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

closes pandas-dev#12541 Author: Giacomo Ferroni <giaferroni@gmail.com> Author: Giacomo <mralgos@users.noreply.github.com> Closes pandas-dev#15196 from mralgos/gh12541 and squashes the following commits: 65d70eb [Giacomo Ferroni] Added Periods to the test 94084b4 [Giacomo] Merge branch 'master' into gh12541 9621315 [Giacomo Ferroni] Added extra test and updated whatsnew cb84935 [Giacomo Ferroni] pylint checks 26c86a5 [Giacomo Ferroni] Test added for GH12541 ea49e77 [Giacomo Ferroni] Fix for GH12541

Author: Giacomo Ferroni

doc/source/whatsnew/v0.20.0.txt

@@ -432,6 +432,7 @@ Bug Fixes
 - Bug in ``pd.pivot_table()`` where no error was raised when values argument was not in the columns (:issue:`14938`)
 
 - Bug in ``.to_json()`` where ``lines=True`` and contents (keys or values) contain escaped characters (:issue:`15096`)
+- Bug in ``.rolling/expanding()`` functions where ``count()`` was not counting ``np.Inf``, nor handling ``object`` dtypes (:issue:`12541`)
 
 - Bug in ``DataFrame.groupby().describe()`` when grouping on ``Index`` containing tuples (:issue:`14848`)
 - Bug in creating a ``MultiIndex`` with tuples and not passing a list of names; this will now raise ``ValueError`` (:issue:`15110`)

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 +207,49 @@ 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)
+
+ result = df.rolling(window=2).count()
+ tm.assert_frame_equal(result, expected)
+
+ result = df.rolling(1).count()
+ expected = df.notnull().astype(float)
+ tm.assert_frame_equal(result, expected)
+
  def test_window_with_args(self):
  tm._skip_if_no_scipy()