review comments

Author: jreback

pandas/core/frame.py

@@ -16,7 +16,7 @@
 import sys
 import warnings
 from textwrap import dedent
-from typing import FrozenSet, List, Optional, Set, Type, Union
+from typing import FrozenSet, List, Optional, Tuple, Set, Type, Union
 
 import numpy as np
 import numpy.ma as ma
@@ -2651,14 +2651,15 @@ def transpose(self, *args, **kwargs):
     def __array__(self, dtype=None):
         return com.values_from_object(self)
 
-    def __array_wrap__(self, result: np.ndarray, context=None) -> 'DataFrame':
+    def __array_wrap__(self, result: np.ndarray,
+                       context: Optional[Tuple] = None) -> 'DataFrame':
         """
         We are called post ufunc; reconstruct the original object and dtypes.
 
         Parameters
         ----------
         result : np.ndarray
-        context
+        context : tuple, optional
 
         Returns
         -------

pandas/core/series.py

@@ -6,6 +6,7 @@
 from shutil import get_terminal_size
 from textwrap import dedent
 import warnings
+from typing import Optional, Tuple
 
 import numpy as np
 
@@ -762,20 +763,20 @@ def __array__(self, dtype=None):
             dtype = 'M8[ns]'
         return np.asarray(self.array, dtype)
 
-    def __array_wrap__(self, result: np.ndarray, context=None) -> 'Series':
+    def __array_wrap__(self, result: np.ndarray,
+                       context: Optional[Tuple] = None) -> 'Series':
         """
         We are called post ufunc; reconstruct the original object and dtypes.
 
         Parameters
         ----------
         result : np.ndarray
-        context
+        context : tuple, optional
 
         Returns
         -------
         Series
         """
-
         result = self._constructor(result, index=self.index,
                                    copy=False)
 

pandas/tests/groupby/test_function.py

@@ -12,7 +12,7 @@
 from pandas import (
     DataFrame, Index, MultiIndex, Series, Timestamp, date_range, isna)
 import pandas.core.nanops as nanops
-from pandas.util import testing as tm
+from pandas.util import _test_decorators as td, testing as tm
 
 
 @pytest.mark.parametrize("agg_func", ['any', 'all'])
@@ -461,11 +461,8 @@ def test_groupby_cumprod():
 
 
 def scipy_sem(*args, **kwargs):
-    try:
-        from scipy.stats import sem
-        return sem(*args, ddof=1, **kwargs)
-    except ImportError:
-        pytest.skip("No Scipy installed")
+    from scipy.stats import sem
+    return sem(*args, ddof=1, **kwargs)
 
 
 @pytest.mark.parametrize(
@@ -481,7 +478,7 @@ def scipy_sem(*args, **kwargs):
      ('first', lambda x: x.iloc[0]),
      ('last', lambda x: x.iloc[-1]),
      ('count', np.size),
-     ('sem', scipy_sem)])
+     pytest.param('sem', scipy_sem, mark=td._skip_if_no_scipy)])
 def test_ops_general(op, targop):
     df = DataFrame(np.random.randn(1000))
     labels = np.random.randint(0, 50, size=1000).astype(float)

pandas/tests/sparse/frame/test_analytics.py

@@ -42,7 +42,7 @@ def test_quantile_multi():
     tm.assert_sp_frame_equal(result, sparse_expected)
 
 
-@pytest.mark.parametrize('func', [np.exp, np.sqrt], ids=lambda x: x.__name__)
+@pytest.mark.parametrize('func', [np.exp, np.sqrt], ids=str)
 def test_ufunc(func):
     # GH 23743
     # assert we preserve the incoming dtype on ufunc operation

pandas/tests/sparse/series/test_analytics.py

@@ -5,7 +5,7 @@
 from pandas.util import testing as tm
 
 
-@pytest.mark.parametrize('func', [np.exp, np.sqrt], ids=lambda x: x.__name__)
+@pytest.mark.parametrize('func', [np.exp, np.sqrt], ids=str)
 def test_ufunc(func):
     # GH 23743
     # assert we preserve the incoming dtype on ufunc operation

pandas/tests/sparse/test_pivot.py

@@ -56,11 +56,14 @@ def test_pivot_table(self):
          'median',
          'first',
          'last'])
-    def test_pivot_table_multi(self, func):
+    @pytest.mark.parametrize('dropna', [True, False])
+    def test_pivot_table_multi(self, func, dropna):
 
-        res_sparse = pd.pivot_table(self.sparse, index='A', columns='B',
-                                    values=['D', 'E'], aggfunc=func)
-        res_dense = pd.pivot_table(self.dense, index='A', columns='B',
-                                   values=['D', 'E'], aggfunc=func)
+        res_sparse = pd.pivot_table(
+            self.sparse, index='A', columns='B',
+            values=['D', 'E'], aggfunc=func, dropna=dropna)
+        res_dense = pd.pivot_table(
+            self.dense, index='A', columns='B',
+            values=['D', 'E'], aggfunc=func, dropna=dropna)
         res_dense = res_dense.apply(lambda x: x.astype("Sparse[float64]"))
         tm.assert_frame_equal(res_sparse, res_dense)