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Pandas 時間增量

2020-04-07 13:46 更新

時間差

Timedelta，時間差，即時間之間的差異，用 日、時、分、秒 等時間單位表示，時間單位可為正，也可為負(fù)。

Timedelta 是 datetime.timedelta 的子類，兩者的操作方式相似，但 Timedelta 兼容 np.timedelta64 等數(shù)據(jù)類型，還支持自定義表示形式、能解析多種類型的數(shù)據(jù)，并支持自有屬性。

#解析數(shù)據(jù)，生成時間差

Timedelta() 支持用多種參數(shù)生成時間差：

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：必須指定關(guān)鍵字參數(shù)
In [6]: pd.Timedelta(days=1, seconds=1)
Out[6]: Timedelta('1 days 00:00:01')

# 用整數(shù)與時間單位生成時間差
In [7]: pd.Timedelta(1, unit='d')
Out[7]: Timedelta('1 days 00:00:00')

# datetime.timedelta 與 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')

# 用字符串表示負(fù)數(shù)時間差
# 更接近 datetime.timedelta
In [10]: pd.Timedelta('-1us')
Out[10]: Timedelta('-1 days +23:59:59.999999')

# 時間差缺失值
In [11]: pd.Timedelta('nan')
Out[11]: NaT

In [12]: pd.Timedelta('nat')
Out[12]: NaT

# ISO8601 時間格式字符串
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 時間格式成時間差。

DateOffsetsDay、Hour、Minute、Second、Milli、Micro、Nano）也可以用來生成時間差。

In [15]: pd.Timedelta(pd.offsets.Second(2))
Out[15]: Timedelta('0 days 00:00:02')

標(biāo)量運(yùn)算生成的也是 Timedelta 標(biāo)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() 可以把符合時間差格式的標(biāo)量、數(shù)組、列表、序列等數(shù)據(jù)轉(zhuǎn)換為Timedelta。輸入數(shù)據(jù)是序列，輸出的就是序列，輸入數(shù)據(jù)是標(biāo)量，輸出的就是標(biāo)量，其它形式的輸入數(shù)據(jù)則輸出 TimedeltaIndex。

to_timedelta() 可以解析單個字符串：

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')

還能解析字符串列表或數(shù)組：

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 關(guān)鍵字參數(shù)指定時間差的單位：

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)

#時間差界限

Pandas 時間差的納秒解析度是 64 位整數(shù)，這就決定了 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')

#運(yùn)算

以時間差為數(shù)據(jù)的 Series 與 DataFrame 支持各種運(yùn)算，datetime64 [ns] 序列或 Timestamps 減法運(yùn)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] 序列的標(biāo)量運(yùn)算：

In [36]: y = s - s[0]

In [37]: y
Out[37]: 
0   0 days
1   1 days
2   2 days
dtype: timedelta64[ns]

時間差序列支持 NaT 值：

In [38]: y = s - s.shift()

In [39]: y
Out[39]: 
0      NaT
1   1 days
2   1 days
dtype: timedelta64[ns]

與 datetime 類似，np.nan 把時間差設(shè)置為 NaT：

In [40]: y[1] = np.nan

In [41]: y
Out[41]: 
0      NaT
1      NaT
2   1 days
dtype: timedelta64[ns]

運(yùn)算符也可以顯示為逆序（序列與單個對象的運(yùn)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 運(yùn)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 運(yùn)算。標(biāo)量計(jì)算結(jié)果為 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

時間差支持 fillna 函數(shù)，參數(shù)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 還支持取反、乘法及絕對值（Abs）運(yùn)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')

#歸約

timedelta64 [ns] 數(shù)值歸約運(yùn)算返回的是 Timedelta 對象。一般情況下，NaT 不計(jì)數(shù)。

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')

#頻率轉(zhuǎn)換

時間差除法把 Timedelta 序列、TimedeltaIndex、Timedelta 標(biāo)量轉(zhuǎn)換為其它“頻率”，astype 也可以將之轉(zhuǎn)換為指定的時間差。這些運(yùn)算生成的是序列，并把 NaT 轉(zhuǎn)換為 nan。注意，NumPy 標(biāo)量除法是真除法，astype 則等同于取底整除（Floor Division）。

說明

Floor Division ，即兩數(shù)的商為向下取整，如，9 / 2 = 4。又譯作地板除或向下取整除，本文譯作取底整除；

擴(kuò)展知識：

Ceiling Division，即兩數(shù)的商為向上取整，如，9 / 2 = 5。又譯作屋頂除或向上取整除，本文譯作取頂整除。

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]

