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Inzynierka/Lib/site-packages/pandas/core/arrays/period.py

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first commit
2023-06-02 12:51:02 +02:00
from __future__ import annotations
from datetime import timedelta
import operator
from typing import (
TYPE_CHECKING,
Any,
Callable,
Literal,
Sequence,
TypeVar,
overload,
)
import numpy as np
from pandas._libs import (
algos as libalgos,
lib,
)
from pandas._libs.arrays import NDArrayBacked
from pandas._libs.tslibs import (
BaseOffset,
NaT,
NaTType,
Timedelta,
astype_overflowsafe,
dt64arr_to_periodarr as c_dt64arr_to_periodarr,
get_unit_from_dtype,
iNaT,
parsing,
period as libperiod,
to_offset,
)
from pandas._libs.tslibs.dtypes import FreqGroup
from pandas._libs.tslibs.fields import isleapyear_arr
from pandas._libs.tslibs.offsets import (
Tick,
delta_to_tick,
)
from pandas._libs.tslibs.period import (
DIFFERENT_FREQ,
IncompatibleFrequency,
Period,
get_period_field_arr,
period_asfreq_arr,
)
from pandas._typing import (
AnyArrayLike,
Dtype,
NpDtype,
npt,
)
from pandas.util._decorators import (
cache_readonly,
doc,
)
from pandas.core.dtypes.common import (
ensure_object,
is_datetime64_any_dtype,
is_datetime64_dtype,
is_dtype_equal,
is_float_dtype,
is_integer_dtype,
is_period_dtype,
pandas_dtype,
)
from pandas.core.dtypes.dtypes import PeriodDtype
from pandas.core.dtypes.generic import (
ABCIndex,
ABCPeriodIndex,
ABCSeries,
ABCTimedeltaArray,
)
from pandas.core.dtypes.missing import isna
import pandas.core.algorithms as algos
from pandas.core.arrays import datetimelike as dtl
import pandas.core.common as com
if TYPE_CHECKING:
from pandas._typing import (
NumpySorter,
NumpyValueArrayLike,
)
from pandas.core.arrays import (
DatetimeArray,
TimedeltaArray,
)
from pandas.core.arrays.base import ExtensionArray
BaseOffsetT = TypeVar("BaseOffsetT", bound=BaseOffset)
_shared_doc_kwargs = {
"klass": "PeriodArray",
}
def _field_accessor(name: str, docstring=None):
def f(self):
base = self.freq._period_dtype_code
result = get_period_field_arr(name, self.asi8, base)
return result
f.__name__ = name
f.__doc__ = docstring
return property(f)
class PeriodArray(dtl.DatelikeOps, libperiod.PeriodMixin):
"""
Pandas ExtensionArray for storing Period data.
Users should use :func:`~pandas.period_array` to create new instances.
Alternatively, :func:`~pandas.array` can be used to create new instances
from a sequence of Period scalars.
Parameters
----------
values : Union[PeriodArray, Series[period], ndarray[int], PeriodIndex]
The data to store. These should be arrays that can be directly
converted to ordinals without inference or copy (PeriodArray,
ndarray[int64]), or a box around such an array (Series[period],
PeriodIndex).
dtype : PeriodDtype, optional
A PeriodDtype instance from which to extract a `freq`. If both
`freq` and `dtype` are specified, then the frequencies must match.
freq : str or DateOffset
The `freq` to use for the array. Mostly applicable when `values`
is an ndarray of integers, when `freq` is required. When `values`
is a PeriodArray (or box around), it's checked that ``values.freq``
matches `freq`.
copy : bool, default False
Whether to copy the ordinals before storing.
Attributes
----------
None
Methods
-------
None
See Also
--------
Period: Represents a period of time.
PeriodIndex : Immutable Index for period data.
period_range: Create a fixed-frequency PeriodArray.
array: Construct a pandas array.
