时间增量#
Timedeltas是时间上的差异，以不同的单位表示，例如天、小时、分钟、秒。它们既可以是积极的，也可以是消极的。
Timedelta 是的子类 datetime.timedelta ，并以类似方式运行，但允许与 np.timedelta64 类型以及大量的自定义表示、解析和属性。
解析#
您可以构造一个 Timedelta 标量通过各种参数，包括 ISO 8601 Duration 弦乐。
In [1]: import datetime
# strings
In [2]: pd.Timedelta("1 days")
Out[2]: Timedelta('1 days 00:00:00')
In [3]: pd.Timedelta("1 days 00:00:00")
Out[3]: Timedelta('1 days 00:00:00')
In [4]: pd.Timedelta("1 days 2 hours")
Out[4]: Timedelta('1 days 02:00:00')
In [5]: pd.Timedelta("-1 days 2 min 3us")
Out[5]: Timedelta('-2 days +23:57:59.999997')
# like datetime.timedelta
# note: these MUST be specified as keyword arguments
In [6]: pd.Timedelta(days=1, seconds=1)
Out[6]: Timedelta('1 days 00:00:01')
# integers with a unit
In [7]: pd.Timedelta(1, unit="d")
Out[7]: Timedelta('1 days 00:00:00')
# from a datetime.timedelta/np.timedelta64
In [8]: pd.Timedelta(datetime.timedelta(days=1, seconds=1))
Out[8]: Timedelta('1 days 00:00:01')
In [9]: pd.Timedelta(np.timedelta64(1, "ms"))
Out[9]: Timedelta('0 days 00:00:00.001000')
# negative Timedeltas have this string repr
# to be more consistent with datetime.timedelta conventions
In [10]: pd.Timedelta("-1us")
Out[10]: Timedelta('-1 days +23:59:59.999999')
# a NaT
In [11]: pd.Timedelta("nan")
Out[11]: NaT
In [12]: pd.Timedelta("nat")
Out[12]: NaT
# ISO 8601 Duration strings
In [13]: pd.Timedelta("P0DT0H1M0S")
Out[13]: Timedelta('0 days 00:01:00')
In [14]: pd.Timedelta("P0DT0H0M0.000000123S")
Out[14]: Timedelta('0 days 00:00:00.000000123')
DateOffsets (Day, Hour, Minute, Second, Milli, Micro, Nano) can also be used in construction.
In [15]: pd.Timedelta(pd.offsets.Second(2))
Out[15]: Timedelta('0 days 00:00:02')
此外，标量之间的运算产生另一个标量 Timedelta 。
In [16]: pd.Timedelta(pd.offsets.Day(2)) + pd.Timedelta(pd.offsets.Second(2)) + pd.Timedelta(
....: "00:00:00.000123"
....: )
....:
Out[16]: Timedelta('2 days 00:00:02.000123')
to_timedelta#
使用顶层 pd.to_timedelta ，您可以将标量、数组、列表或系列从可识别的时间增量格式/值转换为 Timedelta 键入。如果输入是Series，它将构造Series；如果输入是标量，它将构造标量；否则，它将输出 TimedeltaIndex 。
您可以将单个字符串解析为Timedelta：
In [17]: pd.to_timedelta("1 days 06:05:01.00003")
Out[17]: Timedelta('1 days 06:05:01.000030')
In [18]: pd.to_timedelta("15.5us")
Out[18]: Timedelta('0 days 00:00:00.000015500')
或字符串列表/数组：
In [19]: pd.to_timedelta(["1 days 06:05:01.00003", "15.5us", "nan"])
Out[19]: TimedeltaIndex(['1 days 06:05:01.000030', '0 days 00:00:00.000015500', NaT], dtype='timedelta64[ns]', freq=None)
这个 unit 如果输入为数字，则关键字参数指定Timedelta的单位：
In [20]: pd.to_timedelta(np.arange(5), unit="s")
Out[20]:
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:01', '0 days 00:00:02',
'0 days 00:00:03', '0 days 00:00:04'],
dtype='timedelta64[ns]', freq=None)
In [21]: pd.to_timedelta(np.arange(5), unit="d")
Out[21]: TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'], dtype='timedelta64[ns]', freq=None)
警告
