概述
python 日期的范围、频率、重采样以及频率转换
pandas有一整套的标准时间序列频率以及用于重采样、频率推断、生成固定频率日期范围的工具。
生成指定日期范围的范围
pandas.date_range()用于生成指定长度的DatatimeIndex:
1)默认情况下,date_range会按着时间间隔为天的方式生成从给定开始到结束时间的时间戳数组;
2)如果只指定开始或结束时间,还需要periods标定时间长度。
import pandas as pd pd.date_range('2017-6-20','2017-6-27')
DatetimeIndex(['2017-06-20','2017-06-21','2017-06-22','2017-06-23','2017-06-24','2017-06-25','2017-06-26','2017-06-27'],dtype='datetime64[ns]',freq='D')
pd.date_range('2017-6-20 12:59:30','2017-6-27')
DatetimeIndex(['2017-06-20 12:59:30','2017-06-21 12:59:30','2017-06-22 12:59:30','2017-06-23 12:59:30','2017-06-24 12:59:30','2017-06-25 12:59:30','2017-06-26 12:59:30'],periods = 8)
DatetimeIndex(['2017-06-20 12:59:30','2017-06-26 12:59:30','2017-06-27 12:59:30'],periods = 8,normalize = True)
DatetimeIndex(['2017-06-20',freq='D')
频率和日期偏移量
pandas中的频率是由一个基础频率(M、H)也可以是(Hour、Minute、h、min等)
pd.date_range('2017-6-27',periods = 7,freq = '1h30min')
DatetimeIndex(['2017-06-27 00:00:00','2017-06-27 01:30:00','2017-06-27 03:00:00','2017-06-27 04:30:00','2017-06-27 06:00:00','2017-06-27 07:30:00','2017-06-27 09:00:00'],freq='90T')
pd.date_range('2017-6-27',freq = 'M')
DatetimeIndex(['2017-06-30','2017-07-31','2017-08-31','2017-09-30','2017-10-31','2017-11-30','2017-12-31'],freq='M')
pd.date_range('2017-6-27',freq = 'd')
DatetimeIndex(['2017-06-27','2017-06-28','2017-06-29','2017-06-30','2017-07-01','2017-07-02','2017-07-03'],freq='D')
pd.date_range('2017-6-27',freq = 'H')
DatetimeIndex(['2017-06-27 00:00:00','2017-06-27 01:00:00','2017-06-27 02:00:00','2017-06-27 04:00:00','2017-06-27 05:00:00','2017-06-27 06:00:00'],freq='H')
常用的基础频率
上表只展示了部分!
WOM日期(可获得例如“每月第3个星期五”)
pd.date_range('2017-06-01',freq='WOM-3FRI')
DatetimeIndex(['2017-06-16','2017-07-21'],freq='WOM-3FRI')
重采样及频率转换
降采样:高频数据到低频数据
升采样:低频数据到高频数据
主要函数:resample()(pandas对象都会有这个方法)
resample方法的参数
降采样
需考虑:
1)各区间哪边是闭合的(参数:closed)
2)如何标记各聚合面元,用区间的开头还是末尾(参数:label)
ts_index = pd.date_range('2017-06-20',periods =12,freq = '1min')#一分钟采样数据 ts = pd.Series(np.arange(12),index = ts_index)
ts
2017-06-20 00:00:00 0 2017-06-20 00:01:00 1 2017-06-20 00:02:00 2 2017-06-20 00:03:00 3 2017-06-20 00:04:00 4 2017-06-20 00:05:00 5 2017-06-20 00:06:00 6 2017-06-20 00:07:00 7 2017-06-20 00:08:00 8 2017-06-20 00:09:00 9 2017-06-20 00:10:00 10 2017-06-20 00:11:00 11 Freq: T,dtype: int32
聚合到5分钟
ts.resample('5min',how='sum')
C:\Program Files\anaconda\lib\site-packages\ipykernel\__main__.py:1: FutureWarning: how in .resample() is deprecated the new Syntax is .resample(...).sum() if __name__ == '__main__': 2017-06-20 00:00:00 10 2017-06-20 00:05:00 35 2017-06-20 00:10:00 21 Freq: 5T,dtype: int32
ts.resample('5min',how='sum',closed='left')
C:\Program Files\anaconda\lib\site-packages\ipykernel\__main__.py:1: FutureWarning: how in .resample() is deprecated the new Syntax is .resample(...).sum() if __name__ == '__main__': 2017-06-20 00:00:00 10 2017-06-20 00:05:00 35 2017-06-20 00:10:00 21 Freq: 5T,closed='left',label ='left')
C:\Program Files\anaconda\lib\site-packages\ipykernel\__main__.py:1: FutureWarning: how in .resample() is deprecated the new Syntax is .resample(...).sum() if __name__ == '__main__': 2017-06-20 00:00:00 10 2017-06-20 00:05:00 35 2017-06-20 00:10:00 21 Freq: 5T,dtype: int32
通过groupby进行重插样
另外一种降采样方法
ts1_index = pd.date_range('2017-6-01',periods = 100,freq = 'd') ts1 = pd.Series(np.arange(100),index = ts1_index) ts1.head()
2017-06-01 0 2017-06-02 1 2017-06-03 2 2017-06-04 3 2017-06-05 4 Freq: D,dtype: int32
ts1.groupby(lambda x:x.month).mean()
6 14.5 7 45.0 8 76.0 9 95.5 dtype: float64
ts1.groupby(lambda x:x.weekday).mean()
0 49.5 1 50.5 2 51.5 3 49.0 4 50.0 5 47.5 6 48.5 dtype: float64
df1 = pd.DataFrame(np.arange(200).reshape(100,2),index = ts1_index)
df1.groupby(lambda x:x.weekday).mean()
对于具有时间序列索引的pandas数据结构,当groupby传入一个函数时,可以对时间索引对应列进行聚合
升采样
升采样没有聚合,但是需要填充
df2 = pd.DataFrame(np.arange(200).reshape(100,index = ts1_index,columns=['add1','add2']) df2.head()
df2.resample('W-THU',fill_method = 'ffill')
C:\Program Files\anaconda\lib\site-packages\ipykernel\__main__.py:1: FutureWarning: fill_method is deprecated to .resample() the new Syntax is .resample(...).ffill() if __name__ == '__main__':
总结
1)生成指定时间段,指定频率的日期
2)对含有时间索引的pandas数据进行重采样,包括降采样和升采样等。
总结
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