from __future__ import annotations from datetime import ( datetime, timedelta, ) import operator from typing import ( TYPE_CHECKING, Any, Callable, Literal, Sequence, TypeVar, Union, cast, final, overload, ) import warnings import numpy as np from pandas._libs import ( algos, lib, ) from pandas._libs.tslibs import ( BaseOffset, IncompatibleFrequency, NaT, NaTType, Period, Resolution, Tick, Timestamp, delta_to_nanoseconds, iNaT, ints_to_pydatetime, to_offset, ) from pandas._libs.tslibs.fields import ( RoundTo, round_nsint64, ) from pandas._libs.tslibs.timestamps import integer_op_not_supported from pandas._typing import ( ArrayLike, DatetimeLikeScalar, Dtype, DtypeObj, NpDtype, PositionalIndexer2D, PositionalIndexerTuple, ScalarIndexer, SequenceIndexer, npt, ) from pandas.compat.numpy import function as nv from pandas.errors import ( AbstractMethodError, NullFrequencyError, PerformanceWarning, ) from pandas.util._decorators import ( Appender, Substitution, cache_readonly, ) from pandas.util._exceptions import find_stack_level from pandas.core.dtypes.common import ( is_all_strings, is_categorical_dtype, is_datetime64_any_dtype, is_datetime64_dtype, is_datetime64tz_dtype, is_datetime_or_timedelta_dtype, is_dtype_equal, is_float_dtype, is_integer_dtype, is_list_like, is_object_dtype, is_period_dtype, is_string_dtype, is_timedelta64_dtype, is_unsigned_integer_dtype, pandas_dtype, ) from pandas.core.dtypes.dtypes import ExtensionDtype from pandas.core.dtypes.missing import ( is_valid_na_for_dtype, isna, ) from pandas.core import ( nanops, ops, ) from pandas.core.algorithms import ( checked_add_with_arr, isin, mode, unique1d, ) from pandas.core.arraylike import OpsMixin from pandas.core.arrays._mixins import ( NDArrayBackedExtensionArray, ravel_compat, ) import pandas.core.common as com from pandas.core.construction import ( array as pd_array, extract_array, ) from pandas.core.indexers import ( check_array_indexer, check_setitem_lengths, ) from pandas.core.ops.common import unpack_zerodim_and_defer from pandas.core.ops.invalid import ( invalid_comparison, make_invalid_op, ) from pandas.tseries import frequencies if TYPE_CHECKING: from pandas.core.arrays import ( DatetimeArray, TimedeltaArray, ) DTScalarOrNaT = Union[DatetimeLikeScalar, NaTType] DatetimeLikeArrayT = TypeVar("DatetimeLikeArrayT", bound="DatetimeLikeArrayMixin") class InvalidComparison(Exception): """ Raised by _validate_comparison_value to indicate to caller it should return invalid_comparison. """ pass class DatetimeLikeArrayMixin(OpsMixin, NDArrayBackedExtensionArray): """ Shared Base/Mixin class for DatetimeArray, TimedeltaArray, PeriodArray Assumes that __new__/__init__ defines: _data _freq and that the inheriting class has methods: _generate_range """ # _infer_matches -> which infer_dtype strings are close enough to our own _infer_matches: tuple[str, ...] _is_recognized_dtype: Callable[[DtypeObj], bool] _recognized_scalars: tuple[type, ...] _ndarray: np.ndarray @cache_readonly def _can_hold_na(self) -> bool: return True def __init__(self, data, dtype: Dtype | None = None, freq=None, copy=False): raise AbstractMethodError(self) @property def _scalar_type(self) -> type[DatetimeLikeScalar]: """ The scalar associated with this datelike * PeriodArray : Period * DatetimeArray : Timestamp * TimedeltaArray : Timedelta """ raise AbstractMethodError(self) def _scalar_from_string(self, value: str) -> DTScalarOrNaT: """ Construct a scalar type from a string. Parameters ---------- value : str Returns ------- Period, Timestamp, or Timedelta, or NaT Whatever the type of ``self._scalar_type`` is. Notes ----- This should call ``self._check_compatible_with`` before unboxing the result. """ raise AbstractMethodError(self) def _unbox_scalar( self, value: DTScalarOrNaT, setitem: bool = False ) -> np.int64 | np.datetime64 | np.timedelta64: """ Unbox the integer value of a scalar `value`. Parameters ---------- value : Period, Timestamp, Timedelta, or NaT Depending on subclass. setitem : bool, default False Whether to check compatibility with setitem strictness. Returns ------- int Examples -------- >>> self._unbox_scalar(Timedelta("10s")) # doctest: +SKIP 10000000000 """ raise AbstractMethodError(self) def _check_compatible_with( self, other: DTScalarOrNaT, setitem: bool = False ) -> None: """ Verify that `self` and `other` are compatible. * DatetimeArray verifies that the timezones (if any) match * PeriodArray verifies that the freq matches * Timedelta has no verification In each case, NaT is considered compatible. Parameters ---------- other setitem : bool, default False For __setitem__ we may have stricter compatibility restrictions than for comparisons. Raises ------ Exception """ raise AbstractMethodError(self) # ------------------------------------------------------------------ # NDArrayBackedExtensionArray compat @cache_readonly def _data(self) -> np.ndarray: return self._ndarray # ------------------------------------------------------------------ def _box_func(self, x): """ box function to get object from internal representation """ raise AbstractMethodError(self) def _box_values(self, values) -> np.ndarray: """ apply box func to passed values """ return lib.map_infer(values, self._box_func, convert=False) def __iter__(self): if self.ndim > 1: return (self[n] for n in range(len(self))) else: return (self._box_func(v) for v in self.asi8) @property def asi8(self) -> npt.NDArray[np.int64]: """ Integer representation of the values. Returns ------- ndarray An ndarray with int64 dtype. """ # do not cache or you'll create a memory leak return self._ndarray.view("i8") # ---------------------------------------------------------------- # Rendering Methods def _format_native_types(self, *, na_rep="NaT", date_format=None): """ Helper method for astype when converting to strings. Returns ------- ndarray[str] """ raise AbstractMethodError(self) def _formatter(self, boxed: bool = False): # TODO: Remove Datetime & DatetimeTZ formatters. return "'{}'".format # ---------------------------------------------------------------- # Array-Like / EA-Interface Methods def __array__(self, dtype: NpDtype | None = None) -> np.ndarray: # used for Timedelta/DatetimeArray, overwritten by PeriodArray if is_object_dtype(dtype): return np.array(list(self), dtype=object) return self._ndarray @overload def __getitem__(self, item: ScalarIndexer) -> DTScalarOrNaT: ... @overload def __getitem__( self: DatetimeLikeArrayT, item: SequenceIndexer | PositionalIndexerTuple, ) -> DatetimeLikeArrayT: ... def __getitem__( self: DatetimeLikeArrayT, key: PositionalIndexer2D ) -> DatetimeLikeArrayT | DTScalarOrNaT: """ This getitem defers to the underlying array, which by-definition can only handle list-likes, slices, and integer scalars """ # Use cast as we know we will get back a DatetimeLikeArray or DTScalar, # but skip evaluating the Union at runtime for performance # (see https://github.com/pandas-dev/pandas/pull/44624) result = cast( "Union[DatetimeLikeArrayT, DTScalarOrNaT]", super().