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 _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, setitem: bool = False, ) -> 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, setitem=setitem) 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, setitem: bool = False): if other is NaT: return self._require_matching_freq(other) # -------------------------------------------------------------------- # Data / Attributes @cache_readonly def dtype(self) -> PeriodDtype: return self._dtype # error: Read-only property cannot override read-write property @property # type: ignore[misc] 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 _time_shift(self, periods: int, freq=None) -> PeriodArray: """ Shift each value by `periods`. Note this is different from ExtensionArray.shift, which shifts the *position* of each element, padding the end with missing values. Parameters ---------- periods : int Number of periods to shift by. freq : pandas.DateOffset, pandas.Timedelta, or str Frequency increment to shift by. """ if freq is not None: raise TypeError( "`freq` argument is not supported for " f"{type(self).__name__}._time_shift" ) return self + periods 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="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_searchsorted_value(value).view("M8[ns]") # Cast to M8 to get datetime-like NaT placement 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 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) def _quantile( self: PeriodArray, qs: npt.NDArray[np.float64], interpolation: str, ) -> PeriodArray: # dispatch to DatetimeArray implementation dtres = self.view("M8[ns]")._quantile(qs, interpolation) # error: Incompatible return value type (got "Union[ExtensionArray, # ndarray[Any, Any]]", expected "PeriodArray") return dtres.view(self.dtype) # type: ignore[return-value] # ------------------------------------------------------------------ # 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; equivalent to applying `_time_shift` pointwise. 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')]) ['2017', '2018'] Length: 2, dtype: period[A-DEC] >>> period_array([pd.Period('2017', freq='A'), ... pd.Period('2018', freq='A'), ... pd.NaT]) ['2017', '2018', 'NaT'] Length: 3, dtype: period[A-DEC] Integers that look like years are handled >>> period_array([2000, 2001, 2002], freq='D') ['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') ['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=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") elif 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 ]