Notes
-----
There are two components to a PeriodArray
- ordinals : integer ndarray
- freq : pd.tseries.offsets.Offset
The values are physically stored as a 1-D ndarray of integers. These are
called "ordinals" and represent some kind of offset from a base.
The `freq` indicates the span covered by each element of the array.
All elements in the PeriodArray have the same `freq`.
"""
# array priority higher than numpy scalars
__array_priority__ = 1000
_typ = "periodarray" # ABCPeriodArray
_internal_fill_value = np.int64(iNaT)
_recognized_scalars = (Period,)
_is_recognized_dtype = is_period_dtype # check_compatible_with checks freq match
_infer_matches = ("period",)
@property
def _scalar_type(self) -> type[Period]:
return Period
# Names others delegate to us
_other_ops: list[str] = []
_bool_ops: list[str] = ["is_leap_year"]
_object_ops: list[str] = ["start_time", "end_time", "freq"]
_field_ops: list[str] = [
"year",
"month",
"day",
"hour",
"minute",
"second",
"weekofyear",
"weekday",
"week",
"dayofweek",
"day_of_week",
"dayofyear",
"day_of_year",
"quarter",
"qyear",
"days_in_month",
"daysinmonth",
]
_datetimelike_ops: list[str] = _field_ops + _object_ops + _bool_ops
_datetimelike_methods: list[str] = ["strftime", "to_timestamp", "asfreq"]
_dtype: PeriodDtype
# --------------------------------------------------------------------
# Constructors
def __init__(
self, values, dtype: Dtype | None = None, freq=None, copy: bool = False
) -> None:
freq = 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:
raise raise_on_incompatible(values, freq)
values, freq = values._ndarray, values.freq
values = np.array(values, dtype="int64", copy=copy)
if freq is None:
raise ValueError("freq is not specified and cannot be inferred")
NDArrayBacked.__init__(self, values, PeriodDtype(freq))
# error: Signature of "_simple_new" incompatible with supertype "NDArrayBacked"
@classmethod
def _simple_new( # type: ignore[override]
cls,
values: np.ndarray,
freq: BaseOffset | None = None,
dtype: Dtype | None = None,
) -> PeriodArray:
# alias for PeriodArray.__init__
assertion_msg = "Should be numpy array of type i8"
assert isinstance(values, np.ndarray) and values.dtype == "i8", assertion_msg
return cls(values, freq=freq, dtype=dtype)
@classmethod
def _from_sequence(
cls: type[PeriodArray],
scalars: Sequence[Period | None] | AnyArrayLike,
*,
dtype: Dtype | None = None,
copy: bool = False,
) -> PeriodArray:
if dtype and isinstance(dtype, PeriodDtype):
freq = dtype.freq
else:
freq = None
if isinstance(scalars, cls):
validate_dtype_freq(scalars.dtype, freq)
if copy:
scalars = scalars.copy()
return scalars
periods = np.asarray(scalars, dtype=object)
freq = freq or libperiod.extract_freq(periods)
ordinals = libperiod.extract_ordinals(periods, freq)
return cls(ordinals, freq=freq)
@classmethod
def _from_sequence_of_strings(
cls, strings, *, dtype: Dtype | None = None, copy: bool = False
) -> PeriodArray:
return cls._from_sequence(strings, dtype=dtype, copy=copy)
@classmethod
def _from_datetime64(cls, data, freq, tz=None) -> PeriodArray:
"""
Construct a PeriodArray from a datetime64 array
Parameters
----------
data : ndarray[datetime64[ns], datetime64[ns, tz]]
freq : str or Tick
tz : tzinfo, optional
Returns
-------
PeriodArray[freq]
"""
data, freq = dt64arr_to_periodarr(data, freq, tz)
return cls(data, freq=freq)
@classmethod
def _generate_range(cls, start, end, periods, freq, fields):
periods = dtl.validate_periods(periods)
if freq is not None:
freq = Period._maybe_convert_freq(freq)
field_count = len(fields)
if start is not None or end is not None:
if field_count > 0:
raise ValueError(
"Can either instantiate from fields or endpoints, but not both"
)
subarr, freq = _get_ordinal_range(start, end, periods, freq)
elif field_count > 0:
subarr, freq = _range_from_fields(freq=freq, **fields)
else:
raise ValueError("Not enough parameters to construct Period range")
return subarr, freq
# -----------------------------------------------------------------
# DatetimeLike Interface
# error: Argument 1 of "_unbox_scalar" is incompatible with supertype
# "DatetimeLikeArrayMixin"; supertype defines the argument type as
# "Union[Union[Period, Any, Timedelta], NaTType]"
def _unbox_scalar( # type: ignore[override]
self,
value: Period | NaTType,
) -> np.int64:
if value is NaT:
# error: Item "Period" of "Union[Period, NaTType]" has no attribute "value"
return np.int64(value._value) # type: ignore[union-attr]
elif isinstance(value, self._scalar_type):
self._check_compatible_with(value)
return np.int64(value.ordinal)
else:
raise ValueError(f"'value' should be a Period. Got '{value}' instead.")
