Fix bug in window function count should count anything non-null

doc/source/whatsnew/v0.20.0.txt

@@ -447,7 +447,7 @@ Bug Fixes
 
 
 - Bug in ``Series`` constructor when both ``copy=True`` and ``dtype`` arguments are provided (:issue:`15125`)
-- Bug in ``pd.read_csv()`` for the C engine where ``usecols`` were being indexed incorrectly with ``parse_dates`` (:issue:`14792`)
+- Bug in ``pd.read_csv()`` for the C engine where ``usecols`` were being indexed incorrectly with ``parse_dates`` (:issue:`14792
 - Incorrect dtyped ``Series`` was returned by comparison methods (e.g., ``lt``, ``gt``, ...) against a constant for an empty ``DataFrame`` (:issue:`15077`)
 - Bug in ``Series.dt.round`` inconsistent behaviour on NAT's with different arguments (:issue:`14940`)
 
@@ -465,3 +465,5 @@ Bug Fixes
 
 
 - Bug in ``DataFrame.boxplot`` where ``fontsize`` was not applied to the tick labels on both axes (:issue:`15108`)
+
+- Bug in window function ``count`` not counting ``np.Inf`` (:issue:`12541`)

pandas/core/window.py

@@ -762,17 +762,7 @@ def count(self):
 
         results = []
         for b in blocks:
-
-            if needs_i8_conversion(b.values):
-                result = b.notnull().astype(int)
-            else:
-                try:
-                    result = np.isfinite(b).astype(float)
-                except TypeError:
-                    result = np.isfinite(b.astype(float)).astype(float)
-
-                result[pd.isnull(result)] = 0
-
+            result = b.notnull().astype(int)
             result = self._constructor(result, window=window, min_periods=0,
                                        center=self.center).sum()
             results.append(result)

pandas/tests/test_window.py

@@ -207,6 +207,44 @@ def f():
             'A', 'ra', 'std'), ('B', 'rb', 'mean'), ('B', 'rb', 'std')])
         tm.assert_frame_equal(result, expected, check_like=True)
 
+    def test_count_nonnumeric_types(self):
+        # GH12541
+        cols = ['int', 'float', 'string', 'datetime', 'timedelta', 'periods',
+                'fl_inf', 'fl_nan', 'str_nan', 'dt_nat', 'periods_nat']
+
+        df = DataFrame(
+            {'int': [1, 2, 3],
+             'float': [4., 5., 6.],
+             'string': list('abc'),
+             'datetime': pd.date_range('20170101', periods=3),
+             'timedelta': pd.timedelta_range('1 s', periods=3, freq='s'),
+             'periods': [pd.Period('2012-01'), pd.Period('2012-02'), 
+                         pd.Period('2012-03')],
+             'fl_inf': [1., 2., np.Inf],
+             'fl_nan': [1., 2., np.NaN],
+             'str_nan': ['aa', 'bb', np.NaN],
+             'dt_nat': [pd.Timestamp('20170101'), pd.Timestamp('20170203'),
+                        pd.Timestamp(None)],
+             'periods_nat': [pd.Period('2012-01'), pd.Period('2012-02'), 
+                         pd.Period(None)]},
+            columns=cols)
+
+        expected = DataFrame(
+            {'int': [1., 2., 2.],
+             'float': [1., 2., 2.],
+             'string': [1., 2., 2.],
+             'datetime': [1., 2., 2.],
+             'timedelta': [1., 2., 2.],
+             'periods': [1., 2., 2.],
+             'fl_inf': [1., 2., 2.],
+             'fl_nan': [1., 2., 1.],
+             'str_nan': [1., 2., 1.],
+             'dt_nat': [1., 2., 1.],
+             'periods_nat': [1., 2., 1.]},
+            columns=cols)
+
+        self.assert_frame_equal(df.rolling(window=2).count(), expected)
+
     def test_window_with_args(self):
         tm._skip_if_no_scipy()