__getitem__(key) ) if lib.is_scalar(result): return result else: # At this point we know the result is an array. result = cast(DatetimeLikeArrayT, result) result._freq = self._get_getitem_freq(key) return result def _get_getitem_freq(self, key) -> BaseOffset | None: """ Find the `freq` attribute to assign to the result of a __getitem__ lookup. """ is_period = is_period_dtype(self.dtype) if is_period: freq = self.freq elif self.ndim != 1: freq = None else: key = check_array_indexer(self, key) # maybe ndarray[bool] -> slice freq = None if isinstance(key, slice): if self.freq is not None and key.step is not None: freq = key.step * self.freq else: freq = self.freq elif key is Ellipsis: # GH#21282 indexing with Ellipsis is similar to a full slice, # should preserve `freq` attribute freq = self.freq elif com.is_bool_indexer(key): new_key = lib.maybe_booleans_to_slice(key.view(np.uint8)) if isinstance(new_key, slice): return self._get_getitem_freq(new_key) return freq # error: Argument 1 of "__setitem__" is incompatible with supertype # "ExtensionArray"; supertype defines the argument type as "Union[int, # ndarray]" def __setitem__( # type: ignore[override] self, key: int | Sequence[int] | Sequence[bool] | slice, value: NaTType | Any | Sequence[Any], ) -> None: # I'm fudging the types a bit here. "Any" above really depends # on type(self). For PeriodArray, it's Period (or stuff coercible # to a period in from_sequence). For DatetimeArray, it's Timestamp... # I don't know if mypy can do that, possibly with Generics. # https://mypy.readthedocs.io/en/latest/generics.html no_op = check_setitem_lengths(key, value, self) if no_op: return super().__setitem__(key, value) self._maybe_clear_freq() def _maybe_clear_freq(self): # inplace operations like __setitem__ may invalidate the freq of # DatetimeArray and TimedeltaArray pass def astype(self, dtype, copy: bool = True): # Some notes on cases we don't have to handle here in the base class: # 1. PeriodArray.astype handles period -> period # 2. DatetimeArray.astype handles conversion between tz. # 3. DatetimeArray.astype handles datetime -> period dtype = pandas_dtype(dtype) if is_object_dtype(dtype): if self.dtype.kind == "M": # *much* faster than self._box_values # for e.g. test_get_loc_tuple_monotonic_above_size_cutoff i8data = self.asi8.ravel() converted = ints_to_pydatetime( i8data, # error: "DatetimeLikeArrayMixin" has no attribute "tz" tz=self.tz, # type: ignore[attr-defined] freq=self.freq, box="timestamp", ) return converted.reshape(self.shape) return self._box_values(self.asi8.ravel()).reshape(self.shape) elif isinstance(dtype, ExtensionDtype): return super().astype(dtype, copy=copy) elif is_string_dtype(dtype): return self._format_native_types() elif is_integer_dtype(dtype): # we deliberately ignore int32 vs. int64 here. # See https://github.com/pandas-dev/pandas/issues/24381 for more. values = self.asi8 if is_unsigned_integer_dtype(dtype): # Again, we ignore int32 vs. int64 values = values.view("uint64") if copy: values = values.copy() return values elif ( is_datetime_or_timedelta_dtype(dtype) and not is_dtype_equal(self.dtype, dtype) ) or is_float_dtype(dtype): # disallow conversion between datetime/timedelta, # and conversions for any datetimelike to float msg = f"Cannot cast {type(self).__name__} to dtype {dtype}" raise TypeError(msg) else: return np.asarray(self, dtype=dtype) @overload def view(self: DatetimeLikeArrayT) -> DatetimeLikeArrayT: ... @overload def view(self, dtype: Literal["M8[ns]"]) -> DatetimeArray: ... @overload def view(self, dtype: Literal["m8[ns]"]) -> TimedeltaArray: ... @overload def view(self, dtype: Dtype | None = ...) -> ArrayLike: ... def view(self, dtype: Dtype | None = None) -> ArrayLike: # we need to explicitly call super() method as long as the `@overload`s # are present in this file. return super().view(dtype) # ------------------------------------------------------------------ # ExtensionArray Interface @classmethod def _concat_same_type( cls: type[DatetimeLikeArrayT], to_concat: Sequence[DatetimeLikeArrayT], axis: int = 0, ) -> DatetimeLikeArrayT: new_obj = super()._concat_same_type(to_concat, axis) obj = to_concat[0] dtype = obj.dtype new_freq = None if is_period_dtype(dtype): new_freq = obj.freq elif axis == 0: # GH 3232: If the concat result is evenly spaced, we can retain the # original frequency to_concat = [x for x in to_concat if len(x)] if obj.freq is not None and all(x.freq == obj.freq for x in to_concat): pairs = zip(to_concat[:-1], to_concat[1:]) if all(pair[0][-1] + obj.freq == pair[1][0] for pair in pairs): new_freq = obj.freq new_obj._freq = new_freq return new_obj def copy(self: DatetimeLikeArrayT, order="C") -> DatetimeLikeArrayT: # error: Unexpected keyword argument "order" for "copy" new_obj = super().copy(order=order) # type: ignore[call-arg] new_obj._freq = self.freq return new_obj def _values_for_factorize(self): # int64 instead of int ensures we have a "view" method return self._ndarray, np.int64(iNaT) @classmethod def _from_factorized( cls: type[DatetimeLikeArrayT], values, original: DatetimeLikeArrayT ) -> DatetimeLikeArrayT: return cls(values, dtype=original.dtype) # ------------------------------------------------------------------ # Validation Methods # TODO: try to de-duplicate these, ensure identical behavior def _validate_comparison_value(self, other): if isinstance(other, str): try: # GH#18435 strings get a pass from tzawareness compat other = self._scalar_from_string(other) except (ValueError, IncompatibleFrequency): # failed to parse as Timestamp/Timedelta/Period raise InvalidComparison(other) if isinstance(other, self._recognized_scalars) or other is NaT: other = self._scalar_type(other) try: self._check_compatible_with(other) except (TypeError, IncompatibleFrequency) as err: # e.g. tzawareness mismatch raise InvalidComparison(other) from err elif not is_list_like(other): raise