# # 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. # import datetime from functools import partial from typing import Any, Optional, Union, cast, no_type_check import pandas as pd from pandas.api.types import is_hashable # type: ignore[attr-defined] from pandas.tseries.offsets import DateOffset from pyspark._globals import _NoValue from pyspark import pandas as ps from pyspark.pandas.indexes.base import Index from pyspark.pandas.missing.indexes import MissingPandasLikeDatetimeIndex from pyspark.pandas.series import Series, first_series from pyspark.pandas.utils import verify_temp_column_name [docs]class DatetimeIndex(Index): """ Immutable ndarray-like of datetime64 data. Parameters ---------- data : array-like (1-dimensional), optional Optional datetime-like data to construct index with. 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. normalize : bool, default False Normalize start/end dates to midnight before generating date range. closed : {'left', 'right'}, optional Set whether to include `start` and `end` that are on the boundary. The default includes boundary points on either end. ambiguous : 'infer', bool-ndarray, 'NaT', default 'raise' When clocks moved backward due to DST, ambiguous times may arise. For example in Central European Time (UTC+01), when going from 03:00 DST to 02:00 non-DST, 02:30:00 local time occurs both at 00:30:00 UTC and at 01:30:00 UTC. In such a situation, the `ambiguous` parameter dictates how ambiguous times should be handled. - 'infer' will attempt to infer fall dst-transition hours based on order - bool-ndarray where True signifies a DST time, False signifies 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. dayfirst : bool, default False If True, parse dates in `data` with the day first order. yearfirst : bool, default False If True parse dates in `data` with the year first order. dtype : numpy.dtype or str, default None Note that the only NumPy dtype allowed is ‘datetime64[ns]’. copy : bool, default False Make a copy of input ndarray. name : label, default None Name to be stored in the index. See Also -------- Index : The base pandas Index type. to_datetime : Convert argument to datetime. Examples -------- >>> ps.DatetimeIndex(['1970-01-01', '1970-01-01', '1970-01-01']) DatetimeIndex(['1970-01-01', '1970-01-01', '1970-01-01'], dtype='datetime64[ns]', freq=None) From a Series: >>> from datetime import datetime >>> s = ps.Series([datetime(2021, 3, 1), datetime(2021, 3, 2)], index=[10, 20]) >>> ps.DatetimeIndex(s) DatetimeIndex(['2021-03-01', '2021-03-02'], dtype='datetime64[ns]', freq=None) From an Index: >>> idx = ps.DatetimeIndex(['1970-01-01', '1970-01-01', '1970-01-01']) >>> ps.DatetimeIndex(idx) DatetimeIndex(['1970-01-01', '1970-01-01', '1970-01-01'], dtype='datetime64[ns]', freq=None) """ @no_type_check def __new__( cls, data=None, freq=_NoValue, normalize=False, closed=None, ambiguous="raise", dayfirst=False, yearfirst=False, dtype=None, copy=False, name=None, ) -> "DatetimeIndex": if not is_hashable(name): raise TypeError("Index.name must be a hashable type") if isinstance(data, (Series, Index)): if dtype is None: dtype = "datetime64[ns]" return cast(DatetimeIndex, Index(data, dtype=dtype, copy=copy, name=name)) kwargs = dict( data=data, normalize=normalize, closed=closed, ambiguous=ambiguous, dayfirst=dayfirst, yearfirst=yearfirst, dtype=dtype, copy=copy, name=name, ) if freq is not _NoValue: kwargs["freq"] = freq return cast(DatetimeIndex, ps.from_pandas(pd.DatetimeIndex(**kwargs))) def __getattr__(self, item: str) -> Any: if hasattr(MissingPandasLikeDatetimeIndex, item): property_or_func = getattr(MissingPandasLikeDatetimeIndex, item) if isinstance(property_or_func, property): return property_or_func.fget(self) else: return partial(property_or_func, self) raise AttributeError("'DatetimeIndex' object has no attribute '{}'".format(item)) # Properties @property def year(self) -> Index: """ The year of the datetime. """ return Index(self.to_series().dt.year) @property def month(self) -> Index: """ The month of the timestamp as January = 1 December = 12. """ return Index(self.to_series().dt.month) @property def day(self) -> Index: """ The days of the datetime. """ return Index(self.to_series().dt.day) @property def hour(self) -> Index: """ The hours of the datetime. """ return Index(self.to_series().dt.hour) @property def minute(self) -> Index: """ The minutes of the datetime. """ return Index(self.to_series().dt.minute) @property def second(self) -> Index: """ The seconds of the datetime. """ return Index(self.to_series().dt.second) @property def microsecond(self) -> Index: """ The microseconds of the datetime. """ return Index(self.to_series().dt.microsecond) @property def week(self) -> Index: """ The week ordinal of the year. """ return Index(self.to_series().dt.week) @property def weekofyear(self) -> Index: return Index(self.to_series().dt.weekofyear) weekofyear.