# # Copyright (c) 2012-2025 Snowflake Computing Inc. All rights reserved. # # Licensed to Modin Development Team under one or more contributor license agreements. # See the NOTICE file distributed with this work for additional information regarding # copyright ownership. The Modin Development Team 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. # Code in this file may constitute partial or total reimplementation, or modification of # existing code originally distributed by the Modin project, under the Apache License, # Version 2.0. """This module contains TimedeltaIndex docstrings that override modin's docstrings.""" from __future__ import annotations from snowflake.snowpark.modin.plugin.extensions.index import Index class TimedeltaIndex(Index): def __new__(): """ Create new instance of TimedeltaIndex. This overrides behavior of Index.__new__. Parameters ---------- data : array-like (1-dimensional), optional Optional timedelta-like data to construct index with. unit : {'D', 'h', 'm', 's', 'ms', 'us', 'ns'}, optional The unit of ``data``. Deprecated: Use ``pd.to_timedelta`` instead. 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. dtype : numpy.dtype or str, default None Valid ``numpy`` dtypes are ``timedelta64[ns]``, ``timedelta64[us]``, ``timedelta64[ms]``, and ``timedelta64[s]``. copy : bool Make a copy of input array. name : object Name to be stored in the index. Returns: New instance of TimedeltaIndex. """ def __init__() -> None: """ 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 : {'D', 'h', 'm', 's', 'ms', 'us', 'ns'}, optional The unit of ``data``. Deprecated: Use ``pd.to_timedelta`` instead. 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. dtype : numpy.dtype or str, default None Valid ``numpy`` dtypes are ``timedelta64[ns]``, ``timedelta64[us]``, ``timedelta64[ms]``, and ``timedelta64[s]``. copy : bool Make a copy of input array. name : object Name to be stored in the index. Examples -------- >>> pd.TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days']) TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'], dtype='timedelta64[ns]', freq=None) We can also let pandas infer the frequency when possible. >>> pd.TimedeltaIndex(np.arange(5) * 24 * 3600 * 1e9, freq='infer') TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'], dtype='timedelta64[ns]', freq=None) """ @property def days(): """ Number of days for each element. Returns ------- An Index with the days component of the timedelta. Examples -------- >>> idx = pd.to_timedelta(["0 days", "10 days", "20 days"]) >>> idx TimedeltaIndex(['0 days', '10 days', '20 days'], dtype='timedelta64[ns]', freq=None) >>> idx.days Index([0, 10, 20], dtype='int64') """ @property def seconds(): """ Number of seconds (>= 0 and less than 1 day) for each element. Returns ------- An Index with seconds component of the timedelta. Examples -------- >>> idx = pd.to_timedelta([1, 2, 3], unit='s') >>> idx TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:02', '0 days 00:00:03'], dtype='timedelta64[ns]', freq=None) >>> idx.seconds Index([1, 2, 3], dtype='int64') """ @property def microseconds(): """ Number of microseconds (>= 0 and less than 1 second) for each element. Returns ------- An Index with microseconds component of the timedelta. Examples -------- >>> idx = pd.to_timedelta([1, 2, 3], unit='us') >>> idx TimedeltaIndex(['0 days 00:00:00.000001', '0 days 00:00:00.000002', '0 days 00:00:00.000003'], dtype='timedelta64[ns]', freq=None) >>> idx.microseconds Index([1, 2, 3], dtype='int64') """ @property def nanoseconds(): """ Number of nonoseconds (>= 0 and less than 1 microsecond) for each element. Returns ------- An Index with nanoseconds compnent of the timedelta. Examples -------- >>> idx = pd.to_timedelta([1, 2, 3], unit='ns') >>> idx TimedeltaIndex(['0 days 00:00:00.000000001', '0 days 00:00:00.000000002', '0 days 00:00:00.000000003'], dtype='timedelta64[ns]', freq=None) >>> idx.nanoseconds Index([1, 2, 3], dtype='int64') """ @property def components(): """ Return a DataFrame of