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Accessor object for datetimelike properties of the Series values.
|
|
Examples
|
--------
|
>>> seconds_series = pd.Series(pd.date_range("2000-01-01", periods=3, freq="s"))
|
>>> seconds_series
|
0 2000-01-01 00:00:00
|
1 2000-01-01 00:00:01
|
2 2000-01-01 00:00:02
|
dtype: datetime64[ns]
|
>>> seconds_series.dt.second
|
0 0
|
1 1
|
2 2
|
dtype: int32
|
|
>>> hours_series = pd.Series(pd.date_range("2000-01-01", periods=3, freq="h"))
|
>>> hours_series
|
0 2000-01-01 00:00:00
|
1 2000-01-01 01:00:00
|
2 2000-01-01 02:00:00
|
dtype: datetime64[ns]
|
>>> hours_series.dt.hour
|
0 0
|
1 1
|
2 2
|
dtype: int32
|
|
>>> quarters_series = pd.Series(pd.date_range("2000-01-01", periods=3, freq="QE"))
|
>>> quarters_series
|
0 2000-03-31
|
1 2000-06-30
|
2 2000-09-30
|
dtype: datetime64[ns]
|
>>> quarters_series.dt.quarter
|
0 1
|
1 2
|
2 3
|
dtype: int32
|
|
Returns a Series indexed like the original Series.
|
Raises TypeError if the Series does not contain datetimelike values.
|
c ó´ t j dt | « j dt t « ¬« | j « j « S )aö
|
Return the data as an array of :class:`datetime.datetime` objects.
|
|
.. deprecated:: 2.1.0
|
|
The current behavior of dt.to_pydatetime is deprecated.
|
In a future version this will return a Series containing python
|
datetime objects instead of a ndarray.
|
|
Timezone information is retained if present.
|
|
.. warning::
|
|
Python's datetime uses microsecond resolution, which is lower than
|
pandas (nanosecond). The values are truncated.
|
|
Returns
|
-------
|
numpy.ndarray
|
Object dtype array containing native Python datetime objects.
|
|
See Also
|
--------
|
datetime.datetime : Standard library value for a datetime.
|
|
Examples
|
--------
|
>>> s = pd.Series(pd.date_range('20180310', periods=2))
|
>>> s
|
0 2018-03-10
|
1 2018-03-11
|
dtype: datetime64[ns]
|
|
>>> s.dt.to_pydatetime()
|
array([datetime.datetime(2018, 3, 10, 0, 0),
|
datetime.datetime(2018, 3, 11, 0, 0)], dtype=object)
|
|
pandas' nanosecond precision is truncated to microseconds.
|
|
>>> s = pd.Series(pd.date_range('20180310', periods=2, freq='ns'))
|
>>> s
|
0 2018-03-10 00:00:00.000000000
|
1 2018-03-10 00:00:00.000000001
|
dtype: datetime64[ns]
|
|
>>> s.dt.to_pydatetime()
|
array([datetime.datetime(2018, 3, 10, 0, 0),
|
datetime.datetime(2018, 3, 10, 0, 0)], dtype=object)
|
rw rx ry )r{ r| r$ rR r} r r4 r rt s r+ r z DatetimeProperties.to_pydatetimeR sR ôf Ø t Dz× 2Ñ 2Ð 3ð 4,ð ,ô Ü'Ó)õ
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|
Calculate year, week, and day according to the ISO 8601 standard.
|
|
Returns
|
-------
|
DataFrame
|
With columns year, week and day.
|
|
See Also
|
--------
|
Timestamp.isocalendar : Function return a 3-tuple containing ISO year,
|
week number, and weekday for the given Timestamp object.
|
datetime.date.isocalendar : Return a named tuple object with
|
three components: year, week and weekday.
|
|
Examples
|
--------
|
>>> ser = pd.to_datetime(pd.Series(["2010-01-01", pd.NaT]))
|
>>> ser.dt.isocalendar()
|
year week day
|
0 2009 53 5
|
1 <NA> <NA> <NA>
|
>>> ser.dt.isocalendar().week
|
0 53
|
1 <NA>
|
Name: week, dtype: UInt32
|
)r4 r Ú set_indexr% r9 rt s r+ r z DatetimeProperties.isocalendar s1 ð8 × Ñ Ó!×-Ñ-Ó/×9Ñ9¸$¿,¹,×:LÑ:LÓMÐMr- N©rN z
|
np.ndarrayr )rR rS rT Ú__doc__r rW r¡ r rV r- r+ r r s( ñ+óZ;2ðz ñ0ó ð0ô Nr- r c ó8 e Zd ZdZddZed « Zed « Zy)ÚTimedeltaPropertiesa)
|
Accessor object for datetimelike properties of the Series values.
|
|
Returns a Series indexed like the original Series.
|
Raises TypeError if the Series does not contain datetimelike values.
