# -*- coding: utf-8 -*- from datetime import datetime, time, timedelta import textwrap import warnings import numpy as np from pytz import utc from pandas._libs import lib, tslib from pandas._libs.tslibs import ( NaT, Timestamp, ccalendar, conversion, fields, iNaT, normalize_date, resolution as libresolution, timezones) import pandas.compat as compat from pandas.errors import PerformanceWarning from pandas.util._decorators import Appender from pandas.core.dtypes.common import ( _INT64_DTYPE, _NS_DTYPE, is_categorical_dtype, is_datetime64_dtype, is_datetime64_ns_dtype, is_datetime64tz_dtype, is_dtype_equal, is_extension_type, is_float_dtype, is_object_dtype, is_period_dtype, is_string_dtype, is_timedelta64_dtype, pandas_dtype) from pandas.core.dtypes.dtypes import DatetimeTZDtype from pandas.core.dtypes.generic import ( ABCDataFrame, ABCIndexClass, ABCPandasArray, ABCSeries) from pandas.core.dtypes.missing import isna from pandas.core import ops from pandas.core.algorithms import checked_add_with_arr from pandas.core.arrays import datetimelike as dtl from pandas.core.arrays._ranges import generate_regular_range import pandas.core.common as com from pandas.tseries.frequencies import get_period_alias, to_offset from pandas.tseries.offsets import Day, Tick _midnight = time(0, 0) # TODO(GH-24559): Remove warning, int_as_wall_time parameter. _i8_message = """ Passing integer-dtype data and a timezone to DatetimeIndex. Integer values will be interpreted differently in a future version of pandas. Previously, these were viewed as datetime64[ns] values representing the wall time *in the specified timezone*. In the future, these will be viewed as datetime64[ns] values representing the wall time *in UTC*. This is similar to a nanosecond-precision UNIX epoch. To accept the future behavior, use pd.to_datetime(integer_data, utc=True).tz_convert(tz) To keep the previous behavior, use pd.to_datetime(integer_data).tz_localize(tz) """ def tz_to_dtype(tz): """ Return a datetime64[ns] dtype appropriate for the given timezone. Parameters ---------- tz : tzinfo or None Returns ------- np.dtype or Datetime64TZDType """ if tz is None: return _NS_DTYPE else: return DatetimeTZDtype(tz=tz) def _to_M8(key, tz=None): """ Timestamp-like => dt64 """ if not isinstance(key, Timestamp): # this also converts strings key = Timestamp(key) if key.tzinfo is not None and tz is not None: # Don't tz_localize(None) if key is already tz-aware key = key.tz_convert(tz) else: key = key.tz_localize(tz) return np.int64(conversion.pydt_to_i8(key)).view(_NS_DTYPE) def _field_accessor(name, field, docstring=None): def f(self): values = self.asi8 if self.tz is not None and not timezones.is_utc(self.tz): values = self._local_timestamps() if field in self._bool_ops: if field.endswith(('start', 'end')): freq = self.freq month_kw = 12 if freq: kwds = freq.kwds month_kw = kwds.get('startingMonth', kwds.get('month', 12)) result = fields.get_start_end_field(values, field, self.freqstr, month_kw) else: result = fields.get_date_field(values, field) # these return a boolean by-definition return result if field in self._object_ops: result = fields.get_date_name_field(values, field) result = self._maybe_mask_results(result, fill_value=None) else: result = fields.get_date_field(values, field) result = self._maybe_mask_results(result, fill_value=None, convert='float64') return result f.__name__ = name f.__doc__ = "\n{}\n".format(docstring) return property(f) def _dt_array_cmp(cls, op): """ Wrap comparison operations to convert datetime-like to datetime64 """ opname = '__{name}__'.format(name=op.__name__) nat_result = True if opname == '__ne__' else False def wrapper(self, other): if isinstance(other, (ABCDataFrame, ABCSeries, ABCIndexClass)): return NotImplemented other = lib.item_from_zerodim(other) if isinstance(other, (datetime, np.datetime64, compat.string_types)): if isinstance(other, (datetime, np.datetime64)): # GH#18435 strings get a pass from tzawareness compat self._assert_tzawareness_compat(other) try: other = _to_M8(other, tz=self.tz) except ValueError: # string that cannot be parsed to Timestamp return ops.invalid_comparison(self, other, op) result = op(self.asi8, other.view('i8')) if isna(other): result.fill(nat_result) elif lib.is_scalar(other) or np.ndim(other) == 0: return ops.invalid_comparison(self, other, op) elif len(other) != len(self): raise ValueError("Lengths must match") else: if isinstance(other, list): try: other = type(self)._from_sequence(other) except ValueError: other = np.array(other, dtype=np.object_) elif not isinstance(other, (np.ndarray, ABCIndexClass, ABCSeries, DatetimeArray)): # Following Timestamp convention, __eq__ is all-False # and __ne__ is all True, others raise TypeError. return ops.invalid_comparison(self, other, op) if is_object_dtype(other): # We have to use _comp_method_OBJECT_ARRAY instead of numpy # comparison otherwise it would fail to raise when # comparing tz-aware and tz-naive with np.errstate(all='ignore'): result = ops._comp_method_OBJECT_ARRAY(op, self.astype(object), other) o_mask = isna(other) elif not (is_datetime64_dtype(other) or is_datetime64tz_dtype(other)): # e.g. is_timedelta64_dtype(other) return ops.invalid_comparison(self, other, op) else: self._assert_tzawareness_compat(other) if isinstance(other, (ABCIndexClass, ABCSeries)): other = other.array if (is_datetime64_dtype(other) and not is_datetime64_ns_dtype(other) or not hasattr(other, 'asi8')): # e.g. other.dtype == 'datetime64[s]' # or an object-dtype ndarray other = type(self)._from_sequence(other) result = op(self.view('i8'), other.view('i8')) o_mask = other._isnan result = com.values_from_object(result) # Make sure to pass an array to result[...]; indexing with # Series breaks with older version of numpy o_mask = np.array(o_mask) if o_mask.any(): result[o_mask] = nat_result if self._hasnans: result[self._isnan] = nat_result return result return compat.set_function_name(wrapper, opname, cls) class DatetimeArray(dtl.DatetimeLikeArrayMixin, dtl.TimelikeOps, dtl.DatelikeOps): """ Pandas ExtensionArray for tz-naive or tz-aware datetime data. .. versionadded:: 0.24.0 .. warning:: DatetimeArray is currently experimental, and its API may change without warning. In particular, :attr:`DatetimeArray.dtype` is expected to change to always be an instance of an ``ExtensionDtype`` subclass. Parameters ---------- values : Series, Index, DatetimeArray, ndarray The datetime data. For DatetimeArray `values` (or a Series or Index boxing one), `dtype` and `freq` will be extracted from `values`, with precedence given to dtype : numpy.dtype or DatetimeTZDtype Note that the only NumPy dtype allowed is 'datetime64[ns]'. freq : str or Offset, optional copy : bool, default False Whether to copy the underlying array of values. """ _typ = "datetimearray" _scalar_type = Timestamp # define my properties & methods for delegation _bool_ops = ['is_month_start', 'is_month_end', 'is_quarter_start', 'is_quarter_end', 'is_year_start', 'is_year_end', 'is_leap_year'] _object_ops = ['weekday_name', 'freq', 'tz'] _field_ops = ['year', 'month', 'day', 'hour', 'minute', 'second', 'weekofyear', 'week', 'weekday', 'dayofweek', 'dayofyear', 'quarter', 'days_in_month', 'daysinmonth', 'microsecond', 'nanosecond'] _other_ops = ['date', 'time', 'timetz'] _datetimelike_ops = _field_ops + _object_ops + _bool_ops + _other_ops _datetimelike_methods = ['to_period', 'tz_localize', 'tz_convert', 'normalize', 'strftime', 'round', 'floor', 'ceil', 'month_name', 'day_name'] # dummy attribute so that datetime.