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 is_valid_na_for_dtype(obj, dtype: DtypeObj) -> bool:
"""
isna check that excludes incompatible dtypes
Parameters
----------
obj : object
dtype : np.datetime64, np.timedelta64, DatetimeTZDtype, or PeriodDtype
Returns
-------
bool
"""
if not lib.is_scalar(obj) or not isna(obj):
return False
elif dtype.kind == "M":
if isinstance(dtype, np.dtype):
# i.e. not tzaware
return not isinstance(obj, (np.timedelta64, Decimal))
# we have to rule out tznaive dt64("NaT")
return not isinstance(obj, (np.timedelta64, np.datetime64, Decimal))
elif dtype.kind == "m":
return not isinstance(obj, (np.datetime64, Decimal))
elif dtype.kind in ["i", "u", "f", "c"]:
# Numeric
return obj is not NaT and not isinstance(
obj, (np.datetime64, np.timedelta64))
elif dtype.kind == "b":
# We allow pd.NA, None, np.nan in BooleanArray (same as IntervalDtype)
return lib.is_float(obj) or obj is None or obj is libmissing.NA
elif dtype == _dtype_str:
# numpy string dtypes to avoid float np.nan
return not isinstance(obj,
(np.datetime64, np.timedelta64, Decimal, float))
elif dtype == _dtype_object:
# This is needed for Categorical, but is kind of weird
return True
elif isinstance(dtype, PeriodDtype):
return not isinstance(obj, (np.datetime64, np.timedelta64, Decimal))
elif isinstance(dtype, IntervalDtype):
return lib.is_float(obj) or obj is None or obj is libmissing.NA
elif isinstance(dtype, CategoricalDtype):
return is_valid_na_for_dtype(obj, dtype.categories.dtype)
# fallback, default to allowing NaN, None, NA, NaT
return not isinstance(obj, (np.datetime64, np.timedelta64, Decimal))
def __new__(cls, values, freq=None, start=None, end=None, periods=None,
closed=None):
if (freq is not None and not isinstance(freq, DateOffset) and
freq != 'infer'):
freq = to_offset(freq)
if periods is not None:
if lib.is_float(periods):
periods = int(periods)
elif not lib.is_integer(periods):
raise TypeError('`periods` must be a number, got {periods}'
.format(periods=periods))
if values is None:
if freq is None and com._any_none(periods, start, end):
raise ValueError('Must provide freq argument if no data is '
'supplied')
else:
return cls._generate(start, end, periods, freq,
closed=closed)
result = cls._simple_new(values, freq=freq)
if freq == 'infer':
inferred = result.inferred_freq
if inferred:
result._freq = to_offset(inferred)
return result
def cast_scalar_indexer(val, warn_float: bool = False):
"""
To avoid numpy DeprecationWarnings, cast float to integer where valid.
Parameters
----------
val : scalar
warn_float : bool, default False
If True, issue deprecation warning for a float indexer.
Returns
-------
outval : scalar
"""
# assumes lib.is_scalar(val)
if lib.is_float(val) and val.is_integer():
if warn_float:
warnings.warn(
"Indexing with a float is deprecated, and will raise an IndexError "
"in pandas 2.0. You can manually convert to an integer key instead.",
FutureWarning,
stacklevel=3,
)
return int(val)
return val
def validate_periods(periods):
"""
If a `periods` argument is passed to the Datetime/Timedelta Array/Index
constructor, cast it to an integer.
Parameters
----------
periods : None, float, int
Returns
-------
periods : None or int
Raises
------
TypeError
if periods is None, float, or int
"""
if periods is not None:
if lib.is_float(periods):
periods = int(periods)
elif not lib.is_integer(periods):
raise TypeError('periods must be a number, got {periods}'.format(
periods=periods))
return periods
def __new__(cls,
values,
freq=None,
start=None,
end=None,
periods=None,
closed=None):
if (freq is not None and not isinstance(freq, DateOffset)
and freq != 'infer'):
freq = to_offset(freq)
if periods is not None:
if lib.is_float(periods):
periods = int(periods)
elif not lib.is_integer(periods):
raise TypeError(
'`periods` must be a number, got {periods}'.format(
periods=periods))
if values is None:
if freq is None and com._any_none(periods, start, end):
raise ValueError('Must provide freq argument if no data is '
'supplied')
else:
return cls._generate(start, end, periods, freq, closed=closed)
result = cls._simple_new(values, freq=freq)
if freq == 'infer':
inferred = result.inferred_freq
if inferred:
result._freq = to_offset(inferred)
return result
def validate_periods(periods):
"""
If a `periods` argument is passed to the Datetime/Timedelta Array/Index
constructor, cast it to an integer.
Parameters
----------
periods : None, float, int
Returns
-------
periods : None or int
Raises
------
TypeError
if periods is None, float, or int
"""
if periods is not None:
if lib.is_float(periods):
periods = int(periods)
elif not lib.is_integer(periods):
raise TypeError('periods must be a number, got {periods}'
.format(periods=periods))
return periods
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}")
def cast_scalar_indexer(val):
"""
To avoid numpy DeprecationWarnings, cast float to integer where valid.
Parameters
----------
val : scalar
Returns
-------
outval : scalar
"""
# assumes lib.is_scalar(val)
if lib.is_float(val) and val == int(val):
return int(val)
return val
def cast_scalar_indexer(val):
"""
To avoid numpy DeprecationWarnings, cast float to integer where valid.
Parameters
----------
val : scalar
Returns
-------
outval : scalar
"""
# assumes lib.is_scalar(val)
if lib.is_float(val) and val == int(val):
return int(val)
return val
def raise_for_nan(value, method: str):
if lib.is_float(value) and np.isnan(value):
raise ValueError(
f"Cannot perform logical '{method}' with floating NaN")