__doc__ = week.__doc__ @property def dayofweek(self) -> Index: """ The day of the week with Monday=0, Sunday=6. Return the day of the week. It is assumed the week starts on Monday, which is denoted by 0 and ends on Sunday which is denoted by 6. This method is available on both Series with datetime values (using the `dt` accessor) or DatetimeIndex. Returns ------- Series or Index Containing integers indicating the day number. See Also -------- Series.dt.dayofweek : Alias. Series.dt.weekday : Alias. Series.dt.day_name : Returns the name of the day of the week. Examples -------- >>> idx = ps.date_range('2016-12-31', '2017-01-08', freq='D') >>> idx.dayofweek Int64Index([5, 6, 0, 1, 2, 3, 4, 5, 6], dtype='int64') """ return Index(self.to_series().dt.dayofweek) @property def day_of_week(self) -> Index: return self.dayofweek day_of_week.__doc__ = dayofweek.__doc__ @property def weekday(self) -> Index: return Index(self.to_series().dt.weekday) weekday.__doc__ = dayofweek.__doc__ @property def dayofyear(self) -> Index: """ The ordinal day of the year. """ return Index(self.to_series().dt.dayofyear) @property def day_of_year(self) -> Index: return self.dayofyear day_of_year.__doc__ = dayofyear.__doc__ @property def quarter(self) -> Index: """ The quarter of the date. """ return Index(self.to_series().dt.quarter) @property def is_month_start(self) -> Index: """ Indicates whether the date is the first day of the month. Returns ------- Index Returns a Index with boolean values See Also -------- is_month_end : Return a boolean indicating whether the date is the last day of the month. Examples -------- >>> idx = ps.date_range("2018-02-27", periods=3) >>> idx.is_month_start Index([False, False, True], dtype='object') """ return Index(self.to_series().dt.is_month_start) @property def is_month_end(self) -> Index: """ Indicates whether the date is the last day of the month. Returns ------- Index Returns a Index with boolean values. See Also -------- is_month_start : Return a boolean indicating whether the date is the first day of the month. Examples -------- >>> idx = ps.date_range("2018-02-27", periods=3) >>> idx.is_month_end Index([False, True, False], dtype='object') """ return Index(self.to_series().dt.is_month_end) @property def is_quarter_start(self) -> Index: """ Indicator for whether the date is the first day of a quarter. Returns ------- is_quarter_start : Index Returns an Index with boolean values. See Also -------- quarter : Return the quarter of the date. is_quarter_end : Similar property for indicating the quarter start. Examples -------- >>> idx = ps.date_range('2017-03-30', periods=4) >>> idx.is_quarter_start Index([False, False, True, False], dtype='object') """ return Index(self.to_series().dt.is_quarter_start) @property def is_quarter_end(self) -> Index: """ Indicator for whether the date is the last day of a quarter. Returns ------- is_quarter_end : Index Returns an Index with boolean values. See Also -------- quarter : Return the quarter of the date. is_quarter_start : Similar property indicating the quarter start. Examples -------- >>> idx = ps.date_range('2017-03-30', periods=4) >>> idx.is_quarter_end Index([False, True, False, False], dtype='object') """ return Index(self.to_series().dt.is_quarter_end) @property def is_year_start(self) -> Index: """ Indicate whether the date is the first day of a year. Returns ------- Index Returns an Index with boolean values. See Also -------- is_year_end : Similar property indicating the last day of the year. Examples -------- >>> idx = ps.date_range("2017-12-30", periods=3) >>> idx.is_year_start Index([False, False, True], dtype='object') """ return Index(self.to_series().dt.is_year_start) @property def is_year_end(self) -> Index: """ Indicate whether the date is the last day of the year. Returns ------- Index Returns an Index with boolean values. See Also -------- is_year_start : Similar property indicating the start of the year. Examples -------- >>> idx = ps.date_range("2017-12-30", periods=3) >>> idx.is_year_end Index([False, True, False], dtype='object') """ return Index(self.to_series().dt.is_year_end) @property def is_leap_year(self) -> Index: """ Boolean indicator if the date belongs to a leap year. A leap year is a year, which has 366 days (instead of 365) including 29th of February as an intercalary day. Leap years are years which are multiples of four with the exception of years divisible by 100 but not by 400. Returns ------- Index Booleans indicating if dates belong to a leap year. Examples -------- >>> idx = ps.date_range("2012-01-01", "2015-01-01", freq="Y") >>> idx.is_leap_year Index([True, False, False], dtype='object') """ return Index(self.to_series().dt.is_leap_year) @property def daysinmonth(self) -> Index: """ The number of days in the month. """ return Index(self.to_series().dt.daysinmonth) @property def days_in_month(self) -> Index: return Index(self.to_series().dt.days_in_month) days_in_month.