37. Pandas的时间序列数据-period_range
可以通过pandas的period_range函数产生时间序列作为series的index。
import pandas as pd
import numpy as np
att = ["S", "T", "H", "D", "M", "A"]
vi = np.random.randn(5)
for a in att:
pi = pd.period_range('2018-12-19 11:22:33', periods = 5, freq= a)
ts = pd.Series(vi, index = pi)
print ts, "\n"
程序的执行结果:
2018-12-19 11:22:33 -0.275161
2018-12-19 11:22:34 -0.763390
2018-12-19 11:22:35 -2.012351
2018-12-19 11:22:36 -1.126492
2018-12-19 11:22:37 0.843842
Freq: S, dtype: float64
2018-12-19 11:22 -0.275161
2018-12-19 11:23 -0.763390
2018-12-19 11:24 -2.012351
2018-12-19 11:25 -1.126492
2018-12-19 11:26 0.843842
Freq: T, dtype: float64
2018-12-19 11:00 -0.275161
2018-12-19 12:00 -0.763390
2018-12-19 13:00 -2.012351
2018-12-19 14:00 -1.126492
2018-12-19 15:00 0.843842
Freq: H, dtype: float64
2018-12-19 -0.275161
2018-12-20 -0.763390
2018-12-21 -2.012351
2018-12-22 -1.126492
2018-12-23 0.843842
Freq: D, dtype: float64
2018-12 -0.275161
2019-01 -0.763390
2019-02 -2.012351
2019-03 -1.126492
2019-04 0.843842
Freq: M, dtype: float64
2018 -0.275161
2019 -0.763390
2020 -2.012351
2021 -1.126492
2022 0.843842
Freq: A-DEC, dtype: float64