TST: suppress some numpy warnings

pandas/core/algorithms.py

@@ -174,7 +174,7 @@ def isin(comps, values):
                         " to isin(), you passed a "
                         "[{0}]".format(type(values).__name__))
 
-    from pandas import DatetimeIndex, PeriodIndex
+    from pandas import DatetimeIndex, TimedeltaIndex, PeriodIndex
 
     if not isinstance(values, (ABCIndex, ABCSeries, np.ndarray)):
         values = np.array(list(values), dtype='object')
@@ -183,6 +183,9 @@ def isin(comps, values):
         if is_period_dtype(values):
             comps = PeriodIndex(comps)
             values = PeriodIndex(values)
+        elif is_timedelta64_dtype(comps):
+            comps = TimedeltaIndex(comps)
+            values = TimedeltaIndex(values)
         else:
             comps = DatetimeIndex(comps)
             values = DatetimeIndex(values)

pandas/tests/test_algos.py

@@ -431,6 +431,8 @@ def test_basic(self):
         expected = np.array([False, False])
         tm.assert_numpy_array_equal(result, expected)
 
+    def test_i8(self):
+
         arr = pd.date_range('20130101', periods=3).values
         result = algos.isin(arr, [arr[0]])
         expected = np.array([True, False, False])

pandas/tests/test_categorical.py

@@ -2913,10 +2913,12 @@ def test_info(self):
         df['category'] = Series(np.array(list('abcdefghij')).take(
             np.random.randint(0, 10, size=n))).astype('category')
         df.isnull()
-        df.info()
+        buf = compat.StringIO()
+        df.info(buf=buf)
 
         df2 = df[df['category'] == 'd']
-        df2.info()
+        buf = compat.StringIO()
+        df2.info(buf=buf)
 
     def test_groupby_sort(self):
 

pandas/tests/test_nanops.py

@@ -389,9 +389,10 @@ def test_nanstd(self):
     def test_nansem(self):
         tm.skip_if_no_package('scipy', min_version='0.17.0')
         from scipy.stats import sem
-        self.check_funs_ddof(nanops.nansem, sem, allow_complex=False,
-                             allow_str=False, allow_date=False,
-                             allow_tdelta=True, allow_obj='convert')
+        with np.errstate(invalid='ignore'):
+            self.check_funs_ddof(nanops.nansem, sem, allow_complex=False,
+                                 allow_str=False, allow_date=False,
+                                 allow_tdelta=False, allow_obj='convert')
 
     def _minmax_wrap(self, value, axis=None, func=None):
         res = func(value, axis)
@@ -449,16 +450,20 @@ def test_nanskew(self):
         tm.skip_if_no_package('scipy', min_version='0.17.0')
         from scipy.stats import skew
         func = partial(self._skew_kurt_wrap, func=skew)
-        self.check_funs(nanops.nanskew, func, allow_complex=False,
-                        allow_str=False, allow_date=False, allow_tdelta=False)
+        with np.errstate(invalid='ignore'):
+            self.check_funs(nanops.nanskew, func, allow_complex=False,
+                            allow_str=False, allow_date=False,
+                            allow_tdelta=False)
 
     def test_nankurt(self):
         tm.skip_if_no_package('scipy', min_version='0.17.0')
         from scipy.stats import kurtosis
         func1 = partial(kurtosis, fisher=True)
         func = partial(self._skew_kurt_wrap, func=func1)
-        self.check_funs(nanops.nankurt, func, allow_complex=False,
-                        allow_str=False, allow_date=False, allow_tdelta=False)
+        with np.errstate(invalid='ignore'):
+            self.check_funs(nanops.nankurt, func, allow_complex=False,
+                            allow_str=False, allow_date=False,
+                            allow_tdelta=False)
 
     def test_nanprod(self):
         self.check_funs(nanops.nanprod, np.prod, allow_str=False,