from datetime import date, datetime, time, timedelta, tzinfo
import operator
from typing import TYPE_CHECKING, Optional, Tuple
import warnings
import numpy as np
from pandas._libs import NaT, Period, Timestamp, index as libindex, lib
from pandas._libs.tslibs import (
Resolution,
ints_to_pydatetime,
parsing,
timezones,
to_offset,
)
from pandas._libs.tslibs.offsets import prefix_mapping
from pandas._typing import DtypeObj
from pandas.errors import InvalidIndexError
from pandas.util._decorators import cache_readonly, doc
from pandas.core.dtypes.common import (
DT64NS_DTYPE,
is_datetime64_dtype,
is_datetime64tz_dtype,
is_scalar,
)
from pandas.core.dtypes.missing import is_valid_nat_for_dtype
from pandas.core.arrays.datetimes import DatetimeArray, tz_to_dtype
import pandas.core.common as com
from pandas.core.indexes.base import Index, get_unanimous_names, maybe_extract_name
from pandas.core.indexes.datetimelike import DatetimeTimedeltaMixin
from pandas.core.indexes.extension import inherit_names
from pandas.core.tools.times import to_time
if TYPE_CHECKING:
from pandas import DataFrame, Float64Index, PeriodIndex, TimedeltaIndex
def _new_DatetimeIndex(cls, d):
"""
This is called upon unpickling, rather than the default which doesn't
have arguments and breaks __new__
"""
if "data" in d and not isinstance(d["data"], DatetimeIndex):
# Avoid need to verify integrity by calling simple_new directly
data = d.pop("data")
if not isinstance(data, DatetimeArray):
# For backward compat with older pickles, we may need to construct
# a DatetimeArray to adapt to the newer _simple_new signature
tz = d.pop("tz")
freq = d.pop("freq")
dta = DatetimeArray._simple_new(data, dtype=tz_to_dtype(tz), freq=freq)
else:
dta = data
for key in ["tz", "freq"]:
# These are already stored in our DatetimeArray; if they are
# also in the pickle and don't match, we have a problem.
if key in d:
assert d.pop(key) == getattr(dta, key)
result = cls._simple_new(dta, **d)
else:
with warnings.catch_warnings():
# TODO: If we knew what was going in to **d, we might be able to
# go through _simple_new instead
warnings.simplefilter("ignore")
result = cls.__new__(cls, **d)
return result
@inherit_names(
DatetimeArray._field_ops
+ [
method
for method in DatetimeArray._datetimelike_methods
if method not in ("tz_localize", "tz_convert")
],
DatetimeArray,
wrap=True,
)
@inherit_names(["is_normalized", "_resolution_obj"], DatetimeArray, cache=True)
@inherit_names(
[
"_bool_ops",
"_object_ops",
"_field_ops",
"_datetimelike_ops",
"_datetimelike_methods",
"tz",
"tzinfo",
"dtype",
"to_pydatetime",
"_has_same_tz",
"_format_native_types",
"date",
"time",
"timetz",
"std",
]
+ DatetimeArray._bool_ops,
DatetimeArray,
)
class DatetimeIndex(DatetimeTimedeltaMixin):
"""
Immutable ndarray-like of datetime64 data.
Represented internally as int64, and which can be boxed to Timestamp objects
that are subclasses of datetime and carry metadata.
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.
tz : pytz.timezone or dateutil.tz.tzfile or datetime.tzinfo or str
Set the Timezone of the data.
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 DatetimeTZDtype 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.
Attributes
----------
year
month
day
hour
minute
second
microsecond
nanosecond
date
time
timetz
dayofyear
day_of_year
weekofyear
week
dayofweek
day_of_week
weekday
quarter
tz
freq
freqstr
is_month_start
is_month_end
is_quarter_start
is_quarter_end
is_year_start
is_year_end
is_leap_year
inferred_freq
Methods
-------
normalize
strftime
snap
tz_convert
tz_localize
round
floor
ceil
to_period
to_perioddelta
to_pydatetime
to_series
to_frame
month_name
day_name
mean
std
See Also
--------
Index : The base pandas Index type.