InvalidComparison(other) elif len(other) != len(self): raise ValueError("Lengths must match") else: try: other = self._validate_listlike(other, allow_object=True) self._check_compatible_with(other) except (TypeError, IncompatibleFrequency) as err: if is_object_dtype(getattr(other, "dtype", None)): # We will have to operate element-wise pass else: raise InvalidComparison(other) from err return other def _validate_shift_value(self, fill_value): # TODO(2.0): once this deprecation is enforced, use _validate_scalar if is_valid_na_for_dtype(fill_value, self.dtype): fill_value = NaT elif isinstance(fill_value, self._recognized_scalars): fill_value = self._scalar_type(fill_value) else: new_fill: DatetimeLikeScalar # only warn if we're not going to raise if self._scalar_type is Period and lib.is_integer(fill_value): # kludge for #31971 since Period(integer) tries to cast to str new_fill = Period._from_ordinal(fill_value, freq=self.freq) else: new_fill = self._scalar_type(fill_value) # stacklevel here is chosen to be correct when called from # DataFrame.shift or Series.shift warnings.warn( f"Passing {type(fill_value)} to shift is deprecated and " "will raise in a future version, pass " f"{self._scalar_type.__name__} instead.", FutureWarning, # There is no way to hard-code the level since this might be # reached directly or called from the Index or Block method stacklevel=find_stack_level(), ) fill_value = new_fill return self._unbox(fill_value, setitem=True) def _validate_scalar( self, value, *, allow_listlike: bool = False, setitem: bool = True, unbox: bool = True, ): """ Validate that the input value can be cast to our scalar_type. Parameters ---------- value : object allow_listlike: bool, default False When raising an exception, whether the message should say listlike inputs are allowed. setitem : bool, default True Whether to check compatibility with setitem strictness. unbox : bool, default True Whether to unbox the result before returning. Note: unbox=False skips the setitem compatibility check. Returns ------- self._scalar_type or NaT """ if isinstance(value, self._scalar_type): pass elif isinstance(value, str): # NB: Careful about tzawareness try: value = self._scalar_from_string(value) except ValueError as err: msg = self._validation_error_message(value, allow_listlike) raise TypeError(msg) from err elif is_valid_na_for_dtype(value, self.dtype): # GH#18295 value = NaT elif isna(value): # if we are dt64tz and value is dt64("NaT"), dont cast to NaT, # or else we'll fail to raise in _unbox_scalar msg = self._validation_error_message(value, allow_listlike) raise TypeError(msg) elif isinstance(value, self._recognized_scalars): value = self._scalar_type(value) else: msg = self._validation_error_message(value, allow_listlike) raise TypeError(msg) if not unbox: # NB: In general NDArrayBackedExtensionArray will unbox here; # this option exists to prevent a performance hit in # TimedeltaIndex.get_loc return value return self._unbox_scalar(value, setitem=setitem) def _validation_error_message(self, value, allow_listlike: bool = False) -> str: """ Construct an exception message on validation error. Some methods allow only scalar inputs, while others allow either scalar or listlike. Parameters ---------- allow_listlike: bool, default False Returns ------- str """ if allow_listlike: msg = ( f"value should be a '{self._scalar_type.__name__}', 'NaT', " f"or array of those. Got '{type(value).__name__}' instead." ) else: msg = ( f"value should be a '{self._scalar_type.__name__}' or 'NaT'. " f"Got '{type(value).__name__}' instead." ) return msg def _validate_listlike(self, value, allow_object: bool = False): if isinstance(value, type(self)): return value if isinstance(value, list) and len(value) == 0: # We treat empty list as our own dtype. return type(self)._from_sequence([], dtype=self.dtype) if hasattr(value, "dtype") and value.dtype == object: # `array` below won't do inference if value is an Index or Series. # so do so here. in the Index case, inferred_type may be cached. if lib.infer_dtype(value) in self._infer_matches: try: value = type(self)._from_sequence(value) except (ValueError, TypeError): if allow_object: return value msg = self._validation_error_message(value, True) raise TypeError(msg) # Do type inference if necessary up front (after unpacking PandasArray) # e.g. we passed PeriodIndex.values and got an ndarray of Periods value = extract_array(value, extract_numpy=True) value = pd_array(value) value = extract_array(value, extract_numpy=True) if is_all_strings(value): # We got a StringArray try: # TODO: Could use from_sequence_of_strings if implemented # Note: passing dtype is necessary for PeriodArray tests value = type(self)._from_sequence(value, dtype=self.dtype) except ValueError: pass if is_categorical_dtype(value.dtype): # e.g. we have a Categorical holding self.dtype if is_dtype_equal(value.categories.dtype, self.dtype): # TODO: do we need equal dtype or just comparable? value = value._internal_get_values() value = extract_array(value, extract_numpy=True) if allow_object and is_object_dtype(value.dtype): pass elif not type(self)._is_recognized_dtype(value.dtype): msg = self._validation_error_message(value, True) raise TypeError(msg) return value def _validate_searchsorted_value(self, value): if not is_list_like(value): return self._validate_scalar(value, allow_listlike=True, setitem=False) else: value = self._validate_listlike(value) return self._unbox(value) def _validate_setitem_value(self, value): if is_list_like(value): value = self._validate_listlike(value) else: return self._validate_scalar(value, allow_listlike=True) return self._unbox(value, setitem=True) def _unbox( self, other, setitem: bool = False ) -> np.int64 | np.datetime64 | np.timedelta64 | np.ndarray: """ Unbox either a scalar with _unbox_scalar or an instance of our own type. """ if lib.is_scalar(other): other = self._unbox_scalar(other, setitem=setitem) else: # same type as self self._check_compatible_with(other, setitem=setitem) other = other._ndarray return other # ------------------------------------------------------------------ # Additional array methods # These are not part of the EA API, but we implement them because # pandas assumes they're there. @ravel_compat def map(self, mapper): # TODO(GH-23179): Add ExtensionArray.map # Need to figure out if we want ExtensionArray.map first. # If so, then we can refactor IndexOpsMixin._map_values to # a