时间操作datetime,Timestamp,to_datetime,strptime用法
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