TimedeltaIndex : Index of timedelta64 data.
PeriodIndex : Index of Period data.
to_datetime : Convert argument to datetime.
date_range : Create a fixed-frequency DatetimeIndex.
Notes
-----
To learn more about the frequency strings, please see `this link
<https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
"""
_typ = "datetimeindex"
_data_cls = DatetimeArray
_engine_type = libindex.DatetimeEngine
_supports_partial_string_indexing = True
_comparables = ["name", "freqstr", "tz"]
_attributes = ["name", "tz", "freq"]
_is_numeric_dtype = False
_data: DatetimeArray
inferred_freq: Optional[str]
tz: Optional[tzinfo]
# --------------------------------------------------------------------
# methods that dispatch to DatetimeArray and wrap result
@doc(DatetimeArray.strftime)
def strftime(self, date_format) -> Index:
arr = self._data.strftime(date_format)
return Index(arr, name=self.name)
@doc(DatetimeArray.tz_convert)
def tz_convert(self, tz) -> "DatetimeIndex":
arr = self._data.tz_convert(tz)
return type(self)._simple_new(arr, name=self.name)
@doc(DatetimeArray.tz_localize)
def tz_localize(
self, tz, ambiguous="raise", nonexistent="raise"
) -> "DatetimeIndex":
arr = self._data.tz_localize(tz, ambiguous, nonexistent)
return type(self)._simple_new(arr, name=self.name)
@doc(DatetimeArray.to_period)
def to_period(self, freq=None) -> "PeriodIndex":
from pandas.core.indexes.api import PeriodIndex
arr = self._data.to_period(freq)
return PeriodIndex._simple_new(arr, name=self.name)
@doc(DatetimeArray.to_perioddelta)
def to_perioddelta(self, freq) -> "TimedeltaIndex":
from pandas.core.indexes.api import TimedeltaIndex
arr = self._data.to_perioddelta(freq)
return TimedeltaIndex._simple_new(arr, name=self.name)
@doc(DatetimeArray.to_julian_date)
def to_julian_date(self) -> "Float64Index":
from pandas.core.indexes.api import Float64Index
arr = self._data.to_julian_date()
return Float64Index._simple_new(arr, name=self.name)
@doc(DatetimeArray.isocalendar)
def isocalendar(self) -> "DataFrame":
df = self._data.isocalendar()
return df.set_index(self)
# --------------------------------------------------------------------
# Constructors
def __new__(
cls,
data=None,
freq=lib.no_default,
tz=None,
normalize=False,
closed=None,
ambiguous="raise",
dayfirst=False,
yearfirst=False,
dtype=None,
copy=False,
name=None,
):
if is_scalar(data):
raise TypeError(
f"{cls.__name__}() must be called with a "
f"collection of some kind, {repr(data)} was passed"
)
# - Cases checked above all return/raise before reaching here - #
name = maybe_extract_name(name, data, cls)
dtarr = DatetimeArray._from_sequence_not_strict(
data,
dtype=dtype,
copy=copy,
tz=tz,
freq=freq,
dayfirst=dayfirst,
yearfirst=yearfirst,
ambiguous=ambiguous,
)
subarr = cls._simple_new(dtarr, name=name)
return subarr
# --------------------------------------------------------------------
@cache_readonly
def _is_dates_only(self) -> bool:
"""
Return a boolean if we are only dates (and don't have a timezone)
Returns
-------
bool
"""
from pandas.io.formats.format import is_dates_only
return self.tz is None and is_dates_only(self._values)
def __reduce__(self):
# we use a special reduce here because we need
# to simply set the .tz (and not reinterpret it)
d = {"data": self._data}
d.update(self._get_attributes_dict())
return _new_DatetimeIndex, (type(self), d), None
def _validate_fill_value(self, value):
"""
Convert value to be insertable to ndarray.
