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10-时间操作--数据分析

brucexiaogui 发表于 2021/12/30 01:48:29 2021/12/30

【摘要】数据表的时间类型操作 In [59]: ...

数据表的时间类型操作

    In [59]: 
  

import datetime

    In [5]: 
  

 
           dt = datetime.datetime(year=2017,month=11,day=23,hour=7,minute=30) 
           dt

      Out[5]: 
    

datetime.datetime(2017, 11, 23, 7, 30)

    In [6]: 
  

print(dt)

2017-11-23 07:30:00

打印时间戳

    In [19]: 
  

 
           import pandas as pd 
           ts = pd.Timestamp('2019-11-11') 
           ts 
          

      Out[19]: 
    

Timestamp('2019-11-11 00:00:00')

    In [20]: 
  

ts.month

      Out[20]: 
    

    In [21]: 
  

ts.day

      Out[21]: 
    

日期相加计算

    In [22]: 
  

 
           ts + pd.Timedelta('3 day') 
          

      Out[22]: 
    

Timestamp('2019-11-14 00:00:00')

to_datetime() 方法构建时间

    In [23]: 
  

 
           pd.to_datetime('2017-11-23') 
          

      Out[23]: 
    

Timestamp('2017-11-23 00:00:00')

    In [24]: 
  

 
           pd.to_datetime('23/11/2019') 
          

      Out[24]: 
    

Timestamp('2019-11-23 00:00:00')

将数据类型转化为时间类型

s数据的类型是object ts数据的类型是datetime

    In [25]: 
  

 
           s = pd.Series(['2019-11-23 00:00:00','2019-11-24 00:00:00','2019-11-25 00:00:00']) 
           s

      Out[25]: 
    

0    2019-11-23 00:00:00
1    2019-11-24 00:00:00
2    2019-11-25 00:00:00
dtype: object

    In [26]: 
  

 
           ts = pd.to_datetime(s) 
           ts

      Out[26]: 
    

0   2019-11-23
1   2019-11-24
2   2019-11-25
dtype: datetime64[ns]

将数据转化为时间格式后，可以调用pandas库的时间方法。例如下面的方法：

查看数据中的小时

    In [27]: 
  

 
           ts.dt.hour 
          

      Out[27]: 
    

0    0
1    0
2    0
dtype: int64

    In [28]: 
  

 
           ts.dt.weekday 
          

      Out[28]: 
    

0    5
1    6
2    0
dtype: int64

创建数据的时候，直接将需要的字段设置为时间类型

    In [29]: 
  

 
           pd.Series(pd.date_range(start='2019-11-23',periods=10,freq='12H')) 
          

      Out[29]: 
    

0   2019-11-23 00:00:00
1   2019-11-23 12:00:00
2   2019-11-24 00:00:00
3   2019-11-24 12:00:00
4   2019-11-25 00:00:00
5   2019-11-25 12:00:00
6   2019-11-26 00:00:00
7   2019-11-26 12:00:00
8   2019-11-27 00:00:00
9   2019-11-27 12:00:00
dtype: datetime64[ns]

操作包含时间的数据

    In [30]: 
  

 
           data = pd.read_csv('C:/JupyterWork/data/flowdata.csv') 
           data.head()

      Out[30]: 
    

	Time	L06_347	LS06_347	LS06_348
0	2009-01-01 00:00:00	0.137417	0.097500	0.016833
1	2009-01-01 03:00:00	0.131250	0.088833	0.016417
2	2009-01-01 06:00:00	0.113500	0.091250	0.016750
3	2009-01-01 09:00:00	0.135750	0.091500	0.016250
4	2009-01-01 12:00:00	0.140917	0.096167	0.017000

将某列的数据转换为时间类型

    In [31]: 
  

 
           data['Time'] = pd.to_datetime(data['Time']) 
           data = data.set_index('Time') 
           data 
          

      Out[31]: 
    

