| Current File : //usr/local/lib64/python3.6/site-packages/pandas/core/indexes/timedeltas.py |
""" implement the TimedeltaIndex """
from pandas._libs import index as libindex, lib
from pandas._libs.tslibs import Timedelta, to_offset
from pandas._typing import DtypeObj, Label
from pandas.errors import InvalidIndexError
from pandas.util._decorators import doc
from pandas.core.dtypes.common import (
TD64NS_DTYPE,
is_float,
is_integer,
is_scalar,
is_timedelta64_dtype,
is_timedelta64_ns_dtype,
pandas_dtype,
)
from pandas.core.arrays import datetimelike as dtl
from pandas.core.arrays.timedeltas import TimedeltaArray
import pandas.core.common as com
from pandas.core.indexes.base import Index, maybe_extract_name
from pandas.core.indexes.datetimelike import (
DatetimeIndexOpsMixin,
DatetimeTimedeltaMixin,
)
from pandas.core.indexes.extension import inherit_names
@inherit_names(
["__neg__", "__pos__", "__abs__", "total_seconds", "round", "floor", "ceil"]
+ TimedeltaArray._field_ops,
TimedeltaArray,
wrap=True,
)
@inherit_names(
[
"_bool_ops",
"_object_ops",
"_field_ops",
"_datetimelike_ops",
"_datetimelike_methods",
"_other_ops",
"components",
"to_pytimedelta",
"sum",
"std",
"median",
"_format_native_types",
],
TimedeltaArray,
)
class TimedeltaIndex(DatetimeTimedeltaMixin):
"""
Immutable ndarray of timedelta64 data, represented internally as int64, and
which can be boxed to timedelta objects.
Parameters
----------
data : array-like (1-dimensional), optional
Optional timedelta-like data to construct index with.
unit : unit of the arg (D,h,m,s,ms,us,ns) denote the unit, optional
Which is an integer/float number.
freq : str or pandas offset object, optional
One of pandas date offset strings or corresponding objects. The string
'infer' can be passed in order to set the frequency of the index as the
inferred frequency upon creation.
copy : bool
Make a copy of input ndarray.
name : object
Name to be stored in the index.
Attributes
----------
days
seconds
microseconds
nanoseconds
components
inferred_freq
Methods
-------
to_pytimedelta
to_series
round
floor
ceil
to_frame
mean
See Also
--------
Index : The base pandas Index type.
Timedelta : Represents a duration between two dates or times.
DatetimeIndex : Index of datetime64 data.
PeriodIndex : Index of Period data.
timedelta_range : Create a fixed-frequency TimedeltaIndex.
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 = "timedeltaindex"
_engine_type = libindex.TimedeltaEngine
_comparables = ["name", "freq"]
_attributes = ["name", "freq"]
_is_numeric_dtype = True
_data: TimedeltaArray
# -------------------------------------------------------------------
# Constructors
def __new__(
cls,
data=None,
unit=None,
freq=lib.no_default,
closed=None,
dtype=TD64NS_DTYPE,
copy=False,
name=None,
):
name = maybe_extract_name(name, data, cls)
if is_scalar(data):
raise TypeError(
f"{cls.__name__}() must be called with a "
f"collection of some kind, {repr(data)} was passed"
)
if unit in {"Y", "y", "M"}:
raise ValueError(
"Units 'M', 'Y', and 'y' are no longer supported, as they do not "
"represent unambiguous timedelta values durations."
)
if isinstance(data, TimedeltaArray) and freq is lib.no_default:
if copy:
data = data.copy()
return cls._simple_new(data, name=name)
if isinstance(data, TimedeltaIndex) and freq is lib.no_default and name is None:
if copy:
return data.copy()
else:
return data._shallow_copy()
# - Cases checked above all return/raise before reaching here - #
tdarr = TimedeltaArray._from_sequence(
data, freq=freq, unit=unit, dtype=dtype, copy=copy
)
return cls._simple_new(tdarr, name=name)
@classmethod
def _simple_new(cls, values: TimedeltaArray, name: Label = None):
assert isinstance(values, TimedeltaArray)
result = object.__new__(cls)
result._data = values
result._name = name
result._cache = {}
# For groupby perf. See note in indexes/base about _index_data
result._index_data = values._data
result._reset_identity()
return result
# -------------------------------------------------------------------
# Rendering Methods
@property
def _formatter_func(self):
from pandas.io.formats.format import _get_format_timedelta64
return _get_format_timedelta64(self, box=True)
# -------------------------------------------------------------------
@doc(Index.astype)
def astype(self, dtype, copy=True):
dtype = pandas_dtype(dtype)
if is_timedelta64_dtype(dtype) and not is_timedelta64_ns_dtype(dtype):
# Have to repeat the check for 'timedelta64' (not ns) dtype
# so that we can return a numeric index, since pandas will return
# a TimedeltaIndex when dtype='timedelta'
result = self._data.astype(dtype, copy=copy)
if self.hasnans:
return Index(result, name=self.name)
return Index(result.astype("i8"), name=self.name)
return DatetimeIndexOpsMixin.astype(self, dtype, copy=copy)
def _is_comparable_dtype(self, dtype: DtypeObj) -> bool:
"""
Can we compare values of the given dtype to our own?
