时间差#
Timedeltas 是时间上的差异，以不同的单位表示，例如天、小时、分钟、秒。它们可以是正的也可以是负的。
Timedelta 是 datetime.timedelta 的子类，其行为方式类似，但允许与 np.timedelta64 类型兼容，以及一系列自定义表示、解析和属性。
解析#
你可以通过各种参数构建一个 Timedelta 标量，包括 ISO 8601 Duration 字符串。
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')
日期偏移量 (天, 小时, 分钟, 秒, 毫秒, 微秒, 纳秒) 也可以在构造中使用。
In [15]: pd.Timedelta(pd.offsets.Second(2))
Out[15]: Timedelta('0 days 00:00:02')
此外，标量之间的操作产生另一个标量 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#
使用顶级的 pd.to_timedelta，你可以将标量、数组、列表或序列从可识别的时间增量格式/值转换为 Timedelta 类型。如果输入是序列，它将构造序列；如果输入是标量，它将构造标量；否则，它将输出一个 TimedeltaIndex。
你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')
或者是一个字符串的列表/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)
unit 关键字参数指定如果输入是数字，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
如果传递的是字符串或字符串数组作为输入，那么 unit 关键字参数将被忽略。如果传递的是不带单位的字符串，则默认单位为纳秒。
时间增量限制#
pandas 使用 64 位整数以纳秒分辨率表示 Timedeltas。因此，64 位整数限制决定了 Timedelta 的限制。
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')
操作#
你可以对 Series/DataFrames 进行操作，并通过 datetime64[ns] Series 或 Timestamps 的减法操作构建 timedelta64[ns] Series。
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]
对来自 timedelta64[ns] 系列的标量进行操作：
In [36]: y = s - s[0]
In [37]: y
Out[37]:
0 0 days
1 1 days
2 2 days
dtype: timedelta64[ns]
支持带有 NaT 值的时间增量序列：
In [38]: y = s - s.shift()
In [39]: y
Out[39]:
0 NaT
1 1 days
2 1 days
dtype: timedelta64[ns]
元素可以类似地使用 np.nan 设置为 NaT：
In [40]: y[1] = np.nan
In [41]: y
Out[41]:
0 NaT
1 NaT
2 1 days
dtype: timedelta64[ns]
操作数也可以以相反的顺序出现（一个单独的对象与一个 Series 操作）：
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 和相应的 idxmin, idxmax 操作在数据框上受支持：
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 操作在 Series 上也是支持的。标量结果将是一个 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
你可以在 timedeltas 上使用 fillna，传递一个 timedelta 以获取特定值。
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]
你也可以对 Timedeltas 进行否定、乘法和使用 abs 操作：
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')
减少#
timedelta64[ns] 的数值缩减操作将返回 Timedelta 对象。像往常一样，在评估过程中会跳过 NaT。
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')
频率转换#
Timedelta 系列和 TimedeltaIndex，以及 Timedelta 可以通过转换为特定的 timedelta 数据类型来转换为其他频率。
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 seconds
In [77]: td.astype("timedelta64[s]")
Out[77]:
0 31 days 00:00:00
1 31 days 00:00:00
2 31 days 00:05:03
3 NaT
dtype: timedelta64[s]
对于 timedelta64 分辨率（除了支持的 “s”, “ms”, “us”, “ns” 之外），另一种方法是除以另一个 timedelta 对象。请注意，除以 NumPy 标量是真除法，而 astyping 相当于地板除法。
# to days
In [78]: td / np.timedelta64(1, "D")
Out[78]:
0 31.000000
1 31.000000
2 31.003507
3 NaN
dtype: float64
将 timedelta64[ns] 系列除以或乘以一个整数或整数系列会产生另一个 timedelta64[ns] dtypes 系列。
In [79]: td * -1
Out[79]:
0 -31 days +00:00:00
1 -31 days +00:00:00
2 -32 days +23:54:57
3 NaT
dtype: timedelta64[ns]
In [80]: td * pd.Series([1, 2, 3, 4])
Out[80]:
0 31 days 00:00:00
1 62 days 00:00:00
2 93 days 00:15:09
3 NaT
dtype: timedelta64[ns]
将 timedelta64[ns] 系列通过标量 Timedelta 进行圆整除法（向下取整除法）会得到一个整数系列。
In [81]: td // pd.Timedelta(days=3, hours=4)
Out[81]:
0 9.0
1 9.0
2 9.0
3 NaN
dtype: float64
In [82]: pd.Timedelta(days=3, hours=4) // td
