def test_astype_object(self):
tdi = pd.TimedeltaIndex(['1 Day', '3 Hours'])
arr = TimedeltaArrayMixin(tdi)
asobj = arr.astype('O')
assert isinstance(asobj, np.ndarray)
assert asobj.dtype == 'O'
assert list(asobj) == list(tdi)
def test_astype_object(self):
tdi = pd.TimedeltaIndex(['1 Day', '3 Hours'])
arr = TimedeltaArrayMixin(tdi)
asobj = arr.astype('O')
assert isinstance(asobj, np.ndarray)
assert asobj.dtype == 'O'
assert list(asobj) == list(tdi)
def _add_delta(self, delta):
"""
Add a timedelta-like, DateOffset, or TimedeltaIndex-like object
to self.
Parameters
----------
delta : {timedelta, np.timedelta64, DateOffset,
TimedelaIndex, ndarray[timedelta64]}
Returns
-------
result : same type as self
Notes
-----
The result's name is set outside of _add_delta by the calling
method (__add__ or __sub__)
"""
from pandas.core.arrays.timedelta import TimedeltaArrayMixin
if isinstance(delta, (Tick, timedelta, np.timedelta64)):
new_values = self._add_delta_td(delta)
elif is_timedelta64_dtype(delta):
if not isinstance(delta, TimedeltaArrayMixin):
delta = TimedeltaArrayMixin(delta)
new_values = self._add_delta_tdi(delta)
else:
new_values = self.astype('O') + delta
tz = 'UTC' if self.tz is not None else None
result = type(self)(new_values, tz=tz, freq='infer')
if self.tz is not None and self.tz is not utc:
result = result.tz_convert(self.tz)
return result
def total_seconds(self):
"""
Return total duration of each element expressed in seconds.
This method is available directly on TimedeltaIndex and on Series
containing timedelta values under the ``.dt`` namespace.
Returns
-------
seconds : Float64Index or Series
When the calling object is a TimedeltaIndex, the return type is a
Float64Index. When the calling object is a Series, the return type
is Series of type `float64` whose index is the same as the
original.
See Also
--------
datetime.timedelta.total_seconds : Standard library version
of this method.
TimedeltaIndex.components : Return a DataFrame with components of
each Timedelta.
Examples
--------
**Series**
>>> s = pd.Series(pd.to_timedelta(np.arange(5), unit='d'))
>>> s
0 0 days
1 1 days
2 2 days
3 3 days
4 4 days
dtype: timedelta64[ns]
>>> s.dt.total_seconds()
0 0.0
1 86400.0
2 172800.0
3 259200.0
4 345600.0
dtype: float64
**TimedeltaIndex**
>>> idx = pd.to_timedelta(np.arange(5), unit='d')
>>> idx
TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'],
dtype='timedelta64[ns]', freq=None)
>>> idx.total_seconds()
Float64Index([0.0, 86400.0, 172800.0, 259200.00000000003, 345600.0],
dtype='float64')
"""
result = TimedeltaArrayMixin.total_seconds(self)
return Index(result, name=self.name)
def total_seconds(self):
"""
Return total duration of each element expressed in seconds.
This method is available directly on TimedeltaIndex and on Series
containing timedelta values under the ``.dt`` namespace.
Returns
-------
seconds : Float64Index or Series
When the calling object is a TimedeltaIndex, the return type is a
Float64Index. When the calling object is a Series, the return type
is Series of type `float64` whose index is the same as the
original.
See Also
--------
datetime.timedelta.total_seconds : Standard library version
of this method.
TimedeltaIndex.components : Return a DataFrame with components of
each Timedelta.
Examples
--------
**Series**
>>> s = pd.Series(pd.to_timedelta(np.arange(5), unit='d'))
>>> s
0 0 days
1 1 days
2 2 days
3 3 days
4 4 days
dtype: timedelta64[ns]
>>> s.dt.total_seconds()
0 0.0
1 86400.0
2 172800.0
3 259200.0
4 345600.0
dtype: float64
**TimedeltaIndex**
>>> idx = pd.to_timedelta(np.arange(5), unit='d')
>>> idx
TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'],
dtype='timedelta64[ns]', freq=None)
>>> idx.total_seconds()
Float64Index([0.0, 86400.0, 172800.0, 259200.00000000003, 345600.0],
dtype='float64')
"""
result = TimedeltaArrayMixin.total_seconds(self)
return Index(result, name=self.name)
def _evaluate_with_timedelta_like(self, other, op):
result = TimedeltaArrayMixin._evaluate_with_timedelta_like(
self, other, op)
if result is NotImplemented:
return NotImplemented
return Index(result, name=self.name, copy=False)
def total_seconds(self):
result = TimedeltaArrayMixin.total_seconds(self)
return Index(result, name=self.name)
def _evaluate_with_timedelta_like(self, other, op):
result = TimedeltaArrayMixin._evaluate_with_timedelta_like(self, other,
op)
if result is NotImplemented:
return NotImplemented
return Index(result, name=self.name, copy=False)
def test_from_tdi(self):
tdi = pd.TimedeltaIndex(['1 Day', '3 Hours'])
arr = TimedeltaArrayMixin(tdi)
assert list(arr) == list(tdi)