pandas-时间操作 

%matplotlib notebook
import datetime
dt = datetime.datetime(year=2017,month=11,day=24,hour=10,minute=30)
dt

datetime.datetime(2017, 11, 24, 10, 30)

print (dt)

2017-11-24 10:30:00

# Timestamp
import pandas as pd
ts = pd.Timestamp('2017-11-24')
ts

Timestamp('2017-11-24 00:00:00')

ts.month

11

ts.day

24

# 时间计算
ts + pd.Timedelta('5 days')

Timestamp('2017-11-29 00:00:00')

#  转为时间格式1
pd.to_datetime('2017-11-24')

Timestamp('2017-11-24 00:00:00')

#  转为时间格式2
pd.to_datetime('24/11/2017')

Timestamp('2017-11-24 00:00:00')

s = pd.Series(['2017-11-24 00:00:00','2017-11-25 00:00:00','2017-11-26 00:00:00'])
s

0    2017-11-24 00:00:00
1    2017-11-25 00:00:00
2    2017-11-26 00:00:00
dtype: object

ts = pd.to_datetime(s)
ts

0   2017-11-24
1   2017-11-25
2   2017-11-26
dtype: datetime64[ns]

ts.dt.hour

0    0
1    0
2    0
dtype: int64

ts.dt.weekday

0    4
1    5
2    6
dtype: int64

pd.Series(pd.date_range(start='2020-10-01',periods = 10,freq = '12H'))

0   2020-10-01 00:00:00
1   2020-10-01 12:00:00
2   2020-10-02 00:00:00
3   2020-10-02 12:00:00
4   2020-10-03 00:00:00
5   2020-10-03 12:00:00
6   2020-10-04 00:00:00
7   2020-10-04 12:00:00
8   2020-10-05 00:00:00
9   2020-10-05 12:00:00
dtype: datetime64[ns]

data = pd.read_csv('./data/flowdata.csv')
data.head()
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 
data['Time'] = pd.to_datetime(data['Time'])
data = data.set_index('Time')
data
L06_347 LS06_347 LS06_348
Time
2009-01-01 00:00:00 0.137417 0.097500 0.016833
2013-01-02 00:00:00 0.860000 0.860000 0.075000

11697 rows × 3 columns

data.index

DatetimeIndex(['2009-01-01 00:00:00', '2009-01-01 03:00:00',
               '2009-01-01 06:00:00', '2009-01-01 09:00:00',
               '2009-01-01 12:00:00', '2009-01-01 15:00:00',
               '2009-01-01 18:00:00', '2009-01-01 21:00:00',
               '2009-01-02 00:00:00', '2009-01-02 03:00:00',
               ...
               '2012-12-31 21:00:00', '2013-01-01 00:00:00',
               '2013-01-01 03:00:00', '2013-01-01 06:00:00',
               '2013-01-01 09:00:00', '2013-01-01 12:00:00',
               '2013-01-01 15:00:00', '2013-01-01 18:00:00',
               '2013-01-01 21:00:00', '2013-01-02 00:00:00'],
              dtype='datetime64[ns]', name='Time', length=11697, freq=None)

# 读取文件，第一列设为索引，并转为时间格式
data = pd.read_csv('./data/flowdata.csv',index_col = 0,parse_dates = True)
data.head()
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 
data[pd.Timestamp('2012-01-01 09:00'):pd.Timestamp('2012-01-01 19:00')]
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 
data[('2012-01-01 09:00'):('2012-01-01 19:00')]
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 
data.tail(10)
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 
data['2013']
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 
data['2012-01':'2012-03']
L06_347 LS06_347 LS06_348
Time
2012-01-01 00:00:00 0.307167 0.273917 0.028000
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
2012-01-02 12:00:00 1.111000 1.003833 0.062250
2012-01-02 15:00:00 0.738833 0.727083 0.052417
2012-01-02 18:00:00 0.641250 0.603417 0.047667

