def _from_arraylike(cls, data, freq, tz):
if freq is not None:
freq = Period._maybe_convert_freq(freq)
if not isinstance(data, (np.ndarray, PeriodIndex,
DatetimeIndex, Int64Index)):
if is_scalar(data) or isinstance(data, Period):
raise ValueError('PeriodIndex() must be called with a '
'collection of some kind, %s was passed'
% repr(data))
# other iterable of some kind
if not isinstance(data, (list, tuple)):
data = list(data)
try:
data = _ensure_int64(data)
if freq is None:
raise ValueError('freq not specified')
data = np.array([Period(x, freq=freq) for x in data],
dtype=np.int64)
except (TypeError, ValueError):
data = _ensure_object(data)
if freq is None:
freq = period.extract_freq(data)
data = period.extract_ordinals(data, freq)
else:
if isinstance(data, PeriodIndex):
if freq is None or freq == data.freq:
freq = data.freq
data = data._values
else:
base1, _ = _gfc(data.freq)
base2, _ = _gfc(freq)
data = period.period_asfreq_arr(data._values,
base1, base2, 1)
else:
if is_object_dtype(data):
inferred = infer_dtype(data)
if inferred == 'integer':
data = data.astype(np.int64)
if freq is None and is_object_dtype(data):
# must contain Period instance and thus extract ordinals
freq = period.extract_freq(data)
data = period.extract_ordinals(data, freq)
if freq is None:
msg = 'freq not specified and cannot be inferred'
raise ValueError(msg)
if data.dtype != np.int64:
if np.issubdtype(data.dtype, np.datetime64):
data = dt64arr_to_periodarr(data, freq, tz)
else:
data = _ensure_object(data)
data = period.extract_ordinals(data, freq)
return data, freq
def _from_arraylike(cls, data, freq, tz):
if freq is not None:
freq = Period._maybe_convert_freq(freq)
if not isinstance(
data, (np.ndarray, PeriodIndex, DatetimeIndex, Int64Index)):
if is_scalar(data) or isinstance(data, Period):
raise ValueError('PeriodIndex() must be called with a '
'collection of some kind, %s was passed' %
repr(data))
# other iterable of some kind
if not isinstance(data, (list, tuple)):
data = list(data)
try:
data = _ensure_int64(data)
if freq is None:
raise ValueError('freq not specified')
data = np.array([Period(x, freq=freq) for x in data],
dtype=np.int64)
except (TypeError, ValueError):
data = _ensure_object(data)
if freq is None:
freq = period.extract_freq(data)
data = period.extract_ordinals(data, freq)
else:
if isinstance(data, PeriodIndex):
if freq is None or freq == data.freq:
freq = data.freq
data = data._values
else:
base1, _ = _gfc(data.freq)
base2, _ = _gfc(freq)
data = period.period_asfreq_arr(data._values, base1, base2,
1)
else:
if is_object_dtype(data):
inferred = infer_dtype(data)
if inferred == 'integer':
data = data.astype(np.int64)
if freq is None and is_object_dtype(data):
# must contain Period instance and thus extract ordinals
freq = period.extract_freq(data)
data = period.extract_ordinals(data, freq)
if freq is None:
msg = 'freq not specified and cannot be inferred'
raise ValueError(msg)
if data.dtype != np.int64:
if np.issubdtype(data.dtype, np.datetime64):
data = dt64arr_to_periodarr(data, freq, tz)
else:
data = _ensure_object(data)
data = period.extract_ordinals(data, freq)
return data, freq
def __new__(cls,
data=None,
ordinal=None,
freq=None,
start=None,
end=None,
periods=None,
copy=False,
name=None,
tz=None,
dtype=None,
**kwargs):
if periods is not None:
if is_float(periods):
periods = int(periods)
elif not is_integer(periods):
raise ValueError('Periods must be a number, got %s' %
str(periods))
if name is None and hasattr(data, 'name'):
name = data.name
if dtype is not None:
dtype = pandas_dtype(dtype)
if not is_period_dtype(dtype):
raise ValueError('dtype must be PeriodDtype')
if freq is None:
freq = dtype.freq
elif freq != dtype.freq:
msg = 'specified freq and dtype are different'
raise IncompatibleFrequency(msg)
# 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,
kwargs)
return cls._from_ordinals(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._values
else:
base1, _ = _gfc(data.freq)
base2, _ = _gfc(freq)
data = period.period_asfreq_arr(data._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) or isinstance(data, Period):
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._from_ordinals(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._from_ordinals(data, name=name, freq=freq)