时间序列数据分析--Time Series--python日期和时间处理及操作

时间序列分类

• 时间戳（timestamp），特定的时刻
• 固定周期（period），某月或某年
• 时间间隔（interval），由起始时间戳和结束时间戳表示

python日期和时间处理及操作 

1.datetime模块

datetime -> str

• (1)   str(datetime_obj)
• (2)   datetime.strftime()

• from datetime import datetime
  from datetime import date
  
  now = datetime.now()
  print(now)
  
  diff = datetime(2017, 3, 4, 17) - datetime(2017, 2, 18, 15)
  print(type(diff))
  print(diff)
  print('经历了{}天, {}秒。'.format(diff.days, diff.seconds))
  answer:
  
  14 days, 2:00:00
  经历了14天, 7200秒。
  
  datetime -> str
  
  # str()
  dt_obj = datetime(2017, 3, 4)
  str_obj = str(dt_obj)
  print(type(str_obj))
  print(str_obj)
  answer:
  
  2017-03-04 00:00:00
  
  # datetime.strftime()
  str_obj2 = dt_obj.strftime('%d-%m-%Y')
  print(type(str_obj2))
  

   

str -> datetime

• （1）datetime.strptime() 需要指定时间表示的格式
• （2）dateutil.parser.parser() 可以解析大部分时间表示形式
• （3）pd.to_datetime()可以处理缺失值和空字符串

• str -> datetime
  
  # strptime
  dt_str = '2017-02-18'
  dt_obj2 = datetime.strptime(dt_str, '%Y-%m-%d')
  print(type(dt_obj2))
  print(dt_obj2)
  
  # dateutil.parser.parse
  from dateutil.parser import parse
  dt_str2 = '2017/02/18'
  dt_obj3 = parse(dt_str2)
  print(type(dt_obj3))
  print(dt_obj3)
  
  # pd.to_datetime
  import pandas as pd
  s_obj = pd.Series(['2017/02/18', '2017/02/19', '2017-02-25', '2017-02-26'], name='course_time')
  print(s_obj)
  s_obj2 = pd.to_datetime(s_obj)
  print(s_obj2)
  
  # 处理缺失值
  s_obj3 = pd.Series(['2017/02/18', '2017/02/19', '2017-02-25', '2017-02-26'] + [None], 
                     name='course_time')
  print(s_obj3)
  s_obj4 = pd.to_datetime(s_obj3)
  print(s_obj4) # NAT-> Not a Time
  answer:
  0   2017-02-18
  1   2017-02-19
  2   2017-02-25
  3   2017-02-26
  4          NaT
  Name: course_time, dtype: datetime64[ns]