__eq__(DatetimeArray) defers # by returning NotImplemented timetuple = None # Needed so that Timestamp.__richcmp__(DateTimeArray) operates pointwise ndim = 1 # ensure that operations with numpy arrays defer to our implementation __array_priority__ = 1000 # ----------------------------------------------------------------- # Constructors _attributes = ["freq", "tz"] _dtype = None # type: Union[np.dtype, DatetimeTZDtype] _freq = None def __init__(self, values, dtype=_NS_DTYPE, freq=None, copy=False): if isinstance(values, (ABCSeries, ABCIndexClass)): values = values._values inferred_freq = getattr(values, "_freq", None) if isinstance(values, type(self)): # validation dtz = getattr(dtype, 'tz', None) if dtz and values.tz is None: dtype = DatetimeTZDtype(tz=dtype.tz) elif dtz and values.tz: if not timezones.tz_compare(dtz, values.tz): msg = ( "Timezone of the array and 'dtype' do not match. " "'{}' != '{}'" ) raise TypeError(msg.format(dtz, values.tz)) elif values.tz: dtype = values.dtype # freq = validate_values_freq(values, freq) if freq is None: freq = values.freq values = values._data if not isinstance(values, np.ndarray): msg = ( "Unexpected type '{}'. 'values' must be a DatetimeArray " "ndarray, or Series or Index containing one of those." ) raise ValueError(msg.format(type(values).__name__)) if values.dtype == 'i8': # for compat with datetime/timedelta/period shared methods, # we can sometimes get here with int64 values. These represent # nanosecond UTC (or tz-naive) unix timestamps values = values.view(_NS_DTYPE) if values.dtype != _NS_DTYPE: msg = ( "The dtype of 'values' is incorrect. Must be 'datetime64[ns]'." " Got {} instead." ) raise ValueError(msg.format(values.dtype)) dtype = _validate_dt64_dtype(dtype) if freq == "infer": msg = ( "Frequency inference not allowed in DatetimeArray.__init__. " "Use 'pd.array()' instead." ) raise ValueError(msg) if copy: values = values.copy() if freq: freq = to_offset(freq) if getattr(dtype, 'tz', None): # https://github.com/pandas-dev/pandas/issues/18595 # Ensure that we have a standard timezone for pytz objects. # Without this, things like adding an array of timedeltas and # a tz-aware Timestamp (with a tz specific to its datetime) will # be incorrect(ish?) for the array as a whole dtype = DatetimeTZDtype(tz=timezones.tz_standardize(dtype.tz)) self._data = values self._dtype = dtype self._freq = freq if inferred_freq is None and freq is not None: type(self)._validate_frequency(self, freq) @classmethod def _simple_new(cls, values, freq=None, dtype=_NS_DTYPE): assert isinstance(values, np.ndarray) if values.dtype == 'i8': values = values.view(_NS_DTYPE) result = object.__new__(cls) result._data = values result._freq = freq result._dtype = dtype return result @classmethod def _from_sequence(cls, data, dtype=None, copy=False, tz=None, freq=None, dayfirst=False, yearfirst=False, ambiguous='raise', int_as_wall_time=False): freq, freq_infer = dtl.maybe_infer_freq(freq) subarr, tz, inferred_freq = sequence_to_dt64ns( data, dtype=dtype, copy=copy, tz=tz, dayfirst=dayfirst, yearfirst=yearfirst, ambiguous=ambiguous, int_as_wall_time=int_as_wall_time) freq, freq_infer = dtl.validate_inferred_freq(freq, inferred_freq, freq_infer) dtype = tz_to_dtype(tz) result = cls._simple_new(subarr, freq=freq, dtype=dtype) if inferred_freq is None and freq is not None: # this condition precludes `freq_infer` cls._validate_frequency(result, freq, ambiguous=ambiguous) elif freq_infer: # Set _freq directly to bypass duplicative _validate_frequency # check. result._freq = to_offset(result.inferred_freq) return result @classmethod def _generate_range(cls, start, end, periods, freq, tz=None, normalize=False, ambiguous='raise', nonexistent='raise', closed=None): periods = dtl.validate_periods(periods) if freq is None and any(x is None for x in [periods, start, end]): raise ValueError('Must provide freq argument if no data is ' 'supplied') if com.count_not_none(start, end, periods, freq) != 3: raise ValueError('Of the four parameters: start, end, periods, ' 'and freq, exactly three must be specified') freq = to_offset(freq) if start is not None: start = Timestamp(start) if end is not None: end = Timestamp(end) if start is None and end is None: if closed is not None: raise ValueError("Closed has to be None if not both of start" "and end are defined") if start is NaT or end is NaT: raise ValueError("Neither `start` nor `end` can be NaT") left_closed, right_closed = dtl.validate_endpoints(closed) start, end, _normalized = _maybe_normalize_endpoints(start, end, normalize) tz = _infer_tz_from_endpoints(start, end, tz) if tz is not None: # Localize the start and end arguments start = _maybe_localize_point( start, getattr(start, 'tz', None), start, freq, tz ) end = _maybe_localize_point( end, getattr(end, 'tz', None), end, freq, tz ) if freq is not None: # We break Day arithmetic (fixed 24 hour) here and opt for # Day to mean calendar day (23/24/25 hour). Therefore, strip # tz info from start and day to avoid DST arithmetic if isinstance(freq, Day): if start is not None: start = start.tz_localize(None) if end is not None: end = end.tz_localize(None) # TODO: consider re-implementing _cached_range; GH#17914 values, _tz = generate_regular_range(start, end, periods, freq) index = cls._simple_new(values, freq=freq, dtype=tz_to_dtype(_tz)) if tz is not None and index.tz is None: arr = conversion.tz_localize_to_utc( index.asi8, tz, ambiguous=ambiguous, nonexistent=nonexistent) index = cls(arr) # index is localized datetime64 array -> have to convert # start/end as well to compare if start is not None: start = start.tz_localize(tz).asm8 if end is not None: end = end.tz_localize(tz).asm8 else: # Create a linearly spaced date_range in local time # Nanosecond-granularity timestamps aren't always correctly # representable with doubles, so we limit the range that we # pass to np.linspace as much as possible arr = np.linspace( 0, end.value - start.value, periods, dtype='int64') + start.value dtype = tz_to_dtype(tz) index = cls._simple_new( arr.astype('M8[ns]', copy=False), freq=None, dtype=dtype ) if not left_closed and len(index) and index[0] == start: index = index[1:] if not right_closed and len(index) and index[-1] == end: index = index[:-1] dtype = tz_to_dtype(tz) return cls._simple_new(index.asi8, freq=freq, dtype=dtype) # ----------------------------------------------------------------- # DatetimeLike Interface def _unbox_scalar(self, value): if not isinstance(value, self._scalar_type) and value is not NaT: raise ValueError("'value' should be a Timestamp.") if not isna(value): self._check_compatible_with(value) return value.value def _scalar_from_string(self, value): return Timestamp(value, tz=self.tz) def _check_compatible_with(self, other): if other is NaT: return if not timezones.tz_compare(self.tz, other.tz): raise ValueError("Timezones don't match. '{own} != {other}'" .format(own=self.tz, other=other.tz)) def _maybe_clear_freq(self): self._freq = None # ----------------------------------------------------------------- # Descriptive Properties @property def _box_func(self): return lambda x: Timestamp(x, freq=self.freq, tz=self.tz) @property def dtype(self): # type: () -> Union[np.dtype, DatetimeTZDtype] """ The dtype for the DatetimeArray. .. warning:: A future version of pandas will change dtype to never be a ``numpy.dtype``. Instead, :attr:`DatetimeArray.dtype` will always be an instance of an ``ExtensionDtype`` subclass. Returns ------- numpy.dtype or DatetimeTZDtype If the values are tz-naive, then ``np.dtype('datetime64[ns]')`` is returned. If the values are tz-aware, then the ``DatetimeTZDtype`` is returned. """ return self._dtype @property def tz(self): """ Return timezone, if any. Returns ------- datetime.tzinfo, pytz.tzinfo.BaseTZInfo, dateutil.tz.tz.tzfile, or None Returns None when the array is tz-naive. """ # GH 18595 return getattr(self.dtype, "tz", None) @tz.setter def tz(self, value): # GH 3746: Prevent localizing or converting the index by setting tz raise AttributeError("Cannot directly set timezone. Use tz_localize() " "or tz_convert() as appropriate") @property def tzinfo(self): """ Alias for tz attribute """ return self.tz @property # NB: override with cache_readonly in immutable subclasses def _timezone(self): """ Comparable timezone both for pytz / dateutil """ return timezones.get_timezone(self.tzinfo) @property # NB: override with cache_readonly in immutable subclasses def is_normalized(self): """ Returns True if all of the dates are at midnight ("no time") """ return conversion.is_date_array_normalized(self.asi8, self.tz) @property # NB: override with cache_readonly in immutable subclasses def _resolution(self): return libresolution.resolution(self.asi8, self.tz) # ---------------------------------------------------------------- # Array-Like / EA-Interface Methods def __array__(self, dtype=None): if dtype is None and self.tz: # The default for tz-aware is object, to preserve tz info dtype = object return super(DatetimeArray, self).__array__(dtype=dtype) def __iter__(self): """ Return an iterator over the boxed values Yields ------- tstamp : Timestamp """ # convert in chunks of 10k for efficiency data = self.asi8 length = len(self) chunksize = 10000 chunks = int(length / chunksize) + 1 for i in range(chunks): start_i = i * chunksize end_i = min((i + 1) * chunksize, length) converted = tslib.ints_to_pydatetime(data[start_i:end_i], tz=self.tz, freq=self.freq, box="timestamp") for v in converted: yield v def astype(self, dtype, copy=True): # We handle # --> datetime # --> period # DatetimeLikeArrayMixin Super handles the rest. dtype = pandas_dtype(dtype) if (is_datetime64_ns_dtype(dtype) and not is_dtype_equal(dtype, self.dtype)): # GH#18951: datetime64_ns dtype but not equal means different tz new_tz = getattr(dtype, 'tz', None) if getattr(self.dtype, 'tz', None) is None: return self.tz_localize(new_tz) result = self.tz_convert(new_tz) if new_tz is None: # Do we want .astype('datetime64[ns]') to be an ndarray. # The astype in Block._astype expects this to return an # ndarray, but we could maybe work around it there. result = result._data return result elif is_datetime64tz_dtype(self.dtype) and is_dtype_equal(self.dtype, dtype): if copy: return self.copy() return self elif is_period_dtype(dtype): return self.to_period(freq=dtype.freq) return dtl.DatetimeLikeArrayMixin.astype(self, dtype, copy) # ---------------------------------------------------------------- # ExtensionArray Interface @Appender(dtl.DatetimeLikeArrayMixin._validate_fill_value.__doc__) def _validate_fill_value(self, fill_value): if isna(fill_value): fill_value = iNaT elif isinstance(fill_value, (datetime, np.datetime64)): self._assert_tzawareness_compat(fill_value) fill_value = Timestamp(fill_value).value else: raise ValueError("'fill_value' should be a Timestamp. " "Got '{got}'.".format(got=fill_value)) return fill_value # ----------------------------------------------------------------- # Rendering Methods def _format_native_types(self, na_rep='NaT', date_format=None, **kwargs): from pandas.io.formats.format import _get_format_datetime64_from_values fmt = _get_format_datetime64_from_values(self, date_format) return tslib.format_array_from_datetime(self.asi8, tz=self.tz, format=fmt, na_rep=na_rep) # ----------------------------------------------------------------- # Comparison Methods _create_comparison_method = classmethod(_dt_array_cmp) def _has_same_tz(self, other): zzone = self._timezone # vzone sholdn't be None if value is non-datetime like if isinstance(other, np.datetime64): # convert to Timestamp as np.datetime64 doesn't have tz attr other = Timestamp(other) vzone = timezones.get_timezone(getattr(other, 'tzinfo', '__no_tz__')) return zzone == vzone def _assert_tzawareness_compat(self, other): # adapted from _Timestamp._assert_tzawareness_compat other_tz = getattr(other, 'tzinfo', None) if is_datetime64tz_dtype(other): # Get tzinfo from Series dtype other_tz = other.dtype.tz if other is NaT: # pd.NaT quacks both aware and naive pass elif self.tz is None: if other_tz is not None: raise TypeError('Cannot compare tz-naive and tz-aware ' 'datetime-like objects.') elif other_tz is None: raise TypeError('Cannot compare tz-naive and tz-aware ' 'datetime-like objects') # ----------------------------------------------------------------- # Arithmetic Methods def _sub_datetime_arraylike(self, other): """subtract DatetimeArray/Index or ndarray[datetime64]""" if len(self) != len(other): raise ValueError("cannot add indices of unequal length") if isinstance(other, np.ndarray): assert is_datetime64_dtype(other) other = type(self)(other) if not self._has_same_tz(other): # require tz compat raise TypeError("{cls} subtraction must have the same " "timezones or no timezones" .format(cls=type(self).