	L06_347	LS06_347	LS06_348
Time
2009-01-01 00:00:00	0.137417	0.097500	0.016833
2009-01-01 03:00:00	0.131250	0.088833	0.016417
2009-01-01 06:00:00	0.113500	0.091250	0.016750
2009-01-01 09:00:00	0.135750	0.091500	0.016250
2009-01-01 12:00:00	0.140917	0.096167	0.017000
2009-01-01 15:00:00	0.099167	0.091667	0.017583
2009-01-01 18:00:00	0.132667	0.090167	0.016250
2009-01-01 21:00:00	0.109417	0.091167	0.016000
2009-01-02 00:00:00	0.133833	0.090417	0.016083
2009-01-02 03:00:00	0.092083	0.088667	0.016000
2009-01-02 06:00:00	0.112917	0.091417	0.016333
2009-01-02 09:00:00	0.141917	0.097083	0.016417
2009-01-02 12:00:00	0.147833	0.101917	0.016417
2009-01-02 15:00:00	0.107917	0.100250	0.016417
2009-01-02 18:00:00	0.143583	0.098417	0.016750
2009-01-02 21:00:00	0.113083	0.098083	0.016833
2009-01-03 00:00:00	0.135833	0.092167	0.016833
2009-01-03 03:00:00	0.083250	0.080000	0.016083
2009-01-03 06:00:00	0.119417	0.080250	0.015417
2009-01-03 09:00:00	0.124583	0.084417	0.015833
2009-01-03 12:00:00	0.091667	0.088250	0.016250
2009-01-03 15:00:00	0.125000	0.084667	0.016500
2009-01-03 18:00:00	0.121583	0.082083	0.015833
2009-01-03 21:00:00	0.107167	0.092500	0.016000
2009-01-04 00:00:00	0.135250	0.091167	0.016333
2009-01-04 03:00:00	0.135583	0.091583	0.016083
2009-01-04 06:00:00	0.117167	0.095167	0.016000
2009-01-04 09:00:00	0.109000	0.105167	0.018000
2009-01-04 12:00:00	0.157417	0.110750	0.018417
2009-01-04 15:00:00	0.160417	0.113750	0.018417
...	...	...	...
2012-12-29 09:00:00	0.786833	0.786833	0.077000
2012-12-29 12:00:00	0.723750	0.723750	0.072667
2012-12-29 15:00:00	0.690667	0.690667	0.069667
2012-12-29 18:00:00	0.663417	0.663417	0.069667
2012-12-29 21:00:00	0.735917	0.735917	0.072833
2012-12-30 00:00:00	0.753667	0.753667	0.061833
2012-12-30 03:00:00	0.663333	0.663333	0.073667
2012-12-30 06:00:00	0.796833	0.796833	0.095167
2012-12-30 09:00:00	0.916000	0.916000	0.101583
2012-12-30 12:00:00	1.465000	1.465000	0.086833
2012-12-30 15:00:00	1.314167	1.314167	0.085417
2012-12-30 18:00:00	1.239167	1.239167	0.098083
2012-12-30 21:00:00	1.069750	1.069750	0.101417
2012-12-31 00:00:00	0.973333	0.973333	0.085000
2012-12-31 03:00:00	0.850833	0.850833	0.073917
2012-12-31 06:00:00	0.735917	0.735917	0.069417
2012-12-31 09:00:00	0.682750	0.682750	0.066583
2012-12-31 12:00:00	0.651250	0.651250	0.063833
2012-12-31 15:00:00	0.629000	0.629000	0.061833
2012-12-31 18:00:00	0.617333	0.617333	0.060583
2012-12-31 21:00:00	0.846500	0.846500	0.170167
2013-01-01 00:00:00	1.688333	1.688333	0.207333
2013-01-01 03:00:00	2.693333	2.693333	0.201500
2013-01-01 06:00:00	2.220833	2.220833	0.166917
2013-01-01 09:00:00	2.055000	2.055000	0.175667
2013-01-01 12:00:00	1.710000	1.710000	0.129583
2013-01-01 15:00:00	1.420000	1.420000	0.096333
2013-01-01 18:00:00	1.178583	1.178583	0.083083
2013-01-01 21:00:00	0.898250	0.898250	0.077167
2013-01-02 00:00:00	0.860000	0.860000	0.075000

11697 rows × 3 columns

读取数据的时候，直接解析时间格式数据

    In [32]: 
  

 
           data = pd.read_csv('C:/JupyterWork/data/flowdata.csv',index_col=0,parse_dates= True) 
           data.head()

      Out[32]: 
    

