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DOC: Fixing EX01 - Added more examples #53528

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Jun 5, 2023
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DOC: Fixing EX01 - Added more examples #53528

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8 changes: 0 additions & 8 deletions ci/code_checks.sh
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Original file line number Diff line number Diff line change
Expand Up @@ -250,14 +250,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
pandas.DatetimeIndex.mean \
pandas.DatetimeIndex.std \
pandas.TimedeltaIndex \
pandas.TimedeltaIndex.seconds \
pandas.TimedeltaIndex.microseconds \
pandas.TimedeltaIndex.nanoseconds \
pandas.TimedeltaIndex.components \
pandas.TimedeltaIndex.inferred_freq \
pandas.TimedeltaIndex.as_unit \
pandas.TimedeltaIndex.to_pytimedelta \
pandas.TimedeltaIndex.mean \
pandas.core.window.rolling.Rolling.max \
pandas.core.window.rolling.Rolling.cov \
pandas.core.window.rolling.Rolling.skew \
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20 changes: 20 additions & 0 deletions pandas/core/arrays/datetimelike.py
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Expand Up @@ -861,9 +861,20 @@ def inferred_freq(self) -> str | None:

Examples
--------
For DatetimeIndex:

>>> idx = pd.DatetimeIndex(["2018-01-01", "2018-01-03", "2018-01-05"])
>>> idx.inferred_freq
'2D'

For TimedeltaIndex:

>>> tdelta_idx = pd.to_timedelta(["0 days", "10 days", "20 days"])
>>> tdelta_idx
TimedeltaIndex(['0 days', '10 days', '20 days'],
dtype='timedelta64[ns]', freq=None)
>>> tdelta_idx.inferred_freq
'10D'
"""
if self.ndim != 1:
return None
Expand Down Expand Up @@ -1535,6 +1546,15 @@ def mean(self, *, skipna: bool = True, axis: AxisInt | None = 0):
Notes
-----
mean is only defined for Datetime and Timedelta dtypes, not for Period.

Examples
--------
>>> tdelta_idx = pd.to_timedelta([1, 2, 3], unit='D')
>>> tdelta_idx
TimedeltaIndex(['1 days', '2 days', '3 days'],
dtype='timedelta64[ns]', freq=None)
>>> tdelta_idx.mean()
Timedelta('2 days 00:00:00')
"""
if isinstance(self.dtype, PeriodDtype):
# See discussion in GH#24757
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74 changes: 70 additions & 4 deletions pandas/core/arrays/timedeltas.py
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Expand Up @@ -796,6 +796,16 @@ def to_pytimedelta(self) -> npt.NDArray[np.object_]:
Returns
-------
numpy.ndarray

Examples
--------
>>> tdelta_idx = pd.to_timedelta([1, 2, 3], unit='D')
>>> tdelta_idx
TimedeltaIndex(['1 days', '2 days', '3 days'],
dtype='timedelta64[ns]', freq=None)
>>> tdelta_idx.to_pytimedelta()
array([datetime.timedelta(days=1), datetime.timedelta(days=2),
datetime.timedelta(days=3)], dtype=object)
"""
return ints_to_pytimedelta(self._ndarray)

Expand All @@ -804,6 +814,8 @@ def to_pytimedelta(self) -> npt.NDArray[np.object_]:

Examples
--------
For Series:

>>> ser = pd.Series(pd.to_timedelta([1, 2, 3], unit='d'))
>>> ser
0 1 days
Expand All @@ -814,7 +826,16 @@ def to_pytimedelta(self) -> npt.NDArray[np.object_]:
0 1
1 2
2 3
dtype: int64"""
dtype: int64

For TimedeltaIndex:

>>> tdelta_idx = pd.to_timedelta(["0 days", "10 days", "20 days"])
>>> tdelta_idx
TimedeltaIndex(['0 days', '10 days', '20 days'],
dtype='timedelta64[ns]', freq=None)
>>> tdelta_idx.days
Index([0, 10, 20], dtype='int64')"""
)
days = _field_accessor("days", "days", days_docstring)