def _scalar_from_string(self, value: str) -> Period:
return Period(value, freq=self.freq)
def _check_compatible_with(self, other) -> None:
if other is NaT:
return
self._require_matching_freq(other)
# --------------------------------------------------------------------
# Data / Attributes
@cache_readonly
def dtype(self) -> PeriodDtype:
return self._dtype
# error: Cannot override writeable attribute with read-only property
@property # type: ignore[override]
def freq(self) -> BaseOffset:
"""
Return the frequency object for this PeriodArray.
"""
return self.dtype.freq
def __array__(self, dtype: NpDtype | None = None) -> np.ndarray:
if dtype == "i8":
return self.asi8
elif dtype == bool:
return ~self._isnan
# This will raise TypeError for non-object dtypes
return np.array(list(self), dtype=object)
def __arrow_array__(self, type=None):
"""
Convert myself into a pyarrow Array.
"""
import pyarrow
from pandas.core.arrays.arrow.extension_types import ArrowPeriodType
if type is not None:
if pyarrow.types.is_integer(type):
return pyarrow.array(self._ndarray, mask=self.isna(), type=type)
elif isinstance(type, ArrowPeriodType):
# ensure we have the same freq
if self.freqstr != type.freq:
raise TypeError(
"Not supported to convert PeriodArray to array with different "
f"'freq' ({self.freqstr} vs {type.freq})"
)
else:
raise TypeError(
f"Not supported to convert PeriodArray to '{type}' type"
)
period_type = ArrowPeriodType(self.freqstr)
storage_array = pyarrow.array(self._ndarray, mask=self.isna(), type="int64")
return pyarrow.ExtensionArray.from_storage(period_type, storage_array)
# --------------------------------------------------------------------
# Vectorized analogues of Period properties
year = _field_accessor(
"year",
"""
The year of the period.
""",
)
month = _field_accessor(
"month",
"""
The month as January=1, December=12.
""",
)
day = _field_accessor(
"day",
"""
The days of the period.
""",
)
hour = _field_accessor(
"hour",
"""
The hour of the period.
""",
)
minute = _field_accessor(
"minute",
"""
The minute of the period.
""",
)
second = _field_accessor(
"second",
"""
The second of the period.
""",
)
weekofyear = _field_accessor(
"week",
"""
The week ordinal of the year.
""",
)
week = weekofyear
day_of_week = _field_accessor(
"day_of_week",
"""
The day of the week with Monday=0, Sunday=6.
""",
)
dayofweek = day_of_week
weekday = dayofweek
dayofyear = day_of_year = _field_accessor(
"day_of_year",
"""
The ordinal day of the year.
""",
)
quarter = _field_accessor(
"quarter",
"""
The quarter of the date.
""",
)
qyear = _field_accessor("qyear")
days_in_month = _field_accessor(
"days_in_month",
"""
The number of days in the month.