"""
return self._data._validate_setitem_value(value)
def _is_comparable_dtype(self, dtype: DtypeObj) -> bool:
"""
Can we compare values of the given dtype to our own?
"""
if self.tz is not None:
# If we have tz, we can compare to tzaware
return is_datetime64tz_dtype(dtype)
# if we dont have tz, we can only compare to tznaive
return is_datetime64_dtype(dtype)
# --------------------------------------------------------------------
# Rendering Methods
def _mpl_repr(self):
# how to represent ourselves to matplotlib
return ints_to_pydatetime(self.asi8, self.tz)
@property
def _formatter_func(self):
from pandas.io.formats.format import get_format_datetime64
formatter = get_format_datetime64(is_dates_only=self._is_dates_only)
return lambda x: f"'{formatter(x)}'"
# --------------------------------------------------------------------
# Set Operation Methods
def union_many(self, others):
"""
A bit of a hack to accelerate unioning a collection of indexes.
"""
this = self
for other in others:
if not isinstance(this, DatetimeIndex):
this = Index.union(this, other)
continue
if not isinstance(other, DatetimeIndex):
try:
other = DatetimeIndex(other)
except TypeError:
pass
this, other = this._maybe_utc_convert(other)
if this._can_fast_union(other):
this = this._fast_union(other)
else:
this = Index.union(this, other)
res_name = get_unanimous_names(self, *others)[0]
if this.name != res_name:
return this.rename(res_name)
return this
def _maybe_utc_convert(self, other: Index) -> Tuple["DatetimeIndex", Index]:
this = self
if isinstance(other, DatetimeIndex):
if self.tz is not None:
if other.tz is None:
raise TypeError("Cannot join tz-naive with tz-aware DatetimeIndex")
elif other.tz is not None:
raise TypeError("Cannot join tz-naive with tz-aware DatetimeIndex")
if not timezones.tz_compare(self.tz, other.tz):
this = self.tz_convert("UTC")
other = other.tz_convert("UTC")
return this, other
# --------------------------------------------------------------------
def _get_time_micros(self):
"""
Return the number of microseconds since midnight.
Returns
-------
ndarray[int64_t]
"""
values = self._data._local_timestamps()
nanos = values % (24 * 3600 * 1_000_000_000)
micros = nanos // 1000
micros[self._isnan] = -1
return micros
def to_series(self, keep_tz=lib.no_default, index=None, name=None):
"""
Create a Series with both index and values equal to the index keys
useful with map for returning an indexer based on an index.
Parameters
----------
keep_tz : optional, defaults True
Return the data keeping the timezone.
If keep_tz is True:
If the timezone is not set, the resulting
Series will have a datetime64[ns] dtype.
Otherwise the Series will have an datetime64[ns, tz] dtype; the
tz will be preserved.
If keep_tz is False:
Series will have a datetime64[ns] dtype. TZ aware
objects will have the tz removed.
.. versionchanged:: 1.0.0
The default value is now True. In a future version,
this keyword will be removed entirely. Stop passing the
argument to obtain the future behavior and silence the warning.
index : Index, optional
Index of resulting Series. If None, defaults to original index.
name : str, optional
Name of resulting Series. If None, defaults to name of original
index.
Returns
-------
Series
"""
from pandas import Series
if index is None:
index = self._shallow_copy()
if name is None:
name = self.name
if keep_tz is not lib.no_default:
if keep_tz:
warnings.warn(
"The 'keep_tz' keyword in DatetimeIndex.to_series "
"is deprecated and will be removed in a future version. "
"You can stop passing 'keep_tz' to silence this warning.",
FutureWarning,
stacklevel=2,
)
else:
warnings.warn(
"Specifying 'keep_tz=False' is deprecated and this "
"option will be removed in a future release. If "
"you want to remove the timezone information, you "
"can do 'idx.tz_convert(None)' before calling "
"'to_series'.",
FutureWarning,
stacklevel=2,
)
else:
keep_tz = True
if keep_tz and self.tz is not None:
# preserve the tz & copy
values = self.copy(deep=True)
else:
values = self._values.view("M8[ns]").copy()
return Series(values, index=index, name=name)
def snap(self, freq="S"):
"""
Snap time stamps to nearest occurring frequency.