如果将字符串或字符串数组作为输入传递，则 unit 关键字参数将被忽略。如果传递的是不带单位的字符串，则假定默认单位为纳秒。
Timedelta限制#
Pandas代表着 Timedeltas 使用64位整数的纳秒分辨率。因此，64位整数限制决定了 Timedelta 极限。
In [22]: pd.Timedelta.min
Out[22]: Timedelta('-106752 days +00:12:43.145224193')
In [23]: pd.Timedelta.max
Out[23]: Timedelta('106751 days 23:47:16.854775807')
运营#
您可以对Series/DataFrames进行操作并构造 timedelta64[ns] 系列到减法运算 datetime64[ns] 系列，或 Timestamps 。
In [24]: s = pd.Series(pd.date_range("2012-1-1", periods=3, freq="D"))
In [25]: td = pd.Series([pd.Timedelta(days=i) for i in range(3)])
In [26]: df = pd.DataFrame({"A": s, "B": td})
In [27]: df
Out[27]:
A B
0 2012-01-01 0 days
1 2012-01-02 1 days
2 2012-01-03 2 days
In [28]: df["C"] = df["A"] + df["B"]
In [29]: df
Out[29]:
A B C
0 2012-01-01 0 days 2012-01-01
1 2012-01-02 1 days 2012-01-03
2 2012-01-03 2 days 2012-01-05
In [30]: df.dtypes
Out[30]:
A datetime64[ns]
B timedelta64[ns]
C datetime64[ns]
dtype: object
In [31]: s - s.max()
Out[31]:
0 -2 days
1 -1 days
2 0 days
dtype: timedelta64[ns]
In [32]: s - datetime.datetime(2011, 1, 1, 3, 5)
Out[32]:
0 364 days 20:55:00
1 365 days 20:55:00
2 366 days 20:55:00
dtype: timedelta64[ns]
In [33]: s + datetime.timedelta(minutes=5)
Out[33]:
0 2012-01-01 00:05:00
1 2012-01-02 00:05:00
2 2012-01-03 00:05:00
dtype: datetime64[ns]
In [34]: s + pd.offsets.Minute(5)
Out[34]:
0 2012-01-01 00:05:00
1 2012-01-02 00:05:00
2 2012-01-03 00:05:00
dtype: datetime64[ns]
In [35]: s + pd.offsets.Minute(5) + pd.offsets.Milli(5)
Out[35]:
0 2012-01-01 00:05:00.005
1 2012-01-02 00:05:00.005
2 2012-01-03 00:05:00.005
dtype: datetime64[ns]
中的标量进行运算 timedelta64[ns] 系列：
In [36]: y = s - s[0]
In [37]: y
Out[37]:
0 0 days
1 1 days
2 2 days
dtype: timedelta64[ns]
一系列的时间增量 NaT 支持的值包括：
In [38]: y = s - s.shift()
In [39]: y
Out[39]:
0 NaT
1 1 days
2 1 days
dtype: timedelta64[ns]
元素可以设置为 NaT 使用 np.nan 类似于Date Time：
In [40]: y[1] = np.nan
In [41]: y
Out[41]:
0 NaT
1 NaT
2 1 days
dtype: timedelta64[ns]
操作对象也可以以相反的顺序出现(使用系列操作的单数对象)：
In [42]: s.max() - s
Out[42]:
0 2 days
1 1 days
2 0 days
dtype: timedelta64[ns]
In [43]: datetime.datetime(2011, 1, 1, 3, 5) - s
Out[43]:
0 -365 days +03:05:00
1 -366 days +03:05:00
2 -367 days +03:05:00
dtype: timedelta64[ns]
In [44]: datetime.timedelta(minutes=5) + s
Out[44]:
0 2012-01-01 00:05:00
1 2012-01-02 00:05:00
2 2012-01-03 00:05:00
dtype: datetime64[ns]
min, max 以及相应的 idxmin, idxmax 在框架上支持操作：
In [45]: A = s - pd.Timestamp("20120101") - pd.Timedelta("00:05:05")
In [46]: B = s - pd.Series(pd.date_range("2012-1-2", periods=3, freq="D"))
In [47]: df = pd.DataFrame({"A": A, "B": B})
In [48]: df
Out[48]:
A B
0 -1 days +23:54:55 -1 days
1 0 days 23:54:55 -1 days
2 1 days 23:54:55 -1 days
In [49]: df.min()
Out[49]:
A -1 days +23:54:55
B -1 days +00:00:00
dtype: timedelta64[ns]
In [50]: df.min(axis=1)
Out[50]:
0 -1 days
1 -1 days
2 -1 days
dtype: timedelta64[ns]
In [51]: df.idxmin()
Out[51]:
A 0
B 0
dtype: int64
