第十章时间序列（中）

最新推荐文章于 2020-06-29 23:56:08 发布

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最新推荐文章于 2020-06-29 23:56:08 发布

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本文链接：https://blog.youkuaiyun.com/yunini2/article/details/73838610

#带有重复索引的时间序列
dates=pd.DatetimeIndex(['1/1/2000','1/2/2000','1/2/2000','1/2/2000','1/3/2000'])
dup_ts=Series(np.arange(5),index=dates)
#检查索引的is_unique属性
dup_ts.index.is_unique
False
grouped=dup_ts.groupby(level=0)
grouped.mean()
Out[88]: 
2000-01-01    0
2000-01-02    2
2000-01-03    4
dtype: int32
grouped.count()
Out[89]: 
2000-01-01    1
2000-01-02    3
2000-01-03    1
dtype: int64
#日期的范围、频率及移动
ts
Out[90]: 
2011-01-02    1.582904
2011-01-05   -1.439438
2011-01-07   -0.411849
2011-01-08    0.747215
2011-01-10    0.384336
2011-01-12    0.432144
dtype: float64

ts.resample('D')
Out[91]: DatetimeIndexResampler [freq=<Day>, axis=0, closed=left, label=left, convention=start, base=0]

生成日期范围，date_range()按天计算时间点，如果只有起始和结束日期，需要在传入一个表示一段时间的数字

index=pd.date_range('4/1/2017','6/28/2017')

index
Out[93]: 
DatetimeIndex(['2017-04-01', '2017-04-02', '2017-04-03', '2017-04-04',
               '2017-04-05', '2017-04-06', '2017-04-07', '2017-04-08',
               '2017-04-09', '2017-04-10', '2017-04-11', '2017-04-12',
               '2017-04-13', '2017-04-14', '2017-04-15', '2017-04-16',
               '2017-04-17', '2017-04-18', '2017-04-19', '2017-04-20',
               '2017-04-21', '2017-04-22', '2017-04-23', '2017-04-24',
               '2017-04-25', '2017-04-26', '2017-04-27', '2017-04-28',
               '2017-04-29', '2017-04-30', '2017-05-01', '2017-05-02',
               '2017-05-03', '2017-05-04', '2017-05-05', '2017-05-06',
               '2017-05-07', '2017-05-08', '2017-05-09', '2017-05-10',
               '2017-05-11', '2017-05-12', '2017-05-13', '2017-05-14',
               '2017-05-15', '2017-05-16', '2017-05-17', '2017-05-18',
               '2017-05-19', '2017-05-20', '2017-05-21', '2017-05-22',
               '2017-05-23', '2017-05-24', '2017-05-25', '2017-05-26',
               '2017-05-27', '2017-05-28', '2017-05-29', '2017-05-30',
               '2017-05-31', '2017-06-01', '2017-06-02', '2017-06-03',
               '2017-06-04', '2017-06-05', '2017-06-06', '2017-06-07',
               '2017-06-08', '2017-06-09', '2017-06-10', '2017-06-11',
               '2017-06-12', '2017-06-13', '2017-06-14', '2017-06-15',
               '2017-06-16', '2017-06-17', '2017-06-18', '2017-06-19',
               '2017-06-20', '2017-06-21', '2017-06-22', '2017-06-23',
               '2017-06-24', '2017-06-25', '2017-06-26', '2017-06-27',
               '2017-06-28'],
              dtype='datetime64[ns]', freq='D')
pd.date_range(start='4/1/2017',periods=20)
Out[94]: 
DatetimeIndex(['2017-04-01', '2017-04-02', '2017-04-03', '2017-04-04',
               '2017-04-05', '2017-04-06', '2017-04-07', '2017-04-08',
               '2017-04-09', '2017-04-10', '2017-04-11', '2017-04-12',
               '2017-04-13', '2017-04-14', '2017-04-15', '2017-04-16',
               '2017-04-17', '2017-04-18', '2017-04-19', '2017-04-20'],
              dtype='datetime64[ns]', freq='D')

pd.date_range(end='6/1/2017',periods=20)
Out[95]: 
DatetimeIndex(['2017-05-13', '2017-05-14', '2017-05-15', '2017-05-16',
               '2017-05-17', '2017-05-18', '2017-05-19', '2017-05-20',
               '2017-05-21', '2017-05-22', '2017-05-23', '2017-05-24',
               '2017-05-25', '2017-05-26', '2017-05-27', '2017-05-28',
               '2017-05-29', '2017-05-30', '2017-05-31', '2017-06-01'],
              dtype='datetime64[ns]', freq='D')
#想要生成一个每月最后一个工作日组成的日期索引，可传入“BM”频率，得到只包含时间间隔符合频率要求的日期

pd.date_range('1/1/2000','12/1/2000',freq='BM')
Out[98]: 
DatetimeIndex(['2000-01-31', '2000-02-29', '2000-03-31', '2000-04-28',
               '2000-05-31', '2000-06-30', '2000-07-31', '2000-08-31',
               '2000-09-29', '2000-10-31', '2000-11-30'],
              dtype='datetime64[ns]', freq='BM')
#虽然起始和结束日期带有时间信息，若产生一组被规范化午夜的时间戳，可用normalize实现

pd.date_range('5/2/2017 12:56:31',periods=5,normalize=True)
Out[100]: 
DatetimeIndex(['2017-05-02', '2017-05-03', '2017-05-04', '2017-05-05',
               '2017-05-06'],
              dtype='datetime64[ns]', freq='D')