__name__)) self_i8 = self.asi8 other_i8 = other.asi8 arr_mask = self._isnan | other._isnan new_values = checked_add_with_arr(self_i8, -other_i8, arr_mask=arr_mask) if self._hasnans or other._hasnans: new_values[arr_mask] = iNaT return new_values.view('timedelta64[ns]') def _add_offset(self, offset): assert not isinstance(offset, Tick) try: if self.tz is not None: values = self.tz_localize(None) else: values = self result = offset.apply_index(values) if self.tz is not None: result = result.tz_localize(self.tz) except NotImplementedError: warnings.warn("Non-vectorized DateOffset being applied to Series " "or DatetimeIndex", PerformanceWarning) result = self.astype('O') + offset return type(self)._from_sequence(result, freq='infer') def _sub_datetimelike_scalar(self, other): # subtract a datetime from myself, yielding a ndarray[timedelta64[ns]] assert isinstance(other, (datetime, np.datetime64)) assert other is not NaT other = Timestamp(other) if other is NaT: return self - NaT if not self._has_same_tz(other): # require tz compat raise TypeError("Timestamp subtraction must have the same " "timezones or no timezones") i8 = self.asi8 result = checked_add_with_arr(i8, -other.value, arr_mask=self._isnan) result = self._maybe_mask_results(result) return result.view('timedelta64[ns]') def _add_delta(self, delta): """ Add a timedelta-like, Tick, or TimedeltaIndex-like object to self, yielding a new DatetimeArray Parameters ---------- other : {timedelta, np.timedelta64, Tick, TimedeltaIndex, ndarray[timedelta64]} Returns ------- result : DatetimeArray """ new_values = super(DatetimeArray, self)._add_delta(delta) return type(self)._from_sequence(new_values, tz=self.tz, freq='infer') # ----------------------------------------------------------------- # Timezone Conversion and Localization Methods def _local_timestamps(self): """ Convert to an i8 (unix-like nanosecond timestamp) representation while keeping the local timezone and not using UTC. This is used to calculate time-of-day information as if the timestamps were timezone-naive. """ return conversion.tz_convert(self.asi8, utc, self.tz) def tz_convert(self, tz): """ Convert tz-aware Datetime Array/Index from one time zone to another. Parameters ---------- tz : string, pytz.timezone, dateutil.tz.tzfile or None Time zone for time. Corresponding timestamps would be converted to this time zone of the Datetime Array/Index. A `tz` of None will convert to UTC and remove the timezone information. Returns ------- normalized : same type as self Raises ------ TypeError If Datetime Array/Index is tz-naive. See Also -------- DatetimeIndex.tz : A timezone that has a variable offset from UTC. DatetimeIndex.tz_localize : Localize tz-naive DatetimeIndex to a given time zone, or remove timezone from a tz-aware DatetimeIndex. Examples -------- With the `tz` parameter, we can change the DatetimeIndex to other time zones: >>> dti = pd.date_range(start='2014-08-01 09:00', ... freq='H', periods=3, tz='Europe/Berlin') >>> dti DatetimeIndex(['2014-08-01 09:00:00+02:00', '2014-08-01 10:00:00+02:00', '2014-08-01 11:00:00+02:00'], dtype='datetime64[ns, Europe/Berlin]', freq='H') >>> dti.tz_convert('US/Central') DatetimeIndex(['2014-08-01 02:00:00-05:00', '2014-08-01 03:00:00-05:00', '2014-08-01 04:00:00-05:00'], dtype='datetime64[ns, US/Central]', freq='H') With the ``tz=None``, we can remove the timezone (after converting to UTC if necessary): >>> dti = pd.date_range(start='2014-08-01 09:00',freq='H', ... periods=3, tz='Europe/Berlin') >>> dti DatetimeIndex(['2014-08-01 09:00:00+02:00', '2014-08-01 10:00:00+02:00', '2014-08-01 11:00:00+02:00'], dtype='datetime64[ns, Europe/Berlin]', freq='H') >>> dti.tz_convert(None) DatetimeIndex(['2014-08-01 07:00:00', '2014-08-01 08:00:00', '2014-08-01 09:00:00'], dtype='datetime64[ns]', freq='H') """ tz = timezones.maybe_get_tz(tz) if self.tz is None: # tz naive, use tz_localize raise TypeError('Cannot convert tz-naive timestamps, use ' 'tz_localize to localize') # No conversion since timestamps are all UTC to begin with dtype = tz_to_dtype(tz) return self._simple_new(self.asi8, dtype=dtype, freq=self.freq) def tz_localize(self, tz, ambiguous='raise', nonexistent='raise', errors=None): """ Localize tz-naive Datetime Array/Index to tz-aware Datetime Array/Index. This method takes a time zone (tz) naive Datetime Array/Index object and makes this time zone aware. It does not move the time to another time zone. Time zone localization helps to switch from time zone aware to time zone unaware objects. Parameters ---------- tz : string, pytz.timezone, dateutil.tz.tzfile or None Time zone to convert timestamps to. Passing ``None`` will remove the time zone information preserving local time. ambiguous : 'infer', 'NaT', bool array, 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 nonexistent : 'shift_forward', 'shift_backward, 'NaT', timedelta, default 'raise' A nonexistent time does not exist in a particular timezone where clocks moved forward due to DST. - 'shift_forward' will shift the nonexistent time forward to the closest existing time - 'shift_backward' will shift the nonexistent time backward to the closest existing time - 'NaT' will return NaT where there are nonexistent times - timedelta objects will shift nonexistent times by the timedelta - 'raise' will raise an NonExistentTimeError if there are nonexistent times .. versionadded:: 0.24.0 errors : {'raise', 'coerce'}, default None - 'raise' will raise a NonExistentTimeError if a timestamp is not valid in the specified time zone (e.g. due to a transition from or to DST time). Use ``nonexistent='raise'`` instead. - 'coerce' will return NaT if the timestamp can not be converted to the specified time zone. Use ``nonexistent='NaT'`` instead. .. deprecated:: 0.24.0 Returns ------- result : same type as self Array/Index converted to the specified time zone. Raises ------ TypeError If the Datetime Array/Index is tz-aware and tz is not None. See Also -------- DatetimeIndex.tz_convert : Convert tz-aware DatetimeIndex from one time zone to another. Examples -------- >>> tz_naive = pd.date_range('2018-03-01 09:00', periods=3) >>> tz_naive DatetimeIndex(['2018-03-01 09:00:00', '2018-03-02 09:00:00', '2018-03-03 09:00:00'], dtype='datetime64[ns]', freq='D') Localize DatetimeIndex in US/Eastern time zone: >>> tz_aware = tz_naive.tz_localize(tz='US/Eastern') >>> tz_aware DatetimeIndex(['2018-03-01 09:00:00-05:00', '2018-03-02 09:00:00-05:00', '2018-03-03 09:00:00-05:00'], dtype='datetime64[ns, US/Eastern]', freq='D') With the ``tz=None``, we can remove the time zone information while keeping the local time (not converted to UTC): >>> tz_aware.tz_localize(None) DatetimeIndex(['2018-03-01 09:00:00', '2018-03-02 09:00:00', '2018-03-03 09:00:00'], dtype='datetime64[ns]', freq='D') Be careful with DST changes. When there is sequential data, pandas can infer the DST time: >>> s = pd.to_datetime(pd.Series([ ... '2018-10-28 01:30:00', ... '2018-10-28 02:00:00', ... '2018-10-28 02:30:00', ... '2018-10-28 02:00:00', ... '2018-10-28 02:30:00', ... '2018-10-28 03:00:00', ... '2018-10-28 03:30:00'])) >>> s.dt.tz_localize('CET', ambiguous='infer') 2018-10-28 01:30:00+02:00 0 2018-10-28 02:00:00+02:00 1 2018-10-28 02:30:00+02:00 2 2018-10-28 02:00:00+01:00 3 2018-10-28 02:30:00+01:00 4 2018-10-28 03:00:00+01:00 5 2018-10-28 03:30:00+01:00 6 dtype: int64 In some cases, inferring the DST is impossible. In such cases, you can pass an ndarray to the ambiguous parameter to set the DST explicitly >>> s = pd.to_datetime(pd.Series([ ... '2018-10-28 01:20:00', ... '2018-10-28 02:36:00', ... '2018-10-28 03:46:00'])) >>> s.dt.tz_localize('CET', ambiguous=np.array([True, True, False])) 0 2018-10-28 01:20:00+02:00 1 2018-10-28 02:36:00+02:00 2 2018-10-28 03:46:00+01:00 dtype: datetime64[ns, CET] If the DST transition causes nonexistent times, you can shift these dates forward or backwards with a timedelta object or `'shift_forward'` or `'shift_backwards'`. >>> s = pd.to_datetime(pd.Series([ ... '2015-03-29 02:30:00', ... '2015-03-29 