""" implement the TimedeltaIndex """ from __future__ import annotations from pandas._libs import ( index as libindex, lib, ) from pandas._libs.tslibs import ( Timedelta, to_offset, ) from pandas._typing import DtypeObj from pandas.core.dtypes.common import ( TD64NS_DTYPE, is_scalar, is_timedelta64_dtype, ) from pandas.core.arrays import datetimelike as dtl from pandas.core.arrays.timedeltas import TimedeltaArray import pandas.core.common as com from pandas.core.indexes.base import ( Index, maybe_extract_name, ) from pandas.core.indexes.datetimelike import DatetimeTimedeltaMixin from pandas.core.indexes.extension import inherit_names @inherit_names( ["__neg__", "__pos__", "__abs__", "total_seconds", "round", "floor", "ceil"] + TimedeltaArray._field_ops, TimedeltaArray, wrap=True, ) @inherit_names( [ "components", "to_pytimedelta", "sum", "std", "median", "_format_native_types", ], TimedeltaArray, ) class TimedeltaIndex(DatetimeTimedeltaMixin): """ Immutable Index of timedelta64 data. Represented internally as int64, and scalars returned Timedelta objects. Parameters ---------- data : array-like (1-dimensional), optional Optional timedelta-like data to construct index with. unit : unit of the arg (D,h,m,s,ms,us,ns) denote the unit, optional Which is an integer/float number. freq : str or pandas offset object, optional One of pandas date offset strings or corresponding objects. The string 'infer' can be passed in order to set the frequency of the index as the inferred frequency upon creation. copy : bool Make a copy of input ndarray. name : object Name to be stored in the index. Attributes ---------- days seconds microseconds nanoseconds components inferred_freq Methods ------- to_pytimedelta to_series round floor ceil to_frame mean See Also -------- Index : The base pandas Index type. Timedelta : Represents a duration between two dates or times. DatetimeIndex : Index of datetime64 data. PeriodIndex : Index of Period data. timedelta_range : Create a fixed-frequency TimedeltaIndex. Notes ----- To learn more about the frequency strings, please see `this link `__. """ _typ = "timedeltaindex" _data_cls = TimedeltaArray @property def _engine_type(self) -> type[libindex.TimedeltaEngine]: return libindex.TimedeltaEngine _data: TimedeltaArray # Use base class method instead of DatetimeTimedeltaMixin._get_string_slice _get_string_slice = Index._get_string_slice # ------------------------------------------------------------------- # Constructors def __new__( cls, data=None, unit=None, freq=lib.no_default, closed=None, dtype=TD64NS_DTYPE, copy=False, name=None, ): name = maybe_extract_name(name, data, cls) if is_scalar(data): raise cls._scalar_data_error(data) if unit in {"Y", "y", "M"}: raise ValueError( "Units 'M', 'Y', and 'y' are no longer supported, as they do not " "represent unambiguous timedelta values durations." ) # FIXME: need to check for dtype/data match if isinstance(data, TimedeltaArray) and freq is lib.no_default: if copy: data = data.copy() return cls._simple_new(data, name=name) if isinstance(data, TimedeltaIndex) and freq is lib.no_default and name is None: if copy: return data.copy() else: return data._view() # - Cases checked above all return/raise before reaching here - # tdarr = TimedeltaArray._from_sequence_not_strict( data, freq=freq, unit=unit, dtype=dtype, copy=copy ) return cls._simple_new(tdarr, name=name) # ------------------------------------------------------------------- def _is_comparable_dtype(self, dtype: DtypeObj) -> bool: """ Can we compare values of the given dtype to our own? """ return is_timedelta64_dtype(dtype) # aka self._data._is_recognized_dtype # ------------------------------------------------------------------- # Indexing Methods def get_loc(self, key, method=None, tolerance=None): """ Get integer location for requested label Returns ------- loc : int, slice, or ndarray[int] """ self._check_indexing_error(key) try: key = self._data._validate_scalar(key, unbox=False) except TypeError as err: raise KeyError(key) from err return Index.get_loc(self, key, method, tolerance) def _parse_with_reso(self, label: str): # the "with_reso" is a no-op for TimedeltaIndex parsed = Timedelta(label) return parsed, None def _parsed_string_to_bounds(self, reso, parsed: Timedelta): # reso is unused, included to match signature of DTI/PI lbound = parsed.round(parsed.resolution_string) rbound = lbound + to_offset(parsed.resolution_string) - Timedelta(1, "ns") return lbound, rbound # ------------------------------------------------------------------- @property def inferred_type(self) -> str: return "timedelta64" def timedelta_range( start=None, end=None, periods: int | None = None, freq=None, name=None, closed=None, ) -> TimedeltaIndex: """ Return a fixed frequency TimedeltaIndex with day as the default. Parameters ---------- start : str or timedelta-like, default None Left bound for generating timedeltas. end : str or timedelta-like, default None Right bound for generating timedeltas. periods : int, default None Number of periods to generate. freq : str or DateOffset, default 'D' Frequency strings can have multiples, e.g. '5H'. name : str, default None Name of the resulting TimedeltaIndex. closed : str, default None Make the interval closed with respect to the given frequency to the 'left', 'right', or both sides (None). Returns ------- TimedeltaIndex Notes ----- Of the four parameters ``start``, ``end``, ``periods``, and ``freq``, exactly three must be specified. If ``freq`` is omitted, the resulting ``TimedeltaIndex`` will have ``periods`` linearly spaced elements between ``start`` and ``end`` (closed on both sides). To learn more about the frequency strings, please see `this link `__. Examples -------- >>> pd.timedelta_range(start='1 day', periods=4) TimedeltaIndex(['1 days', '2 days', '3 days', '4 days'], dtype='timedelta64[ns]', freq='D') The ``closed`` parameter specifies which endpoint is included. The default behavior is to include both endpoints. >>> pd.timedelta_range(start='1 day', periods=4, closed='right') TimedeltaIndex(['2 days', '3 days', '4 days'], dtype='timedelta64[ns]', freq='D') The ``freq`` parameter specifies the frequency of the TimedeltaIndex. Only fixed frequencies can be passed, non-fixed frequencies such as 'M' (month end) will raise. >>> pd.timedelta_range(start='1 day', end='2 days', freq='6H') TimedeltaIndex(['1 days 00:00:00', '1 days 06:00:00', '1 days 12:00:00', '1 days 18:00:00', '2 days 00:00:00'], dtype='timedelta64[ns]', freq='6H') Specify ``start``, ``end``, and ``periods``; the frequency is generated automatically (linearly spaced). >>> pd.timedelta_range(start='1 day', end='5 days', periods=4) TimedeltaIndex(['1 days 00:00:00', '2 days 08:00:00', '3 days 16:00:00', '5 days 00:00:00'], dtype='timedelta64[ns]', freq=None) """ if freq is None and com.any_none(periods, start, end): freq = "D" freq, _ = dtl.maybe_infer_freq(freq) tdarr = TimedeltaArray._generate_range(start, end, periods, freq, closed=closed) return TimedeltaIndex._simple_new(tdarr, name=name)