Fix value_counts and other feedback

Fix errors that crept into value_counts concerning na_type.

And canonicalize na_value in quantities a bit more generally.  As stated in a previous commit comment, if we leave naked NA/NaNs in our Quantity array, the code gracefully handles unit addition/subtraction, but unit multiplication/division loses.

Signed-off-by: Michael Tiemann <[email protected]>

Author: MichaelTiemannOSC

pint_pandas/pint_array.py

@@ -598,7 +598,7 @@ def value_counts(self, dropna=True):
         # compute counts on the data with no nans
         data = self._data
         nafilt = pd.isna(data)
-        na_value = self.dtype.na_value
+        na_value = pd.NA  # NA value for index, not data, so not quantified
         data = data[~nafilt]
         index = list(set(data))
 
@@ -607,9 +607,9 @@ def value_counts(self, dropna=True):
 
         if not dropna:
             index.append(na_value)
-            array.append(len(nafilt))
+            array.append(nafilt.sum())
 
-        return Series(array, index=index)
+        return Series(np.asarray(array), index=index)
 
     def unique(self):
         """Compute the PintArray of unique values.
@@ -773,7 +773,7 @@ def __array__(self, dtype=None, copy=False):
     def _to_array_of_quantity(self, copy=False):
         qtys = [
             self._Q(item, self._dtype.units)
-            if item is not self.dtype.na_value
+            if not pd.isna(item)
             else self.dtype.na_value
             for item in self._data
         ]

pint_pandas/testsuite/test_pandas_extensiontests.py

@@ -316,6 +316,10 @@ def test_groupby_extension_no_sort(self, data_for_grouping):
         tm.assert_series_equal(result, expected)
 
 
+class TestInterface(base.BaseInterfaceTests):
+    pass
+
+
 class TestMethods(base.BaseMethodsTests):
     def test_apply_simple_series(self, data):
         result = pd.Series(data).apply(lambda x: x * 2 + ureg.Quantity(1, x.u))
@@ -537,10 +541,7 @@ def test_reduce_scaling(self, data, all_numeric_reductions, skipna):
         # min/max with empty produce numpy warnings
         with warnings.catch_warnings():
             warnings.simplefilter("ignore", RuntimeWarning)
-            # try:
             r_nm = getattr(s_nm, op_name)(skipna=skipna)
-            # except AttributeError:
-            #     pytest.skip("bye!")
             r_mm = getattr(s_mm, op_name)(skipna=skipna)
             if isinstance(r_nm, ureg.Quantity):
                 # convert both results to the same units, then take the magnitude