strip out period validation

Author: ResidentMario

pandas/tests/types/test_missing.py

@@ -322,12 +322,6 @@ def test_valid_fill_value(value, dtype):
     assert valid_fill_value(value, dtype)
 
 
-@pytest.mark.parametrize('value',
-                         [pd.Period('1970-01-01'), np.nan])
-def test_valid_period_fill_value(value):
-    assert valid_fill_value(value, 'O', is_period=True)
-
-
 @pytest.mark.parametrize(('value', 'dtype'),
                          [(0, bool), (0.0, bool), (0j, bool), ('0', bool),
                           ('0', int),
@@ -341,9 +335,3 @@ def test_valid_period_fill_value(value):
                           (pd.Period('1970-01-01'), np.dtype('timedelta64'))])
 def test_invalid_fill_value(value, dtype):
     assert not valid_fill_value(value, dtype)
-
-
-@pytest.mark.parametrize('value',
-                         [datetime(1970, 1, 1), timedelta(0)])
-def test_invalid_period_fill_value(value):
-    assert not valid_fill_value(value, 'O', is_period=True)

pandas/types/missing.py

@@ -396,18 +396,14 @@ def na_value_for_dtype(dtype):
     return np.nan
 
 
-def valid_fill_value(value, dtype, is_period=False):
+def valid_fill_value(value, dtype):
     """
     Makes sure the fill value is appropriate for the given dtype.
 
     Parameters
     ----------
     value : scalar
     dtype: string / dtype
-    is_period : bool
-        Period-type columns retain a numpy 'O' dtype, but we still want to
-        potentially restrict their contents to period types only. To implement
-        this we pass whether or not a column consists of periods separately.
     """
     if not is_scalar(value):
         # maybe always raise?
@@ -424,6 +420,4 @@ def valid_fill_value(value, dtype, is_period=False):
         return isinstance(value, (np.datetime64, datetime))
     elif is_timedelta64_dtype(dtype):
         return isinstance(value, (np.timedelta64, timedelta))
-    elif is_period:
-        return is_period_arraylike(np.array([value]))
     return True