	L06_347	LS06_347	LS06_348
Time
2009-01-01 00:00:00	0.137417	0.097500	0.016833
2009-01-01 03:00:00	0.131250	0.088833	0.016417
2009-01-01 06:00:00	0.113500	0.091250	0.016750
2009-01-01 09:00:00	0.135750	0.091500	0.016250
2009-01-01 12:00:00	0.140917	0.096167	0.017000

使用Timestamp（）对时间列做分片处理

    In [34]: 
  

 
           data[pd.Timestamp('2012-01-01 09:00'):pd.Timestamp('2012-01-01 19:00')] 
          

      Out[34]: 
    

	L06_347	LS06_347	LS06_348
Time
2012-01-01 09:00:00	0.330750	0.293583	0.029750
2012-01-01 12:00:00	0.295000	0.285167	0.031750
2012-01-01 15:00:00	0.301417	0.287750	0.031417
2012-01-01 18:00:00	0.322083	0.304167	0.038083

时间类型索引，可以直接使用分片获取数据

    In [36]: 
  

 
           data[('2012-01-01 09:10'):('2012-01-02 09:10')] 
          

      Out[36]: 
    

	L06_347	LS06_347	LS06_348
Time
2012-01-01 12:00:00	0.295000	0.285167	0.031750
2012-01-01 15:00:00	0.301417	0.287750	0.031417
2012-01-01 18:00:00	0.322083	0.304167	0.038083
2012-01-01 21:00:00	0.355417	0.346500	0.080917
2012-01-02 00:00:00	1.069333	0.970000	0.071917
2012-01-02 03:00:00	0.886667	0.817417	0.070833
2012-01-02 06:00:00	1.231000	1.153083	0.150750
2012-01-02 09:00:00	1.647500	1.476667	0.076583

tail（）显示表格中最后的5行数据

    In [37]: 
  

data.tail()

      Out[37]: 
    

	L06_347	LS06_347	LS06_348
Time
2013-01-01 12:00:00	1.710000	1.710000	0.129583
2013-01-01 15:00:00	1.420000	1.420000	0.096333
2013-01-01 18:00:00	1.178583	1.178583	0.083083
2013-01-01 21:00:00	0.898250	0.898250	0.077167
2013-01-02 00:00:00	0.860000	0.860000	0.075000

tail() 设置最后获取的数据量

    In [38]: 
  

 
           data.tail(10)

      Out[38]: 
    

	L06_347	LS06_347	LS06_348
Time
2012-12-31 21:00:00	0.846500	0.846500	0.170167
2013-01-01 00:00:00	1.688333	1.688333	0.207333
2013-01-01 03:00:00	2.693333	2.693333	0.201500
2013-01-01 06:00:00	2.220833	2.220833	0.166917
2013-01-01 09:00:00	2.055000	2.055000	0.175667
2013-01-01 12:00:00	1.710000	1.710000	0.129583
2013-01-01 15:00:00	1.420000	1.420000	0.096333
2013-01-01 18:00:00	1.178583	1.178583	0.083083
2013-01-01 21:00:00	0.898250	0.898250	0.077167
2013-01-02 00:00:00	0.860000	0.860000	0.075000

获取某一年的数据

    In [41]: 
  

data['2013']

      Out[41]: 
    

	L06_347	LS06_347	LS06_348
Time
2013-01-01 00:00:00	1.688333	1.688333	0.207333
2013-01-01 03:00:00	2.693333	2.693333	0.201500
2013-01-01 06:00:00	2.220833	2.220833	0.166917
2013-01-01 09:00:00	2.055000	2.055000	0.175667
2013-01-01 12:00:00	1.710000	1.710000	0.129583
2013-01-01 15:00:00	1.420000	1.420000	0.096333
2013-01-01 18:00:00	1.178583	1.178583	0.083083
2013-01-01 21:00:00	0.898250	0.898250	0.077167
2013-01-02 00:00:00	0.860000	0.860000	0.075000

获取一个时间段的数据

    In [44]: 
  

 
           data['2012-02':'2012-03'] 
          

      Out[44]: 
    