03:30:00'])) >>> s.dt.tz_localize('Europe/Warsaw', nonexistent='shift_forward') 0 2015-03-29 03:00:00+02:00 1 2015-03-29 03:30:00+02:00 dtype: datetime64[ns, 'Europe/Warsaw'] >>> s.dt.tz_localize('Europe/Warsaw', nonexistent='shift_backward') 0 2015-03-29 01:59:59.999999999+01:00 1 2015-03-29 03:30:00+02:00 dtype: datetime64[ns, 'Europe/Warsaw'] >>> s.dt.tz_localize('Europe/Warsaw', nonexistent=pd.Timedelta('1H')) 0 2015-03-29 03:30:00+02:00 1 2015-03-29 03:30:00+02:00 dtype: datetime64[ns, 'Europe/Warsaw'] """ if errors is not None: warnings.warn("The errors argument is deprecated and will be " "removed in a future release. Use " "nonexistent='NaT' or nonexistent='raise' " "instead.", FutureWarning) if errors == 'coerce': nonexistent = 'NaT' elif errors == 'raise': nonexistent = 'raise' else: raise ValueError("The errors argument must be either 'coerce' " "or 'raise'.") nonexistent_options = ('raise', 'NaT', 'shift_forward', 'shift_backward') if nonexistent not in nonexistent_options and not isinstance( nonexistent, timedelta): raise ValueError("The nonexistent argument must be one of 'raise'," " 'NaT', 'shift_forward', 'shift_backward' or" " a timedelta object") if self.tz is not None: if tz is None: new_dates = conversion.tz_convert(self.asi8, timezones.UTC, self.tz) else: raise TypeError("Already tz-aware, use tz_convert to convert.") else: tz = timezones.maybe_get_tz(tz) # Convert to UTC new_dates = conversion.tz_localize_to_utc( self.asi8, tz, ambiguous=ambiguous, nonexistent=nonexistent, ) new_dates = new_dates.view(_NS_DTYPE) dtype = tz_to_dtype(tz) return self._simple_new(new_dates, dtype=dtype, freq=self.freq) # ---------------------------------------------------------------- # Conversion Methods - Vectorized analogues of Timestamp methods def to_pydatetime(self): """ Return Datetime Array/Index as object ndarray of datetime.datetime objects Returns ------- datetimes : ndarray """ return tslib.ints_to_pydatetime(self.asi8, tz=self.tz) def normalize(self): """ 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, and directly on Datetime Array/Index. Returns ------- DatetimeArray, DatetimeIndex or Series The same type as the original data. Series will have the same name and index. DatetimeIndex will have the same name. See Also -------- floor : Floor the datetimes to the specified freq. ceil : Ceil the datetimes to the specified freq. round : Round the datetimes to the specified freq. Examples -------- >>> idx = pd.date_range(start='2014-08-01 10:00', freq='H', ... periods=3, tz='Asia/Calcutta') >>> idx DatetimeIndex(['2014-08-01 10:00:00+05:30', '2014-08-01 11:00:00+05:30', '2014-08-01 12:00:00+05:30'], dtype='datetime64[ns, Asia/Calcutta]', freq='H') >>> idx.normalize() DatetimeIndex(['2014-08-01 00:00:00+05:30', '2014-08-01 00:00:00+05:30', '2014-08-01 00:00:00+05:30'], dtype='datetime64[ns, Asia/Calcutta]', freq=None) """ if self.tz is None or timezones.is_utc(self.tz): not_null = ~self.isna() DAY_NS = ccalendar.DAY_SECONDS * 1000000000 new_values = self.asi8.copy() adjustment = (new_values[not_null] % DAY_NS) new_values[not_null] = new_values[not_null] - adjustment else: new_values = conversion.normalize_i8_timestamps(self.asi8, self.tz) return type(self)._from_sequence(new_values, freq='infer').tz_localize(self.tz) def to_period(self, freq=None): """ Cast to PeriodArray/Index at a particular frequency. Converts DatetimeArray/Index to PeriodArray/Index. Parameters ---------- freq : string or Offset, optional One of pandas' :ref:`offset strings ` or an Offset object. Will be inferred by default. Returns ------- PeriodArray/Index Raises ------ ValueError When converting a DatetimeArray/Index with non-regular values, so that a frequency cannot be inferred. See Also -------- PeriodIndex: Immutable ndarray holding ordinal values. DatetimeIndex.to_pydatetime: Return DatetimeIndex as object. Examples -------- >>> df = pd.DataFrame({"y": [1,2,3]}, ... index=pd.to_datetime(["2000-03-31 00:00:00", ... "2000-05-31 00:00:00", ... "2000-08-31 00:00:00"])) >>> df.index.to_period("M") PeriodIndex(['2000-03', '2000-05', '2000-08'], dtype='period[M]', freq='M') Infer the daily frequency >>> idx = pd.date_range("2017-01-01", periods=2) >>> idx.to_period() PeriodIndex(['2017-01-01', '2017-01-02'], dtype='period[D]', freq='D') """ from pandas.core.arrays import PeriodArray if self.tz is not None: warnings.warn("Converting to PeriodArray/Index representation " "will drop timezone information.", UserWarning) if freq is None: freq = self.freqstr or self.inferred_freq if freq is None: raise ValueError("You must pass a freq argument as " "current index has none.") freq = get_period_alias(freq) return PeriodArray._from_datetime64(self._data, freq, tz=self.tz) def to_perioddelta(self, freq): """ Calculate TimedeltaArray of difference between index values and index converted to PeriodArray at specified freq. Used for vectorized offsets Parameters ---------- freq : Period frequency Returns ------- TimedeltaArray/Index """ # TODO: consider privatizing (discussion in GH#23113) from pandas.core.arrays.timedeltas import TimedeltaArray i8delta = self.asi8 - self.to_period(freq).to_timestamp().asi8 m8delta = i8delta.view('m8[ns]') return TimedeltaArray(m8delta) # ----------------------------------------------------------------- # Properties - Vectorized Timestamp Properties/Methods def month_name(self, locale=None): """ Return the month names of the DateTimeIndex with specified locale. .. versionadded:: 0.23.0 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 = pd.date_range(start='2018-01', freq='M', periods=3) >>> idx DatetimeIndex(['2018-01-31', '2018-02-28', '2018-03-31'], dtype='datetime64[ns]', freq='M') >>> idx.month_name() Index(['January', 'February', 'March'], dtype='object') """ if self.tz is not None and not timezones.is_utc(self.tz): values = self._local_timestamps() else: values = self.asi8 result = fields.get_date_name_field(values, 'month_name', locale=locale) result = self._maybe_mask_results(result, fill_value=None) return result def day_name(self, locale=None): """ Return the day names of the DateTimeIndex with specified locale. .. versionadded:: 0.23.0 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 = pd.date_range(start='2018-01-01', freq='D', periods=3) >>> idx DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03'], dtype='datetime64[ns]', freq='D') >>> idx.day_name() Index(['Monday', 'Tuesday', 'Wednesday'], dtype='object') """ if self.tz is not None and not timezones.is_utc(self.tz): values = self._local_timestamps() else: values = self.asi8 result = fields.get_date_name_field(values, 'day_name', locale=locale) result = self._maybe_mask_results(result, fill_value=None) return result @property def time(self): """ Returns numpy array of datetime.time. The time part of the Timestamps. """ # If the Timestamps have a timezone that is not UTC, # convert them into their i8 representation while # keeping their timezone and not using UTC if self.tz is not None and not timezones.is_utc(self.tz): timestamps = self._local_timestamps() else: timestamps = self.asi8 