standalone function and call from here.. # Else, just rewrite _map_infer_values to do the right thing. from pandas import Index return Index(self).map(mapper).array def isin(self, values) -> npt.NDArray[np.bool_]: """ Compute boolean array of whether each value is found in the passed set of values. Parameters ---------- values : set or sequence of values Returns ------- ndarray[bool] """ if not hasattr(values, "dtype"): values = np.asarray(values) if values.dtype.kind in ["f", "i", "u", "c"]: # TODO: de-duplicate with equals, validate_comparison_value return np.zeros(self.shape, dtype=bool) if not isinstance(values, type(self)): inferable = [ "timedelta", "timedelta64", "datetime", "datetime64", "date", "period", ] if values.dtype == object: inferred = lib.infer_dtype(values, skipna=False) if inferred not in inferable: if inferred == "string": pass elif "mixed" in inferred: return isin(self.astype(object), values) else: return np.zeros(self.shape, dtype=bool) try: values = type(self)._from_sequence(values) except ValueError: return isin(self.astype(object), values) try: self._check_compatible_with(values) except (TypeError, ValueError): # Includes tzawareness mismatch and IncompatibleFrequencyError return np.zeros(self.shape, dtype=bool) return isin(self.asi8, values.asi8) # ------------------------------------------------------------------ # Null Handling def isna(self) -> npt.NDArray[np.bool_]: return self._isnan @property # NB: override with cache_readonly in immutable subclasses def _isnan(self) -> npt.NDArray[np.bool_]: """ return if each value is nan """ return self.asi8 == iNaT @property # NB: override with cache_readonly in immutable subclasses def _hasna(self) -> bool: """ return if I have any nans; enables various perf speedups """ return bool(self._isnan.any()) def _maybe_mask_results( self, result: np.ndarray, fill_value=iNaT, convert=None ) -> np.ndarray: """ Parameters ---------- result : np.ndarray fill_value : object, default iNaT convert : str, dtype or None Returns ------- result : ndarray with values replace by the fill_value mask the result if needed, convert to the provided dtype if its not None This is an internal routine. """ if self._hasna: if convert: result = result.astype(convert) if fill_value is None: fill_value = np.nan np.putmask(result, self._isnan, fill_value) return result # ------------------------------------------------------------------ # Frequency Properties/Methods @property def freq(self): """ Return the frequency object if it is set, otherwise None. """ return self._freq @freq.setter def freq(self, value): if value is not None: value = to_offset(value) self._validate_frequency(self, value) if self.ndim > 1: raise ValueError("Cannot set freq with ndim > 1") self._freq = value @property def freqstr(self) -> str | None: """ Return the frequency object as a string if its set, otherwise None. """ if self.freq is None: return None return self.freq.freqstr @property # NB: override with cache_readonly in immutable subclasses def inferred_freq(self) -> str | None: """ Tries to return a string representing a frequency guess, generated by infer_freq. Returns None if it can't autodetect the frequency. """ if self.ndim != 1: return None try: return frequencies.infer_freq(self) except ValueError: return None @property # NB: override with cache_readonly in immutable subclasses def _resolution_obj(self) -> Resolution | None: freqstr = self.freqstr if freqstr is None: return None try: return Resolution.get_reso_from_freq(freqstr) except KeyError: return None @property # NB: override with cache_readonly in immutable subclasses def resolution(self) -> str: """ Returns day, hour, minute, second, millisecond or microsecond """ # error: Item "None" of "Optional[Any]" has no attribute "attrname" return self._resolution_obj.attrname # type: ignore[union-attr] @classmethod def _validate_frequency(cls, index, freq, **kwargs): """ Validate that a frequency is compatible with the values of a given Datetime Array/Index or Timedelta Array/Index Parameters ---------- index : DatetimeIndex or TimedeltaIndex The index on which to determine if the given frequency is valid freq : DateOffset The frequency to validate """ # TODO: this is not applicable to PeriodArray, move to correct Mixin inferred = index.inferred_freq if index.size == 0 or inferred == freq.freqstr: return None try: on_freq = cls._generate_range( start=index[0], end=None, periods=len(index), freq=freq, **kwargs ) if not np.array_equal(index.asi8, on_freq.asi8): raise ValueError except ValueError as e: if "non-fixed" in str(e): # non-fixed frequencies are not meaningful for timedelta64; # we retain that error message raise e # GH#11587 the main way this is reached is if the `np.array_equal` # check above is False. This can also be reached if index[0] # is `NaT`, in which case the call to `cls._generate_range` will # raise a ValueError, which we re-raise with a more targeted # message. raise ValueError( f"Inferred frequency {inferred} from passed values " f"does not conform to passed frequency {freq.freqstr}" ) from e @classmethod def _generate_range( cls: type[DatetimeLikeArrayT], start, end, periods, freq, *args, **kwargs ) -> DatetimeLikeArrayT: raise AbstractMethodError(cls) # monotonicity/uniqueness properties are called via frequencies.infer_freq, # see GH#23789 @property def _is_monotonic_increasing(self) -> bool: return algos.is_monotonic(self.asi8, timelike=True)[0] @property def _is_monotonic_decreasing(self) -> bool: return algos.is_monotonic(self.asi8, timelike=True)[1] @property def _is_unique(self) -> bool: return len(unique1d(self.asi8.ravel("K"))) == self.size # ------------------------------------------------------------------ # Arithmetic Methods def _cmp_method(self, other, op): if self.ndim > 1 and getattr(other, "shape", None) == self.shape: # TODO: handle 2D-like listlikes return op(self.ravel(), other.ravel()).reshape(self.shape) try: other = self._validate_comparison_value(other) except InvalidComparison: return invalid_comparison(self, other, op) dtype = getattr(other, "dtype", None) if is_object_dtype(dtype): # We have to use comp_method_OBJECT_ARRAY instead of numpy # comparison otherwise it would fail to raise when # comparing tz-aware and tz-naive with np.errstate(all="ignore"): result = ops.comp_method_OBJECT_ARRAY( op, np.asarray(self.astype(object)), other ) return result other_vals = self._unbox(other) # GH#37462 comparison on i8 values