def na_op(x, y):
# dispatch to the categorical if we have a categorical
# in either operand
if is_categorical_dtype(x):
return op(x, y)
elif is_categorical_dtype(y) and not is_scalar(y):
return op(y, x)
if is_object_dtype(x.dtype):
result = _comp_method_OBJECT_ARRAY(op, x, y)
else:
# we want to compare like types
# we only want to convert to integer like if
# we are not NotImplemented, otherwise
# we would allow datetime64 (but viewed as i8) against
# integer comparisons
if is_datetimelike_v_numeric(x, y):
raise TypeError("invalid type comparison")
# numpy does not like comparisons vs None
if is_scalar(y) and isna(y):
if name == '__ne__':
return np.ones(len(x), dtype=bool)
else:
return np.zeros(len(x), dtype=bool)
# we have a datetime/timedelta and may need to convert
mask = None
if (needs_i8_conversion(x)
or (not is_scalar(y) and needs_i8_conversion(y))):
if is_scalar(y):
mask = isna(x)
y = libindex.convert_scalar(x, com._values_from_object(y))
else:
mask = isna(x) | isna(y)
y = y.view('i8')
x = x.view('i8')
try:
with np.errstate(all='ignore'):
result = getattr(x, name)(y)
if result is NotImplemented:
raise TypeError("invalid type comparison")
except AttributeError:
result = op(x, y)
if mask is not None and mask.any():
result[mask] = masker
return result
def na_op(x, y):
# dispatch to the categorical if we have a categorical
# in either operand
if is_categorical_dtype(x):
return op(x, y)
elif is_categorical_dtype(y) and not is_scalar(y):
return op(y, x)
if is_object_dtype(x.dtype):
result = _comp_method_OBJECT_ARRAY(op, x, y)
else:
# we want to compare like types
# we only want to convert to integer like if
# we are not NotImplemented, otherwise
# we would allow datetime64 (but viewed as i8) against
# integer comparisons
if is_datetimelike_v_numeric(x, y):
raise TypeError("invalid type comparison")
# numpy does not like comparisons vs None
if is_scalar(y) and isna(y):
if name == '__ne__':
return np.ones(len(x), dtype=bool)
else:
return np.zeros(len(x), dtype=bool)
# we have a datetime/timedelta and may need to convert
mask = None
if (needs_i8_conversion(x) or
(not is_scalar(y) and needs_i8_conversion(y))):
if is_scalar(y):
mask = isna(x)
y = libindex.convert_scalar(x, com._values_from_object(y))
else:
mask = isna(x) | isna(y)
y = y.view('i8')
x = x.view('i8')
try:
with np.errstate(all='ignore'):
result = getattr(x, name)(y)
if result is NotImplemented:
raise TypeError("invalid type comparison")
except AttributeError:
result = op(x, y)
if mask is not None and mask.any():
result[mask] = masker
return result
def _set_values(self, key, value):
# this might be inefficient as we have to recreate the sparse array
# rather than setting individual elements, but have to convert
# the passed slice/boolean that's in dense space into a sparse indexer
# not sure how to do that!
if isinstance(key, Series):
key = key.values
values = self.values.to_dense()
values[key] = libindex.convert_scalar(values, value)
values = SparseArray(values, fill_value=self.fill_value, kind=self.kind)
self._data = SingleBlockManager(values, self.index)
def _set_values(self, key, value):
# this might be inefficient as we have to recreate the sparse array
# rather than setting individual elements, but have to convert
# the passed slice/boolean that's in dense space into a sparse indexer
# not sure how to do that!
if isinstance(key, Series):
key = key.values
values = self.values.to_dense()
values[key] = _index.convert_scalar(values, value)
values = SparseArray(values, fill_value=self.fill_value,
kind=self.kind)
self._data = SingleBlockManager(values, self.index)