	L06_347	LS06_347	LS06_348
Time
2012-02-01 00:00:00	0.150917	0.208083	0.022250
2012-02-01 03:00:00	0.140917	0.200250	0.022083
2012-02-01 06:00:00	0.130667	0.191250	0.020250
2012-02-01 09:00:00	0.135583	0.186750	0.020000
2012-02-01 12:00:00	0.131750	0.183750	0.020917
2012-02-01 15:00:00	0.133333	0.177417	0.020667
2012-02-01 18:00:00	0.119333	0.168917	0.020667
2012-02-01 21:00:00	0.124417	0.174500	0.019333
2012-02-02 00:00:00	0.116167	0.167500	0.019000
2012-02-02 03:00:00	0.107333	0.157167	0.017417
2012-02-02 06:00:00	0.147750	0.217750	0.017167
2012-02-02 09:00:00	0.230583	0.313333	0.017000
2012-02-02 12:00:00	0.122250	0.174333	0.018000
2012-02-02 15:00:00	0.104083	0.152583	0.017500
2012-02-02 18:00:00	0.090917	0.150250	0.017083
2012-02-02 21:00:00	0.090667	0.145750	0.015750
2012-02-03 00:00:00	0.093833	0.153250	0.014417
2012-02-03 03:00:00	0.113083	0.193250	0.014667
2012-02-03 06:00:00	0.121333	0.221750	0.014083
2012-02-03 09:00:00	0.122917	0.195750	0.014000
2012-02-03 12:00:00	0.086667	0.140250	0.015500
2012-02-03 15:00:00	0.087833	0.133250	0.016083
2012-02-03 18:00:00	0.088417	0.138833	0.016667
2012-02-03 21:00:00	0.079000	0.137333	0.012833
2012-02-04 00:00:00	0.076333	0.131333	0.013917
2012-02-04 03:00:00	0.051750	0.131500	0.011417
2012-02-04 06:00:00	0.068667	0.169250	0.012333
2012-02-04 09:00:00	0.104750	0.178917	0.014333
2012-02-04 12:00:00	0.106500	0.165833	0.012667
2012-02-04 15:00:00	0.083000	0.140000	0.011583
...	...	...	...
2012-03-28 06:00:00	0.079583	0.122333	0.010750
2012-03-28 09:00:00	0.095333	0.120167	0.013250
2012-03-28 12:00:00	0.081500	0.121000	0.011417
2012-03-28 15:00:00	0.090917	0.119583	0.011250
2012-03-28 18:00:00	0.068417	0.122667	0.010500
2012-03-28 21:00:00	0.050583	0.123083	0.009083
2012-03-29 00:00:00	0.059083	0.121417	0.009917
2012-03-29 03:00:00	0.063833	0.121750	0.009250
2012-03-29 06:00:00	0.088500	0.122583	0.010917
2012-03-29 09:00:00	0.104917	0.121250	0.013250
2012-03-29 12:00:00	0.090167	0.121083	0.012083
2012-03-29 15:00:00	0.083667	0.121333	0.011583
2012-03-29 18:00:00	0.076833	0.120750	0.011667
2012-03-29 21:00:00	0.064667	0.119750	0.010667
2012-03-30 00:00:00	0.059083	0.118250	0.009500
2012-03-30 03:00:00	0.065583	0.119083	0.009417
2012-03-30 06:00:00	0.088500	0.122167	0.010750
2012-03-30 09:00:00	0.108500	0.121250	0.013500
2012-03-30 12:00:00	0.097417	0.123083	0.012750
2012-03-30 15:00:00	0.092833	0.121083	0.012167
2012-03-30 18:00:00	0.091083	0.121417	0.012417
2012-03-30 21:00:00	0.088500	0.123250	0.012083
2012-03-31 00:00:00	0.087417	0.123000	0.011000
2012-03-31 03:00:00	0.090833	0.123167	0.012500
2012-03-31 06:00:00	0.099417	0.124167	0.011667
2012-03-31 09:00:00	0.104917	0.125000	0.012417
2012-03-31 12:00:00	0.098333	0.124417	0.011833
2012-03-31 15:00:00	0.091917	0.123917	0.011500
2012-03-31 18:00:00	0.085750	0.121417	0.011000
2012-03-31 21:00:00	0.068417	0.119750	0.010417

480 rows × 3 columns

显示数据表中每年中月份为1月的数据

    In [45]: 
  

 
           data[data.index.month == 1]