return tslib.ints_to_pydatetime(timestamps, box="time") @property def timetz(self): """ Returns numpy array of datetime.time also containing timezone information. The time part of the Timestamps. """ return tslib.ints_to_pydatetime(self.asi8, self.tz, box="time") @property def date(self): """ Returns numpy array of python datetime.date objects (namely, the date part of Timestamps without timezone information). """ # If the Timestamps have a timezone that is not UTC, # convert them into their i8 representation while # keeping their timezone and not using UTC if self.tz is not None and not timezones.is_utc(self.tz): timestamps = self._local_timestamps() else: timestamps = self.asi8 return tslib.ints_to_pydatetime(timestamps, box="date") year = _field_accessor('year', 'Y', "The year of the datetime.") month = _field_accessor('month', 'M', "The month as January=1, December=12. ") day = _field_accessor('day', 'D', "The days of the datetime.") hour = _field_accessor('hour', 'h', "The hours of the datetime.") minute = _field_accessor('minute', 'm', "The minutes of the datetime.") second = _field_accessor('second', 's', "The seconds of the datetime.") microsecond = _field_accessor('microsecond', 'us', "The microseconds of the datetime.") nanosecond = _field_accessor('nanosecond', 'ns', "The nanoseconds of the datetime.") weekofyear = _field_accessor('weekofyear', 'woy', "The week ordinal of the year.") week = weekofyear _dayofweek_doc = """ 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 -------- >>> s = pd.date_range('2016-12-31', '2017-01-08', freq='D').to_series() >>> s.dt.dayofweek 2016-12-31 5 2017-01-01 6 2017-01-02 0 2017-01-03 1 2017-01-04 2 2017-01-05 3 2017-01-06 4 2017-01-07 5 2017-01-08 6 Freq: D, dtype: int64 """ dayofweek = _field_accessor('dayofweek', 'dow', _dayofweek_doc) weekday = dayofweek weekday_name = _field_accessor( 'weekday_name', 'weekday_name', "The name of day in a week (ex: Friday)\n\n.. deprecated:: 0.23.0") dayofyear = _field_accessor('dayofyear', 'doy', "The ordinal day of the year.") quarter = _field_accessor('quarter', 'q', "The quarter of the date.") days_in_month = _field_accessor( 'days_in_month', 'dim', "The number of days in the month.") daysinmonth = days_in_month _is_month_doc = """ Indicates whether the date is the {first_or_last} day of the month. Returns ------- Series or array For Series, returns a Series with boolean values. For DatetimeIndex, returns a boolean array. See Also -------- is_month_start : Return a boolean indicating whether the date is the first day of the month. is_month_end : Return a boolean indicating whether the date is the last day of the month. Examples -------- This method is available on Series with datetime values under the ``.dt`` accessor, and directly on DatetimeIndex. >>> s = pd.Series(pd.date_range("2018-02-27", periods=3)) >>> s 0 2018-02-27 1 2018-02-28 2 2018-03-01 dtype: datetime64[ns] >>> s.dt.is_month_start 0 False 1 False 2 True dtype: bool >>> s.dt.is_month_end 0 False 1 True 2 False dtype: bool >>> idx = pd.date_range("2018-02-27", periods=3) >>> idx.is_month_start array([False, False, True]) >>> idx.is_month_end array([False, True, False]) """ is_month_start = _field_accessor( 'is_month_start', 'is_month_start', _is_month_doc.format(first_or_last='first')) is_month_end = _field_accessor( 'is_month_end', 'is_month_end', _is_month_doc.format(first_or_last='last')) is_quarter_start = _field_accessor( 'is_quarter_start', 'is_quarter_start', """ Indicator for whether the date is the first day of a quarter. Returns ------- is_quarter_start : Series or DatetimeIndex The same type as the original data with boolean values. Series will have the same name and index. DatetimeIndex will have the same name. See Also -------- quarter : Return the quarter of the date. is_quarter_end : Similar property for indicating the quarter start. Examples -------- This method is available on Series with datetime values under the ``.dt`` accessor, and directly on DatetimeIndex. >>> df = pd.DataFrame({'dates': pd.date_range("2017-03-30", ... periods=4)}) >>> df.assign(quarter=df.dates.dt.quarter, ... is_quarter_start=df.dates.dt.is_quarter_start) dates quarter is_quarter_start 0 2017-03-30 1 False 1 2017-03-31 1 False 2 2017-04-01 2 True 3 2017-04-02 2 False >>> idx = pd.date_range('2017-03-30', periods=4) >>> idx DatetimeIndex(['2017-03-30', '2017-03-31', '2017-04-01', '2017-04-02'], dtype='datetime64[ns]', freq='D') >>> idx.is_quarter_start array([False, False, True, False]) """) is_quarter_end = _field_accessor( 'is_quarter_end', 'is_quarter_end', """ Indicator for whether the date is the last day of a quarter. Returns ------- is_quarter_end : Series or DatetimeIndex The same type as the original data with boolean values. Series will have the same name and index. DatetimeIndex will have the same name. See Also -------- quarter : Return the quarter of the date. is_quarter_start : Similar property indicating the quarter start. Examples -------- This method is available on Series with datetime values under the ``.dt`` accessor, and directly on DatetimeIndex. >>> df = pd.DataFrame({'dates': pd.date_range("2017-03-30", ... periods=4)}) >>> df.assign(quarter=df.dates.dt.quarter, ... is_quarter_end=df.dates.dt.is_quarter_end) dates quarter is_quarter_end 0 2017-03-30 1 False 1 2017-03-31 1 True 2 2017-04-01 2 False 3 2017-04-02 2 False >>> idx = pd.date_range('2017-03-30', periods=4) >>> idx DatetimeIndex(['2017-03-30', '2017-03-31', '2017-04-01', '2017-04-02'], dtype='datetime64[ns]', freq='D') >>> idx.is_quarter_end array([False, True, False, False]) """) is_year_start = _field_accessor( 'is_year_start', 'is_year_start', """ Indicate whether the date is the first day of a year. Returns ------- Series or DatetimeIndex The same type as the original data with boolean values. Series will have the same name and index. DatetimeIndex will have the same name. See Also -------- is_year_end : Similar property indicating the last day of the year. Examples -------- This method is available on Series with datetime values under the ``.dt`` accessor, and directly on DatetimeIndex. >>> dates = pd.Series(pd.date_range("2017-12-30", periods=3)) >>> dates 0 2017-12-30 1 2017-12-31 2 2018-01-01 dtype: datetime64[ns] >>> dates.dt.is_year_start 0 False 1 False 2 True dtype: bool >>> idx = pd.date_range("2017-12-30", periods=3) >>> idx DatetimeIndex(['2017-12-30', '2017-12-31', '2018-01-01'], dtype='datetime64[ns]', freq='D') >>> idx.is_year_start array([False, False, True]) """) is_year_end = _field_accessor( 'is_year_end', 'is_year_end', """ Indicate whether the date is the last day of the year. Returns ------- Series or DatetimeIndex The same type as the original data with boolean values. Series will have the same name and index. DatetimeIndex will have the same name. See Also -------- is_year_start : Similar property indicating the start of the year. Examples -------- This method is available on Series with datetime values under the ``.dt`` accessor, and directly on DatetimeIndex. >>> dates = pd.Series(pd.date_range("2017-12-30", periods=3)) >>> dates 