Returns
-------
DatetimeIndex
"""
# Superdumb, punting on any optimizing
freq = to_offset(freq)
snapped = np.empty(len(self), dtype=DT64NS_DTYPE)
for i, v in enumerate(self):
s = v
if not freq.is_on_offset(s):
t0 = freq.rollback(s)
t1 = freq.rollforward(s)
if abs(s - t0) < abs(t1 - s):
s = t0
else:
s = t1
snapped[i] = s
dta = DatetimeArray(snapped, dtype=self.dtype)
return DatetimeIndex._simple_new(dta, name=self.name)
# --------------------------------------------------------------------
# Indexing Methods
def _parsed_string_to_bounds(self, reso: Resolution, parsed: datetime):
"""
Calculate datetime bounds for parsed time string and its resolution.
Parameters
----------
reso : str
Resolution provided by parsed string.
parsed : datetime
Datetime from parsed string.
Returns
-------
lower, upper: pd.Timestamp
"""
assert isinstance(reso, Resolution), (type(reso), reso)
valid_resos = {
"year",
"month",
"quarter",
"day",
"hour",
"minute",
"second",
"minute",
"second",
"microsecond",
}
if reso.attrname not in valid_resos:
raise KeyError
grp = reso.freq_group
per = Period(parsed, freq=grp)
start, end = per.start_time, per.end_time
# GH 24076
# If an incoming date string contained a UTC offset, need to localize
# the parsed date to this offset first before aligning with the index's
# timezone
if parsed.tzinfo is not None:
if self.tz is None:
raise ValueError(
"The index must be timezone aware when indexing "
"with a date string with a UTC offset"
)
start = start.tz_localize(parsed.tzinfo).tz_convert(self.tz)
end = end.tz_localize(parsed.tzinfo).tz_convert(self.tz)
elif self.tz is not None:
start = start.tz_localize(self.tz)
end = end.tz_localize(self.tz)
return start, end
def _validate_partial_date_slice(self, reso: Resolution):
assert isinstance(reso, Resolution), (type(reso), reso)
if (
self.is_monotonic
and reso.attrname in ["day", "hour", "minute", "second"]
and self._resolution_obj >= reso
):
# These resolution/monotonicity validations came from GH3931,
# GH3452 and GH2369.
# See also GH14826
raise KeyError
if reso == "microsecond":
# _partial_date_slice doesn't allow microsecond resolution, but
# _parsed_string_to_bounds allows it.
raise KeyError
def _deprecate_mismatched_indexing(self, key):
# GH#36148
# we get here with isinstance(key, self._data._recognized_scalars)
try:
self._data._assert_tzawareness_compat(key)
except TypeError:
if self.tz is None:
msg = (
"Indexing a timezone-naive DatetimeIndex with a "
"timezone-aware datetime is deprecated and will "
"raise KeyError in a future version. "
"Use a timezone-naive object instead."
)
else:
msg = (
"Indexing a timezone-aware DatetimeIndex with a "
"timezone-naive datetime is deprecated and will "
"raise KeyError in a future version. "
"Use a timezone-aware object instead."
