【python数据分析】pandas库时间序列

DatetimeIndex， TimeSeries ，TimeSeries

1、DatetimeIndex

      是pandas库的时间索引

import pandas as pd
import numpy as np
from datetime import datetime

rng = pd.DatetimeIndex(['12/1/2019','12/2/2019','12/3/2019'])
print(rng,type(rng))

------------------------结果-------------------
DatetimeIndex(['2019-12-01', '2019-12-02', '2019-12-03'], dtype='datetime64[ns]', freq=None) 

2、TimeSeries

      以DatetimeIndex为索引的Series，称为时间序列

import pandas as pd
import numpy as np
from datetime import datetime
st = pd.Series(np.random.rand(len(rng)),index=rng)
print(st,type(st))

------------------------结果--------------------
2019-12-01    0.539328
2019-12-02    0.093855
2019-12-03    0.344785
dtype: float64 

生成方式：1.start+end创建    2.start/end +periods 创建，频率默认为天

import pandas as pd
import numpy as np
from datetime import datetime

# date_range 生成日期范围
# 两种生成方式，1，start+end 2，start/end +periods ，频率默认为天
rng1 = pd.date_range('1/1/2019','1/10/2019',normalize=True)
rng2 = pd.date_range('1/1/2019 10:00:00','1/10/2019',normalize=False)
print(rng1)
print(rng2)

rng3 = pd.date_range('1/1/2019',periods=10,normalize=True)
print(rng3)
print('-------------')

# close指定左开或者又开
print(pd.date_range('1/1/2019','1/10/2019',closed='right'))
print(pd.date_range('1/1/2019','1/10/2019',closed='left'))

# bdate_range默认频率为工作日
print(pd.bdate_range('1/1/2019','1/10/2019'))

-----------------------------结果------------------------
DatetimeIndex(['2019-01-01', '2019-01-02', '2019-01-03', '2019-01-04',
               '2019-01-05', '2019-01-06', '2019-01-07', '2019-01-08',
               '2019-01-09', '2019-01-10'],
              dtype='datetime64[ns]', freq='D')
DatetimeIndex(['2019-01-01 10:00:00', '2019-01-02 10:00:00',
               '2019-01-03 10:00:00', '2019-01-04 10:00:00',
               '2019-01-05 10:00:00', '2019-01-06 10:00:00',
               '2019-01-07 10:00:00', '2019-01-08 10:00:00',
               '2019-01-09 10:00:00'],
              dtype='datetime64[ns]', freq='D')
DatetimeIndex(['2019-01-01', '2019-01-02', '2019-01-03', '2019-01-04',
               '2019-01-05', '2019-01-06', '2019-01-07', '2019-01-08',
               '2019-01-09', '2019-01-10'],
              dtype='datetime64[ns]', freq='D')
-------------
DatetimeIndex(['2019-01-02', '2019-01-03', '2019-01-04', '2019-01-05',
               '2019-01-06', '2019-01-07', '2019-01-08', '2019-01-09',
               '2019-01-10'],
              dtype='datetime64[ns]', freq='D')
DatetimeIndex(['2019-01-01', '2019-01-02', '2019-01-03', '2019-01-04',
               '2019-01-05', '2019-01-06', '2019-01-07', '2019-01-08',
               '2019-01-09'],
              dtype='datetime64[ns]', freq='D')
DatetimeIndex(['2019-01-01', '2019-01-02', '2019-01-03', '2019-01-04',
               '2019-01-07', '2019-01-08', '2019-01-09', '2019-01-10'],
              dtype='datetime64[ns]', freq='B')

3、一个时间构造器

import pandas as pd
import numpy as np
from datetime import datetime

# pd.period()创建时期,生成一个默认以月为频率的时间构造器
p = pd.Period('2019',freq = 'M')
print(p)
print(p +1)

prng = pd.period_range('1/1/2019','1/1/2020')
print(prng)

# freq可以指定为其他，H为小时
p = pd.Period('2019',freq = 'H')
print(p)
print(p +1)
print(p -1)

----------------------结果---------------------
2019-01
2019-02
PeriodIndex(['2019-01-01', '2019-01-02', '2019-01-03', '2019-01-04',
             '2019-01-05', '2019-01-06', '2019-01-07', '2019-01-08',
             '2019-01-09', '2019-01-10',
             ...
             '2019-12-23', '2019-12-24', '2019-12-25', '2019-12-26',
             '2019-12-27', '2019-12-28', '2019-12-29', '2019-12-30',
             '2019-12-31', '2020-01-01'],
            dtype='period[D]', length=366, freq='D')
2019-01-01 00:00
2019-01-01 01:00
2018-12-31 23:00