is almost 2x faster than M8/m8 result = op(self._ndarray.view("i8"), other_vals.view("i8")) o_mask = isna(other) mask = self._isnan | o_mask if mask.any(): nat_result = op is operator.ne np.putmask(result, mask, nat_result) return result # pow is invalid for all three subclasses; TimedeltaArray will override # the multiplication and division ops __pow__ = make_invalid_op("__pow__") __rpow__ = make_invalid_op("__rpow__") __mul__ = make_invalid_op("__mul__") __rmul__ = make_invalid_op("__rmul__") __truediv__ = make_invalid_op("__truediv__") __rtruediv__ = make_invalid_op("__rtruediv__") __floordiv__ = make_invalid_op("__floordiv__") __rfloordiv__ = make_invalid_op("__rfloordiv__") __mod__ = make_invalid_op("__mod__") __rmod__ = make_invalid_op("__rmod__") __divmod__ = make_invalid_op("__divmod__") __rdivmod__ = make_invalid_op("__rdivmod__") def _add_datetimelike_scalar(self, other): # Overridden by TimedeltaArray raise TypeError(f"cannot add {type(self).__name__} and {type(other).__name__}") _add_datetime_arraylike = _add_datetimelike_scalar def _sub_datetimelike_scalar(self, other): # Overridden by DatetimeArray assert other is not NaT raise TypeError(f"cannot subtract a datelike from a {type(self).__name__}") _sub_datetime_arraylike = _sub_datetimelike_scalar def _sub_period(self, other): # Overridden by PeriodArray raise TypeError(f"cannot subtract Period from a {type(self).__name__}") def _add_period(self, other: Period): # Overridden by TimedeltaArray raise TypeError(f"cannot add Period to a {type(self).__name__}") def _add_offset(self, offset): raise AbstractMethodError(self) def _add_timedeltalike_scalar(self, other): """ Add a delta of a timedeltalike Returns ------- Same type as self """ if isna(other): # i.e np.timedelta64("NaT"), not recognized by delta_to_nanoseconds new_values = np.empty(self.shape, dtype="i8") new_values.fill(iNaT) return type(self)(new_values, dtype=self.dtype) inc = delta_to_nanoseconds(other) new_values = checked_add_with_arr(self.asi8, inc, arr_mask=self._isnan) new_values = new_values.view("i8") new_values = self._maybe_mask_results(new_values) new_values = new_values.view(self._ndarray.dtype) new_freq = None if isinstance(self.freq, Tick) or is_period_dtype(self.dtype): # adding a scalar preserves freq new_freq = self.freq # error: Unexpected keyword argument "freq" for "_simple_new" of "NDArrayBacked" return type(self)._simple_new( # type: ignore[call-arg] new_values, dtype=self.dtype, freq=new_freq ) def _add_timedelta_arraylike(self, other): """ Add a delta of a TimedeltaIndex Returns ------- Same type as self """ # overridden by PeriodArray if len(self) != len(other): raise ValueError("cannot add indices of unequal length") if isinstance(other, np.ndarray): # ndarray[timedelta64]; wrap in TimedeltaIndex for op from pandas.core.arrays import TimedeltaArray other = TimedeltaArray._from_sequence(other) self_i8 = self.asi8 other_i8 = other.asi8 new_values = checked_add_with_arr( self_i8, other_i8, arr_mask=self._isnan, b_mask=other._isnan ) if self._hasna or other._hasna: mask = self._isnan | other._isnan np.putmask(new_values, mask, iNaT) return type(self)(new_values, dtype=self.dtype) @final def _add_nat(self): """ Add pd.NaT to self """ if is_period_dtype(self.dtype): raise TypeError( f"Cannot add {type(self).__name__} and {type(NaT).__name__}" ) # GH#19124 pd.NaT is treated like a timedelta for both timedelta # and datetime dtypes result = np.empty(self.shape, dtype=np.int64) result.fill(iNaT) return type(self)(result, dtype=self.dtype, freq=None) @final def _sub_nat(self): """ Subtract pd.NaT from self """ # GH#19124 Timedelta - datetime is not in general well-defined. # We make an exception for pd.NaT, which in this case quacks # like a timedelta. # For datetime64 dtypes by convention we treat NaT as a datetime, so # this subtraction returns a timedelta64 dtype. # For period dtype, timedelta64 is a close-enough return dtype. result = np.empty(self.shape, dtype=np.int64) result.fill(iNaT) return result.view("timedelta64[ns]") def _sub_period_array(self, other): # Overridden by PeriodArray raise TypeError( f"cannot subtract {other.dtype}-dtype from {type(self).__name__}" ) def _addsub_object_array(self, other: np.ndarray, op): """ Add or subtract array-like of DateOffset objects Parameters ---------- other : np.ndarray[object] op : {operator.add, operator.sub} Returns ------- result : same class as self """ assert op in [operator.add, operator.sub] if len(other) == 1 and self.ndim == 1: # If both 1D then broadcasting is unambiguous return op(self, other[0]) warnings.warn( "Adding/subtracting object-dtype array to " f"{type(self).__name__} not vectorized.", PerformanceWarning, ) # Caller is responsible for broadcasting if necessary assert self.shape == other.shape, (self.shape, other.shape) with warnings.catch_warnings(): # filter out warnings about Timestamp.freq warnings.filterwarnings("ignore", category=FutureWarning) res_values = op(self.astype("O"), np.asarray(other)) result = pd_array(res_values.ravel()) # error: Item "ExtensionArray" of "Union[Any, ExtensionArray]" has no attribute # "reshape" result = extract_array( result, extract_numpy=True ).reshape( # type: ignore[union-attr] self.shape ) return result def _time_shift( self: DatetimeLikeArrayT, periods: int, freq=None ) -> DatetimeLikeArrayT: """ 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 and freq != self.freq: if isinstance(freq, str): freq = to_offset(freq) offset = periods * freq return self + offset if periods == 0 or len(self) == 0: # GH#14811 empty case return self.copy() if self.freq is None: raise NullFrequencyError("Cannot shift with no freq") start = self[0] + periods * self.freq end = self[-1] + periods * self.freq # Note: in the DatetimeTZ case, _generate_range will infer the # appropriate timezone from `start` and `end`, so tz does not need # to be passed explicitly. return self._generate_range(start=start, end=end, periods=None, freq=self.freq) @unpack_zerodim_and_defer("__add__") def __add__(self, other): other_dtype = getattr(other, "dtype", None) # scalar others if other is NaT: result = self._add_nat() elif isinstance(other, (Tick, timedelta, np.timedelta64)): result = self._add_timedeltalike_scalar(other) elif isinstance(other, BaseOffset): # specifically _not_ a Tick result = self._add_offset(other) elif isinstance(other, (datetime, np.datetime64)): result = self._add_datetimelike_scalar(other) elif