In [52]: df.idxmax()
Out[52]:
A 2
B 0
dtype: int64
min, max, idxmin, idxmax 在系列上也支持操作。标量结果将是 Timedelta 。
In [53]: df.min().max()
Out[53]: Timedelta('-1 days +23:54:55')
In [54]: df.min(axis=1).min()
Out[54]: Timedelta('-1 days +00:00:00')
In [55]: df.min().idxmax()
Out[55]: 'A'
In [56]: df.min(axis=1).idxmin()
Out[56]: 0
您可以在时间增量上填充NA，传递一个时间增量来获得特定值。
In [57]: y.fillna(pd.Timedelta(0))
Out[57]:
0 0 days
1 0 days
2 1 days
dtype: timedelta64[ns]
In [58]: y.fillna(pd.Timedelta(10, unit="s"))
Out[58]:
0 0 days 00:00:10
1 0 days 00:00:10
2 1 days 00:00:00
dtype: timedelta64[ns]
In [59]: y.fillna(pd.Timedelta("-1 days, 00:00:05"))
Out[59]:
0 -1 days +00:00:05
1 -1 days +00:00:05
2 1 days 00:00:00
dtype: timedelta64[ns]
您还可以对其求反、相乘和使用 abs 在……上面 Timedeltas ：
In [60]: td1 = pd.Timedelta("-1 days 2 hours 3 seconds")
In [61]: td1
Out[61]: Timedelta('-2 days +21:59:57')
In [62]: -1 * td1
Out[62]: Timedelta('1 days 02:00:03')
In [63]: -td1
Out[63]: Timedelta('1 days 02:00:03')
In [64]: abs(td1)
Out[64]: Timedelta('1 days 02:00:03')
减量#
的数值归约运算 timedelta64[ns] 会回来的 Timedelta 物体。像往常一样 NaT 在评估过程中被跳过。
In [65]: y2 = pd.Series(
....: pd.to_timedelta(["-1 days +00:00:05", "nat", "-1 days +00:00:05", "1 days"])
....: )
....:
In [66]: y2
Out[66]:
0 -1 days +00:00:05
1 NaT
2 -1 days +00:00:05
3 1 days 00:00:00
dtype: timedelta64[ns]
In [67]: y2.mean()
Out[67]: Timedelta('-1 days +16:00:03.333333334')
In [68]: y2.median()
Out[68]: Timedelta('-1 days +00:00:05')
In [69]: y2.quantile(0.1)
Out[69]: Timedelta('-1 days +00:00:05')
In [70]: y2.sum()
Out[70]: Timedelta('-1 days +00:00:10')
变频式#
Timedelta系列， TimedeltaIndex ，以及 Timedelta 标量可以通过除以另一个时间增量或通过键入特定的时间增量类型来转换为其他“频率”。这些操作产生系列并传播 NaT -> nan 。请注意，除以NumPy标量是真除法，而astype等效于楼层除法。
In [71]: december = pd.Series(pd.date_range("20121201", periods=4))
In [72]: january = pd.Series(pd.date_range("20130101", periods=4))
In [73]: td = january - december
In [74]: td[2] += datetime.timedelta(minutes=5, seconds=3)
In [75]: td[3] = np.nan
In [76]: td
Out[76]:
0 31 days 00:00:00
1 31 days 00:00:00
2 31 days 00:05:03
3 NaT
dtype: timedelta64[ns]
# to days
In [77]: td / np.timedelta64(1, "D")
Out[77]:
0 31.000000
1 31.000000
2 31.003507
3 NaN
dtype: float64
In [78]: td.astype("timedelta64[D]")
Out[78]:
0 31.0
1 31.0
2 31.0
3 NaN
dtype: float64
# to seconds
In [79]: td / np.timedelta64(1, "s")
Out[79]:
0 2678400.0
1 2678400.0
2 2678703.0
3 NaN
dtype: float64
In [80]: td.astype("timedelta64[s]")
Out[80]:
0 2678400.0
1 2678400.0
2 2678703.0
3 NaN
dtype: float64
# to months (these are constant months)
In [81]: td / np.timedelta64(1, "M")
Out[81]:
0 1.018501
1 1.018501
2 1.018617
3 NaN
dtype: float64
除以或乘以 timedelta64[ns] 整数的级数或整数的级数产生另一个 timedelta64[ns] DTYPE系列。
In [82]: td * -1
Out[82]:
0 -31 days +00:00:00
1 -31 days +00:00:00
2 -32 days +23:54:57
3 NaT
dtype: timedelta64[ns]
In [83]: td * pd.Series([1, 2, 3, 4])
