def na_op(x, y):
if com.is_categorical_dtype(x) != (not np.isscalar(y) and com.is_categorical_dtype(y)):
msg = "Cannot compare a Categorical for op {op} with type {typ}. If you want to \n" \
"compare values, use 'series <op> np.asarray(cat)'."
raise TypeError(msg.format(op=op,typ=type(y)))
if x.dtype == np.object_:
if isinstance(y, list):
y = lib.list_to_object_array(y)
if isinstance(y, (pa.Array, pd.Series)):
if y.dtype != np.object_:
result = lib.vec_compare(x, y.astype(np.object_), op)
else:
result = lib.vec_compare(x, y, op)
else:
result = lib.scalar_compare(x, y, op)
else:
try:
result = getattr(x, name)(y)
if result is NotImplemented:
raise TypeError("invalid type comparison")
except (AttributeError):
result = op(x, y)
return result
def na_op(x, y):
# dispatch to the categorical if we have a categorical
# in either operand
if com.is_categorical_dtype(x):
return op(x, y)
elif com.is_categorical_dtype(y) and not lib.isscalar(y):
return op(y, x)
if x.dtype == np.object_:
if isinstance(y, list):
y = lib.list_to_object_array(y)
if isinstance(y, (np.ndarray, pd.Series)):
if y.dtype != np.object_:
result = lib.vec_compare(x, y.astype(np.object_), op)
else:
result = lib.vec_compare(x, y, op)
else:
result = lib.scalar_compare(x, y, op)
else:
try:
result = getattr(x, name)(y)
if result is NotImplemented:
raise TypeError("invalid type comparison")
except (AttributeError):
result = op(x, y)
return result
def na_op(x, y):
if com.is_categorical_dtype(x) != com.is_categorical_dtype(y):
msg = "Cannot compare a Categorical for op {op} with type {typ}. If you want to \n" \
"compare values, use 'series <op> np.asarray(cat)'."
raise TypeError(msg.format(op=op, typ=type(y)))
if x.dtype == np.object_:
if isinstance(y, list):
y = lib.list_to_object_array(y)
if isinstance(y, (pa.Array, pd.Series)):
if y.dtype != np.object_:
result = lib.vec_compare(x, y.astype(np.object_), op)
else:
result = lib.vec_compare(x, y, op)
else:
result = lib.scalar_compare(x, y, op)
else:
try:
result = getattr(x, name)(y)
if result is NotImplemented:
raise TypeError("invalid type comparison")
except (AttributeError):
result = op(x, y)
return result
def na_op(x, y):
# dispatch to the categorical if we have a categorical
# in either operand
if com.is_categorical_dtype(x):
return op(x,y)
elif com.is_categorical_dtype(y) and not lib.isscalar(y):
return op(y,x)
if x.dtype == np.object_:
if isinstance(y, list):
y = lib.list_to_object_array(y)
if isinstance(y, (np.ndarray, pd.Series)):
if y.dtype != np.object_:
result = lib.vec_compare(x, y.astype(np.object_), op)
else:
result = lib.vec_compare(x, y, op)
else:
result = lib.scalar_compare(x, y, op)
else:
try:
result = getattr(x, name)(y)
if result is NotImplemented:
raise TypeError("invalid type comparison")
except (AttributeError):
result = op(x, y)
return result
def na_op(x, y):
if x.dtype == np.object_:
if isinstance(y, list):
y = lib.list_to_object_array(y)
if isinstance(y, (pa.Array, pd.Series)):
if y.dtype != np.object_:
result = lib.vec_compare(x, y.astype(np.object_), op)
else:
result = lib.vec_compare(x, y, op)
else:
result = lib.scalar_compare(x, y, op)
else:
try:
result = getattr(x,name)(y)
if result is NotImplemented:
raise TypeError("invalid type comparison")
except (AttributeError):
result = op(x, y)
return result
def na_op(x, y):
if x.dtype == np.object_:
if isinstance(y, list):
y = lib.list_to_object_array(y)
if isinstance(y, (pa.Array, pd.Series)):
if y.dtype != np.object_:
result = lib.vec_compare(x, y.astype(np.object_), op)
else:
result = lib.vec_compare(x, y, op)
else:
result = lib.scalar_compare(x, y, op)
else:
try:
result = getattr(x, name)(y)
if result is NotImplemented:
raise TypeError("invalid type comparison")
except (AttributeError):
result = op(x, y)
return result
def _comp_method_OBJECT_ARRAY(op, x, y):
if isinstance(y, list):
y = lib.list_to_object_array(y)
if isinstance(y, (np.ndarray, ABCSeries, ABCIndex)):
if not is_object_dtype(y.dtype):
y = y.astype(np.object_)
if isinstance(y, (ABCSeries, ABCIndex)):
y = y.values
result = lib.vec_compare(x, y, op)
else:
result = lib.scalar_compare(x, y, op)
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 isscalar(y):
return op(y,x)
if is_object_dtype(x.dtype):
if isinstance(y, list):
y = lib.list_to_object_array(y)
if isinstance(y, (np.ndarray, pd.Series)):
if not is_object_dtype(y.dtype):
result = lib.vec_compare(x, y.astype(np.object_), op)
else:
result = lib.vec_compare(x, y, op)
else:
result = lib.scalar_compare(x, y, op)
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 isscalar(y) and isnull(y):
y = np.nan
# we have a datetime/timedelta and may need to convert
mask = None
if needs_i8_conversion(x) or (not isscalar(y) and needs_i8_conversion(y)):
if isscalar(y):
y = _index.convert_scalar(x,_values_from_object(y))
else:
y = y.view('i8')
if name == '__ne__':
mask = notnull(x)
else:
mask = isnull(x)
x = x.view('i8')
try:
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] = False
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 isscalar(y):
return op(y,x)
if is_object_dtype(x.dtype):
if isinstance(y, list):
y = lib.list_to_object_array(y)
if isinstance(y, (np.ndarray, pd.Series)):
if not is_object_dtype(y.dtype):
result = lib.vec_compare(x, y.astype(np.object_), op)
else:
result = lib.vec_compare(x, y, op)
else:
result = lib.scalar_compare(x, y, op)
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 isscalar(y) and isnull(y):
y = np.nan
# we have a datetime/timedelta and may need to convert
mask = None
if needs_i8_conversion(x) or (not isscalar(y) and needs_i8_conversion(y)):
if isscalar(y):
y = _index.convert_scalar(x,_values_from_object(y))
else:
y = y.view('i8')
if name == '__ne__':
mask = notnull(x)
else:
mask = isnull(x)
x = x.view('i8')
try:
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] = False
return result
