# # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from typing import cast, no_type_check, Any from functools import partial import pandas as pd from pandas.api.types import is_hashable # type: ignore[attr-defined] from pyspark import pandas as ps from pyspark._globals import _NoValue from pyspark.pandas.indexes.base import Index from pyspark.pandas.missing.indexes import MissingPandasLikeTimedeltaIndex from pyspark.pandas.series import Series from pyspark.pandas.spark import functions as SF from pyspark.sql import functions as F HOURS_PER_DAY = 24 MINUTES_PER_HOUR = 60 SECONDS_PER_MINUTE = 60 MILLIS_PER_SECOND = 1000 MICROS_PER_MILLIS = 1000 SECONDS_PER_HOUR = MINUTES_PER_HOUR * SECONDS_PER_MINUTE SECONDS_PER_DAY = HOURS_PER_DAY * SECONDS_PER_HOUR MICROS_PER_SECOND = MILLIS_PER_SECOND * MICROS_PER_MILLIS [docs]class TimedeltaIndex(Index): """ Immutable ndarray-like of timedelta64 data, represented internally as int64, and which can be boxed to 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. See Also -------- Index : The base pandas Index type. Examples -------- >>> from datetime import timedelta >>> ps.TimedeltaIndex([timedelta(1), timedelta(microseconds=2)]) ... # doctest: +NORMALIZE_WHITESPACE TimedeltaIndex(['1 days 00:00:00', '0 days 00:00:00.000002'], dtype='timedelta64[ns]', freq=None) From an Series: >>> s = ps.Series([timedelta(1), timedelta(microseconds=2)], index=[10, 20]) >>> ps.TimedeltaIndex(s) ... # doctest: +NORMALIZE_WHITESPACE TimedeltaIndex(['1 days 00:00:00', '0 days 00:00:00.000002'], dtype='timedelta64[ns]', freq=None) From an Index: >>> idx = ps.TimedeltaIndex([timedelta(1), timedelta(microseconds=2)]) >>> ps.TimedeltaIndex(idx) ... # doctest: +NORMALIZE_WHITESPACE TimedeltaIndex(['1 days 00:00:00', '0 days 00:00:00.000002'], dtype='timedelta64[ns]', freq=None) """ @no_type_check def __new__( cls, data=None, unit=None, freq=_NoValue, closed=None, dtype=None, copy=False, name=None, ) -> "TimedeltaIndex": if not is_hashable(name): raise TypeError("Index.name must be a hashable type") if isinstance(data, (Series, Index)): if dtype is None: dtype = "timedelta64[ns]" return cast(TimedeltaIndex, Index(data, dtype=dtype, copy=copy, name=name)) kwargs = dict( data=data, unit=unit, closed=closed, dtype=dtype, copy=copy, name=name, ) if freq is not _NoValue: kwargs["freq"] = freq return cast(TimedeltaIndex, ps.from_pandas(pd.TimedeltaIndex(**kwargs))) def __getattr__(self, item: str) -> Any: if hasattr(MissingPandasLikeTimedeltaIndex, item): property_or_func = getattr(MissingPandasLikeTimedeltaIndex, item) if isinstance(property_or_func, property): return property_or_func.fget(self) else: return partial(property_or_func, self) raise AttributeError("'TimedeltaIndex' object has no attribute '{}'".format(item)) @property def days(self) -> Index: """ Number of days for each element. """ def pandas_days(x) -> int: # type: ignore[no-untyped-def] return x.days return Index(self.to_series().transform(pandas_days)) @property def seconds(self) -> Index: """ Number of seconds (>= 0 and less than 1 day) for each element. """ @no_type_check def get_seconds(scol): hour_scol = SF.date_part("HOUR", scol) minute_scol = SF.date_part("MINUTE", scol) second_scol = SF.date_part("SECOND", scol) return ( F.when( hour_scol < 0, SECONDS_PER_DAY + hour_scol * SECONDS_PER_HOUR, ).otherwise(hour_scol * SECONDS_PER_HOUR) + F.when( minute_scol < 0, SECONDS_PER_DAY + minute_scol * SECONDS_PER_MINUTE, ).otherwise(minute_scol * SECONDS_PER_MINUTE) + F.when( second_scol < 0, SECONDS_PER_DAY + second_scol, ).otherwise(second_scol) ).cast("int") return Index(self.to_series().spark.transform(get_seconds)) @property def microseconds(self) -> Index: """ Number of microseconds (>= 0 and less than 1 second) for each element. """ @no_type_check def get_microseconds(scol): second_scol = SF.date_part("SECOND", scol) return ( ( F.when( (second_scol >= 0) & (second_scol < 1), second_scol, ) .when(second_scol < 0, 1 + second_scol) .otherwise(0) ) * MICROS_PER_SECOND ).cast("int") return Index(self.to_series().spark.transform(get_microseconds)) @no_type_check def all(self, *args, **kwargs) -> None: raise TypeError("Cannot perform 'all' with this index type: %s" % type(self).__name__)