""",
)
daysinmonth = days_in_month
@property
def is_leap_year(self) -> np.ndarray:
"""
Logical indicating if the date belongs to a leap year.
"""
return isleapyear_arr(np.asarray(self.year))
def to_timestamp(self, freq=None, how: str = "start") -> DatetimeArray:
"""
Cast to DatetimeArray/Index.
Parameters
----------
freq : str or DateOffset, optional
Target frequency. The default is 'D' for week or longer,
'S' otherwise.
how : {'s', 'e', 'start', 'end'}
Whether to use the start or end of the time period being converted.
Returns
-------
DatetimeArray/Index
"""
from pandas.core.arrays import DatetimeArray
how = libperiod.validate_end_alias(how)
end = how == "E"
if end:
if freq == "B" or self.freq == "B":
# roll forward to ensure we land on B date
adjust = Timedelta(1, "D") - Timedelta(1, "ns")
return self.to_timestamp(how="start") + adjust
else:
adjust = Timedelta(1, "ns")
return (self + self.freq).to_timestamp(how="start") - adjust
if freq is None:
freq = self._dtype._get_to_timestamp_base()
base = freq
else:
freq = Period._maybe_convert_freq(freq)
base = freq._period_dtype_code
new_parr = self.asfreq(freq, how=how)
new_data = libperiod.periodarr_to_dt64arr(new_parr.asi8, base)
dta = DatetimeArray(new_data)
if self.freq.name == "B":
# See if we can retain BDay instead of Day in cases where
# len(self) is too small for infer_freq to distinguish between them
diffs = libalgos.unique_deltas(self.asi8)
if len(diffs) == 1:
diff = diffs[0]
if diff == self.freq.n:
dta._freq = self.freq
elif diff == 1:
dta._freq = self.freq.base
# TODO: other cases?
return dta
else:
return dta._with_freq("infer")
# --------------------------------------------------------------------
def _box_func(self, x) -> Period | NaTType:
return Period._from_ordinal(ordinal=x, freq=self.freq)
@doc(**_shared_doc_kwargs, other="PeriodIndex", other_name="PeriodIndex")
def asfreq(self, freq=None, how: str = "E") -> PeriodArray:
"""
Convert the {klass} to the specified frequency `freq`.
Equivalent to applying :meth:`pandas.Period.asfreq` with the given arguments
to each :class:`~pandas.Period` in this {klass}.
Parameters
----------
freq : str
A frequency.
how : str {{'E', 'S'}}, default 'E'
Whether the elements should be aligned to the end
or start within pa period.
* 'E', 'END', or 'FINISH' for end,
* 'S', 'START', or 'BEGIN' for start.
January 31st ('END') vs. January 1st ('START') for example.
Returns
-------
{klass}
The transformed {klass} with the new frequency.
See Also
--------
{other}.asfreq: Convert each Period in a {other_name} to the given frequency.
Period.asfreq : Convert a :class:`~pandas.Period` object to the given frequency.
