def _shallow_copy(self, values=None, **kwargs):
# TODO: simplify, figure out type of values
if values is None:
values = self._data
if isinstance(values, type(self)):
values = values._values
if not isinstance(values, PeriodArray):
if (isinstance(values, np.ndarray) and
is_integer_dtype(values.dtype)):
values = PeriodArray(values, freq=self.freq)
else:
# in particular, I would like to avoid period_array here.
# Some people seem to be calling use with unexpected types
# Index.difference -> ndarray[Period]
# DatetimelikeIndexOpsMixin.repeat -> ndarray[ordinal]
# I think that once all of Datetime* are EAs, we can simplify
# this quite a bit.
values = period_array(values, freq=self.freq)
# We don't allow changing `freq` in _shallow_copy.
validate_dtype_freq(self.dtype, kwargs.get('freq'))
attributes = self._get_attributes_dict()
attributes.update(kwargs)
if not len(values) and 'dtype' not in kwargs:
attributes['dtype'] = self.dtype
return self._simple_new(values, **attributes)
def _shallow_copy(self, values=None, **kwargs):
# TODO: simplify, figure out type of values
if values is None:
values = self._data
if isinstance(values, type(self)):
values = values._values
if not isinstance(values, PeriodArray):
if (isinstance(values, np.ndarray) and
is_integer_dtype(values.dtype)):
values = PeriodArray(values, freq=self.freq)
else:
# in particular, I would like to avoid period_array here.
# Some people seem to be calling use with unexpected types
# Index.difference -> ndarray[Period]
# DatetimelikeIndexOpsMixin.repeat -> ndarray[ordinal]
# I think that once all of Datetime* are EAs, we can simplify
# this quite a bit.
values = period_array(values, freq=self.freq)
# I don't like overloading shallow_copy with freq changes.
# See if it's used anywhere outside of test_resample_empty_dataframe
attributes = self._get_attributes_dict()
freq = kwargs.pop("freq", None)
if freq:
values = values.asfreq(freq)
attributes.pop("freq", None)
attributes.update(kwargs)
if not len(values) and 'dtype' not in kwargs:
attributes['dtype'] = self.dtype
return self._simple_new(values, **attributes)
def __new__(
cls,
data=None,
ordinal=None,
freq=None,
tz=None,
dtype=None,
copy=False,
name=None,
**fields,
):
valid_field_set = {
"year",
"month",
"day",
"quarter",
"hour",
"minute",
"second",
}
if not set(fields).issubset(valid_field_set):
argument = list(set(fields) - valid_field_set)[0]
raise TypeError(f"__new__() got an unexpected keyword argument {argument}")
name = maybe_extract_name(name, data, cls)
if data is None and ordinal is None:
# range-based.
data, freq2 = PeriodArray._generate_range(None, None, None, freq, fields)
# PeriodArray._generate range does validation that fields is
# empty when really using the range-based constructor.
freq = freq2
data = PeriodArray(data, freq=freq)
else:
freq = validate_dtype_freq(dtype, freq)
# PeriodIndex allow PeriodIndex(period_index, freq=different)
# Let's not encourage that kind of behavior in PeriodArray.
if freq and isinstance(data, cls) and data.freq != freq:
# TODO: We can do some of these with no-copy / coercion?
# e.g. D -> 2D seems to be OK
data = data.asfreq(freq)
if data is None and ordinal is not None:
# we strangely ignore `ordinal` if data is passed.
ordinal = np.asarray(ordinal, dtype=np.int64)
data = PeriodArray(ordinal, freq)
else:
# don't pass copy here, since we copy later.
data = period_array(data=data, freq=freq)
if copy:
data = data.copy()
return cls._simple_new(data, name=name)
def __new__(cls, data=None, ordinal=None, freq=None, start=None, end=None,
periods=None, tz=None, dtype=None, copy=False, name=None,
**fields):
valid_field_set = {'year', 'month', 'day', 'quarter',
'hour', 'minute', 'second'}
if not set(fields).issubset(valid_field_set):
raise TypeError('__new__() got an unexpected keyword argument {}'.
format(list(set(fields) - valid_field_set)[0]))
if name is None and hasattr(data, 'name'):
name = data.name
if data is None and ordinal is None:
# range-based.
if periods is not None:
if is_float(periods):
periods = int(periods)
elif not is_integer(periods):
msg = 'periods must be a number, got {periods}'
raise TypeError(msg.format(periods=periods))
data, freq = PeriodArray._generate_range(start, end, periods,
freq, fields)
data = PeriodArray(data, freq=freq)
else:
if freq is None and dtype is not None:
freq = PeriodDtype(dtype).freq
elif freq and dtype:
freq = PeriodDtype(freq).freq
dtype = PeriodDtype(dtype).freq
if freq != dtype:
msg = "specified freq and dtype are different"
raise IncompatibleFrequency(msg)
# PeriodIndex allow PeriodIndex(period_index, freq=different)
# Let's not encourage that kind of behavior in PeriodArray.
if freq and isinstance(data, cls) and data.freq != freq:
# TODO: We can do some of these with no-copy / coercion?
# e.g. D -> 2D seems to be OK
data = data.asfreq(freq)
if data is None and ordinal is not None:
# we strangely ignore `ordinal` if data is passed.
ordinal = np.asarray(ordinal, dtype=np.int64)
data = PeriodArray(ordinal, freq)
else:
# don't pass copy here, since we copy later.
data = period_array(data=data, freq=freq)
if copy:
data = data.copy()
return cls._simple_new(data, name=name)
def __new__(cls, data=None, ordinal=None, freq=None, start=None, end=None,
periods=None, tz=None, dtype=None, copy=False, name=None,
**fields):
valid_field_set = {'year', 'month', 'day', 'quarter',
'hour', 'minute', 'second'}
if not set(fields).issubset(valid_field_set):
raise TypeError('__new__() got an unexpected keyword argument {}'.