      Out[45]: 
    

	L06_347	LS06_347	LS06_348
Time
2009-01-01 00:00:00	0.137417	0.097500	0.016833
2009-01-01 03:00:00	0.131250	0.088833	0.016417
2009-01-01 06:00:00	0.113500	0.091250	0.016750
2009-01-01 09:00:00	0.135750	0.091500	0.016250
2009-01-01 12:00:00	0.140917	0.096167	0.017000
2009-01-01 15:00:00	0.099167	0.091667	0.017583
2009-01-01 18:00:00	0.132667	0.090167	0.016250
2009-01-01 21:00:00	0.109417	0.091167	0.016000
2009-01-02 00:00:00	0.133833	0.090417	0.016083
2009-01-02 03:00:00	0.092083	0.088667	0.016000
2009-01-02 06:00:00	0.112917	0.091417	0.016333
2009-01-02 09:00:00	0.141917	0.097083	0.016417
2009-01-02 12:00:00	0.147833	0.101917	0.016417
2009-01-02 15:00:00	0.107917	0.100250	0.016417
2009-01-02 18:00:00	0.143583	0.098417	0.016750
2009-01-02 21:00:00	0.113083	0.098083	0.016833
2009-01-03 00:00:00	0.135833	0.092167	0.016833
2009-01-03 03:00:00	0.083250	0.080000	0.016083
2009-01-03 06:00:00	0.119417	0.080250	0.015417
2009-01-03 09:00:00	0.124583	0.084417	0.015833
2009-01-03 12:00:00	0.091667	0.088250	0.016250
2009-01-03 15:00:00	0.125000	0.084667	0.016500
2009-01-03 18:00:00	0.121583	0.082083	0.015833
2009-01-03 21:00:00	0.107167	0.092500	0.016000
2009-01-04 00:00:00	0.135250	0.091167	0.016333
2009-01-04 03:00:00	0.135583	0.091583	0.016083
2009-01-04 06:00:00	0.117167	0.095167	0.016000
2009-01-04 09:00:00	0.109000	0.105167	0.018000
2009-01-04 12:00:00	0.157417	0.110750	0.018417
2009-01-04 15:00:00	0.160417	0.113750	0.018417
...	...	...	...
2012-01-29 09:00:00	0.296833	0.315833	0.034750
2012-01-29 12:00:00	0.294000	0.311917	0.034333
2012-01-29 15:00:00	0.269500	0.308000	0.033000
2012-01-29 18:00:00	0.259417	0.304417	0.031833
2012-01-29 21:00:00	0.254583	0.296250	0.031333
2012-01-30 00:00:00	0.243500	0.287333	0.030917
2012-01-30 03:00:00	0.236250	0.281667	0.030250
2012-01-30 06:00:00	0.230333	0.272167	0.029417
2012-01-30 09:00:00	0.221833	0.263250	0.027833
2012-01-30 12:00:00	0.224250	0.262583	0.029250
2012-01-30 15:00:00	0.206000	0.256750	0.028917
2012-01-30 18:00:00	0.200417	0.258417	0.028250
2012-01-30 21:00:00	0.192750	0.251083	0.027250
2012-01-31 00:00:00	0.191250	0.247417	0.025917
2012-01-31 03:00:00	0.181083	0.241583	0.025833
2012-01-31 06:00:00	0.188750	0.236750	0.026000
2012-01-31 09:00:00	0.191000	0.231250	0.025583
2012-01-31 12:00:00	0.183333	0.227167	0.025917
2012-01-31 15:00:00	0.163417	0.221000	0.023750
2012-01-31 18:00:00	0.157083	0.220667	0.023167
2012-01-31 21:00:00	0.160083	0.214750	0.023333
2013-01-01 00:00:00	1.688333	1.688333	0.207333
2013-01-01 03:00:00	2.693333	2.693333	0.201500
2013-01-01 06:00:00	2.220833	2.220833	0.166917
2013-01-01 09:00:00	2.055000	2.055000	0.175667
2013-01-01 12:00:00	1.710000	1.710000	0.129583
2013-01-01 15:00:00	1.420000	1.420000	0.096333
2013-01-01 18:00:00	1.178583	1.178583	0.083083
2013-01-01 21:00:00	0.898250	0.898250	0.077167
2013-01-02 00:00:00	0.860000	0.860000	0.075000