)
warnings.warn(msg, FutureWarning, stacklevel=5)
def get_loc(self, key, method=None, tolerance=None):
"""
Get integer location for requested label
Returns
-------
loc : int
"""
if not is_scalar(key):
raise InvalidIndexError(key)
orig_key = key
if is_valid_nat_for_dtype(key, self.dtype):
key = NaT
if isinstance(key, self._data._recognized_scalars):
# needed to localize naive datetimes
self._deprecate_mismatched_indexing(key)
key = self._maybe_cast_for_get_loc(key)
elif isinstance(key, str):
try:
return self._get_string_slice(key)
except (TypeError, KeyError, ValueError, OverflowError):
pass
try:
key = self._maybe_cast_for_get_loc(key)
except ValueError as err:
raise KeyError(key) from err
elif isinstance(key, timedelta):
# GH#20464
raise TypeError(
f"Cannot index {type(self).__name__} with {type(key).__name__}"
)
elif isinstance(key, time):
if method is not None:
raise NotImplementedError(
"cannot yet lookup inexact labels when key is a time object"
)
return self.indexer_at_time(key)
else:
# unrecognized type
raise KeyError(key)
try:
return Index.get_loc(self, key, method, tolerance)
except KeyError as err:
raise KeyError(orig_key) from err
def _maybe_cast_for_get_loc(self, key) -> Timestamp:
# needed to localize naive datetimes or dates (GH 35690)
key = Timestamp(key)
if key.tzinfo is None:
key = key.tz_localize(self.tz)
else:
key = key.tz_convert(self.tz)
return key
def _maybe_cast_slice_bound(self, label, side: str, kind):
"""
If label is a string, cast it to datetime according to resolution.
Parameters
----------
label : object
side : {'left', 'right'}
kind : {'loc', 'getitem'} or None
Returns
-------
label : object
Notes
-----
Value of `side` parameter should be validated in caller.
"""
assert kind in ["loc", "getitem", None]
if isinstance(label, str):
freq = getattr(self, "freqstr", getattr(self, "inferred_freq", None))
try:
parsed, reso = parsing.parse_time_string(label, freq)
except parsing.DateParseError as err:
raise self._invalid_indexer("slice", label) from err
reso = Resolution.from_attrname(reso)
lower, upper = self._parsed_string_to_bounds(reso, parsed)
# lower, upper form the half-open interval:
# [parsed, parsed + 1 freq)
# because label may be passed to searchsorted
# the bounds need swapped if index is reverse sorted and has a
# length > 1 (is_monotonic_decreasing gives True for empty
# and length 1 index)
if self._is_strictly_monotonic_decreasing and len(self) > 1:
return upper if side == "left" else lower
return lower if side == "left" else upper
elif isinstance(label, (self._data._recognized_scalars, date)):
self._deprecate_mismatched_indexing(label)
else:
raise self._invalid_indexer("slice", label)
return self._maybe_cast_for_get_loc(label)
def _get_string_slice(self, key: str):
freq = getattr(self, "freqstr", getattr(self, "inferred_freq", None))
parsed, reso = parsing.parse_time_string(key, freq)
reso = Resolution.from_attrname(reso)
loc = self._partial_date_slice(reso, parsed)
return loc
def slice_indexer(self, start=None, end=None, step=None, kind=None):
"""
Return indexer for specified label slice.
Index.slice_indexer, customized to handle time slicing.
In addition to functionality provided by Index.slice_indexer, does the
following:
- if both `start` and `end` are instances of `datetime.time`, it
invokes `indexer_between_time`
- if `start` and `end` are both either string or None perform
value-based selection in non-monotonic cases.
"""
# For historical reasons DatetimeIndex supports slices between two
# instances of datetime.time as if it were applying a slice mask to
# an array of (self.hour, self.minute, self.seconds, self.microsecond).
if isinstance(start, time) and isinstance(end, time):
if step is not None and step != 1:
raise ValueError("Must have step size of 1 with time slices")
return self.indexer_between_time(start, end)
if isinstance(start, time) or isinstance(end, time):
raise KeyError("Cannot mix time and non-time slice keys")
