(this) == 0:
             return np.nan
+        this_values = np.asarray(this.values)
+        other_values = np.asarray(other.values)
         return nanops.nancov(
-            this.values, other.values, min_periods=min_periods, ddof=ddof
+            this_values, other_values, min_periods=min_periods, ddof=ddof
         )
 
     @doc(

pandas/tests/frame/methods/test_cov_corr.py

@@ -356,7 +356,7 @@ def test_corrwith_mixed_dtypes(self, numeric_only):
         else:
             with pytest.raises(
                 TypeError,
-                match=r"unsupported operand type\(s\) for /: 'str' and 'int'",
+                match=r"Could not convert \['a' 'b' 'c' 'd'\] to numeric",
             ):
                 df.corrwith(s, numeric_only=numeric_only)
 

pandas/tests/series/methods/test_cov_corr.py

@@ -42,13 +42,14 @@ def test_cov(self, datetime_series):
         assert isna(ts1.cov(ts2, min_periods=12))
 
     @pytest.mark.parametrize("test_ddof", [None, 0, 1, 2, 3])
-    def test_cov_ddof(self, test_ddof):
+    @pytest.mark.parametrize("dtype", ["float64", "Float64"])
+    def test_cov_ddof(self, test_ddof, dtype):
         # GH#34611
         np_array1 = np.random.rand(10)
         np_array2 = np.random.rand(10)
 
-        s1 = Series(np_array1)
-        s2 = Series(np_array2)
+        s1 = Series(np_array1, dtype=dtype)
+        s2 = Series(np_array2, dtype=dtype)
 
         result = s1.cov(s2, ddof=test_ddof)
         expected = np.cov(np_array1, np_array2, ddof=test_ddof)[0][1]
@@ -57,9 +58,12 @@ def test_cov_ddof(self, test_ddof):
 
 class TestSeriesCorr:
     @td.skip_if_no_scipy
-    def test_corr(self, datetime_series):
+    @pytest.mark.parametrize("dtype", ["float64", "Float64"])
+    def test_corr(self, datetime_series, dtype):
         from scipy import stats
 
+        datetime_series = datetime_series.astype(dtype)
+
         # full overlap
         tm.assert_almost_equal(datetime_series.corr(datetime_series), 1)