def _validate_setitem_value(self, value):
"""
Check if we have a scalar that we can cast losslessly.
Raises
------
TypeError
"""
kind = self.dtype.kind
# TODO: get this all from np_can_hold_element?
if kind == "b":
if lib.is_bool(value):
return value
elif kind == "f":
if lib.is_integer(value) or lib.is_float(value):
return value
else:
if lib.is_integer(value) or (lib.is_float(value)
and value.is_integer()):
return value
# TODO: unsigned checks
# Note: without the "str" here, the f-string rendering raises in
# py38 builds.
raise TypeError(f"Invalid value '{str(value)}' for dtype {self.dtype}")
def _logical_method(self, other, op):
assert op.__name__ in {"or_", "ror_", "and_", "rand_", "xor", "rxor"}
other_is_booleanarray = isinstance(other, BooleanArray)
other_is_scalar = lib.is_scalar(other)
mask = None
if other_is_booleanarray:
other, mask = other._data, other._mask
elif is_list_like(other):
other = np.asarray(other, dtype="bool")
if other.ndim > 1:
raise NotImplementedError("can only perform ops with 1-d structures")
other, mask = coerce_to_array(other, copy=False)
elif isinstance(other, np.bool_):
other = other.item()
if other_is_scalar and other is not libmissing.NA and not lib.is_bool(other):
raise TypeError(
"'other' should be pandas.NA or a bool. "
f"Got {type(other).__name__} instead."
)
if not other_is_scalar and len(self) != len(other):
raise ValueError("Lengths must match to compare")
if op.__name__ in {"or_", "ror_"}:
result, mask = ops.kleene_or(self._data, other, self._mask, mask)
elif op.__name__ in {"and_", "rand_"}:
result, mask = ops.kleene_and(self._data, other, self._mask, mask)
elif op.__name__ in {"xor", "rxor"}:
result, mask = ops.kleene_xor(self._data, other, self._mask, mask)
return BooleanArray(result, mask)
def validate_cum_func_with_skipna(skipna, args, kwargs, name):
"""
If this function is called via the 'numpy' library, the third parameter in
its signature is 'dtype', which takes either a 'numpy' dtype or 'None', so
check if the 'skipna' parameter is a boolean or not
"""
if not is_bool(skipna):
args = (skipna, ) + args
skipna = True
validate_cum_func(args, kwargs, fname=name)
return skipna
def validate_cum_func_with_skipna(skipna, args, kwargs, name):
"""
If this function is called via the 'numpy' library, the third
parameter in its signature is 'dtype', which takes either a
'numpy' dtype or 'None', so check if the 'skipna' parameter is
a boolean or not
"""
if not is_bool(skipna):
args = (skipna,) + args
skipna = True
validate_cum_func(args, kwargs, fname=name)
return skipna
def _validate_parse_dates_arg(parse_dates):
"""
Check whether or not the 'parse_dates' parameter
is a non-boolean scalar. Raises a ValueError if
that is the case.
"""
msg = ("Only booleans, lists, and dictionaries are accepted "
"for the 'parse_dates' parameter")
if parse_dates is not None:
if is_scalar(parse_dates):
if not lib.is_bool(parse_dates):
raise TypeError(msg)
elif not isinstance(parse_dates, (list, dict)):
raise TypeError(msg)
return parse_dates
def logical_method(self, other):
if isinstance(other, (ABCDataFrame, ABCSeries, ABCIndexClass)):
# Rely on pandas to unbox and dispatch to us.
return NotImplemented
assert op.__name__ in {"or_", "ror_", "and_", "rand_", "xor", "rxor"}
other = lib.item_from_zerodim(other)
other_is_booleanarray = isinstance(other, BooleanArray)
other_is_scalar = lib.is_scalar(other)
mask = None
if other_is_booleanarray:
other, mask = other._data, other._mask
elif is_list_like(other):
other = np.asarray(other, dtype="bool")
if other.ndim > 1:
raise NotImplementedError(
"can only perform ops with 1-d structures"
)
other, mask = coerce_to_array(other, copy=False)
elif isinstance(other, np.bool_):
other = other.item()
if other_is_scalar and not (other is libmissing.NA or lib.is_bool(other)):
raise TypeError(
"'other' should be pandas.NA or a bool. Got {} instead.".format(
type(other).__name__
)
)
if not other_is_scalar and len(self) != len(other):
raise ValueError("Lengths must match to compare")
if op.__name__ in {"or_", "ror_"}:
result, mask = ops.kleene_or(self._data, other, self._mask, mask)
elif op.__name__ in {"and_", "rand_"}:
result, mask = ops.kleene_and(self._data, other, self._mask, mask)
elif op.__name__ in {"xor", "rxor"}:
result, mask = ops.kleene_xor(self._data, other, self._mask, mask)
return BooleanArray(result, mask)
def _validate_setitem_value(self, value):
"""
Check if we have a scalar that we can cast losslessly.
Raises
------
TypeError
"""
kind = self.dtype.kind
# TODO: get this all from np_can_hold_element?
if kind == "b":
if lib.is_bool(value):
return value
elif kind == "f":
if lib.is_integer(value) or lib.is_float(value):
return value
else:
if lib.is_integer(value) or (lib.is_float(value) and value.is_integer()):
return value
# TODO: unsigned checks
raise TypeError(f"Invalid value '{value}' for dtype {self.dtype}")
