Pandas 时间增量
时间差
Timedelta，时间差，即时间之间的差异，用 日、时、分、秒 等时间单位表示，时间单位可为正，也可为负。
Timedelta 是 datetime.timedelta 的子类，两者的操作方式相似，但 Timedelta 兼容 np.timedelta64 等数据类型，还支持自定义表示形式、能解析多种类型的数据，并支持自有属性。
#解析数据，生成时间差
Timedelta() 支持用多种参数生成时间差：
In [1]: import datetime
# 字符串
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')
# datetime.timedelta
# 注意：必须指定关键字参数
In [6]: pd.Timedelta(days=1, seconds=1)
Out[6]: Timedelta('1 days 00:00:01')
# 用整数与时间单位生成时间差
In [7]: pd.Timedelta(1, unit='d')
Out[7]: Timedelta('1 days 00:00:00')
# datetime.timedelta 与 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')
# 用字符串表示负数时间差
# 更接近 datetime.timedelta
In [10]: pd.Timedelta('-1us')
Out[10]: Timedelta('-1 days +23:59:59.999999')
# 时间差缺失值
In [11]: pd.Timedelta('nan')
Out[11]: NaT
In [12]: pd.Timedelta('nat')
Out[12]: NaT
# ISO8601 时间格式字符串
In [13]: pd.Timedelta('P0DT0H1M0S')
Out[13]: Timedelta('0 days 00:01:00')
In [14]: pd.Timedelta('P0DT0H0M0.000000123S')
Out[14]: Timedelta('0 days 00:00:00.000000')
0.23.0 版新增：增加了用 ISO8601 时间格式成时间差。
DateOffsetsDay、Hour、Minute、Second、Milli、Micro、Nano）也可以用来生成时间差。
In [15]: pd.Timedelta(pd.offsets.Second(2))
Out[15]: Timedelta('0 days 00:00:02')
标量运算生成的也是 Timedelta 标量。
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
pd.to_timedelta() 可以把符合时间差格式的标量、数组、列表、序列等数据转换为Timedelta。输入数据是序列，输出的就是序列，输入数据是标量，输出的就是标量，其它形式的输入数据则输出 TimedeltaIndex。
to_timedelta() 可以解析单个字符串：
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 00:00:00.000015')
还能解析字符串列表或数组：
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 00:00:00.000015', NaT], dtype='timedelta64[ns]', freq=None)
unit 关键字参数指定时间差的单位：
In [20]: pd.to_timedelta(np.arange(5), unit='s')
Out[20]: TimedeltaIndex(['00:00:00', '00:00:01', '00:00:02', '00:00:03', '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)
#时间差界限
Pandas 时间差的纳秒解析度是 64 位整数，这就决定了 Timedelta 的上下限。
In [22]: pd.Timedelta.min
Out[22]: Timedelta('-106752 days +00:12:43.145224')
In [23]: pd.Timedelta.max
Out[23]: Timedelta('106751 days 23:47:16.854775')
#运算
以时间差为数据的 Series 与 DataFrame 支持各种运算，datetime64 [ns] 序列或 Timestamps 减法运算生成的是timedelta64 [ns] 序列。
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]
timedelta64 [ns] 序列的标量运算：
In [36]: y = s - s[0]
In [37]: y
Out[37]:
0 0 days
1 1 days
2 2 days
dtype: timedelta64[ns]
时间差序列支持 NaT 值：
In [38]: y = s - s.shift()
In [39]: y
Out[39]:
0 NaT
1 1 days
2 1 days
dtype: timedelta64[ns]
与 datetime 类似，np.nan 把时间差设置为 NaT：
In [40]: y[1] = np.nan
In [41]: y
Out[41]:
0 NaT
1 NaT
2 1 days
dtype: timedelta64[ns]
运算符也可以显示为逆序（序列与单个对象的运算）：
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]
DataFrame 支持 min、max 及 idxmin、idxmax 运算：
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
Series 也支持min、max 及 idxmin、idxmax 运算。标量计算结果为 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
时间差支持 fillna 函数，参数是 Timedelta，用于指定填充值。
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]
Timedelta 还支持取反、乘法及绝对值（Abs）运算：
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')
#归约
timedelta64 [ns] 数值归约运算返回的是 Timedelta 对象。 一般情况下，NaT 不计数。
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 +16:00:03.333333')
In [68]: y2.median()
Out[68]: Timedelta('-1 days +00:00:05')
In [69]: y2.quantile(.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
时间差除法把 Timedelta 序列、TimedeltaIndex、Timedelta 标量转换为其它“频率”，astype 也可以将之转换为指定的时间差。这些运算生成的是序列，并把 NaT 转换为 nan。 注意，NumPy 标量除法是真除法，astype 则等同于取底整除（Floor Division）。
说明
Floor Division ，即两数的商为向下取整，如，9 / 2 = 4。又译作地板除或向下取整除，本文译作取底整除；
扩展知识：
Ceiling Division，即两数的商为向上取整，如，9 / 2 = 5。又译作屋顶除或向上取整除，本文译作取顶整除。
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]
# 转为日
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
