时间序列

时间序列数据在很多领域都是重要的结构化数据形式

1.python标准库中的datetime

1.datetime
from datetime import datetime
now = datetime.now()
print(now)
2019-11-09 09:58:45.827047


2.两个时间相减
delta = datetime.now()-datetime(2015,6,23)
print(delta)
1600 days, 9:58:45.827047

print(delta.days)
1600

print(delta.seconds)
35925


3.strfime():按照一定格式将datetime转换为字符串,
stamp = datetime.now()
print(str(stamp))
2019-11-09 14:37:20.217164
        
print(stamp.strftime('%Y-%m-%d'))
2019-11-09


4.strptime(字符串，转换格式)：将字符串转换为日期，
value = '2015-08-08'
print(datetime.strptime(value,'%Y-%m-%d'))
2015-08-08 00:00:00

2.时间序列基础

1.将时间序列作为索引
dates = [datetime(2011,1,2),datetime(2011,1,2),datetime(2011,1,3)]
ts = Series(np.random.randn(3),index=dates)
print(ts)


2.data_range('日期'，periods='')
longer = Series(np.random.randn(1000),index=pd.date_range('1/1/2000',periods=1000))
print(longer)
2000-01-01   -0.449904
2000-01-02   -0.513678
2000-01-03    2.371833
2000-01-04   -1.846488
2000-01-05    1.234716
                ...   
2002-09-22    1.560118
2002-09-23   -0.380624
2002-09-24   -0.593029
2002-09-25   -0.142115
2002-09-26    0.627853
Freq: D, Length: 1000, dtype: float64
            
print(longer['2000']):字符串‘2000’被解释为一个年份，并选择相应的时间区间
2000-01-01   -0.063944
2000-01-02   -0.783158
2000-01-03   -0.513641
2000-01-04   -1.881692
2000-01-05   -0.773866
                ...   
2000-12-27   -1.342624
2000-12-28   -0.321939
2000-12-29   -1.816298
2000-12-30   -0.312869
2000-12-31   -1.283336
Freq: D, Length: 366, dtype: float64
            
            
print(longer['2001-5'])
2001-05-01    0.385213
2001-05-02    0.673710
2001-05-03    0.574347
2001-05-04    0.888069
2001-05-05   -0.072358
2001-05-06   -0.094635
2001-05-07   -1.020520
2001-05-08   -0.624349
2001-05-09   -0.632038
2001-05-10   -0.534303
2001-05-11   -0.337141
2001-05-12    0.297579
2001-05-13   -0.091500
2001-05-14    0.223502
2001-05-15   -0.261659
2001-05-16    2.310869
2001-05-17    0.922134
2001-05-18    2.034180
2001-05-19    1.085959
2001-05-20   -0.198433
2001-05-21   -1.300043
2001-05-22   -0.837715
2001-05-23   -0.116093
2001-05-24   -0.800779
2001-05-25    0.274794
2001-05-26   -0.620030
2001-05-27   -0.050057
2001-05-28   -0.543447
2001-05-29    0.056641
2001-05-30   -0.913786
2001-05-31   -1.618820
Freq: D, dtype: float64


        
3.生成日期范围：默认情况下，data_range生成的是每月的时间戳
index = pd.data_range(start=,end=,periods=):当只传入一个起始或结尾日期，我们必须传递periods的值
index = pd.date_range('2019-01-01','2019-11-09')
print(index)
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',
               ...
               '2019-10-31', '2019-11-01', '2019-11-02', '2019-11-03',
               '2019-11-04', '2019-11-05', '2019-11-06', '2019-11-07',
               '2019-11-08', '2019-11-09'],
              dtype='datetime64[ns]', length=313, freq='D')


4.data_range的freq参数：表明时间间隔的频率
index = pd.date_range('2019-01-01','2019-11-09',freq = '4h')
print(index)
DatetimeIndex(['2019-01-01 00:00:00', '2019-01-01 04:00:00',
               '2019-01-01 08:00:00', '2019-01-01 12:00:00',
               '2019-01-01 16:00:00', '2019-01-01 20:00:00',
               '2019-01-02 00:00:00', '2019-01-02 04:00:00',
               '2019-01-02 08:00:00', '2019-01-02 12:00:00',
               ...
               '2019-11-07 12:00:00', '2019-11-07 16:00:00',
               '2019-11-07 20:00:00', '2019-11-08 00:00:00',
               '2019-11-08 04:00:00', '2019-11-08 08:00:00',
               '2019-11-08 12:00:00', '2019-11-08 16:00:00',
               '2019-11-08 20:00:00', '2019-11-09 00:00:00'],
              dtype='datetime64[ns]', length=1873, freq='4H')