0 2017-12-30 1 2017-12-31 2 2018-01-01 dtype: datetime64[ns] >>> dates.dt.is_year_end 0 False 1 True 2 False dtype: bool >>> idx = pd.date_range("2017-12-30", periods=3) >>> idx DatetimeIndex(['2017-12-30', '2017-12-31', '2018-01-01'], dtype='datetime64[ns]', freq='D') >>> idx.is_year_end array([False, True, False]) """) is_leap_year = _field_accessor( 'is_leap_year', 'is_leap_year', """ 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 ------- Series or ndarray Booleans indicating if dates belong to a leap year. Examples -------- This method is available on Series with datetime values under the ``.dt`` accessor, and directly on DatetimeIndex. >>> idx = pd.date_range("2012-01-01", "2015-01-01", freq="Y") >>> idx DatetimeIndex(['2012-12-31', '2013-12-31', '2014-12-31'], dtype='datetime64[ns]', freq='A-DEC') >>> idx.is_leap_year array([ True, False, False], dtype=bool) >>> dates = pd.Series(idx) >>> dates_series 0 2012-12-31 1 2013-12-31 2 2014-12-31 dtype: datetime64[ns] >>> dates_series.dt.is_leap_year 0 True 1 False 2 False dtype: bool """) def to_julian_date(self): """ Convert Datetime Array to float64 ndarray of Julian Dates. 0 Julian date is noon January 1, 4713 BC. http://en.wikipedia.org/wiki/Julian_day """ # http://mysite.verizon.net/aesir_research/date/jdalg2.htm year = np.asarray(self.year) month = np.asarray(self.month) day = np.asarray(self.day) testarr = month < 3 year[testarr] -= 1 month[testarr] += 12 return (day + np.fix((153 * month - 457) / 5) + 365 * year + np.floor(year / 4) - np.floor(year / 100) + np.floor(year / 400) + 1721118.5 + (self.hour + self.minute / 60.0 + self.second / 3600.0 + self.microsecond / 3600.0 / 1e+6 + self.nanosecond / 3600.0 / 1e+9 ) / 24.0) DatetimeArray._add_comparison_ops() # ------------------------------------------------------------------- # Constructor Helpers def sequence_to_dt64ns(data, dtype=None, copy=False, tz=None, dayfirst=False, yearfirst=False, ambiguous='raise', int_as_wall_time=False): """ Parameters ---------- data : list-like dtype : dtype, str, or None, default None copy : bool, default False tz : tzinfo, str, or None, default None dayfirst : bool, default False yearfirst : bool, default False ambiguous : str, bool, or arraylike, default 'raise' See pandas._libs.tslibs.conversion.tz_localize_to_utc int_as_wall_time : bool, default False Whether to treat ints as wall time in specified timezone, or as nanosecond-precision UNIX epoch (wall time in UTC). This is used in DatetimeIndex.__init__ to deprecate the wall-time behaviour. ..versionadded:: 0.24.0 Returns ------- result : numpy.ndarray The sequence converted to a numpy array with dtype ``datetime64[ns]``. tz : tzinfo or None Either the user-provided tzinfo or one inferred from the data. inferred_freq : Tick or None The inferred frequency of the sequence. Raises ------ TypeError : PeriodDType data is passed """ inferred_freq = None dtype = _validate_dt64_dtype(dtype) if not hasattr(data, "dtype"): # e.g. list, tuple if np.ndim(data) == 0: # i.e. generator data = list(data) data = np.asarray(data) copy = False elif isinstance(data, ABCSeries): data = data._values if isinstance(data, ABCPandasArray): data = data.to_numpy() if hasattr(data, "freq"): # i.e. DatetimeArray/Index inferred_freq = data.freq # if dtype has an embedded tz, capture it tz = validate_tz_from_dtype(dtype, tz) if isinstance(data, ABCIndexClass): data = data._data # By this point we are assured to have either a numpy array or Index data, copy = maybe_convert_dtype(data, copy) if is_object_dtype(data) or is_string_dtype(data): # TODO: We do not have tests specific to string-dtypes, # also complex or categorical or other extension copy = False if lib.infer_dtype(data, skipna=False) == 'integer': data = data.astype(np.int64) else: # data comes back here as either i8 to denote UTC timestamps # or M8[ns] to denote wall times data, inferred_tz = objects_to_datetime64ns( data, dayfirst=dayfirst, yearfirst=yearfirst) tz = maybe_infer_tz(tz, inferred_tz) # When a sequence of timestamp objects is passed, we always # want to treat the (now i8-valued) data as UTC timestamps, # not wall times. int_as_wall_time = False # `data` may have originally been a Categorical[datetime64[ns, tz]], # so we need to handle these types. if is_datetime64tz_dtype(data): # DatetimeArray -> ndarray tz = maybe_infer_tz(tz, data.tz) result = data._data elif is_datetime64_dtype(data): # tz-naive DatetimeArray or ndarray[datetime64] data = getattr(data, "_data", data) if data.dtype != _NS_DTYPE: data = conversion.ensure_datetime64ns(data) if tz is not None: # Convert tz-naive to UTC tz = timezones.maybe_get_tz(tz) data = conversion.tz_localize_to_utc(data.view('i8'), tz, ambiguous=ambiguous) data = data.view(_NS_DTYPE) assert data.dtype == _NS_DTYPE, data.dtype result = data else: # must be integer dtype otherwise # assume this data are epoch timestamps if tz: tz = timezones.maybe_get_tz(tz) if data.dtype != _INT64_DTYPE: data = data.astype(np.int64, copy=False) if int_as_wall_time and tz is not None and not timezones.is_utc(tz): warnings.warn(_i8_message, FutureWarning, stacklevel=4) data = conversion.tz_localize_to_utc(data.view('i8'), tz, ambiguous=ambiguous) data = data.view(_NS_DTYPE) result = data.view(_NS_DTYPE) if copy: # TODO: should this be deepcopy? result = result.copy() assert isinstance(result, np.ndarray), type(result) assert result.dtype == 'M8[ns]', result.dtype # We have to call this again after possibly inferring a tz above validate_tz_from_dtype(dtype, tz) return result, tz, inferred_freq def objects_to_datetime64ns(data, dayfirst, yearfirst, utc=False, errors="raise", require_iso8601=False, allow_object=False): """ Convert data to array of timestamps. Parameters ---------- data : np.ndarray[object] dayfirst : bool yearfirst : bool utc : bool, default False Whether to convert timezone-aware timestamps to UTC errors : {'raise', 'ignore', 'coerce'} allow_object : bool Whether to return an object-dtype ndarray instead of raising if the data contains more than one timezone. Returns ------- result : ndarray np.int64 dtype if returned values represent UTC timestamps np.datetime64[ns] if returned values represent wall times object if mixed timezones inferred_tz : tzinfo or None Raises ------ ValueError : if data cannot be converted to datetimes """ assert errors in ["raise", "ignore", "coerce"] # if str-dtype, convert data = np.array(data, copy=False, dtype=np.object_) try: result, tz_parsed = tslib.array_to_datetime( data, errors=errors, utc=utc, dayfirst=dayfirst, yearfirst=yearfirst, require_iso8601=require_iso8601 ) except ValueError as e: try: values, tz_parsed = conversion.datetime_to_datetime64(data) # If tzaware, these values represent unix timestamps, so we # return them as i8 to distinguish from wall times return values.view('i8'), tz_parsed except (ValueError, TypeError): raise e if tz_parsed is not None: # We can take a shortcut since the datetime64 numpy array # is in UTC # Return i8 values to denote unix timestamps return result.view('i8'), tz_parsed elif is_datetime64_dtype(result): # returning