def __init__(self, values, freq=None, dtype=None, copy=False):
freq = dtl.validate_dtype_freq(dtype, freq)
if freq is not None:
freq = Period._maybe_convert_freq(freq)
if isinstance(values, ABCSeries):
values = values._values
if not isinstance(values, type(self)):
raise TypeError("Incorrect dtype")
elif isinstance(values, ABCPeriodIndex):
values = values._values
if isinstance(values, type(self)):
if freq is not None and freq != values.freq:
msg = DIFFERENT_FREQ_INDEX.format(values.freq.freqstr,
freq.freqstr)
raise IncompatibleFrequency(msg)
values, freq = values._data, values.freq
values = np.array(values, dtype='int64', copy=copy)
self._data = values
if freq is None:
raise ValueError('freq is not specified and cannot be inferred')
self._dtype = PeriodDtype(freq)
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 _simple_new(cls, values, freq=None, **kwargs):
"""
Values can be any type that can be coerced to Periods.
Ordinals in an ndarray are fastpath-ed to `_from_ordinals`
"""
if is_period_dtype(values):
freq = dtl.validate_dtype_freq(values.dtype, freq)
values = values.asi8
if not is_integer_dtype(values):
values = np.array(values, copy=False)
if len(values) > 0 and is_float_dtype(values):
raise TypeError("{cls} can't take floats"
.format(cls=cls.__name__))
return cls(values, freq=freq, **kwargs)
return cls._from_ordinals(values, freq=freq, **kwargs)
def _simple_new(cls, values, freq=None, **kwargs):
"""
Values can be any type that can be coerced to Periods.
Ordinals in an ndarray are fastpath-ed to `_from_ordinals`
"""
if is_period_dtype(values):
freq = dtl.validate_dtype_freq(values.dtype, freq)
values = values.asi8
if not is_integer_dtype(values):
values = np.array(values, copy=False)
if len(values) > 0 and is_float_dtype(values):
raise TypeError(
"{cls} can't take floats".format(cls=cls.__name__))
return cls(values, freq=freq, **kwargs)
return cls._from_ordinals(values, freq=freq, **kwargs)
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]))
periods = dtl.validate_periods(periods)
if name is None and hasattr(data, 'name'):
name = data.name
freq = dtl.validate_dtype_freq(dtype, freq)
# coerce freq to freq object, otherwise it can be coerced elementwise
# which is slow
if freq:
freq = Period._maybe_convert_freq(freq)
if data is None:
if ordinal is not None:
data = np.asarray(ordinal, dtype=np.int64)
else:
data, freq = cls._generate_range(start, end, periods, freq,
fields)
return cls._simple_new(data, name=name, freq=freq)
if isinstance(data, PeriodIndex):
if freq is None or freq == data.freq: # no freq change
freq = data.freq
data = data._ndarray_values
else:
base1, _ = _gfc(data.freq)
base2, _ = _gfc(freq)
data = period.period_asfreq_arr(data._ndarray_values, base1,
base2, 1)
return cls._simple_new(data, name=name, freq=freq)
# not array / index
if not isinstance(
data, (np.ndarray, PeriodIndex, DatetimeIndex, Int64Index)):
if is_scalar(data):
cls._scalar_data_error(data)
# other iterable of some kind
if not isinstance(data, (list, tuple)):
data = list(data)
data = np.asarray(data)
# datetime other than period
if is_datetime64_dtype(data.dtype):
data = dt64arr_to_periodarr(data, freq, tz)
return cls._simple_new(data, name=name, freq=freq)
# check not floats
if infer_dtype(data) == 'floating' and len(data) > 0:
raise TypeError("PeriodIndex does not allow "
"floating point in construction")
# anything else, likely an array of strings or periods
data = ensure_object(data)
freq = freq or period.extract_freq(data)
data = period.extract_ordinals(data, freq)
return cls._simple_new(data, name=name, freq=freq)
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]))
periods = dtl.validate_periods(periods)
if name is None and hasattr(data, 'name'):
name = data.name
freq = dtl.validate_dtype_freq(dtype, freq)
# coerce freq to freq object, otherwise it can be coerced elementwise
# which is slow
if freq:
freq = Period._maybe_convert_freq(freq)
if data is None:
if ordinal is not None:
data = np.asarray(ordinal, dtype=np.int64)
else:
data, freq = cls._generate_range(start, end, periods,
freq, fields)
return cls._simple_new(data, name=name, freq=freq)
if isinstance(data, PeriodIndex):
if freq is None or freq == data.freq: # no freq change
freq = data.freq
data = data._ndarray_values
else:
base1, _ = _gfc(data.freq)
base2, _ = _gfc(freq)
data = period.period_asfreq_arr(data._ndarray_values,
base1, base2, 1)
return cls._simple_new(data, name=name, freq=freq)
# not array / index
if not isinstance(data, (np.ndarray, PeriodIndex,
DatetimeIndex, Int64Index)):
if is_scalar(data):
cls._scalar_data_error(data)
# other iterable of some kind
if not isinstance(data, (list, tuple)):
data = list(data)
data = np.asarray(data)
# datetime other than period
if is_datetime64_dtype(data.dtype):
data = dt64arr_to_periodarr(data, freq, tz)
return cls._simple_new(data, name=name, freq=freq)
# check not floats
if infer_dtype(data) == 'floating' and len(data) > 0:
raise TypeError("PeriodIndex does not allow "
"floating point in construction")
# anything else, likely an array of strings or periods
data = ensure_object(data)
freq = freq or period.extract_freq(data)
data = period.extract_ordinals(data, freq)
return cls._simple_new(data, name=name, freq=freq)