# Pandas supports slicing with dates, treated as datetimes at midnight.
# https://github.com/pandas-dev/pandas/issues/31501
if isinstance(start, date) and not isinstance(start, datetime):
start = datetime.combine(start, time(0, 0))
if isinstance(end, date) and not isinstance(end, datetime):
end = datetime.combine(end, time(0, 0))
try:
return Index.slice_indexer(self, start, end, step, kind=kind)
except KeyError:
# For historical reasons DatetimeIndex by default supports
# value-based partial (aka string) slices on non-monotonic arrays,
# let's try that.
if (start is None or isinstance(start, str)) and (
end is None or isinstance(end, str)
):
mask = np.array(True)
deprecation_mask = np.array(True)
if start is not None:
start_casted = self._maybe_cast_slice_bound(start, "left", kind)
mask = start_casted <= self
deprecation_mask = start_casted == self
if end is not None:
end_casted = self._maybe_cast_slice_bound(end, "right", kind)
mask = (self <= end_casted) & mask
deprecation_mask = (end_casted == self) | deprecation_mask
if not deprecation_mask.any():
warnings.warn(
"Value based partial slicing on non-monotonic DatetimeIndexes "
"with non-existing keys is deprecated and will raise a "
"KeyError in a future Version.",
FutureWarning,
stacklevel=5,
)
indexer = mask.nonzero()[0][::step]
if len(indexer) == len(self):
return slice(None)
else:
return indexer
else:
raise
# --------------------------------------------------------------------
@property
def inferred_type(self) -> str:
# b/c datetime is represented as microseconds since the epoch, make
# sure we can't have ambiguous indexing
return "datetime64"
def indexer_at_time(self, time, asof=False):
"""
Return index locations of values at particular time of day
(e.g. 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 : array of integers
See Also
--------
indexer_between_time : Get index locations of values between particular
times of day.
DataFrame.at_time : Select values at particular time of day.
"""
if asof:
raise NotImplementedError("'asof' argument is not supported")
if isinstance(time, str):
from dateutil.parser import parse
time = parse(time).time()
if time.tzinfo:
if self.tz is None:
raise ValueError("Index must be timezone aware.")
time_micros = self.tz_convert(time.tzinfo)._get_time_micros()
else:
time_micros = self._get_time_micros()
micros = _time_to_micros(time)
return (micros == time_micros).nonzero()[0]
def indexer_between_time(
self, start_time, end_time, include_start=True, include_end=True
):
"""
Return index locations of values between particular times of day
(e.g., 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 : array of integers
See Also
--------
indexer_at_time : Get index locations of values at particular time of day.
DataFrame.between_time : Select values between particular times of day.
"""
start_time = to_time(start_time)
end_time = to_time(end_time)
time_micros = self._get_time_micros()
start_micros = _time_to_micros(start_time)
end_micros = _time_to_micros(end_time)
if include_start and include_end:
lop = rop = operator.le
elif include_start:
lop = operator.le
rop = operator.lt
elif include_end:
lop = operator.lt
rop = operator.le
else:
lop = rop = operator.lt
if start_time <= end_time:
join_op = operator.and_
else:
join_op = operator.or_
mask = join_op(lop(start_micros, time_micros), rop(time_micros, end_micros))
return mask.nonzero()[0]
def date_range(
start=None,
end=None,
periods=None,
freq=None,
tz=None,
normalize=False,
name=None,
closed=None,
**kwargs,
) -> DatetimeIndex:
"""
Return a fixed frequency DatetimeIndex.
Parameters
----------
start : str or datetime-like, optional
Left bound for generating dates.
end : str or datetime-like, optional
Right bound for generating dates.
periods : int, optional
Number of periods to generate.
freq : str or DateOffset, default 'D'
Frequency strings can have multiples, e.g. '5H'. See
:ref:`here <timeseries.offset_aliases>` for a list of
frequency aliases.
tz : str or tzinfo, optional
Time zone name for returning localized DatetimeIndex, for example
'Asia/Hong_Kong'. By default, the resulting DatetimeIndex is
timezone-naive.
normalize : bool, default False
Normalize start/end dates to midnight before generating date range.
name : str, default None
Name of the resulting DatetimeIndex.
closed : {None, 'left', 'right'}, optional
Make the interval closed with respect to the given frequency to
the 'left', 'right', or both sides (None, the default).