# 轉(zhuǎn)為日
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

# 轉(zhuǎn)為秒
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

# 轉(zhuǎn)為月 （此處用常量表示）
In [81]: td / np.timedelta64(1, 'M')
Out[81]: 
0    1.018501
1    1.018501
2    1.018617
3         NaN
dtype: float64

timedelta64 [ns] 序列與整數(shù)或整數(shù)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] 序列與 Timedelta 標(biāo)量相除的結(jié)果為取底整除的整數(shù)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(%)）與除余（divmod）運(yùn)算，支持時間差與數(shù)值參數(shù)。

In [86]: pd.Timedelta(hours=37) % datetime.timedelta(hours=2)
Out[86]: Timedelta('0 days 01:00:00')

# 除余運(yùn)算的參數(shù)為時間差時，返回一對值（int, Timedelta）
In [87]: divmod(datetime.timedelta(hours=2), pd.Timedelta(minutes=11))
Out[87]: (10, Timedelta('0 days 00:10:00'))

# 除余運(yùn)算的參數(shù)為數(shù)值時，也返回一對值（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'))

#屬性

Timedelta 或 TimedeltaIndex 的組件可以直接訪問 days、seconds、microseconds、nanoseconds 等屬性。這些屬性與datetime.timedelta 的返回值相同，例如，.seconds 屬性表示大于等于 0 天且小于 1 天的秒數(shù)。帶符號的 Timedelta 返回的值也帶符號。

Series 的 .dt 屬性也可以直接訪問這些數(shù)據(jù)。

注意

這些屬性不是 Timedelta 顯示的值。.components 可以提取顯示的值。

對于 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

直接訪問 Timedelta 標(biāo)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 屬性可以快速訪問時間差的組件，返回結(jié)果是 DataFrame。下列代碼輸出 Timedelta 的顯示值。

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 轉(zhuǎn)換為 ISO8601 時間格式字符串。

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()可以生成時間差索引。

TimedeltaIndex 支持字符串型的 Timedelta、timedelta 或 np.timedelta64對象。

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 關(guān)鍵字參數(shù)為 infer 時，TimedeltaIndex 可以自行推斷時間頻率：

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')

#生成時間差范圍

與 date_range()相似，timedelta_range()可以生成定頻 TimedeltaIndex，timedelta_range 的默認(rèn)頻率是日歷日：

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 三個參數(shù)：

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 參數(shù)支持各種頻率別名

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 可以生成等寬時間差范圍，其中，start 與 end（含）是起止兩端的時間，periods 為 TimedeltaIndex 里的元素?cái)?shù)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 應(yīng)用

與 DatetimeIndex、PeriodIndex 等 datetime 型索引類似，TimedeltaIndex 也可當(dāng)作 pandas 對象的索引。

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

選擇操作也差不多，可以強(qiáng)制轉(zhuǎn)換字符串與切片：

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 還支持局部字符串選擇，并且可以推斷選擇范圍：

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 運(yùn)算

TimedeltaIndex 與 DatetimeIndex 運(yùn)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')]

#轉(zhuǎn)換

與 Series 頻率轉(zhuǎn)換類似，可以把 TimedeltaIndex 轉(zhuǎn)換為其它索引。

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')

與標(biāo)量操作類似，會返回不同類型的索引。

# 時間差與日期相加，結(jié)果為日期型索引（DatetimeIndex）
In [121]: tdi + pd.Timestamp('20130101')
Out[121]: DatetimeIndex(['2013-01-02', 'NaT', '2013-01-03'], dtype='datetime64[ns]', freq=None)

# 日期與時間戳相減，結(jié)果為日期型數(shù)據(jù)（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')]

# 時間差與時間差相加，結(jié)果還是時間差索引
In [123]: tdi + pd.Timedelta('10 days')
Out[123]: TimedeltaIndex(['11 days', NaT, '12 days'], dtype='timedelta64[ns]', freq=None)

# 除數(shù)是整數(shù)，則結(jié)果為時間差索引
In [124]: tdi / 2
Out[124]: TimedeltaIndex(['0 days 12:00:00', NaT, '1 days 00:00:00'], dtype='timedelta64[ns]', freq=None)

# 除數(shù)是時間差，則結(jié)果為 Float64Index
In [125]: tdi / tdi[0]
Out[125]: Float64Index([1.0, nan, 2.0], dtype='float64')

#重采樣

與時間序列重采樣一樣，TimedeltaIndex 也支持重采樣。

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

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