M8[ns] denotes wall-times; since tz is None # the distinction is a thin one return result, tz_parsed elif is_object_dtype(result): # GH#23675 when called via `pd.to_datetime`, returning an object-dtype # array is allowed. When called via `pd.DatetimeIndex`, we can # only accept datetime64 dtype, so raise TypeError if object-dtype # is returned, as that indicates the values can be recognized as # datetimes but they have conflicting timezones/awareness if allow_object: return result, tz_parsed raise TypeError(result) else: # pragma: no cover # GH#23675 this TypeError should never be hit, whereas the TypeError # in the object-dtype branch above is reachable. raise TypeError(result) def maybe_convert_dtype(data, copy): """ Convert data based on dtype conventions, issuing deprecation warnings or errors where appropriate. Parameters ---------- data : np.ndarray or pd.Index copy : bool Returns ------- data : np.ndarray or pd.Index copy : bool Raises ------ TypeError : PeriodDType data is passed """ if is_float_dtype(data): # Note: we must cast to datetime64[ns] here in order to treat these # as wall-times instead of UTC timestamps. data = data.astype(_NS_DTYPE) copy = False # TODO: deprecate this behavior to instead treat symmetrically # with integer dtypes. See discussion in GH#23675 elif is_timedelta64_dtype(data): warnings.warn("Passing timedelta64-dtype data is deprecated, will " "raise a TypeError in a future version", FutureWarning, stacklevel=5) data = data.view(_NS_DTYPE) elif is_period_dtype(data): # Note: without explicitly raising here, PeriodIndex # test_setops.test_join_does_not_recur fails raise TypeError("Passing PeriodDtype data is invalid. " "Use `data.to_timestamp()` instead") elif is_categorical_dtype(data): # GH#18664 preserve tz in going DTI->Categorical->DTI # TODO: cases where we need to do another pass through this func, # e.g. the categories are timedelta64s data = data.categories.take(data.codes, fill_value=NaT)._values copy = False elif is_extension_type(data) and not is_datetime64tz_dtype(data): # Includes categorical # TODO: We have no tests for these data = np.array(data, dtype=np.object_) copy = False return data, copy # ------------------------------------------------------------------- # Validation and Inference def maybe_infer_tz(tz, inferred_tz): """ If a timezone is inferred from data, check that it is compatible with the user-provided timezone, if any. Parameters ---------- tz : tzinfo or None inferred_tz : tzinfo or None Returns ------- tz : tzinfo or None Raises ------ TypeError : if both timezones are present but do not match """ if tz is None: tz = inferred_tz elif inferred_tz is None: pass elif not timezones.tz_compare(tz, inferred_tz): raise TypeError('data is already tz-aware {inferred_tz}, unable to ' 'set specified tz: {tz}' .format(inferred_tz=inferred_tz, tz=tz)) return tz def _validate_dt64_dtype(dtype): """ Check that a dtype, if passed, represents either a numpy datetime64[ns] dtype or a pandas DatetimeTZDtype. Parameters ---------- dtype : object Returns ------- dtype : None, numpy.dtype, or DatetimeTZDtype Raises ------ ValueError : invalid dtype Notes ----- Unlike validate_tz_from_dtype, this does _not_ allow non-existent tz errors to go through """ if dtype is not None: dtype = pandas_dtype(dtype) if is_dtype_equal(dtype, np.dtype("M8")): # no precision, warn dtype = _NS_DTYPE msg = textwrap.dedent("""\ Passing in 'datetime64' dtype with no precision is deprecated and will raise in a future version. Please pass in 'datetime64[ns]' instead.""") warnings.warn(msg, FutureWarning, stacklevel=5) if ((isinstance(dtype, np.dtype) and dtype != _NS_DTYPE) or not isinstance(dtype, (np.dtype, DatetimeTZDtype))): raise ValueError("Unexpected value for 'dtype': '{dtype}'. " "Must be 'datetime64[ns]' or DatetimeTZDtype'." .format(dtype=dtype)) return dtype def validate_tz_from_dtype(dtype, tz): """ If the given dtype is a DatetimeTZDtype, extract the implied tzinfo object from it and check that it does not conflict with the given tz. Parameters ---------- dtype : dtype, str tz : None, tzinfo Returns ------- tz : consensus tzinfo Raises ------ ValueError : on tzinfo mismatch """ if dtype is not None: if isinstance(dtype, compat.string_types): try: dtype = DatetimeTZDtype.construct_from_string(dtype) except TypeError: # Things like `datetime64[ns]`, which is OK for the # constructors, but also nonsense, which should be validated # but not by us. We *do* allow non-existent tz errors to # go through pass dtz = getattr(dtype, 'tz', None) if dtz is not None: if tz is not None and not timezones.tz_compare(tz, dtz): raise ValueError("cannot supply both a tz and a dtype" " with a tz") tz = dtz if tz is not None and is_datetime64_dtype(dtype): # We also need to check for the case where the user passed a # tz-naive dtype (i.e. datetime64[ns]) if tz is not None and not timezones.tz_compare(tz, dtz): raise ValueError("cannot supply both a tz and a " "timezone-naive dtype (i.e. datetime64[ns]") return tz def _infer_tz_from_endpoints(start, end, tz): """ If a timezone is not explicitly given via `tz`, see if one can be inferred from the `start` and `end` endpoints. If more than one of these inputs provides a timezone, require that they all agree. Parameters ---------- start : Timestamp end : Timestamp tz : tzinfo or None Returns ------- tz : tzinfo or None Raises ------ TypeError : if start and end timezones do not agree """ try: inferred_tz = timezones.infer_tzinfo(start, end) except Exception: raise TypeError('Start and end cannot both be tz-aware with ' 'different timezones') inferred_tz = timezones.maybe_get_tz(inferred_tz) tz = timezones.maybe_get_tz(tz) if tz is not None and inferred_tz is not None: if not timezones.tz_compare(inferred_tz, tz): raise AssertionError("Inferred time zone not equal to passed " "time zone") elif inferred_tz is not None: tz = inferred_tz return tz def _maybe_normalize_endpoints(start, end, normalize): _normalized = True if start is not None: if normalize: start = normalize_date(start) _normalized = True else: _normalized = _normalized and start.time() == _midnight if end is not None: if normalize: end = normalize_date(end) _normalized = True else: _normalized = _normalized and end.time() == _midnight return start, end, _normalized def _maybe_localize_point(ts, is_none, is_not_none, freq, tz): """ Localize a start or end Timestamp to the timezone of the corresponding start or end Timestamp Parameters ---------- ts : start or end Timestamp to potentially localize is_none : argument that should be None is_not_none : argument that should not be None freq : Tick, DateOffset, or None tz : str, timezone object or None Returns ------- ts : Timestamp """ # Make sure start and end are timezone localized if: # 1) freq = a Timedelta-like frequency (Tick) # 2) freq = None i.e. generating a linspaced range if isinstance(freq, Tick) or freq is None: localize_args = {'tz': tz, 'ambiguous': False} else: localize_args = {'tz': None} if is_none is None and is_not_none is not None: ts = ts.tz_localize(**localize_args) return ts