**kwargs
For compatibility. Has no effect on the result.
Returns
-------
rng : DatetimeIndex
See Also
--------
DatetimeIndex : An immutable container for datetimes.
timedelta_range : Return a fixed frequency TimedeltaIndex.
period_range : Return a fixed frequency PeriodIndex.
interval_range : Return a fixed frequency IntervalIndex.
Notes
-----
Of the four parameters ``start``, ``end``, ``periods``, and ``freq``,
exactly three must be specified. If ``freq`` is omitted, the resulting
``DatetimeIndex`` will have ``periods`` linearly spaced elements between
``start`` and ``end`` (closed on both sides).
To learn more about the frequency strings, please see `this link
<https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
Examples
--------
**Specifying the values**
The next four examples generate the same `DatetimeIndex`, but vary
the combination of `start`, `end` and `periods`.
Specify `start` and `end`, with the default daily frequency.
>>> pd.date_range(start='1/1/2018', end='1/08/2018')
DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',
'2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08'],
dtype='datetime64[ns]', freq='D')
Specify `start` and `periods`, the number of periods (days).
>>> pd.date_range(start='1/1/2018', periods=8)
DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',
'2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08'],
dtype='datetime64[ns]', freq='D')
Specify `end` and `periods`, the number of periods (days).
>>> pd.date_range(end='1/1/2018', periods=8)
DatetimeIndex(['2017-12-25', '2017-12-26', '2017-12-27', '2017-12-28',
'2017-12-29', '2017-12-30', '2017-12-31', '2018-01-01'],
dtype='datetime64[ns]', freq='D')
Specify `start`, `end`, and `periods`; the frequency is generated
automatically (linearly spaced).
>>> pd.date_range(start='2018-04-24', end='2018-04-27', periods=3)
DatetimeIndex(['2018-04-24 00:00:00', '2018-04-25 12:00:00',
'2018-04-27 00:00:00'],
dtype='datetime64[ns]', freq=None)
**Other Parameters**
Changed the `freq` (frequency) to ``'M'`` (month end frequency).
>>> pd.date_range(start='1/1/2018', periods=5, freq='M')
DatetimeIndex(['2018-01-31', '2018-02-28', '2018-03-31', '2018-04-30',
'2018-05-31'],
dtype='datetime64[ns]', freq='M')
Multiples are allowed
>>> pd.date_range(start='1/1/2018', periods=5, freq='3M')
DatetimeIndex(['2018-01-31', '2018-04-30', '2018-07-31', '2018-10-31',
'2019-01-31'],
dtype='datetime64[ns]', freq='3M')
`freq` can also be specified as an Offset object.
>>> pd.date_range(start='1/1/2018', periods=5, freq=pd.offsets.MonthEnd(3))
DatetimeIndex(['2018-01-31', '2018-04-30', '2018-07-31', '2018-10-31',
'2019-01-31'],
dtype='datetime64[ns]', freq='3M')
Specify `tz` to set the timezone.
>>> pd.date_range(start='1/1/2018', periods=5, tz='Asia/Tokyo')
DatetimeIndex(['2018-01-01 00:00:00+09:00', '2018-01-02 00:00:00+09:00',
'2018-01-03 00:00:00+09:00', '2018-01-04 00:00:00+09:00',
'2018-01-05 00:00:00+09:00'],
dtype='datetime64[ns, Asia/Tokyo]', freq='D')
`closed` controls whether to include `start` and `end` that are on the
boundary. The default includes boundary points on either end.