isinstance(other, Period) and is_timedelta64_dtype(self.dtype): result = self._add_period(other) elif lib.is_integer(other): # This check must come after the check for np.timedelta64 # as is_integer returns True for these if not is_period_dtype(self.dtype): raise integer_op_not_supported(self) result = self._time_shift(other) # array-like others elif is_timedelta64_dtype(other_dtype): # TimedeltaIndex, ndarray[timedelta64] result = self._add_timedelta_arraylike(other) elif is_object_dtype(other_dtype): # e.g. Array/Index of DateOffset objects result = self._addsub_object_array(other, operator.add) elif is_datetime64_dtype(other_dtype) or is_datetime64tz_dtype(other_dtype): # DatetimeIndex, ndarray[datetime64] return self._add_datetime_arraylike(other) elif is_integer_dtype(other_dtype): if not is_period_dtype(self.dtype): raise integer_op_not_supported(self) result = self._addsub_int_array(other, operator.add) else: # Includes Categorical, other ExtensionArrays # For PeriodDtype, if self is a TimedeltaArray and other is a # PeriodArray with a timedelta-like (i.e. Tick) freq, this # operation is valid. Defer to the PeriodArray implementation. # In remaining cases, this will end up raising TypeError. return NotImplemented if isinstance(result, np.ndarray) and is_timedelta64_dtype(result.dtype): from pandas.core.arrays import TimedeltaArray return TimedeltaArray(result) return result def __radd__(self, other): # alias for __add__ return self.__add__(other) @unpack_zerodim_and_defer("__sub__") def __sub__(self, other): other_dtype = getattr(other, "dtype", None) # scalar others if other is NaT: result = self._sub_nat() elif isinstance(other, (Tick, timedelta, np.timedelta64)): result = self._add_timedeltalike_scalar(-other) elif isinstance(other, BaseOffset): # specifically _not_ a Tick result = self._add_offset(-other) elif isinstance(other, (datetime, np.datetime64)): result = self._sub_datetimelike_scalar(other) elif lib.is_integer(other): # This check must come after the check for np.timedelta64 # as is_integer returns True for these if not is_period_dtype(self.dtype): raise integer_op_not_supported(self) result = self._time_shift(-other) elif isinstance(other, Period): result = self._sub_period(other) # array-like others elif is_timedelta64_dtype(other_dtype): # TimedeltaIndex, ndarray[timedelta64] result = self._add_timedelta_arraylike(-other) elif is_object_dtype(other_dtype): # e.g. Array/Index of DateOffset objects result = self._addsub_object_array(other, operator.sub) elif is_datetime64_dtype(other_dtype) or is_datetime64tz_dtype(other_dtype): # DatetimeIndex, ndarray[datetime64] result = self._sub_datetime_arraylike(other) elif is_period_dtype(other_dtype): # PeriodIndex result = self._sub_period_array(other) elif is_integer_dtype(other_dtype): if not is_period_dtype(self.dtype): raise integer_op_not_supported(self) result = self._addsub_int_array(other, operator.sub) else: # Includes ExtensionArrays, float_dtype return NotImplemented if isinstance(result, np.ndarray) and is_timedelta64_dtype(result.dtype): from pandas.core.arrays import TimedeltaArray return TimedeltaArray(result) return result def __rsub__(self, other): other_dtype = getattr(other, "dtype", None) if is_datetime64_any_dtype(other_dtype) and is_timedelta64_dtype(self.dtype): # ndarray[datetime64] cannot be subtracted from self, so # we need to wrap in DatetimeArray/Index and flip the operation if lib.is_scalar(other): # i.e. np.datetime64 object return Timestamp(other) - self if not isinstance(other, DatetimeLikeArrayMixin): # Avoid down-casting DatetimeIndex from pandas.core.arrays import DatetimeArray other = DatetimeArray(other) return other - self elif ( is_datetime64_any_dtype(self.dtype) and hasattr(other, "dtype") and not is_datetime64_any_dtype(other.dtype) ): # GH#19959 datetime - datetime is well-defined as timedelta, # but any other type - datetime is not well-defined. raise TypeError( f"cannot subtract {type(self).__name__} from {type(other).__name__}" ) elif is_period_dtype(self.dtype) and is_timedelta64_dtype(other_dtype): # TODO: Can we simplify/generalize these cases at all? raise TypeError(f"cannot subtract {type(self).__name__} from {other.dtype}") elif is_timedelta64_dtype(self.dtype): self = cast("TimedeltaArray", self) return (-self) + other # We get here with e.g. datetime objects return -(self - other) def __iadd__(self, other): result = self + other self[:] = result[:] if not is_period_dtype(self.dtype): # restore freq, which is invalidated by setitem self._freq = result.freq return self def __isub__(self, other): result = self - other self[:] = result[:] if not is_period_dtype(self.dtype): # restore freq, which is invalidated by setitem self._freq = result.freq return self # -------------------------------------------------------------- # Reductions def min(self, *, axis: int | None = None, skipna: bool = True, **kwargs): """ Return the minimum value of the Array or minimum along an axis. See Also -------- numpy.ndarray.min Index.min : Return the minimum value in an Index. Series.min : Return the minimum value in a Series. """ nv.validate_min((), kwargs) nv.validate_minmax_axis(axis, self.ndim) if is_period_dtype(self.dtype): # pass datetime64 values to nanops to get correct NaT semantics result = nanops.nanmin( self._ndarray.view("M8[ns]"), axis=axis, skipna=skipna ) if result is NaT: return NaT result = result.view("i8") if axis is None or self.ndim == 1: return self._box_func(result) return self._from_backing_data(result) result = nanops.nanmin(self._ndarray, axis=axis, skipna=skipna) return self._wrap_reduction_result(axis, result) def max(self, *, axis: int | None = None, skipna: bool = True, **kwargs): """ Return the maximum value of the Array or maximum along an axis. See Also -------- numpy.ndarray.max Index.max : Return the maximum value in an Index. Series.max : Return the maximum value in a Series. """ nv.validate_max((), kwargs) nv.validate_minmax_axis(axis, self.ndim) if is_period_dtype(self.dtype): # pass datetime64 values to nanops to get correct NaT semantics result = nanops.nanmax( self._ndarray.view("M8[ns]"), axis=axis, skipna=skipna ) if result is NaT: return result result = result.view("i8") if axis is None or self.ndim == 1: return self._box_func(result) return self._from_backing_data(result) result = nanops.nanmax(self._ndarray, axis=axis, skipna=skipna) return self._wrap_reduction_result(axis, result) def mean(self, *, skipna: bool = True, axis: int | None = 0): """ Return the mean value of the Array. .. versionadded:: 0.25.0 Parameters ---------- skipna : bool, default True Whether to ignore any NaT elements. axis : int, optional, default 0 Returns ------- scalar Timestamp or Timedelta. See Also -------- numpy.ndarray.mean : Returns the average of array elements along a given axis. Series.mean : Return the mean value in a Series. Notes ----- mean is only defined for Datetime and Timedelta dtypes, not for Period. """ if is_period_dtype(self.dtype): # See discussion in GH#24757 raise TypeError( f"mean is not implemented for {type(self).