728 rows × 3 columns

data[data.index.month == 1]
>
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

1001 rows × 3 columns

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

1462 rows × 4 columns

data.between_time('08:00','12:00')
L06_347 LS06_347 LS06_348 month
Time
2009-01-01 09:00:00 0.135750 0.091500 0.016250 1
2009-01-06 09:00:00 0.100083 0.065583 0.015500 1
2009-01-07 09:00:00 0.138500 0.093917 0.015000 1
2009-01-08 09:00:00 0.101333 0.066417 0.016833 1
2009-01-09 09:00:00 0.061750 0.059417 0.015167 1
L06_347 LS06_347 LS06_348 month
Time
2009-01-01 09:00:00 0.135750 0.091500 0.016250 1
2009-01-01 12:00:00 0.140917 0.096167 0.017000 1
2009-01-02 09:00:00 0.141917 0.097083 0.016417 1
2009-01-02 12:00:00 0.147833 0.101917 0.016417 1
2009-01-03 09:00:00 0.124583 0.084417 0.015833 1

2924 rows × 4 columns

data.head()
L06_347 LS06_347 LS06_348 month
Time
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 1
2009-01-01 06:00:00 0.113500 0.091250 0.016750 1
2009-01-01 09:00:00 0.135750 0.091500 0.016250 1
2009-01-01 12:00:00 0.140917 0.096167 0.017000 1 
data.resample('D').mean().head()
L06_347 LS06_347 LS06_348 month
Time
2009-01-01 0.125010 0.092281 0.016635 1
2009-01-02 0.124146 0.095781 0.016406 1
2009-01-03 0.113562 0.085542 0.016094 1
2009-01-04 0.140198 0.102708 0.017323 1
2009-01-05 0.128812 0.104490 0.018167 1 
data.resample('D',how='mean').head()

C:\Anaconda3\lib\site-packages\ipykernel\__main__.py:1: FutureWarning: how in .resample() is deprecated
the new syntax is .resample(...).mean()
  if __name__ == '__main__':
L06_347 LS06_347 LS06_348 month
Time
2009-01-01 0.125010 0.092281 0.016635 1
2009-01-02 0.124146 0.095781 0.016406 1
2009-01-03 0.113562 0.085542 0.016094 1
2009-01-04 0.140198 0.102708 0.017323 1
2009-01-05 0.128812 0.104490 0.018167 1 
data.resample('D').max().head()
L06_347 LS06_347 LS06_348 month
Time
2009-01-01 0.140917 0.097500 0.017583 1
2009-01-02 0.147833 0.101917 0.016833 1
2009-01-03 0.135833 0.092500 0.016833 1
2009-01-04 0.160417 0.113750 0.018417 1
2009-01-05 0.161500 0.115167 0.021583 1 
data.resample('3D').mean().head()
L06_347 LS06_347 LS06_348 month
Time
2009-01-01 0.120906 0.091201 0.016378 1.0
2009-01-04 0.121594 0.091708 0.016670 1.0
2009-01-07 0.097042 0.070740 0.014479 1.0
2009-01-10 0.115941 0.086340 0.014545 1.0
2009-01-13 0.346962 0.364549 0.034198 1.0 
data.resample('M').mean().head()
L06_347 LS06_347 LS06_348 month
Time
2009-01-31 0.517864 0.536660 0.045597 1
2009-02-28 0.516847 0.529987 0.047238 2
2009-03-31 0.373157 0.383172 0.037508 3
2009-04-30 0.163182 0.129354 0.021356 4
2009-05-31 0.178588 0.160616 0.020744 5 

data.resample('M').mean().plot()

<IPython.core.display.Javascript object>

<matplotlib.axes._subplots.AxesSubplot at 0x232129ce080>
posted @ 2021-07-04 18:00  风hua  阅读(95)  评论(0)  编辑  收藏  举报