Out[83]:
0 31 days 00:00:00
1 62 days 00:00:00
2 93 days 00:15:09
3 NaT
dtype: timedelta64[ns]
圆角分割(楼层分割) timedelta64[ns] 按标量级数 Timedelta 给出一系列整数。
In [84]: td // pd.Timedelta(days=3, hours=4)
Out[84]:
0 9.0
1 9.0
2 9.0
3 NaN
dtype: float64
In [85]: pd.Timedelta(days=3, hours=4) // td
Out[85]:
0 0.0
1 0.0
2 0.0
3 NaN
dtype: float64
Mod(%)和divmod运算是为 Timedelta 当使用另一个类时间增量或使用数值参数进行操作时。
In [86]: pd.Timedelta(hours=37) % datetime.timedelta(hours=2)
Out[86]: Timedelta('0 days 01:00:00')
# divmod against a timedelta-like returns a pair (int, Timedelta)
In [87]: divmod(datetime.timedelta(hours=2), pd.Timedelta(minutes=11))
Out[87]: (10, Timedelta('0 days 00:10:00'))
# divmod against a numeric returns a pair (Timedelta, Timedelta)
In [88]: divmod(pd.Timedelta(hours=25), 86400000000000)
Out[88]: (Timedelta('0 days 00:00:00.000000001'), Timedelta('0 days 01:00:00'))
属性#
您可以访问的各种组件 Timedelta 或 TimedeltaIndex 直接使用属性 days,seconds,microseconds,nanoseconds 。它们与返回的值相同 datetime.timedelta ，例如，因为 .seconds 属性表示大于等于0且小于1天的秒数。这些签名是根据是否 Timedelta 已经签署了。
这些操作也可以通过 .dt 属性的属性 Series 也是。
备注
请注意，这些属性不是 Timedelta 。使用 .components 以检索显示的值。
对于 Series ：
In [89]: td.dt.days
Out[89]:
0 31.0
1 31.0
2 31.0
3 NaN
dtype: float64
In [90]: td.dt.seconds
Out[90]:
0 0.0
1 0.0
2 303.0
3 NaN
dtype: float64
您可以访问标量的字段值 Timedelta 直接去吧。
In [91]: tds = pd.Timedelta("31 days 5 min 3 sec")
In [92]: tds.days
Out[92]: 31
In [93]: tds.seconds
Out[93]: 303
In [94]: (-tds).seconds
Out[94]: 86097
您可以使用 .components 属性访问时间增量的简化形式。这将返回一个 DataFrame 索引类似于 Series 。这些是 显示的 的价值 Timedelta 。
In [95]: td.dt.components
Out[95]:
days hours minutes seconds milliseconds microseconds nanoseconds
0 31.0 0.0 0.0 0.0 0.0 0.0 0.0
1 31.0 0.0 0.0 0.0 0.0 0.0 0.0
2 31.0 0.0 5.0 3.0 0.0 0.0 0.0
3 NaN NaN NaN NaN NaN NaN NaN
In [96]: td.dt.components.seconds
Out[96]:
0 0.0
1 0.0
2 3.0
3 NaN
Name: seconds, dtype: float64
您可以将一个 Timedelta 转到一个 ISO 8601 Duration 字符串用 .isoformat 方法
In [97]: pd.Timedelta(
....: days=6, minutes=50, seconds=3, milliseconds=10, microseconds=10, nanoseconds=12
....: ).isoformat()
....:
Out[97]: 'P6DT0H50M3.010010012S'
TimedeltaIndex#
若要生成具有时间增量的索引，可以使用 TimedeltaIndex 或者 timedelta_range() 构造函数。
使用 TimedeltaIndex 您可以传递字符串形式的、 Timedelta ， timedelta ，或 np.timedelta64 物体。传球 np.nan/pd.NaT/nat 将表示缺少的值。
In [98]: pd.TimedeltaIndex(
....: [
....: "1 days",
....: "1 days, 00:00:05",
....: np.timedelta64(2, "D"),
....: datetime.timedelta(days=2, seconds=2),
....: ]
....: )
....:
Out[98]:
TimedeltaIndex(['1 days 00:00:00', '1 days 00:00:05', '2 days 00:00:00',
'2 days 00:00:02'],
dtype='timedelta64[ns]', freq=None)
可以传递字符串‘INFER’，以便在创建时将索引的频率设置为推断频率：
In [99]: pd.TimedeltaIndex(["0 days", "10 days", "20 days"], freq="infer")
Out[99]: TimedeltaIndex(['0 days', '10 days', '20 days'], dtype='timedelta64[ns]', freq='10D')
生成时间增量的范围#