Examples
--------
>>> pidx = pd.period_range('2010-01-01', '2015-01-01', freq='A')
>>> pidx
PeriodIndex(['2010', '2011', '2012', '2013', '2014', '2015'],
dtype='period[A-DEC]')
>>> pidx.asfreq('M')
PeriodIndex(['2010-12', '2011-12', '2012-12', '2013-12', '2014-12',
'2015-12'], dtype='period[M]')
>>> pidx.asfreq('M', how='S')
PeriodIndex(['2010-01', '2011-01', '2012-01', '2013-01', '2014-01',
'2015-01'], dtype='period[M]')
"""
how = libperiod.validate_end_alias(how)
freq = Period._maybe_convert_freq(freq)
base1 = self._dtype._dtype_code
base2 = freq._period_dtype_code
asi8 = self.asi8
# self.freq.n can't be negative or 0
end = how == "E"
if end:
ordinal = asi8 + self.freq.n - 1
else:
ordinal = asi8
new_data = period_asfreq_arr(ordinal, base1, base2, end)
if self._hasna:
new_data[self._isnan] = iNaT
return type(self)(new_data, freq=freq)
# ------------------------------------------------------------------
# Rendering Methods
def _formatter(self, boxed: bool = False):
if boxed:
return str
return "'{}'".format
@dtl.ravel_compat
def _format_native_types(
self, *, na_rep: str | float = "NaT", date_format=None, **kwargs
) -> npt.NDArray[np.object_]:
"""
actually format my specific types
"""
values = self.astype(object)
# Create the formatter function
if date_format:
formatter = lambda per: per.strftime(date_format)
else:
# Uses `_Period.str` which in turn uses `format_period`
formatter = lambda per: str(per)
# Apply the formatter to all values in the array, possibly with a mask
if self._hasna:
mask = self._isnan
values[mask] = na_rep
imask = ~mask
values[imask] = np.array([formatter(per) for per in values[imask]])
else:
values = np.array([formatter(per) for per in values])
return values
# ------------------------------------------------------------------
def astype(self, dtype, copy: bool = True):
# We handle Period[T] -> Period[U]
# Our parent handles everything else.
dtype = pandas_dtype(dtype)
if is_dtype_equal(dtype, self._dtype):
if not copy:
return self
else:
return self.copy()
if is_period_dtype(dtype):
return self.asfreq(dtype.freq)
if is_datetime64_any_dtype(dtype):
# GH#45038 match PeriodIndex behavior.
tz = getattr(dtype, "tz", None)
return self.to_timestamp().tz_localize(tz)
return super().astype(dtype, copy=copy)
def searchsorted(
self,
value: NumpyValueArrayLike | ExtensionArray,
side: Literal["left", "right"] = "left",
sorter: NumpySorter = None,
) -> npt.NDArray[np.intp] | np.intp:
npvalue = self._validate_setitem_value(value).view("M8[ns]")
# Cast to M8 to get datetime-like NaT placement,
# similar to dtl._period_dispatch
m8arr = self._ndarray.view("M8[ns]")
return m8arr.searchsorted(npvalue, side=side, sorter=sorter)
def fillna(self, value=None, method=None, limit=None) -> PeriodArray:
if method is not None:
# view as dt64 so we get treated as timelike in core.missing,
# similar to dtl._period_dispatch
dta = self.view("M8[ns]")
result = dta.fillna(value=value, method=method, limit=limit)
# error: Incompatible return value type (got "Union[ExtensionArray,
# ndarray[Any, Any]]", expected "PeriodArray")
return result.view(self.dtype) # type: ignore[return-value]
return super().fillna(value=value, method=method, limit=limit)
# ------------------------------------------------------------------
# Arithmetic Methods
def _addsub_int_array_or_scalar(
self, other: np.ndarray | int, op: Callable[[Any, Any], Any]
) -> PeriodArray:
"""
Add or subtract array of integers.
Parameters
----------
other : np.ndarray[int64] or int
op : {operator.add, operator.sub}
Returns
-------
result : PeriodArray
"""
assert op in [operator.add, operator.sub]
if op is operator.sub:
other = -other
res_values = algos.checked_add_with_arr(self.asi8, other, arr_mask=self._isnan)
return type(self)(res_values, freq=self.freq)
def _add_offset(self, other: BaseOffset):
assert not isinstance(other, Tick)
self._require_matching_freq(other, base=True)
return self._addsub_int_array_or_scalar(other.n, operator.add)