__name__} since the " "meaning is ambiguous. An alternative is " "obj.to_timestamp(how='start').mean()" ) result = nanops.nanmean( self._ndarray, axis=axis, skipna=skipna, mask=self.isna() ) return self._wrap_reduction_result(axis, result) def median(self, *, axis: int | None = None, skipna: bool = True, **kwargs): nv.validate_median((), kwargs) if axis is not None and abs(axis) >= self.ndim: raise ValueError("abs(axis) must be less than ndim") if is_period_dtype(self.dtype): # pass datetime64 values to nanops to get correct NaT semantics result = nanops.nanmedian( self._ndarray.view("M8[ns]"), axis=axis, skipna=skipna ) result = result.view("i8") if axis is None or self.ndim == 1: return self._box_func(result) return self._from_backing_data(result) result = nanops.nanmedian(self._ndarray, axis=axis, skipna=skipna) return self._wrap_reduction_result(axis, result) def _mode(self, dropna: bool = True): values = self if dropna: mask = values.isna() values = values[~mask] i8modes = mode(values.view("i8")) npmodes = i8modes.view(self._ndarray.dtype) npmodes = cast(np.ndarray, npmodes) return self._from_backing_data(npmodes) class DatelikeOps(DatetimeLikeArrayMixin): """ Common ops for DatetimeIndex/PeriodIndex, but not TimedeltaIndex. """ @Substitution( URL="https://docs.python.org/3/library/datetime.html" "#strftime-and-strptime-behavior" ) def strftime(self, date_format: str) -> npt.NDArray[np.object_]: """ Convert to Index using specified date_format. Return an Index of formatted strings specified by date_format, which supports the same string format as the python standard library. Details of the string format can be found in `python string format doc <%(URL)s>`__. Parameters ---------- date_format : str Date format string (e.g. "%%Y-%%m-%%d"). Returns ------- ndarray[object] NumPy ndarray of formatted strings. See Also -------- to_datetime : Convert the given argument to datetime. DatetimeIndex.normalize : Return DatetimeIndex with times to midnight. DatetimeIndex.round : Round the DatetimeIndex to the specified freq. DatetimeIndex.floor : Floor the DatetimeIndex to the specified freq. Examples -------- >>> rng = pd.date_range(pd.Timestamp("2018-03-10 09:00"), ... periods=3, freq='s') >>> rng.strftime('%%B %%d, %%Y, %%r') Index(['March 10, 2018, 09:00:00 AM', 'March 10, 2018, 09:00:01 AM', 'March 10, 2018, 09:00:02 AM'], dtype='object') """ result = self._format_native_types(date_format=date_format, na_rep=np.nan) return result.astype(object, copy=False) _round_doc = """ Perform {op} operation on the data to the specified `freq`. Parameters ---------- freq : str or Offset The frequency level to {op} the index to. Must be a fixed frequency like 'S' (second) not 'ME' (month end). See :ref:`frequency aliases ` for a list of possible `freq` values. ambiguous : 'infer', bool-ndarray, 'NaT', default 'raise' Only relevant for DatetimeIndex: - 'infer' will attempt to infer fall dst-transition hours based on order - bool-ndarray where True signifies a DST time, False designates a non-DST time (note that this flag is only applicable for ambiguous times) - 'NaT' will return NaT where there are ambiguous times - 'raise' will raise an AmbiguousTimeError if there are ambiguous times. nonexistent : 'shift_forward', 'shift_backward', 'NaT', timedelta, default 'raise' A nonexistent time does not exist in a particular timezone where clocks moved forward due to DST. - 'shift_forward' will shift the nonexistent time forward to the closest existing time - 'shift_backward' will shift the nonexistent time backward to the closest existing time - 'NaT' will return NaT where there are nonexistent times - timedelta objects will shift nonexistent times by the timedelta - 'raise' will raise an NonExistentTimeError if there are nonexistent times. Returns ------- DatetimeIndex, TimedeltaIndex, or Series Index of the same type for a DatetimeIndex or TimedeltaIndex, or a Series with the same index for a Series. Raises ------ ValueError if the `freq` cannot be converted. Notes ----- If the timestamps have a timezone, {op}ing will take place relative to the local ("wall") time and re-localized to the same timezone. When {op}ing near daylight savings time, use ``nonexistent`` and ``ambiguous`` to control the re-localization behavior. Examples -------- **DatetimeIndex** >>> rng = pd.date_range('1/1/2018 11:59:00', periods=3, freq='min') >>> rng DatetimeIndex(['2018-01-01 11:59:00', '2018-01-01 12:00:00', '2018-01-01 12:01:00'], dtype='datetime64[ns]', freq='T') """ _round_example = """>>> rng.round('H') DatetimeIndex(['2018-01-01 12:00:00', '2018-01-01 12:00:00', '2018-01-01 12:00:00'], dtype='datetime64[ns]', freq=None) **Series** >>> pd.Series(rng).dt.round("H") 0 2018-01-01 12:00:00 1 2018-01-01 12:00:00 2 2018-01-01 12:00:00 dtype: datetime64[ns] When rounding near a daylight savings time transition, use ``ambiguous`` or ``nonexistent`` to control how the timestamp should be re-localized. >>> rng_tz = pd.DatetimeIndex(["2021-10-31 03:30:00"], tz="Europe/Amsterdam") >>> rng_tz.floor("2H", ambiguous=False) DatetimeIndex(['2021-10-31 02:00:00+01:00'], dtype='datetime64[ns, Europe/Amsterdam]', freq=None) >>> rng_tz.floor("2H", ambiguous=True) DatetimeIndex(['2021-10-31 02:00:00+02:00'], dtype='datetime64[ns, Europe/Amsterdam]', freq=None) """ _floor_example = """>>> rng.floor('H') DatetimeIndex(['2018-01-01 11:00:00', '2018-01-01 12:00:00', '2018-01-01 12:00:00'], dtype='datetime64[ns]', freq=None) **Series** >>> pd.Series(rng).dt.floor("H") 0 2018-01-01 11:00:00 1 2018-01-01 12:00:00 2 2018-01-01 12:00:00 dtype: datetime64[ns] When rounding near a daylight savings time transition, use ``ambiguous`` or ``nonexistent`` to control how the timestamp should be re-localized. >>> rng_tz = pd.DatetimeIndex(["2021-10-31 