类似于 date_range() ，则可以构造规则范围的 TimedeltaIndex 使用 timedelta_range() 。的默认频率 timedelta_range 是日历日：
In [100]: pd.timedelta_range(start="1 days", periods=5)
Out[100]: TimedeltaIndex(['1 days', '2 days', '3 days', '4 days', '5 days'], dtype='timedelta64[ns]', freq='D')
不同的组合 start ， end ，以及 periods 可以与一起使用 timedelta_range ：
In [101]: pd.timedelta_range(start="1 days", end="5 days")
Out[101]: TimedeltaIndex(['1 days', '2 days', '3 days', '4 days', '5 days'], dtype='timedelta64[ns]', freq='D')
In [102]: pd.timedelta_range(end="10 days", periods=4)
Out[102]: TimedeltaIndex(['7 days', '8 days', '9 days', '10 days'], dtype='timedelta64[ns]', freq='D')
这个 freq 参数可以传递各种 frequency aliases ：
In [103]: pd.timedelta_range(start="1 days", end="2 days", freq="30T")
Out[103]:
TimedeltaIndex(['1 days 00:00:00', '1 days 00:30:00', '1 days 01:00:00',
'1 days 01:30:00', '1 days 02:00:00', '1 days 02:30:00',
'1 days 03:00:00', '1 days 03:30:00', '1 days 04:00:00',
'1 days 04:30:00', '1 days 05:00:00', '1 days 05:30:00',
'1 days 06:00:00', '1 days 06:30:00', '1 days 07:00:00',
'1 days 07:30:00', '1 days 08:00:00', '1 days 08:30:00',
'1 days 09:00:00', '1 days 09:30:00', '1 days 10:00:00',
'1 days 10:30:00', '1 days 11:00:00', '1 days 11:30:00',
'1 days 12:00:00', '1 days 12:30:00', '1 days 13:00:00',
'1 days 13:30:00', '1 days 14:00:00', '1 days 14:30:00',
'1 days 15:00:00', '1 days 15:30:00', '1 days 16:00:00',
'1 days 16:30:00', '1 days 17:00:00', '1 days 17:30:00',
'1 days 18:00:00', '1 days 18:30:00', '1 days 19:00:00',
'1 days 19:30:00', '1 days 20:00:00', '1 days 20:30:00',
'1 days 21:00:00', '1 days 21:30:00', '1 days 22:00:00',
'1 days 22:30:00', '1 days 23:00:00', '1 days 23:30:00',
'2 days 00:00:00'],
dtype='timedelta64[ns]', freq='30T')
In [104]: pd.timedelta_range(start="1 days", periods=5, freq="2D5H")
Out[104]:
TimedeltaIndex(['1 days 00:00:00', '3 days 05:00:00', '5 days 10:00:00',
'7 days 15:00:00', '9 days 20:00:00'],
dtype='timedelta64[ns]', freq='53H')
指定 start ， end ，以及 periods 将生成一系列均匀分布的时间增量 start 至 end 包括，与 periods 结果中的元素数 TimedeltaIndex ：
In [105]: pd.timedelta_range("0 days", "4 days", periods=5)
Out[105]: TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'], dtype='timedelta64[ns]', freq=None)
In [106]: pd.timedelta_range("0 days", "4 days", periods=10)
Out[106]:
TimedeltaIndex(['0 days 00:00:00', '0 days 10:40:00', '0 days 21:20:00',
'1 days 08:00:00', '1 days 18:40:00', '2 days 05:20:00',
'2 days 16:00:00', '3 days 02:40:00', '3 days 13:20:00',
'4 days 00:00:00'],
dtype='timedelta64[ns]', freq=None)
使用TimedeltaIndex#
类似于其他类似DateTime的索引， DatetimeIndex 和 PeriodIndex ，您可以使用 TimedeltaIndex 作为Pandas对象的索引。
In [107]: s = pd.Series(
.....: np.arange(100),
.....: index=pd.timedelta_range("1 days", periods=100, freq="h"),
.....: )
.....:
In [108]: s
Out[108]:
1 days 00:00:00 0
1 days 01:00:00 1
1 days 02:00:00 2
1 days 03:00:00 3
1 days 04:00:00 4
..