format(list(set(fields) - valid_field_set)[0]))
if name is None and hasattr(data, 'name'):
name = data.name
if data is None and ordinal is None:
# range-based.
data, freq = PeriodArray._generate_range(start, end, periods,
freq, fields)
data = PeriodArray(data, freq=freq)
else:
freq = dtl.validate_dtype_freq(dtype, freq)
# PeriodIndex allow PeriodIndex(period_index, freq=different)
# Let's not encourage that kind of behavior in PeriodArray.
if freq and isinstance(data, cls) and data.freq != freq:
# TODO: We can do some of these with no-copy / coercion?
# e.g. D -> 2D seems to be OK
data = data.asfreq(freq)
if data is None and ordinal is not None:
# we strangely ignore `ordinal` if data is passed.
ordinal = np.asarray(ordinal, dtype=np.int64)
data = PeriodArray(ordinal, freq)
else:
# don't pass copy here, since we copy later.
data = period_array(data=data, freq=freq)
if copy:
data = data.copy()
return cls._simple_new(data, name=name)
def __new__(cls,
data=None,
ordinal=None,
freq=None,
start=None,
end=None,
periods=None,
tz=None,
dtype=None,
copy=False,
name=None,
**fields):
valid_field_set = {
"year",
"month",
"day",
"quarter",
"hour",
"minute",
"second",
}
if not set(fields).issubset(valid_field_set):
raise TypeError(
"__new__() got an unexpected keyword argument {}".format(
list(set(fields) - valid_field_set)[0]))
if name is None and hasattr(data, "name"):
name = data.name
if data is None and ordinal is None:
# range-based.
data, freq2 = PeriodArray._generate_range(start, end, periods,
freq, fields)
# PeriodArray._generate range does validate that fields is
# empty when really using the range-based constructor.
if not fields:
msg = ("Creating a PeriodIndex by passing range "
"endpoints is deprecated. Use "
"`pandas.period_range` instead.")
# period_range differs from PeriodIndex for cases like
# start="2000", periods=4
# PeriodIndex interprets that as A-DEC freq.
# period_range interprets it as 'D' freq.
cond = freq is None and (
(start and not isinstance(start, Period)) or
(end and not isinstance(end, Period)))
if cond:
msg += (" Note that the default `freq` may differ. Pass "
"'freq=\"{}\"' to ensure the same output.").format(
freq2.freqstr)
warnings.warn(msg, FutureWarning, stacklevel=2)
freq = freq2
data = PeriodArray(data, freq=freq)
else:
freq = validate_dtype_freq(dtype, freq)
# PeriodIndex allow PeriodIndex(period_index, freq=different)
# Let's not encourage that kind of behavior in PeriodArray.
if freq and isinstance(data, cls) and data.freq != freq:
# TODO: We can do some of these with no-copy / coercion?
# e.g. D -> 2D seems to be OK
data = data.asfreq(freq)
if data is None and ordinal is not None:
# we strangely ignore `ordinal` if data is passed.
ordinal = np.asarray(ordinal, dtype=np.int64)
data = PeriodArray(ordinal, freq)
else:
# don't pass copy here, since we copy later.
data = period_array(data=data, freq=freq)
if copy:
data = data.copy()
return cls._simple_new(data, name=name)
def __new__(cls, data=None, ordinal=None, freq=None, start=None, end=None,
periods=None, tz=None, dtype=None, copy=False, name=None,
**fields):
valid_field_set = {'year', 'month', 'day', 'quarter',
'hour', 'minute', 'second'}
if not set(fields).issubset(valid_field_set):
raise TypeError('__new__() got an unexpected keyword argument {}'.
format(list(set(fields) - valid_field_set)[0]))
if name is None and hasattr(data, 'name'):
name = data.name
if data is None and ordinal is None:
# range-based.
data, freq2 = PeriodArray._generate_range(start, end, periods,
freq, fields)
# PeriodArray._generate range does validate that fields is
# empty when really using the range-based constructor.
if not fields:
msg = ("Creating a PeriodIndex by passing range "
"endpoints is deprecated. Use "
"`pandas.period_range` instead.")
# period_range differs from PeriodIndex for cases like
# start="2000", periods=4
# PeriodIndex interprets that as A-DEC freq.
# period_range interprets it as 'D' freq.
cond = (
freq is None and (
(start and not isinstance(start, Period)) or
(end and not isinstance(end, Period))
)
)
if cond:
msg += (
" Note that the default `freq` may differ. Pass "
"'freq=\"{}\"' to ensure the same output."
).format(freq2.freqstr)
warnings.warn(msg, FutureWarning, stacklevel=2)
freq = freq2
data = PeriodArray(data, freq=freq)
else:
freq = validate_dtype_freq(dtype, freq)
# PeriodIndex allow PeriodIndex(period_index, freq=different)
# Let's not encourage that kind of behavior in PeriodArray.
if freq and isinstance(data, cls) and data.freq != freq:
# TODO: We can do some of these with no-copy / coercion?
# e.g. D -> 2D seems to be OK
data = data.asfreq(freq)
if data is None and ordinal is not None:
# we strangely ignore `ordinal` if data is passed.
ordinal = np.asarray(ordinal, dtype=np.int64)
data = PeriodArray(ordinal, freq)
else:
# don't pass copy here, since we copy later.
data = period_array(data=data, freq=freq)
if copy:
data = data.copy()
return cls._simple_new(data, name=name)