# TODO: can we de-duplicate with Period._add_timedeltalike_scalar?
def _add_timedeltalike_scalar(self, other):
"""
Parameters
----------
other : timedelta, Tick, np.timedelta64
Returns
-------
PeriodArray
"""
if not isinstance(self.freq, Tick):
# We cannot add timedelta-like to non-tick PeriodArray
raise raise_on_incompatible(self, other)
if isna(other):
# i.e. np.timedelta64("NaT")
return super()._add_timedeltalike_scalar(other)
td = np.asarray(Timedelta(other).asm8)
return self._add_timedelta_arraylike(td)
def _add_timedelta_arraylike(
self, other: TimedeltaArray | npt.NDArray[np.timedelta64]
) -> PeriodArray:
"""
Parameters
----------
other : TimedeltaArray or ndarray[timedelta64]
Returns
-------
PeriodArray
"""
freq = self.freq
if not isinstance(freq, Tick):
# We cannot add timedelta-like to non-tick PeriodArray
raise TypeError(
f"Cannot add or subtract timedelta64[ns] dtype from {self.dtype}"
)
dtype = np.dtype(f"m8[{freq._td64_unit}]")
try:
delta = astype_overflowsafe(
np.asarray(other), dtype=dtype, copy=False, round_ok=False
)
except ValueError as err:
# e.g. if we have minutes freq and try to add 30s
# "Cannot losslessly convert units"
raise IncompatibleFrequency(
"Cannot add/subtract timedelta-like from PeriodArray that is "
"not an integer multiple of the PeriodArray's freq."
) from err
b_mask = np.isnat(delta)
res_values = algos.checked_add_with_arr(
self.asi8, delta.view("i8"), arr_mask=self._isnan, b_mask=b_mask
)
np.putmask(res_values, self._isnan | b_mask, iNaT)
return type(self)(res_values, freq=self.freq)
def _check_timedeltalike_freq_compat(self, other):
"""
Arithmetic operations with timedelta-like scalars or array `other`
are only valid if `other` is an integer multiple of `self.freq`.
If the operation is valid, find that integer multiple. Otherwise,
raise because the operation is invalid.
Parameters
----------
other : timedelta, np.timedelta64, Tick,
ndarray[timedelta64], TimedeltaArray, TimedeltaIndex
Returns
-------
multiple : int or ndarray[int64]
Raises
------
IncompatibleFrequency
"""
assert isinstance(self.freq, Tick) # checked by calling function
dtype = np.dtype(f"m8[{self.freq._td64_unit}]")
if isinstance(other, (timedelta, np.timedelta64, Tick)):
td = np.asarray(Timedelta(other).asm8)
else:
td = np.asarray(other)
try:
delta = astype_overflowsafe(td, dtype=dtype, copy=False, round_ok=False)
except ValueError as err:
raise raise_on_incompatible(self, other) from err
delta = delta.view("i8")
return lib.item_from_zerodim(delta)
def raise_on_incompatible(left, right):
"""
Helper function to render a consistent error message when raising
IncompatibleFrequency.
Parameters
----------
left : PeriodArray
right : None, DateOffset, Period, ndarray, or timedelta-like
Returns
-------
IncompatibleFrequency
Exception to be raised by the caller.
"""
# GH#24283 error message format depends on whether right is scalar
if isinstance(right, (np.ndarray, ABCTimedeltaArray)) or right is None:
other_freq = None
elif isinstance(right, (ABCPeriodIndex, PeriodArray, Period, BaseOffset)):
other_freq = right.freqstr
else:
other_freq = delta_to_tick(Timedelta(right)).freqstr
msg = DIFFERENT_FREQ.format(
cls=type(left).__name__, own_freq=left.freqstr, other_freq=other_freq
)
return IncompatibleFrequency(msg)
# -------------------------------------------------------------------
# Constructor Helpers
def period_array(
data: Sequence[Period | str | None] | AnyArrayLike,
freq: str | Tick | None = None,
copy: bool = False,
) -> PeriodArray:
"""
Construct a new PeriodArray from a sequence of Period scalars.
Parameters
----------
data : Sequence of Period objects
A sequence of Period objects. These are required to all have
the same ``freq.`` Missing values can be indicated by ``None``
or ``pandas.NaT``.
freq : str, Tick, or Offset
The frequency of every element of the array. This can be specified
to avoid inferring the `freq` from `data`.
copy : bool, default False
Whether to ensure a copy of the data is made.