4 days 23:00:00 95
5 days 00:00:00 96
5 days 01:00:00 97
5 days 02:00:00 98
5 days 03:00:00 99
Freq: H, Length: 100, dtype: int64
所选内容的工作原理与此类似，但会强制使用类似字符串的内容和切片：
In [109]: s["1 day":"2 day"]
Out[109]:
1 days 00:00:00 0
1 days 01:00:00 1
1 days 02:00:00 2
1 days 03:00:00 3
1 days 04:00:00 4
..
2 days 19:00:00 43
2 days 20:00:00 44
2 days 21:00:00 45
2 days 22:00:00 46
2 days 23:00:00 47
Freq: H, Length: 48, dtype: int64
In [110]: s["1 day 01:00:00"]
Out[110]: 1
In [111]: s[pd.Timedelta("1 day 1h")]
Out[111]: 1
此外，您可以使用部分字符串选择，范围将被推断：
In [112]: s["1 day":"1 day 5 hours"]
Out[112]:
1 days 00:00:00 0
1 days 01:00:00 1
1 days 02:00:00 2
1 days 03:00:00 3
1 days 04:00:00 4
1 days 05:00:00 5
Freq: H, dtype: int64
运营#
最后，两种技术的结合 TimedeltaIndex 使用 DatetimeIndex 允许保留NAT的某些组合操作：
In [113]: tdi = pd.TimedeltaIndex(["1 days", pd.NaT, "2 days"])
In [114]: tdi.to_list()
Out[114]: [Timedelta('1 days 00:00:00'), NaT, Timedelta('2 days 00:00:00')]
In [115]: dti = pd.date_range("20130101", periods=3)
In [116]: dti.to_list()
Out[116]:
[Timestamp('2013-01-01 00:00:00', freq='D'),
Timestamp('2013-01-02 00:00:00', freq='D'),
Timestamp('2013-01-03 00:00:00', freq='D')]
In [117]: (dti + tdi).to_list()
Out[117]: [Timestamp('2013-01-02 00:00:00'), NaT, Timestamp('2013-01-05 00:00:00')]
In [118]: (dti - tdi).to_list()
Out[118]: [Timestamp('2012-12-31 00:00:00'), NaT, Timestamp('2013-01-01 00:00:00')]
转换#
类似于在一台 Series 如上所述，您可以将这些索引转换为另一个索引。
In [119]: tdi / np.timedelta64(1, "s")
Out[119]: Float64Index([86400.0, nan, 172800.0], dtype='float64')
In [120]: tdi.astype("timedelta64[s]")
Out[120]: Float64Index([86400.0, nan, 172800.0], dtype='float64')
标量类型的运算也同样有效。它们可能会返回一个 不同 索引的类型。
# adding or timedelta and date -> datelike
In [121]: tdi + pd.Timestamp("20130101")
Out[121]: DatetimeIndex(['2013-01-02', 'NaT', '2013-01-03'], dtype='datetime64[ns]', freq=None)
# subtraction of a date and a timedelta -> datelike
# note that trying to subtract a date from a Timedelta will raise an exception
In [122]: (pd.Timestamp("20130101") - tdi).to_list()
Out[122]: [Timestamp('2012-12-31 00:00:00'), NaT, Timestamp('2012-12-30 00:00:00')]
# timedelta + timedelta -> timedelta
In [123]: tdi + pd.Timedelta("10 days")
Out[123]: TimedeltaIndex(['11 days', NaT, '12 days'], dtype='timedelta64[ns]', freq=None)
# division can result in a Timedelta if the divisor is an integer
In [124]: tdi / 2
Out[124]: TimedeltaIndex(['0 days 12:00:00', NaT, '1 days 00:00:00'], dtype='timedelta64[ns]', freq=None)
# or a Float64Index if the divisor is a Timedelta
In [125]: tdi / tdi[0]
Out[125]: Float64Index([1.0, nan, 2.0], dtype='float64')
重采样#
类似于 timeseries resampling ，我们可以用一个 TimedeltaIndex 。
In [126]: s.resample("D").mean()
Out[126]:
1 days 11.5
2 days 35.5
3 days 59.5
4 days 83.5
5 days 97.5
Freq: D, dtype: float64