03:30:00"], tz="Europe/Amsterdam") >>> rng_tz.floor("2H", ambiguous=False) DatetimeIndex(['2021-10-31 02:00:00+01:00'], dtype='datetime64[ns, Europe/Amsterdam]', freq=None) >>> rng_tz.floor("2H", ambiguous=True) DatetimeIndex(['2021-10-31 02:00:00+02:00'], dtype='datetime64[ns, Europe/Amsterdam]', freq=None) """ _ceil_example = """>>> rng.ceil('H') DatetimeIndex(['2018-01-01 12:00:00', '2018-01-01 12:00:00', '2018-01-01 13:00:00'], dtype='datetime64[ns]', freq=None) **Series** >>> pd.Series(rng).dt.ceil("H") 0 2018-01-01 12:00:00 1 2018-01-01 12:00:00 2 2018-01-01 13:00:00 dtype: datetime64[ns] When rounding near a daylight savings time transition, use ``ambiguous`` or ``nonexistent`` to control how the timestamp should be re-localized. >>> rng_tz = pd.DatetimeIndex(["2021-10-31 01:30:00"], tz="Europe/Amsterdam") >>> rng_tz.ceil("H", ambiguous=False) DatetimeIndex(['2021-10-31 02:00:00+01:00'], dtype='datetime64[ns, Europe/Amsterdam]', freq=None) >>> rng_tz.ceil("H", ambiguous=True) DatetimeIndex(['2021-10-31 02:00:00+02:00'], dtype='datetime64[ns, Europe/Amsterdam]', freq=None) """ TimelikeOpsT = TypeVar("TimelikeOpsT", bound="TimelikeOps") class TimelikeOps(DatetimeLikeArrayMixin): """ Common ops for TimedeltaIndex/DatetimeIndex, but not PeriodIndex. """ def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs): if ( ufunc in [np.isnan, np.isinf, np.isfinite] and len(inputs) == 1 and inputs[0] is self ): # numpy 1.18 changed isinf and isnan to not raise on dt64/td64 return getattr(ufunc, method)(self._ndarray, **kwargs) return super().__array_ufunc__(ufunc, method, *inputs, **kwargs) def _round(self, freq, mode, ambiguous, nonexistent): # round the local times if is_datetime64tz_dtype(self.dtype): # operate on naive timestamps, then convert back to aware self = cast("DatetimeArray", self) naive = self.tz_localize(None) result = naive._round(freq, mode, ambiguous, nonexistent) return result.tz_localize( self.tz, ambiguous=ambiguous, nonexistent=nonexistent ) values = self.view("i8") values = cast(np.ndarray, values) nanos = to_offset(freq).nanos result_i8 = round_nsint64(values, mode, nanos) result = self._maybe_mask_results(result_i8, fill_value=iNaT) result = result.view(self._ndarray.dtype) return self._simple_new(result, dtype=self.dtype) @Appender((_round_doc + _round_example).format(op="round")) def round(self, freq, ambiguous="raise", nonexistent="raise"): return self._round(freq, RoundTo.NEAREST_HALF_EVEN, ambiguous, nonexistent) @Appender((_round_doc + _floor_example).format(op="floor")) def floor(self, freq, ambiguous="raise", nonexistent="raise"): return self._round(freq, RoundTo.MINUS_INFTY, ambiguous, nonexistent) @Appender((_round_doc + _ceil_example).format(op="ceil")) def ceil(self, freq, ambiguous="raise", nonexistent="raise"): return self._round(freq, RoundTo.PLUS_INFTY, ambiguous, nonexistent) # -------------------------------------------------------------- # Reductions def any(self, *, axis: int | None = None, skipna: bool = True): # GH#34479 discussion of desired behavior long-term return nanops.nanany(self._ndarray, axis=axis, skipna=skipna, mask=self.isna()) def all(self, *, axis: int | None = None, skipna: bool = True): # GH#34479 discussion of desired behavior long-term return nanops.nanall(self._ndarray, axis=axis, skipna=skipna, mask=self.isna()) # -------------------------------------------------------------- # Frequency Methods def _maybe_clear_freq(self) -> None: self._freq = None def _with_freq(self, freq): """ Helper to get a view on the same data, with a new freq. Parameters ---------- freq : DateOffset, None, or "infer" Returns ------- Same type as self """ # GH#29843 if freq is None: # Always valid pass elif len(self) == 0 and isinstance(freq, BaseOffset): # Always valid. In the TimedeltaArray case, we assume this # is a Tick offset. pass else: # As an internal method, we can ensure this assertion always holds assert freq == "infer" freq = to_offset(self.inferred_freq) arr = self.view() arr._freq = freq return arr # -------------------------------------------------------------- def factorize(self, na_sentinel=-1, sort: bool = False): if self.freq is not None: # We must be unique, so can short-circuit (and retain freq) codes = np.arange(len(self), dtype=np.intp) uniques = self.copy() # TODO: copy or view? if sort and self.freq.n < 0: codes = codes[::-1] uniques = uniques[::-1] return codes, uniques # FIXME: shouldn't get here; we are ignoring sort return super().factorize(na_sentinel=na_sentinel) # ------------------------------------------------------------------- # Shared Constructor Helpers def validate_periods(periods): """ If a `periods` argument is passed to the Datetime/Timedelta Array/Index constructor, cast it to an integer. Parameters ---------- periods : None, float, int Returns ------- periods : None or int Raises ------ TypeError if periods is None, float, or int """ if periods is not None: if lib.is_float(periods): periods = int(periods) elif not lib.is_integer(periods): raise TypeError(f"periods must be a number, got {periods}") return periods def validate_inferred_freq(freq, inferred_freq, freq_infer): """ If the user passes a freq and another freq is inferred from passed data, require that they match. Parameters ---------- freq : DateOffset or None inferred_freq : DateOffset or None freq_infer : bool Returns ------- freq : DateOffset or None freq_infer : bool Notes ----- We assume at this point that `maybe_infer_freq` has been called, so `freq` is either a DateOffset object or None. """ if inferred_freq is not None: if freq is not None and freq != inferred_freq: raise ValueError( f"Inferred frequency {inferred_freq} from passed " "values does not conform to passed frequency " f"{freq.freqstr}" ) elif freq is None: freq = inferred_freq freq_infer = False return freq, freq_infer def maybe_infer_freq(freq): """ Comparing a DateOffset to the string "infer" raises, so we need to be careful about comparisons. Make a dummy variable `freq_infer` to signify the case where the given freq is "infer" and set freq to None to avoid comparison trouble later on. Parameters ---------- freq : {DateOffset, None, str} Returns ------- freq : {DateOffset, None} freq_infer : bool Whether we should inherit the freq of passed data. """ freq_infer = False if not isinstance(freq, BaseOffset): # if a passed freq is None, don't infer automatically if freq != "infer": freq = to_offset(freq) else: freq_infer = True freq = None return freq, freq_infer