Returns
-------
PeriodArray
See Also
--------
PeriodArray
pandas.PeriodIndex
Examples
--------
>>> period_array([pd.Period('2017', freq='A'),
... pd.Period('2018', freq='A')])
<PeriodArray>
['2017', '2018']
Length: 2, dtype: period[A-DEC]
>>> period_array([pd.Period('2017', freq='A'),
... pd.Period('2018', freq='A'),
... pd.NaT])
<PeriodArray>
['2017', '2018', 'NaT']
Length: 3, dtype: period[A-DEC]
Integers that look like years are handled
>>> period_array([2000, 2001, 2002], freq='D')
<PeriodArray>
['2000-01-01', '2001-01-01', '2002-01-01']
Length: 3, dtype: period[D]
Datetime-like strings may also be passed
>>> period_array(['2000-Q1', '2000-Q2', '2000-Q3', '2000-Q4'], freq='Q')
<PeriodArray>
['2000Q1', '2000Q2', '2000Q3', '2000Q4']
Length: 4, dtype: period[Q-DEC]
"""
data_dtype = getattr(data, "dtype", None)
if is_datetime64_dtype(data_dtype):
return PeriodArray._from_datetime64(data, freq)
if is_period_dtype(data_dtype):
return PeriodArray(data, freq=freq)
# other iterable of some kind
if not isinstance(data, (np.ndarray, list, tuple, ABCSeries)):
data = list(data)
arrdata = np.asarray(data)
dtype: PeriodDtype | None
if freq:
dtype = PeriodDtype(freq)
else:
dtype = None
if is_float_dtype(arrdata) and len(arrdata) > 0:
raise TypeError("PeriodIndex does not allow floating point in construction")
if is_integer_dtype(arrdata.dtype):
arr = arrdata.astype(np.int64, copy=False)
# error: Argument 2 to "from_ordinals" has incompatible type "Union[str,
# Tick, None]"; expected "Union[timedelta, BaseOffset, str]"
ordinals = libperiod.from_ordinals(arr, freq) # type: ignore[arg-type]
return PeriodArray(ordinals, dtype=dtype)
data = ensure_object(arrdata)
return PeriodArray._from_sequence(data, dtype=dtype)
@overload
def validate_dtype_freq(dtype, freq: BaseOffsetT) -> BaseOffsetT:
...
@overload
def validate_dtype_freq(dtype, freq: timedelta | str | None) -> BaseOffset:
...
def validate_dtype_freq(
dtype, freq: BaseOffsetT | timedelta | str | None
) -> BaseOffsetT:
"""
If both a dtype and a freq are available, ensure they match. If only
dtype is available, extract the implied freq.
Parameters
----------
dtype : dtype
freq : DateOffset or None
Returns
-------
freq : DateOffset
Raises
------
ValueError : non-period dtype
IncompatibleFrequency : mismatch between dtype and freq
"""
if freq is not None:
# error: Incompatible types in assignment (expression has type
# "BaseOffset", variable has type "Union[BaseOffsetT, timedelta,
# str, None]")
freq = to_offset(freq) # type: ignore[assignment]
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:
raise IncompatibleFrequency("specified freq and dtype are different")
# error: Incompatible return value type (got "Union[BaseOffset, Any, None]",
# expected "BaseOffset")
return freq # type: ignore[return-value]
def dt64arr_to_periodarr(
data, freq, tz=None
) -> tuple[npt.NDArray[np.int64], BaseOffset]:
"""
Convert an datetime-like array to values Period ordinals.
Parameters
----------
data : Union[Series[datetime64[ns]], DatetimeIndex, ndarray[datetime64ns]]
freq : Optional[Union[str, Tick]]
Must match the `freq` on the `data` if `data` is a DatetimeIndex
or Series.
tz : Optional[tzinfo]
Returns
-------
ordinals : ndarray[int64]
freq : Tick
The frequency extracted from the Series or DatetimeIndex if that's
used.