Out[82]:
0 0.0
1 0.0
2 0.0
3 NaN
dtype: float64
当与另一个类似时间增量的对象或数值参数进行操作时，Timedelta 定义了 mod (%) 和 divmod 操作。
In [83]: pd.Timedelta(hours=37) % datetime.timedelta(hours=2)
Out[83]: Timedelta('0 days 01:00:00')
# divmod against a timedelta-like returns a pair (int, Timedelta)
In [84]: divmod(datetime.timedelta(hours=2), pd.Timedelta(minutes=11))
Out[84]: (10, Timedelta('0 days 00:10:00'))
# divmod against a numeric returns a pair (Timedelta, Timedelta)
In [85]: divmod(pd.Timedelta(hours=25), 86400000000000)
Out[85]: (Timedelta('0 days 00:00:00.000000001'), Timedelta('0 days 01:00:00'))
属性#
你可以通过属性 days,seconds,microseconds,nanoseconds 直接访问 Timedelta 或 TimedeltaIndex 的各个组件。这些与 datetime.timedelta 返回的值相同，例如，.seconds 属性表示大于等于0且小于1天的秒数。这些根据 Timedelta 的符号是有符号的。
这些操作也可以通过 Series 的 .dt 属性直接访问。
备注
请注意，属性不是 Timedelta 的显示值。使用 .components 来检索显示值。
对于一个 Series：
In [86]: td.dt.days
Out[86]:
0 31.0
1 31.0
2 31.0
3 NaN
dtype: float64
In [87]: td.dt.seconds
Out[87]:
0 0.0
1 0.0
2 303.0
3 NaN
dtype: float64
你可以直接访问标量 Timedelta 的字段值。
In [88]: tds = pd.Timedelta("31 days 5 min 3 sec")
In [89]: tds.days
Out[89]: 31
In [90]: tds.seconds
Out[90]: 303
In [91]: (-tds).seconds
Out[91]: 86097
你可以使用 .components 属性来访问 timedelta 的简化形式。这将返回一个与 Series 索引相似的 DataFrame。这些是 Timedelta 的 显示 值。
In [92]: td.dt.components
Out[92]:
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 [93]: td.dt.components.seconds
Out[93]:
0 0.0
1 0.0
2 3.0
3 NaN
Name: seconds, dtype: float64
你可以使用 .isoformat 方法将 Timedelta 转换为 ISO 8601 Duration 字符串
In [94]: pd.Timedelta(
....: days=6, minutes=50, seconds=3, milliseconds=10, microseconds=10, nanoseconds=12
....: ).isoformat()
....:
Out[94]: 'P6DT0H50M3.010010012S'
TimedeltaIndex#
要生成一个带有时间增量的索引，你可以使用 TimedeltaIndex 或 timedelta_range() 构造函数。
使用 TimedeltaIndex 你可以传递字符串形式的、Timedelta、timedelta 或 np.timedelta64 对象。传递 np.nan/pd.NaT/nat 将表示缺失值。
In [95]: pd.TimedeltaIndex(
....: [
....: "1 days",
....: "1 days, 00:00:05",
....: np.timedelta64(2, "D"),
....: datetime.timedelta(days=2, seconds=2),
....: ]
....: )
....:
Out[95]:
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)
字符串 ‘infer’ 可以传递，以便在创建索引时将其频率设置为推断的频率：
In [96]: pd.TimedeltaIndex(["0 days", "10 days", "20 days"], freq="infer")
Out[96]: TimedeltaIndex(['0 days', '10 days', '20 days'], dtype='timedelta64[ns]', freq='10D')
生成时间增量范围#
类似于 date_range()，你可以使用 timedelta_range() 构建 TimedeltaIndex 的常规范围。timedelta_range 的默认频率是日历日：
In [97]: pd.timedelta_range(start="1 days", periods=5)
Out[97]: TimedeltaIndex(['1 days', '2 days', '3 days', '4 days', '5 days'], dtype='timedelta64[ns]', freq='D')
start、end 和 periods 的各种组合可以与 timedelta_range 一起使用：
In [98]: pd.timedelta_range(start="1 days", end="5 days")
Out[98]: TimedeltaIndex(['1 days', '2 days', '3 days', '4 days', '5 days'], dtype='timedelta64[ns]', freq='D')
In [99]: pd.timedelta_range(end="10 days", periods=4)
Out[99]: TimedeltaIndex(['7 days', '8 days', '9 days', '10 days'], dtype='timedelta64[ns]', freq='D')
freq 参数可以传递各种 频率别名:
In [100]: pd.timedelta_range(start="1 days", end="2 days", freq="30min")
Out[100]:
