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import warnings
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]
import numpy as np
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.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 closed is not None:
warnings.warn(
"The 'closed' keyword in TimedeltaIndex construction is deprecated "
"and will be removed in a future version.",
FutureWarning,
)
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) -> np.int64: # 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 = F.date_part(F.lit("HOUR"), scol)
minute_scol = F.date_part(F.lit("MINUTE"), scol)
second_scol = F.date_part(F.lit("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 = F.date_part(F.lit("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__)