# 转为秒
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
# 转为月 （此处用常量表示）
In [81]: td / np.timedelta64(1, 'M')
Out[81]:
0 1.018501
1 1.018501
2 1.018617
3 NaN
dtype: float64
timedelta64 [ns] 序列与整数或整数序列相乘或相除，生成的也是 timedelta64 [ns] 序列。
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]
timedelta64 [ns] 序列与 Timedelta 标量相除的结果为取底整除的整数序列。
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
Timedelta 的求余（mod(%)）与除余（divmod）运算，支持时间差与数值参数。
In [86]: pd.Timedelta(hours=37) % datetime.timedelta(hours=2)
Out[86]: Timedelta('0 days 01:00:00')
# 除余运算的参数为时间差时，返回一对值（int, Timedelta）
In [87]: divmod(datetime.timedelta(hours=2), pd.Timedelta(minutes=11))
Out[87]: (10, Timedelta('0 days 00:10:00'))
# 除余运算的参数为数值时，也返回一对值（Timedelta, Timedelta）
In [88]: divmod(pd.Timedelta(hours=25), 86400000000000)
Out[88]: (Timedelta('0 days 00:00:00.000000'), Timedelta('0 days 01:00:00'))
#属性
Timedelta 或 TimedeltaIndex 的组件可以直接访问 days、seconds、microseconds、nanoseconds 等属性。这些属性与datetime.timedelta 的返回值相同，例如，.seconds 属性表示大于等于 0 天且小于 1 天的秒数。带符号的 Timedelta 返回的值也带符号。
Series 的 .dt 属性也可以直接访问这些数据。
注意
这些属性不是 Timedelta 显示的值。.components 可以提取显示的值。
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
直接访问 Timedelta 标量字段值。
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
.components 属性可以快速访问时间差的组件，返回结果是 DataFrame。 下列代码输出 Timedelta 的显示值。
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
.isoformat 方法可以把 Timedelta 转换为 ISO8601 时间格式字符串。
0.20.0 版新增。
In [97]: pd.Timedelta(days=6, minutes=50, seconds=3,
....: milliseconds=10, microseconds=10,
....: nanoseconds=12).isoformat()
....:
Out[97]: 'P6DT0H50M3.010010012S'
#TimedeltaIndex
TimedeltaIndex或 timedelta_range()可以生成时间差索引。
TimedeltaIndex 支持字符串型的 Timedelta、timedelta 或 np.timedelta64对象。
np.nan、pd.NaT、nat 代表缺失值。
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)
freq 关键字参数为 infer 时，TimedeltaIndex 可以自行推断时间频率：
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')
#生成时间差范围
与 date_range()相似，timedelta_range()可以生成定频 TimedeltaIndex，timedelta_range 的默认频率是日历日：
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')
timedelta_range 支持 start、end、periods 三个参数：
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')
freq 参数支持各种频率别名
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')
0.23.0 版新增。
用 start、end、period 可以生成等宽时间差范围，其中，start 与 end（含）是起止两端的时间，periods 为 TimedeltaIndex 里的元素数量：
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)
#TimedeltaIndex 应用
与 DatetimeIndex、PeriodIndex 等 datetime 型索引类似，TimedeltaIndex 也可当作 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
选择操作也差不多，可以强制转换字符串与切片：
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
TimedeltaIndex 还支持局部字符串选择，并且可以推断选择范围：
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
#TimedeltaIndex 运算
TimedeltaIndex 与 DatetimeIndex 运算可以保留 NaT 值：
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
与 Series 频率转换类似，可以把 TimedeltaIndex 转换为其它索引。
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')
与标量操作类似，会返回不同类型的索引。
# 时间差与日期相加，结果为日期型索引（DatetimeIndex）
In [121]: tdi + pd.Timestamp('20130101')
Out[121]: DatetimeIndex(['2013-01-02', 'NaT', '2013-01-03'], dtype='datetime64[ns]', freq=None)
# 日期与时间戳相减，结果为日期型数据（Timestamp）
# 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')]
# 时间差与时间差相加，结果还是时间差索引
In [123]: tdi + pd.Timedelta('10 days')
Out[123]: TimedeltaIndex(['11 days', NaT, '12 days'], dtype='timedelta64[ns]', freq=None)
# 除数是整数，则结果为时间差索引
In [124]: tdi / 2
Out[124]: TimedeltaIndex(['0 days 12:00:00', NaT, '1 days 00:00:00'], dtype='timedelta64[ns]', freq=None)
# 除数是时间差，则结果为 Float64Index
In [125]: tdi / tdi[0]
Out[125]: Float64Index([1.0, nan, 2.0], dtype='float64')
#重采样
与时间序列重采样一样，TimedeltaIndex 也支持重采样。
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