__doc__ = daysinmonth.__doc__ # Methods [docs] def ceil(self, freq: Union[str, DateOffset], *args: Any, **kwargs: Any) -> "DatetimeIndex": """ 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). Returns ------- DatetimeIndex Raises ------ ValueError if the `freq` cannot be converted. Examples -------- >>> rng = ps.date_range('1/1/2018 11:59:00', periods=3, freq='min') >>> rng.ceil('H') # doctest: +NORMALIZE_WHITESPACE DatetimeIndex(['2018-01-01 12:00:00', '2018-01-01 12:00:00', '2018-01-01 13:00:00'], dtype='datetime64[ns]', freq=None) """ disallow_nanoseconds(freq) return DatetimeIndex(self.to_series().dt.ceil(freq, *args, **kwargs)) [docs] def floor(self, freq: Union[str, DateOffset], *args: Any, **kwargs: Any) -> "DatetimeIndex": """ 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). Returns ------- DatetimeIndex Raises ------ ValueError if the `freq` cannot be converted. Examples -------- >>> rng = ps.date_range('1/1/2018 11:59:00', periods=3, freq='min') >>> rng.floor("H") # doctest: +NORMALIZE_WHITESPACE DatetimeIndex(['2018-01-01 11:00:00', '2018-01-01 12:00:00', '2018-01-01 12:00:00'], dtype='datetime64[ns]', freq=None) """ disallow_nanoseconds(freq) return DatetimeIndex(self.to_series().dt.floor(freq, *args, **kwargs)) [docs] def round(self, freq: Union[str, DateOffset], *args: Any, **kwargs: Any) -> "DatetimeIndex": """ 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). Returns ------- DatetimeIndex Raises ------ ValueError if the `freq` cannot be converted. Examples -------- >>> rng = ps.date_range('1/1/2018 11:59:00', periods=3, freq='min') >>> rng.round("H") # doctest: +NORMALIZE_WHITESPACE DatetimeIndex(['2018-01-01 12:00:00', '2018-01-01 12:00:00', '2018-01-01 12:00:00'], dtype='datetime64[ns]', freq=None) """ disallow_nanoseconds(freq) return DatetimeIndex(self.to_series().dt.round(freq, *args, **kwargs)) [docs] def month_name(self, locale: Optional[str] = None) -> Index: """ Return the month names of the DatetimeIndex with specified locale. Parameters ---------- locale : str, optional Locale determining the language in which to return the month name. Default is English locale. Returns ------- Index Index of month names. Examples -------- >>> idx = ps.date_range(start='2018-01', freq='M', periods=3) >>> idx.month_name() Index(['January', 'February', 'March'], dtype='object') """ return Index(self.to_series().dt.month_name(locale)) [docs] def day_name(self, locale: Optional[str] = None) -> Index: """ Return the day names of the series with specified locale. Parameters ---------- locale : str, optional Locale determining the language in which to return the day name. Default is English locale. Returns ------- Index Index of day names. Examples -------- >>> idx = ps.date_range(start='2018-01-01', freq='D', periods=3) >>> idx.day_name() Index(['Monday', 'Tuesday', 'Wednesday'], dtype='object') """ return Index(self.to_series().dt.day_name(locale)) [docs] def normalize(self) -> "DatetimeIndex": """ Convert times to midnight. The time component of the date-time is converted to midnight i.e. 00:00:00. This is useful in cases, when the time does not matter. Length is unaltered. The timezones are unaffected. This method is available on Series with datetime values under the ``.dt`` accessor. Returns ------- DatetimeIndex The same type as the original data. See Also -------- floor : Floor the series to the specified freq. ceil : Ceil the series to the specified freq. round : Round the series to the specified freq. Examples -------- >>> idx = ps.date_range(start='2014-08-01 10:00', freq='H', periods=3) >>> idx.normalize() DatetimeIndex(['2014-08-01', '2014-08-01', '2014-08-01'], dtype='datetime64[ns]', freq=None) """ return DatetimeIndex(self.to_series().dt.normalize()) [docs] def strftime(self, date_format: str) -> Index: """ Convert to a string 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. Parameters ---------- date_format : str Date format string (example: "%%Y-%%m-%%d"). Returns ------- Index Index of formatted strings. See Also -------- normalize : Return series with times to midnight. round : Round the series to the specified freq. floor : Floor the series to the specified freq. Examples -------- >>> idx = ps.date_range(pd.Timestamp("2018-03-10 09:00"), periods=3, freq='s') >>> idx.strftime('%B %d, %Y, %r') # doctest: +NORMALIZE_WHITESPACE Index(['March 10, 2018, 09:00:00 AM', 'March 10, 2018, 09:00:01 AM', 'March 10, 2018, 09:00:02 AM'], dtype='object') """ return Index(self.to_series().dt.strftime(date_format)) [docs] def indexer_between_time( self, start_time: Union[datetime.time, str], end_time: Union[datetime.time, str], include_start: bool = True, include_end: bool = True, ) -> Index: """ Return index locations of values between particular times of day (example: 9:00-9:30AM). Parameters ---------- start_time, end_time : datetime.time, str Time passed either as object (datetime.time) or as string in appropriate format ("%H:%M", "%H%M", "%I:%M%p", "%I%M%p", "%H:%M:%S", "%H%M%S", "%I:%M:%S%p","%I%M%S%p"). include_start : bool, default True include_end : bool, default True Returns ------- values_between_time : Index of integers Examples -------- >>> psidx = ps.date_range("2000-01-01", periods=3, freq="T") >>> psidx # doctest: +NORMALIZE_WHITESPACE DatetimeIndex(['2000-01-01 00:00:00', '2000-01-01 00:01:00', '2000-01-01 00:02:00'], dtype='datetime64[ns]', freq=None) >>> psidx.indexer_between_time("00:01", "00:02").sort_values() Int64Index([1, 2], dtype='int64') >>> psidx.indexer_between_time("00:01", "00:02", include_end=False) Int64Index([1], dtype='int64') >>> psidx.indexer_between_time("00:01", "00:02", include_start=False) Int64Index([2], dtype='int64') """ def pandas_between_time(pdf) -> ps.DataFrame[int]: # type: ignore[no-untyped-def] return pdf.between_time(start_time, end_time, include_start, include_end) psdf = self.to_frame()[[]] id_column_name = verify_temp_column_name(psdf, "__id_column__") psdf = psdf.pandas_on_spark.attach_id_column("distributed-sequence", id_column_name) with ps.option_context("compute.default_index_type", "distributed"): # The attached index in the statement below will be dropped soon, # so we enforce “distributed” default index type psdf = psdf.pandas_on_spark.apply_batch(pandas_between_time) return ps.Index(first_series(psdf).rename(self.name)) [docs] def indexer_at_time(self, time: Union[datetime.time, str], asof: bool = False) -> Index: """ Return index locations of values at particular time of day (example: 9:30AM). Parameters ---------- time : datetime.time or str Time passed in either as object (datetime.time) or as string in appropriate format ("%H:%M", "%H%M", "%I:%M%p", "%I%M%p", "%H:%M:%S", "%H%M%S", "%I:%M:%S%p", "%I%M%S%p"). Returns ------- values_at_time : Index of integers Examples -------- >>> psidx = ps.date_range("2000-01-01", periods=3, freq="T") >>> psidx # doctest: +NORMALIZE_WHITESPACE DatetimeIndex(['2000-01-01 00:00:00', '2000-01-01 00:01:00', '2000-01-01 00:02:00'], dtype='datetime64[ns]', freq=None) >>> psidx.indexer_at_time("00:00") Int64Index([0], dtype='int64') >>> psidx.indexer_at_time("00:01") Int64Index([1], dtype='int64') """ if asof: raise NotImplementedError("'asof' argument is not supported") def pandas_at_time(pdf) -> ps.DataFrame[int]: # type: ignore[no-untyped-def] return pdf.at_time(time, asof) psdf = self.to_frame()[[]] id_column_name = verify_temp_column_name(psdf, "__id_column__") psdf = psdf.pandas_on_spark.attach_id_column("distributed-sequence", id_column_name) with ps.option_context("compute.default_index_type", "distributed"): # The attached index in the statement below will be dropped soon, # so we enforce “distributed” default index type psdf = psdf.pandas_on_spark.apply_batch(pandas_at_time) return ps.Index(first_series(psdf).rename(self.name)) @no_type_check def all(self, *args, **kwargs) -> None: raise TypeError("Cannot perform 'all' with this index type: %s" % type(self).__name__) def disallow_nanoseconds(freq: Union[str, DateOffset]) -> None: if freq in ["N", "ns"]: raise ValueError("nanoseconds is not supported") def _test() -> None: import os import doctest import sys from pyspark.sql import SparkSession import pyspark.pandas.indexes.datetimes os.chdir(os.environ["SPARK_HOME"]) globs = pyspark.pandas.indexes.datetimes.__dict__.copy() globs["ps"] = pyspark.pandas spark = ( SparkSession.builder.master("local[4]") .appName("pyspark.pandas.indexes.datetimes tests") .getOrCreate() ) (failure_count, test_count) = doctest.testmod( pyspark.pandas.indexes.datetimes, globs=globs, optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE, ) spark.stop() if failure_count: sys.exit(-1) if __name__ == "__main__": _test()