the individual resolution components of the Timedeltas. The components (days, hours, minutes seconds, milliseconds, microseconds, nanoseconds) are returned as columns in a DataFrame. Returns ------- A DataFrame Examples -------- >>> idx = pd.to_timedelta(['1 day 3 min 2 us 42 ns']) # doctest: +SKIP >>> idx # doctest: +SKIP TimedeltaIndex(['1 days 00:03:00.000002042'], dtype='timedelta64[ns]', freq=None) >>> idx.components # doctest: +SKIP days hours minutes seconds milliseconds microseconds nanoseconds 0 1 0 3 0 0 2 42 """ @property def inferred_freq(): """ Tries to return a string representing a frequency generated by infer_freq. Returns None if it can't autodetect the frequency. Examples -------- >>> idx = pd.to_timedelta(["0 days", "10 days", "20 days"]) # doctest: +SKIP >>> idx # doctest: +SKIP TimedeltaIndex(['0 days', '10 days', '20 days'], dtype='timedelta64[ns]', freq=None) >>> idx.inferred_freq # doctest: +SKIP '10D' """ def round(): """ Perform round operation on the data to the specified `freq`. Parameters ---------- freq : str or Offset The frequency level to round the index to. Must be a fixed frequency like 'S' (second) not 'ME' (month end). See frequency aliases for a list of possible `freq` values. Returns ------- TimedeltaIndex with round values. Raises ------ ValueError if the `freq` cannot be converted. """ def floor(): """ Perform floor operation on the data to the specified `freq`. Parameters ---------- freq : str or Offset The frequency level to floor the index to. Must be a fixed frequency like 'S' (second) not 'ME' (month end). See frequency aliases for a list of possible `freq` values. Returns ------- TimedeltaIndex with floor values. Raises ------ ValueError if the `freq` cannot be converted. """ def ceil(): """ Perform ceil operation on the data to the specified `freq`. Parameters ---------- freq : str or Offset The frequency level to ceil the index to. Must be a fixed frequency like 'S' (second) not 'ME' (month end). See frequency aliases for a list of possible `freq` values. Returns ------- TimedeltaIndex with ceil values. Raises ------ ValueError if the `freq` cannot be converted. """ def to_pytimedelta(): """ Return an ndarray of datetime.timedelta objects. Returns ------- numpy.ndarray Examples -------- >>> idx = pd.to_timedelta([1, 2, 3], unit='D') # doctest: +SKIP >>> idx # doctest: +SKIP TimedeltaIndex(['1 days', '2 days', '3 days'], dtype='timedelta64[ns]', freq=None) >>> idx.to_pytimedelta() # doctest: +SKIP array([datetime.timedelta(days=1), datetime.timedelta(days=2), datetime.timedelta(days=3)], dtype=object) """ def mean(): """ Return the mean value of the Timedelta values. Parameters ---------- skipna : bool, default True Whether to ignore any NaT elements. axis : int, optional, default 0 Returns ------- scalar Timedelta Examples -------- >>> idx = pd.to_timedelta([1, 2, 3, 1], unit='D') >>> idx TimedeltaIndex(['1 days', '2 days', '3 days', '1 days'], dtype='timedelta64[ns]', freq=None) >>> idx.mean() Timedelta('1 days 18:00:00') See Also -------- numpy.ndarray.mean : Returns the average of array elements along a given axis. Series.mean : Return the mean value in a Series. """ def as_unit(): """ Convert to a dtype with the given unit resolution. Parameters ---------- unit : {'s', 'ms', 'us', 'ns'} Returns ------- DatetimeIndex Examples -------- >>> idx = pd.to_timedelta(['1 day 3 min 2 us 42 ns']) # doctest: +SKIP >>> idx # doctest: +SKIP TimedeltaIndex(['1 days 00:03:00.000002042'], dtype='timedelta64[ns]', freq=None) >>> idx.as_unit('s') # doctest: +SKIP TimedeltaIndex(['1 days 00:03:00'], dtype='timedelta64[s]', freq=None) """ def total_seconds(): """ Return total duration of each element expressed in seconds. Returns ------- An Index with float type. Examples: -------- >>> idx = pd.to_timedelta(np.arange(5), unit='d') >>> idx TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'], dtype='timedelta64[ns]', freq=None) >>> idx.total_seconds() Index([0.0, 86400.0, 172800.0, 259200.0, 345600.0], dtype='float64') """