时间操作datetime,Timestamp,to_datetime,strptime用法

参考链接： Python strptime()

一, datetime.datetime() 

import datetime

dt = datetime.datetime(year=2019,month=11,day=4,hour=10,minute=30)

dt

datetime.datetime(2019, 11, 4, 10, 30) 

print(dt)

2019-11-04 10:30:00 

二, pd.Timestamp() 

import pandas as pd

ts = pd.Timestamp('2019-11-4')

print(ts)

print(ts.month)

print(ts.day)

2019-11-04 00:00:00 11 4 

ts + pd.Timedelta('5 days')

Timestamp(‘2019-11-09 00:00:00’) 

三, pd.to_datetime() 

pd.to_datetime('2019-11-4')

# pd.to_datetime('11/4/2019')

Timestamp(‘2019-11-04 00:00:00’) 

s = pd.Series(['2019-11-4 00:00:00','2019-11-5 00:00:00','2019-11-6 00:00:00'])

s

Out:

0    2019-11-4 00:00:00

1    2019-11-5 00:00:00

2    2019-11-6 00:00:00

dtype: object

ts = pd.to_datetime(s)

ts

# ts.dt.hour

# ts.dt.weekday

Out:

0   2019-11-04

1   2019-11-05

2   2019-11-06

dtype: datetime64[ns]

四, pandas数据中to_datetime的运用 

data = pd.read_csv('flowdata.csv')

data.head()

Out:

             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') # 以时间为索引

data.head()

Out:

                    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

或者读取数据时,直接指定时间为数据的索引 

data = pd.read_csv('flowdata.csv', index_col=0, parse_dates=True)

data.head() #结果跟上面一样

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

# data[('2012-01-01 09:00'):('2012-01-01 19:00')] #简略写法

Out:

                    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

五, datetime.strptime() 

语法: datetime.strptime(str, ‘%Y-%m-%d’) 

import pandas as pd

data = pd.read_csv('temps.csv')

data.head()

Out:

    year month day week temp_2    temp_1    average    actual    friend

0    2016    1    1    Fri        45    45        45.6    45        29

1    2016    1    2    Sat        44    45        45.7    44        61

2    2016    1    3    Sun        45    44        45.8    41        56

3    2016    1    4    Mon        44    41        45.9    40        53

4    2016    1    5    Tues     41    40        46.0    44        41

import datetime

years = data['year']

months = data['month']

days = data['day']

dates = [str(int(year))+'-'+str(int(month))+'-'+str(int(day)) for year,month,day in zip(years,months,days)]

dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in dates]

dates[:5]

[datetime.datetime(2016, 1, 1, 0, 0), datetime.datetime(2016, 1, 2, 0, 0), datetime.datetime(2016, 1, 3, 0, 0), datetime.datetime(2016, 1, 4, 0, 0), datetime.datetime(2016, 1, 5, 0, 0)] 

更多时间相关操作见: https://blog.csdn.net/sanjianjixiang/article/details/103018564