1001 rows × 3 columns

使用boolean表达式帅选符合条件的数据

    In [46]: 
  

 
           data[(data.index.hour>8) & (data.index.hour<12)]

      Out[46]: 
    

	L06_347	LS06_347	LS06_348
Time
2009-01-01 09:00:00	0.135750	0.091500	0.016250
2009-01-02 09:00:00	0.141917	0.097083	0.016417
2009-01-03 09:00:00	0.124583	0.084417	0.015833
2009-01-04 09:00:00	0.109000	0.105167	0.018000
2009-01-05 09:00:00	0.161500	0.114583	0.021583
2009-01-06 09:00:00	0.100083	0.065583	0.015500
2009-01-07 09:00:00	0.138500	0.093917	0.015000
2009-01-08 09:00:00	0.101333	0.066417	0.016833
2009-01-09 09:00:00	0.061750	0.059417	0.015167
2009-01-10 09:00:00	0.193500	0.147000	0.013000
2009-01-11 09:00:00	0.080250	0.077417	0.013583
2009-01-12 09:00:00	0.132500	0.089167	0.016833
2009-01-13 09:00:00	0.196500	0.192667	0.045333
2009-01-14 09:00:00	0.322917	0.299250	0.029333
2009-01-15 09:00:00	0.210750	0.167500	0.025000
2009-01-16 09:00:00	0.157833	0.153917	0.023000
2009-01-17 09:00:00	0.218667	0.173333	0.022917
2009-01-18 09:00:00	0.633000	0.745667	0.077000
2009-01-19 09:00:00	1.042167	1.398500	0.133667
2009-01-20 09:00:00	0.753000	0.773000	0.065583
2009-01-21 09:00:00	0.398500	0.398500	0.042500
2009-01-22 09:00:00	0.362417	0.351250	0.036667
2009-01-23 09:00:00	8.237500	8.560000	0.383750
2009-01-24 09:00:00	1.857500	2.356667	0.099750
2009-01-25 09:00:00	0.575583	0.657750	0.059000
2009-01-26 09:00:00	0.305417	0.279917	0.044167
2009-01-27 09:00:00	0.279917	0.274917	0.032500
2009-01-28 09:00:00	0.287083	0.253833	0.031083
2009-01-29 09:00:00	0.260750	0.221833	0.028167
2009-01-30 09:00:00	0.242000	0.200167	0.024750
...	...	...	...
2012-12-03 09:00:00	0.144500	0.144500	0.074667
2012-12-04 09:00:00	0.292083	0.292083	0.041083
2012-12-05 09:00:00	0.775250	0.775250	0.075667
2012-12-06 09:00:00	0.467917	0.467917	0.060750
2012-12-07 09:00:00	0.509833	0.509833	0.096583
2012-12-08 09:00:00	0.457583	0.457583	0.064667
2012-12-09 09:00:00	0.288750	0.288750	0.053167
2012-12-10 09:00:00	0.289250	0.289250	0.060083
2012-12-11 09:00:00	0.226083	0.226083	0.037833
2012-12-12 09:00:00	0.201333	0.201333	0.035167
2012-12-13 09:00:00	0.175750	0.175750	0.034500
2012-12-14 09:00:00	0.165833	0.165833	0.035417
2012-12-15 09:00:00	0.576833	0.576833	0.065083
2012-12-16 09:00:00	0.381750	0.381750	0.046417
2012-12-17 09:00:00	0.305833	0.305833	0.050917
2012-12-18 09:00:00	0.302167	0.302167	0.070667
2012-12-19 09:00:00	0.282917	0.282917	0.041333
2012-12-20 09:00:00	0.306083	0.306083	0.068250
2012-12-21 09:00:00	0.550333	0.550333	0.059250
2012-12-22 09:00:00	0.378833	0.378833	0.069667
2012-12-23 09:00:00	5.917500	5.917500	0.286583
2012-12-24 09:00:00	1.638333	1.638333	0.151333
2012-12-25 09:00:00	1.719167	1.719167	0.146250
2012-12-26 09:00:00	1.354167	1.354167	0.127583
2012-12-27 09:00:00	1.076667	1.076667	0.103000
2012-12-28 09:00:00	0.961500	0.961500	0.092417
2012-12-29 09:00:00	0.786833	0.786833	0.077000
2012-12-30 09:00:00	0.916000	0.916000	0.101583
2012-12-31 09:00:00	0.682750	0.682750	0.066583
2013-01-01 09:00:00	2.055000	2.055000	0.175667