|
|
Examples
|
--------
|
>>> seconds_series = pd.Series(
|
... pd.timedelta_range(start="1 second", periods=3, freq="s")
|
... )
|
>>> seconds_series
|
0 0 days 00:00:01
|
1 0 days 00:00:02
|
2 0 days 00:00:03
|
dtype: timedelta64[ns]
|
>>> seconds_series.dt.seconds
|
0 1
|
1 2
|
2 3
|
dtype: int32
|
c ó> | j « j « S )a!
|
Return an array of native :class:`datetime.timedelta` objects.
|
|
Python's standard `datetime` library uses a different representation
|
timedelta's. This method converts a Series of pandas Timedeltas
|
to `datetime.timedelta` format with the same length as the original
|
Series.
|
|
Returns
|
-------
|
numpy.ndarray
|
Array of 1D containing data with `datetime.timedelta` type.
|
|
See Also
|
--------
|
datetime.timedelta : A duration expressing the difference
|
between two date, time, or datetime.
|
|
Examples
|
--------
|
>>> 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.to_pytimedelta()
|
array([datetime.timedelta(0), datetime.timedelta(days=1),
|
datetime.timedelta(days=2), datetime.timedelta(days=3),
|
datetime.timedelta(days=4)], dtype=object)
|
)r4 ru rt s r+ ru z"TimedeltaProperties.to_pytimedeltaÒ s ðF × Ñ Ó!×0Ñ0Ó2Ð2r- c ó® | j « j j | j j « j | j « S )a£
|
Return a Dataframe of the components of the Timedeltas.
|
|
Returns
|
-------
|
DataFrame
|
|
Examples
|
--------
|
>>> s = pd.Series(pd.to_timedelta(np.arange(5), unit='s'))
|
>>> s
|
0 0 days 00:00:00
|
1 0 days 00:00:01
|
2 0 days 00:00:02
|
3 0 days 00:00:03
|
4 0 days 00:00:04
|
dtype: timedelta64[ns]
|
>>> s.dt.components
|
days hours minutes seconds milliseconds microseconds nanoseconds
|
0 0 0 0 0 0 0 0
|
1 0 0 0 1 0 0 0
|
2 0 0 0 2 0 0 0
|
3 0 0 0 3 0 0 0
|
4 0 0 0 4 0 0 0
|
)r4 r r¤ r% r9 r? rt s r+ r z TimedeltaProperties.components÷ s; ð8 × Ñ Ó ß Z $§,¡,×"4Ñ"4Ó5ß \$,,Ó 'ð
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r- c ó6 | j « j S rr r rt s r+ r¡ zTimedeltaProperties.freq r¢ r- Nr¥ )rR rS rT r¦ ru rW r r¡ rV r- r+ r¨ r¨ ² s6 ñó.#3ðJ ñ
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ó ð
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ð@ ñ0ó ñ0r- r¨ c ó e Zd ZdZy)ÚPeriodPropertiesaQ
|
Accessor object for datetimelike properties of the Series values.
|
|
Returns a Series indexed like the original Series.
|
Raises TypeError if the Series does not contain datetimelike values.
|
|
Examples
|
--------
|
>>> seconds_series = pd.Series(
|
... pd.period_range(
|
... start="2000-01-01 00:00:00", end="2000-01-01 00:00:03", freq="s"
|
... )
|
... )
|
>>> seconds_series
|
0 2000-01-01 00:00:00
|
1 2000-01-01 00:00:01
|
2 2000-01-01 00:00:02
|
3 2000-01-01 00:00:03
|
dtype: period[s]
|
>>> seconds_series.dt.second
|
0 0
|
1 1
|
2 2
|
3 3
|
dtype: int64
|
|
>>> hours_series = pd.Series(
|
... pd.period_range(start="2000-01-01 00:00", end="2000-01-01 03:00", freq="h")
|
... )
|
>>> hours_series
|
0 2000-01-01 00:00
|
1 2000-01-01 01:00
|
2 2000-01-01 02:00
|
3 2000-01-01 03:00
|
dtype: period[h]
|
>>> hours_series.dt.hour
|
0 0
|
1 1
|
2 2
|
3 3
|
dtype: int64
|
|
>>> quarters_series = pd.Series(
|
... pd.period_range(start="2000-01-01", end="2000-12-31", freq="Q-DEC")
|
... )
|
>>> quarters_series
|
0 2000Q1
|
1 2000Q2
|
2 2000Q3
|
3 2000Q4
|
dtype: period[Q-DEC]
|
>>> quarters_series.dt.quarter
|
0 1
|
1 2
|
2 3
|
3 4
|
dtype: int64
|
N)rR rS rT r¦ rV r- r+ r r s ò9r- r c ó e Zd ZddZy)Ú CombinedDatetimelikePropertiesc ó t |t « st dt |« d« t |j t
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