频率和日期偏移量

#对于每个基础频率都有一个被称为日期偏移量的对象与之对应
from pandas.tseries.offsets import Hour,Minute
hour = Hour()
#传入一个整数即可定义偏移量的倍数
four_hours=Hour(4)
four_hours
<4 * Hours>
pd.date_range('1/1/2017','1/3/2017',freq='4h')
Out[107]: 
DatetimeIndex(['2017-01-01 00:00:00', '2017-01-01 04:00:00',
               '2017-01-01 08:00:00', '2017-01-01 12:00:00',
               '2017-01-01 16:00:00', '2017-01-01 20:00:00',
               '2017-01-02 00:00:00', '2017-01-02 04:00:00',
               '2017-01-02 08:00:00', '2017-01-02 12:00:00',
               '2017-01-02 16:00:00', '2017-01-02 20:00:00',
               '2017-01-03 00:00:00'],
              dtype='datetime64[ns]', freq='4H')
Hour(2)+Minute(30)
Out[108]: <150 * Minutes>

WOM日期：可以获得每月第三个星期五之类的

rng=pd.date_range('1/1/2012','9/1/2012',freq='WOM-3FRI')

list(rng)
Out[111]: 
[Timestamp('2012-01-20 00:00:00', freq='WOM-3FRI'),
 Timestamp('2012-02-17 00:00:00', freq='WOM-3FRI'),
 Timestamp('2012-03-16 00:00:00', freq='WOM-3FRI'),
 Timestamp('2012-04-20 00:00:00', freq='WOM-3FRI'),
 Timestamp('2012-05-18 00:00:00', freq='WOM-3FRI'),
 Timestamp('2012-06-15 00:00:00', freq='WOM-3FRI'),
 Timestamp('2012-07-20 00:00:00', freq='WOM-3FRI'),
 Timestamp('2012-08-17 00:00:00', freq='WOM-3FRI')]

移动（超前和滞后）的数据

ts=Series(np.random.randn(4),index=pd.date_range('1/1/2000',periods=4,freq='M'))

ts
Out[113]: 
2000-01-31   -0.098800
2000-02-29    1.454941
2000-03-31   -0.052731
2000-04-30   -0.522302
Freq: M, dtype: float64

ts.shift(2)
Out[114]: 
2000-01-31         NaN
2000-02-29         NaN
2000-03-31   -0.098800
2000-04-30    1.454941
Freq: M, dtype: float64

ts.shift(-2)
Out[115]: 
2000-01-31   -0.052731
2000-02-29   -0.522302
2000-03-31         NaN
2000-04-30         NaN
Freq: M, dtype: float64
ts/ts.shift(1)-1
Out[117]: 
2000-01-31          NaN
2000-02-29   -15.726113
2000-03-31    -1.036243
2000-04-30     8.904943
Freq: M, dtype: float64

ts.shift(2,freq='M')
Out[118]: 
2000-03-31   -0.098800
2000-04-30    1.454941
2000-05-31   -0.052731
2000-06-30   -0.522302
Freq: M, dtype: float64
ts.shift(3,freq='D')
Out[119]: 
2000-02-03   -0.098800
2000-03-03    1.454941
2000-04-03   -0.052731
2000-05-03   -0.522302
dtype: float64

ts.shift(1,freq='3D')
Out[120]: 
2000-02-03   -0.098800
2000-03-03    1.454941
2000-04-03   -0.052731
2000-05-03   -0.522302
dtype: float64

通过偏移量对日期进行位移

from pandas.tseries.offsets import Day,MonthEnd
now=datetime(2011,11,17)
now+3*Day()

Out[123]: Timestamp('2011-11-20 00:00:00')
now+MonthEnd()
Out[124]: Timestamp('2011-11-30 00:00:00')

now+MonthEnd(2)
Out[125]: Timestamp('2011-12-31 00:00:00')

offset=MonthEnd()
offset.rollforward(now)

Out[127]: Timestamp('2011-11-30 00:00:00')
offset.rollback(now)
Out[128]: Timestamp('2011-10-31 00:00:00')
ts=Series(np.random.randn(20),index=pd.date_range('1/15/2000',periods=20,freq='4d'))
ts.groupby(offset.rollforward).mean()
Out[130]: 
2000-01-31   -0.081986
2000-02-29   -0.045495
2000-03-31    0.100962
dtype: float64
ts.resample('M',how='mean')
Out[131]: 
2000-01-31   -0.081986
2000-02-29   -0.045495
2000-03-31    0.100962
Freq: M, dtype: float64