>>> pd.date_range(start='2017-01-01', end='2017-01-04', closed=None)
DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04'],
dtype='datetime64[ns]', freq='D')
Use ``closed='left'`` to exclude `end` if it falls on the boundary.
>>> pd.date_range(start='2017-01-01', end='2017-01-04', closed='left')
DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03'],
dtype='datetime64[ns]', freq='D')
Use ``closed='right'`` to exclude `start` if it falls on the boundary.
>>> pd.date_range(start='2017-01-01', end='2017-01-04', closed='right')
DatetimeIndex(['2017-01-02', '2017-01-03', '2017-01-04'],
dtype='datetime64[ns]', freq='D')
"""
if freq is None and com.any_none(periods, start, end):
freq = "D"
dtarr = DatetimeArray._generate_range(
start=start,
end=end,
periods=periods,
freq=freq,
tz=tz,
normalize=normalize,
closed=closed,
**kwargs,
)
return DatetimeIndex._simple_new(dtarr, name=name)
def bdate_range(
start=None,
end=None,
periods=None,
freq="B",
tz=None,
normalize=True,
name=None,
weekmask=None,
holidays=None,
closed=None,
**kwargs,
) -> DatetimeIndex:
"""
Return a fixed frequency DatetimeIndex, with business day as the default
frequency.
Parameters
----------
start : str or datetime-like, default None
Left bound for generating dates.
end : str or datetime-like, default None
Right bound for generating dates.
periods : int, default None
Number of periods to generate.
freq : str or DateOffset, default 'B' (business daily)
Frequency strings can have multiples, e.g. '5H'.
tz : str or None
Time zone name for returning localized DatetimeIndex, for example
Asia/Beijing.
normalize : bool, default False
Normalize start/end dates to midnight before generating date range.
name : str, default None
Name of the resulting DatetimeIndex.
weekmask : str or None, default None
Weekmask of valid business days, passed to ``numpy.busdaycalendar``,
only used when custom frequency strings are passed. The default
value None is equivalent to 'Mon Tue Wed Thu Fri'.
holidays : list-like or None, default None
Dates to exclude from the set of valid business days, passed to
``numpy.busdaycalendar``, only used when custom frequency strings
are passed.
closed : str, default None
Make the interval closed with respect to the given frequency to
the 'left', 'right', or both sides (None).
**kwargs
For compatibility. Has no effect on the result.
Returns
-------
DatetimeIndex
Notes
-----
Of the four parameters: ``start``, ``end``, ``periods``, and ``freq``,
exactly three must be specified. Specifying ``freq`` is a requirement
for ``bdate_range``. Use ``date_range`` if specifying ``freq`` is not
desired.
To learn more about the frequency strings, please see `this link
<https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
Examples
--------
Note how the two weekend days are skipped in the result.
>>> pd.bdate_range(start='1/1/2018', end='1/08/2018')
DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',
'2018-01-05', '2018-01-08'],
dtype='datetime64[ns]', freq='B')
"""
if freq is None:
msg = "freq must be specified for bdate_range; use date_range instead"
raise TypeError(msg)
if isinstance(freq, str) and freq.startswith("C"):
try:
weekmask = weekmask or "Mon Tue Wed Thu Fri"
freq = prefix_mapping[freq](holidays=holidays, weekmask=weekmask)
except (KeyError, TypeError) as err:
msg = f"invalid custom frequency string: {freq}"
raise ValueError(msg) from err
elif holidays or weekmask:
msg = (
"a custom frequency string is required when holidays or "
f"weekmask are passed, got frequency {freq}"
)
raise ValueError(msg)
return date_range(
start=start,
end=end,
periods=periods,
freq=freq,
tz=tz,
normalize=normalize,
name=name,
closed=closed,
**kwargs,
)
def _time_to_micros(time_obj: time) -> int:
seconds = time_obj.hour * 60 * 60 + 60 * time_obj.minute + time_obj.second
return 1_000_000 * seconds + time_obj.microsecond