Expand All @@ -823,6 +844,8 @@ def to_pytimedelta(self) -> npt.NDArray[np.object_]:

Examples
--------
For Series:

>>> ser = pd.Series(pd.to_timedelta([1, 2, 3], unit='S'))
>>> ser
0 0 days 00:00:01
Expand All @@ -833,7 +856,16 @@ def to_pytimedelta(self) -> npt.NDArray[np.object_]:
0 1
1 2
2 3
dtype: int32"""
dtype: int32

For TimedeltaIndex:

>>> tdelta_idx = pd.to_timedelta([1, 2, 3], unit='S')
>>> tdelta_idx
TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:02', '0 days 00:00:03'],
dtype='timedelta64[ns]', freq=None)
>>> tdelta_idx.seconds
Index([1, 2, 3], dtype='int32')"""
)
seconds = _field_accessor(
"seconds",
Expand All @@ -846,6 +878,8 @@ def to_pytimedelta(self) -> npt.NDArray[np.object_]:

Examples
--------
For Series:

>>> ser = pd.Series(pd.to_timedelta([1, 2, 3], unit='U'))
>>> ser
0 0 days 00:00:00.000001
Expand All @@ -856,7 +890,17 @@ def to_pytimedelta(self) -> npt.NDArray[np.object_]:
0 1
1 2
2 3
dtype: int32"""
dtype: int32

For TimedeltaIndex:

>>> tdelta_idx = pd.to_timedelta([1, 2, 3], unit='U')
>>> tdelta_idx
TimedeltaIndex(['0 days 00:00:00.000001', '0 days 00:00:00.000002',
'0 days 00:00:00.000003'],
dtype='timedelta64[ns]', freq=None)
>>> tdelta_idx.microseconds
Index([1, 2, 3], dtype='int32')"""
)
microseconds = _field_accessor(
"microseconds",
Expand All @@ -869,6 +913,8 @@ def to_pytimedelta(self) -> npt.NDArray[np.object_]:

Examples
--------
For Series:

>>> ser = pd.Series(pd.to_timedelta([1, 2, 3], unit='N'))
>>> ser
0 0 days 00:00:00.000000001
Expand All @@ -879,7 +925,17 @@ def to_pytimedelta(self) -> npt.NDArray[np.object_]:
0 1
1 2
2 3
dtype: int32"""
dtype: int32

For TimedeltaIndex:

>>> tdelta_idx = pd.to_timedelta([1, 2, 3], unit='N')
>>> tdelta_idx
TimedeltaIndex(['0 days 00:00:00.000000001', '0 days 00:00:00.000000002',
'0 days 00:00:00.000000003'],
dtype='timedelta64[ns]', freq=None)
>>> tdelta_idx.nanoseconds
Index([1, 2, 3], dtype='int32')"""
)
nanoseconds = _field_accessor(
"nanoseconds",
Expand All @@ -898,6 +954,16 @@ def components(self) -> DataFrame:
Returns
-------
DataFrame

Examples
--------
>>> tdelta_idx = pd.to_timedelta(['1 day 3 min 2 us 42 ns'])
>>> tdelta_idx
TimedeltaIndex(['1 days 00:03:00.000002042'],
dtype='timedelta64[ns]', freq=None)
>>> tdelta_idx.components
days hours minutes seconds milliseconds microseconds nanoseconds
0 1 0 3 0 0 2 42
"""
from pandas import DataFrame

Expand Down
9 changes: 9 additions & 0 deletions pandas/core/indexes/datetimelike.py
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Expand Up @@ -431,6 +431,15 @@ def as_unit(self, unit: str) -> Self:
Returns
-------
same type as self

Examples
--------
>>> tdelta_idx = pd.to_timedelta(['1 day 3 min 2 us 42 ns'])
>>> tdelta_idx
TimedeltaIndex(['1 days 00:03:00.000002042'],
dtype='timedelta64[ns]', freq=None)
>>> tdelta_idx.as_unit('s')
TimedeltaIndex(['1 days 00:03:00'], dtype='timedelta64[s]', freq=None)
"""
arr = self._data.as_unit(unit)
return type(self)._simple_new(arr, name=self.name)
Expand Down