1462 rows × 3 columns

between_time() 获取一个闭合范围的数据

    In [47]: 
  

 
           data.between_time('08:00','09:00')

      Out[47]: 
    

	L06_347	LS06_347	LS06_348
Time
2009-01-01 09:00:00	0.135750	0.091500	0.016250
2009-01-02 09:00:00	0.141917	0.097083	0.016417
2009-01-03 09:00:00	0.124583	0.084417	0.015833
2009-01-04 09:00:00	0.109000	0.105167	0.018000
2009-01-05 09:00:00	0.161500	0.114583	0.021583
2009-01-06 09:00:00	0.100083	0.065583	0.015500
2009-01-07 09:00:00	0.138500	0.093917	0.015000
2009-01-08 09:00:00	0.101333	0.066417	0.016833
2009-01-09 09:00:00	0.061750	0.059417	0.015167
2009-01-10 09:00:00	0.193500	0.147000	0.013000
2009-01-11 09:00:00	0.080250	0.077417	0.013583
2009-01-12 09:00:00	0.132500	0.089167	0.016833
2009-01-13 09:00:00	0.196500	0.192667	0.045333
2009-01-14 09:00:00	0.322917	0.299250	0.029333
2009-01-15 09:00:00	0.210750	0.167500	0.025000
2009-01-16 09:00:00	0.157833	0.153917	0.023000
2009-01-17 09:00:00	0.218667	0.173333	0.022917
2009-01-18 09:00:00	0.633000	0.745667	0.077000
2009-01-19 09:00:00	1.042167	1.398500	0.133667
2009-01-20 09:00:00	0.753000	0.773000	0.065583
2009-01-21 09:00:00	0.398500	0.398500	0.042500
2009-01-22 09:00:00	0.362417	0.351250	0.036667
2009-01-23 09:00:00	8.237500	8.560000	0.383750
2009-01-24 09:00:00	1.857500	2.356667	0.099750
2009-01-25 09:00:00	0.575583	0.657750	0.059000
2009-01-26 09:00:00	0.305417	0.279917	0.044167
2009-01-27 09:00:00	0.279917	0.274917	0.032500
2009-01-28 09:00:00	0.287083	0.253833	0.031083
2009-01-29 09:00:00	0.260750	0.221833	0.028167
2009-01-30 09:00:00	0.242000	0.200167	0.024750
...	...	...	...
2012-12-03 09:00:00	0.144500	0.144500	0.074667
2012-12-04 09:00:00	0.292083	0.292083	0.041083
2012-12-05 09:00:00	0.775250	0.775250	0.075667
2012-12-06 09:00:00	0.467917	0.467917	0.060750
2012-12-07 09:00:00	0.509833	0.509833	0.096583
2012-12-08 09:00:00	0.457583	0.457583	0.064667
2012-12-09 09:00:00	0.288750	0.288750	0.053167
2012-12-10 09:00:00	0.289250	0.289250	0.060083
2012-12-11 09:00:00	0.226083	0.226083	0.037833
2012-12-12 09:00:00	0.201333	0.201333	0.035167
2012-12-13 09:00:00	0.175750	0.175750	0.034500
2012-12-14 09:00:00	0.165833	0.165833	0.035417
2012-12-15 09:00:00	0.576833	0.576833	0.065083
2012-12-16 09:00:00	0.381750	0.381750	0.046417
2012-12-17 09:00:00	0.305833	0.305833	0.050917
2012-12-18 09:00:00	0.302167	0.302167	0.070667
2012-12-19 09:00:00	0.282917	0.282917	0.041333
2012-12-20 09:00:00	0.306083	0.306083	0.068250
2012-12-21 09:00:00	0.550333	0.550333	0.059250
2012-12-22 09:00:00	0.378833	0.378833	0.069667
2012-12-23 09:00:00	5.917500	5.917500	0.286583
2012-12-24 09:00:00	1.638333	1.638333	0.151333
2012-12-25 09:00:00	1.719167	1.719167	0.146250
2012-12-26 09:00:00	1.354167	1.354167	0.127583
2012-12-27 09:00:00	1.076667	1.076667	0.103000
2012-12-28 09:00:00	0.961500	0.961500	0.092417
2012-12-29 09:00:00	0.786833	0.786833	0.077000
2012-12-30 09:00:00	0.916000	0.916000	0.101583
2012-12-31 09:00:00	0.682750	0.682750	0.066583
2013-01-01 09:00:00	2.055000	2.055000	0.175667