"""
return is_timedelta64_dtype(dtype)
def get_loc(self, key, method=None, tolerance=None):
"""
Get integer location for requested label
Returns
-------
loc : int, slice, or ndarray[int]
"""
if not is_scalar(key):
raise InvalidIndexError(key)
try:
key = self._data._validate_scalar(key, cast_str=True)
except TypeError as err:
raise KeyError(key) from err
return Index.get_loc(self, key, method, tolerance)
def _maybe_cast_slice_bound(self, label, side: str, kind):
"""
If label is a string, cast it to timedelta according to resolution.
Parameters
----------
label : object
side : {'left', 'right'}
kind : {'loc', 'getitem'} or None
Returns
-------
label : object
"""
assert kind in ["loc", "getitem", None]
if isinstance(label, str):
parsed = Timedelta(label)
lbound = parsed.round(parsed.resolution_string)
if side == "left":
return lbound
else:
return lbound + to_offset(parsed.resolution_string) - Timedelta(1, "ns")
elif is_integer(label) or is_float(label):
self._invalid_indexer("slice", label)
return label
def is_type_compatible(self, typ) -> bool:
return typ == self.inferred_type or typ == "timedelta"
@property
def inferred_type(self) -> str:
return "timedelta64"
TimedeltaIndex._add_logical_methods_disabled()
def timedelta_range(
start=None, end=None, periods=None, freq=None, name=None, closed=None
) -> TimedeltaIndex:
"""
Return a fixed frequency TimedeltaIndex, with day as the default
frequency.
Parameters
----------
start : str or timedelta-like, default None
Left bound for generating timedeltas.
end : str or timedelta-like, default None
Right bound for generating timedeltas.
periods : int, default None
Number of periods to generate.
freq : str or DateOffset, default 'D'
Frequency strings can have multiples, e.g. '5H'.
name : str, default None
Name of the resulting TimedeltaIndex.
closed : str, default None
Make the interval closed with respect to the given frequency to
the 'left', 'right', or both sides (None).
Returns
-------
rng : TimedeltaIndex
Notes
-----
Of the four parameters ``start``, ``end``, ``periods``, and ``freq``,
exactly three must be specified. If ``freq`` is omitted, the resulting
``TimedeltaIndex`` 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
--------
>>> pd.timedelta_range(start='1 day', periods=4)
TimedeltaIndex(['1 days', '2 days', '3 days', '4 days'],
dtype='timedelta64[ns]', freq='D')
The ``closed`` parameter specifies which endpoint is included. The default
behavior is to include both endpoints.
>>> pd.timedelta_range(start='1 day', periods=4, closed='right')
TimedeltaIndex(['2 days', '3 days', '4 days'],
dtype='timedelta64[ns]', freq='D')
The ``freq`` parameter specifies the frequency of the TimedeltaIndex.
Only fixed frequencies can be passed, non-fixed frequencies such as
'M' (month end) will raise.
>>> pd.timedelta_range(start='1 day', end='2 days', freq='6H')
TimedeltaIndex(['1 days 00:00:00', '1 days 06:00:00', '1 days 12:00:00',
'1 days 18:00:00', '2 days 00:00:00'],
dtype='timedelta64[ns]', freq='6H')
Specify ``start``, ``end``, and ``periods``; the frequency is generated
automatically (linearly spaced).
>>> pd.timedelta_range(start='1 day', end='5 days', periods=4)
TimedeltaIndex(['1 days 00:00:00', '2 days 08:00:00', '3 days 16:00:00',
'5 days 00:00:00'],
dtype='timedelta64[ns]', freq=None)
"""
if freq is None and com.any_none(periods, start, end):
freq = "D"
freq, _ = dtl.maybe_infer_freq(freq)
tdarr = TimedeltaArray._generate_range(start, end, periods, freq, closed=closed)
return TimedeltaIndex._simple_new(tdarr, name=name)