"""
if not isinstance(data.dtype, np.dtype) or data.dtype.kind != "M":
raise ValueError(f"Wrong dtype: {data.dtype}")
if freq is None:
if isinstance(data, ABCIndex):
data, freq = data._values, data.freq
elif isinstance(data, ABCSeries):
data, freq = data._values, data.dt.freq
elif isinstance(data, (ABCIndex, ABCSeries)):
data = data._values
reso = get_unit_from_dtype(data.dtype)
freq = Period._maybe_convert_freq(freq)
base = freq._period_dtype_code
return c_dt64arr_to_periodarr(data.view("i8"), base, tz, reso=reso), freq
def _get_ordinal_range(start, end, periods, freq, mult: int = 1):
if com.count_not_none(start, end, periods) != 2:
raise ValueError(
"Of the three parameters: start, end, and periods, "
"exactly two must be specified"
)
if freq is not None:
freq = to_offset(freq)
mult = freq.n
if start is not None:
start = Period(start, freq)
if end is not None:
end = Period(end, freq)
is_start_per = isinstance(start, Period)
is_end_per = isinstance(end, Period)
if is_start_per and is_end_per and start.freq != end.freq:
raise ValueError("start and end must have same freq")
if start is NaT or end is NaT:
raise ValueError("start and end must not be NaT")
if freq is None:
if is_start_per:
freq = start.freq
elif is_end_per:
freq = end.freq
else: # pragma: no cover
raise ValueError("Could not infer freq from start/end")
if periods is not None:
periods = periods * mult
if start is None:
data = np.arange(
end.ordinal - periods + mult, end.ordinal + 1, mult, dtype=np.int64
)
else:
data = np.arange(
start.ordinal, start.ordinal + periods, mult, dtype=np.int64
)
else:
data = np.arange(start.ordinal, end.ordinal + 1, mult, dtype=np.int64)
return data, freq
def _range_from_fields(
year=None,
month=None,
quarter=None,
day=None,
hour=None,
minute=None,
second=None,
freq=None,
) -> tuple[np.ndarray, BaseOffset]:
if hour is None:
hour = 0
if minute is None:
minute = 0
if second is None:
second = 0
if day is None:
day = 1
ordinals = []
if quarter is not None:
if freq is None:
freq = to_offset("Q")
base = FreqGroup.FR_QTR.value
else:
freq = to_offset(freq)
base = libperiod.freq_to_dtype_code(freq)
if base != FreqGroup.FR_QTR.value:
raise AssertionError("base must equal FR_QTR")
freqstr = freq.freqstr
year, quarter = _make_field_arrays(year, quarter)
for y, q in zip(year, quarter):
y, m = parsing.quarter_to_myear(y, q, freqstr)
val = libperiod.period_ordinal(y, m, 1, 1, 1, 1, 0, 0, base)
ordinals.append(val)
else:
freq = to_offset(freq)
base = libperiod.freq_to_dtype_code(freq)
arrays = _make_field_arrays(year, month, day, hour, minute, second)
for y, mth, d, h, mn, s in zip(*arrays):
ordinals.append(libperiod.period_ordinal(y, mth, d, h, mn, s, 0, 0, base))
return np.array(ordinals, dtype=np.int64), freq
def _make_field_arrays(*fields) -> list[np.ndarray]:
length = None
for x in fields:
if isinstance(x, (list, np.ndarray, ABCSeries)):
if length is not None and len(x) != length:
raise ValueError("Mismatched Period array lengths")
if length is None:
length = len(x)
# error: Argument 2 to "repeat" has incompatible type "Optional[int]"; expected
# "Union[Union[int, integer[Any]], Union[bool, bool_], ndarray, Sequence[Union[int,
# integer[Any]]], Sequence[Union[bool, bool_]], Sequence[Sequence[Any]]]"
return [
np.asarray(x)
if isinstance(x, (np.ndarray, list, ABCSeries))
else np.repeat(x, length) # type: ignore[arg-type]
for x in fields
]
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