python_pandas DAY_19(3)时间数据

学习内容
时间数据操作
重点

import pandas as pd
import datetime
import numpy as np

now = datetime.datetime.now()#获取当前时间
print(now)
2020-01-27 23:14:27.039777
print(now.year)#显示当前年份
2020
date1 = datetime.datetime(2020, 1, 27)
date2 = datetime.datetime(2020, 2, 1)
delta = date2 - date1#日期之差
print(delta)
5 days, 0:00:00
print(delta + datetime.datetime(2020, 1, 20))#日期可以加减运算
2020-01-25 00:00:00
print(str(date1))#转化成字符串
2020-01-27 00:00:00
print(pd.period_range("2012-01", periods=10, freq="Q"))
PeriodIndex(['2012Q1', '2012Q2', '2012Q3', '2012Q4', '2013Q1', '2013Q2',
             '2013Q3', '2013Q4', '2014Q1', '2014Q2'],
            dtype='period[Q-DEC]', freq='Q-DEC')
#2012-01为起点，10个周期，每个周期以季度为单位
b=pd.Period("2020")
print(b+2)#产生周期2020年，并加上2年
2022
c=pd.Period("2020",freq="M")
2020-03
print(c+2)#单位是月，加2就是加上两个月
a=pd.Period('2020')
print(a.asfreq("M"))#年变成月
2020-12
p=pd.Period('2020-07')
print(p.asfreq("A-MAR"))#以年为单位，每年三个月，那么就变成了2021年
2021
d=pd.Period('2020Q1',"Q-JAN")
print(d.asfreq("M",how='start'))#转换成月份，显示开始时间
2019-02
print(d.asfreq("M",how='end'))#转成月份显示结束时间
2019-04
s=pd.Series(np.random.rand(5),index=pd.date_range("20200128",periods=5,freq="M"))
print(s.to_period())
2020-01    0.652192
2020-02    0.080791
2020-03    0.509225
2020-04    0.392480
2020-05    0.356542
Freq: M, dtype: float64
print(s.to_period("D"))
2020-01-31    0.652192
2020-02-29    0.080791
2020-03-31    0.509225
2020-04-30    0.392480
2020-05-31    0.356542
Freq: D, dtype: float64
print(s.to_period("A"))
2020    0.652192
2020    0.080791
2020    0.509225
2020    0.392480
2020    0.356542
Freq: A-DEC, dtype: float64
print(s.to_period("T"))
2020-01-31 00:00    0.652192
2020-02-29 00:00    0.080791
2020-03-31 00:00    0.509225
2020-04-30 00:00    0.392480
2020-05-31 00:00    0.356542
Freq: T, dtype: float64
print(s.groupby(level=0).sum())
print(s.to_period("D").to_timestamp(how="end"))
#转化成周期才能使用to_timestamp指令
2020-01-31 23:59:59.999999999    0.518218
2020-02-29 23:59:59.999999999    0.464856
2020-03-31 23:59:59.999999999    0.198433
2020-04-30 23:59:59.999999999    0.158730
2020-05-31 23:59:59.999999999    0.490003
Freq: M, dtype: float64