1462 rows × 3 columns

resample('D') 重采样方法，表示按照传入的条件进行数据采样。参数D：day日的意思，表示按照以天为单位进行采样

例如按照每天为单位采样计算每天的平均值

    In [48]: 
  

 
           data.resample('D').mean().head()

      Out[48]: 
    

	L06_347	LS06_347	LS06_348
Time
2009-01-01	0.125010	0.092281	0.016635
2009-01-02	0.124146	0.095781	0.016406
2009-01-03	0.113562	0.085542	0.016094
2009-01-04	0.140198	0.102708	0.017323
2009-01-05	0.128812	0.104490	0.018167

    In [49]: 
  

 
           data.resample('D').max().head() 
          

      Out[49]: 
    

	L06_347	LS06_347	LS06_348
Time
2009-01-01	0.140917	0.097500	0.017583
2009-01-02	0.147833	0.101917	0.016833
2009-01-03	0.135833	0.092500	0.016833
2009-01-04	0.160417	0.113750	0.018417
2009-01-05	0.161500	0.115167	0.021583

以多天为单位进行采样

    In [50]: 
  

 
           data.resample('3D').mean().head() 
          

      Out[50]: 
    

	L06_347	LS06_347	LS06_348
Time
2009-01-01	0.120906	0.091201	0.016378
2009-01-04	0.121594	0.091708	0.016670
2009-01-07	0.097042	0.070740	0.014479
2009-01-10	0.115941	0.086340	0.014545
2009-01-13	0.346962	0.364549	0.034198

###

    In [ ]: 
  

文章来源: brucelong.blog.csdn.net，作者：Bruce小鬼，版权归原作者所有，如需转载，请联系作者。

原文链接：brucelong.blog.csdn.net/article/details/80751888

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10-时间操作--数据分析

数据表的时间类型操作

打印时间戳

日期相加计算

to_datetime() 方法构建时间

将数据类型转化为时间类型

将数据转化为时间格式后，可以调用pandas库的时间方法。例如下面的方法：

创建数据的时候，直接将需要的字段设置为时间类型

操作包含时间的数据

将某列的数据转换为时间类型

读取数据的时候，直接解析时间格式数据

使用Timestamp（）对时间列做分片处理

时间类型索引，可以直接使用分片获取数据

tail（）显示表格中最后的5行数据

tail() 设置最后获取的数据量

获取某一年的数据

获取一个时间段的数据

显示数据表中每年中月份为1月的数据

使用boolean表达式帅选符合条件的数据

between_time() 获取一个闭合范围的数据

resample('D') 重采样方法，表示按照传入的条件进行数据采样。参数D：day日的意思，表示按照以天为单位进行采样

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加入云驻计划，成为创作者

10-时间操作--数据分析

数据表的时间类型操作

打印时间戳

日期相加计算

to_datetime() 方法构建时间

将数据类型转化为时间类型

将数据转化为时间格式后，可以调用pandas库的时间方法。例如下面的方法：

创建数据的时候，直接将需要的字段设置为时间类型

操作包含时间的数据

将某列的数据转换为时间类型

读取数据的时候，直接解析时间格式数据

使用Timestamp（）对时间列做分片处理

时间类型索引，可以直接使用分片获取数据

tail（）显示表格中最后的5行数据

tail() 设置最后获取的数据量

获取某一年的数据

获取一个时间段的数据

显示数据表中每年中月份为1月的数据

使用boolean表达式帅选符合条件的数据

between_time() 获取一个闭合范围的数据

resample('D') 重采样方法，表示按照传入的条件进行数据采样。参数D：day日的意思，表示按照以天为单位进行采样

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