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='30min')
In [101]: pd.timedelta_range(start="1 days", periods=5, freq="2D5h")
Out[101]:
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 和 periods 将生成从 start 到 end 范围内均匀间隔的 timedeltas，结果 TimedeltaIndex 中包含 periods 个元素：
In [102]: pd.timedelta_range("0 days", "4 days", periods=5)
Out[102]: TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'], dtype='timedelta64[ns]', freq=None)
In [103]: pd.timedelta_range("0 days", "4 days", periods=10)
Out[103]:
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#
与其他类似日期时间的索引，如 DatetimeIndex 和 PeriodIndex 类似，你可以使用 TimedeltaIndex 作为 pandas 对象的索引。
In [104]: s = pd.Series(
.....: np.arange(100),
.....: index=pd.timedelta_range("1 days", periods=100, freq="h"),
.....: )
.....:
In [105]: s
Out[105]:
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
选择操作类似，对类似字符串的对象进行强制转换和切片：
In [106]: s["1 day":"2 day"]
Out[106]:
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 [107]: s["1 day 01:00:00"]
Out[107]: 1
In [108]: s[pd.Timedelta("1 day 1h")]
Out[108]: 1
此外，您可以使用部分字符串选择，范围将被推断：
In [109]: s["1 day":"1 day 5 hours"]
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
1 days 05:00:00 5
Freq: h, dtype: int64
操作#
最后，TimedeltaIndex 与 DatetimeIndex 的组合允许某些保留 NaT 的组合操作：
In [110]: tdi = pd.TimedeltaIndex(["1 days", pd.NaT, "2 days"])
In [111]: tdi.to_list()
Out[111]: [Timedelta('1 days 00:00:00'), NaT, Timedelta('2 days 00:00:00')]
In [112]: dti = pd.date_range("20130101", periods=3)
In [113]: dti.to_list()
Out[113]:
[Timestamp('2013-01-01 00:00:00'),
Timestamp('2013-01-02 00:00:00'),
Timestamp('2013-01-03 00:00:00')]
In [114]: (dti + tdi).to_list()
Out[114]: [Timestamp('2013-01-02 00:00:00'), NaT, Timestamp('2013-01-05 00:00:00')]
In [115]: (dti - tdi).to_list()
Out[115]: [Timestamp('2012-12-31 00:00:00'), NaT, Timestamp('2013-01-01 00:00:00')]
转换#
与上述 Series 上的频率转换类似，您可以将这些索引转换为生成另一个索引。
In [116]: tdi / np.timedelta64(1, "s")
Out[116]: Index([86400.0, nan, 172800.0], dtype='float64')
In [117]: tdi.astype("timedelta64[s]")
Out[117]: TimedeltaIndex(['1 days', NaT, '2 days'], dtype='timedelta64[s]', freq=None)
标量类型操作也同样有效。这些操作可能会返回一个 不同 类型的索引。
# adding or timedelta and date -> datelike
In [118]: tdi + pd.Timestamp("20130101")
Out[118]: 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 [119]: (pd.Timestamp("20130101") - tdi).to_list()
Out[119]: [Timestamp('2012-12-31 00:00:00'), NaT, Timestamp('2012-12-30 00:00:00')]
# timedelta + timedelta -> timedelta
In [120]: tdi + pd.Timedelta("10 days")
Out[120]: TimedeltaIndex(['11 days', NaT, '12 days'], dtype='timedelta64[ns]', freq=None)
# division can result in a Timedelta if the divisor is an integer
In [121]: tdi / 2
Out[121]: TimedeltaIndex(['0 days 12:00:00', NaT, '1 days 00:00:00'], dtype='timedelta64[ns]', freq=None)
# or a float64 Index if the divisor is a Timedelta
In [122]: tdi / tdi[0]
Out[122]: Index([1.0, nan, 2.0], dtype='float64')
重采样#
类似于 时间序列重采样 ，我们可以使用 TimedeltaIndex 进行重采样。
In [123]: s.resample("D").mean()
Out[123]:
1 days 11.5
2 days 35.5
3 days 59.5
4 days 83.5
5 days 97.5
Freq: D, dtype: float64