前言
时间日期相加减，单独看没啥意思，但是比如要预测从当前时间开始，未来12个月的股票变化情况，你只拿到了股票的价格的序列值一共12个，需要把它扑到未来12个月，中间可能跨年。
安装插件
python-dateutil
1 | pip install python-dateutil |
一、python时间处理月份加减
1 2 3 4 5 6 | import datetimefrom dateutil.relativedelta import relativedelta datetime_now = datetime.datetime.now()datetime_three_month_ago = datetime_now - relativedelta(months=3)print(datetime_three_month_ago) |
二、python日期相减（秒、小时、天、月、年）
1.例子
代码如下（示例）：
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | import datetimetoday=datetime.datetime.now()datetime1=today-datetime.timedelta(seconds=10)#减10秒datetime2=today-datetime.timedelta(minutes=10)#减10分钟datetime3=today-datetime.timedelta(hours=1)#减1小时datetime4=today-datetime.timedelta(days=7)#减1天datetime5=today-datetime.timedelta(weeks=1)#减1周datetime6=today.strftime('%Y-%m-%d')#将时间格式化为字符串print('today:',today)print('second:',datetime1)print('minute:',datetime2)print('hour:',datetime3)print('day:',datetime4)print('week:',datetime5)print('时间格式化为字符串:',datetime6) import dateutil.relativedeltadatetime7= datetime.datetime.strptime(datetime6, '%Y-%m-%d')#将字符串格式为时间datetime8 = datetime7 - dateutil.relativedelta.relativedelta(seconds=10)#减10秒datetime9 = datetime7 - dateutil.relativedelta.relativedelta(minutes=10)#减10分钟datetime10 = datetime7 - dateutil.relativedelta.relativedelta(hours=1)#减减1小时datetime11 = datetime7 - dateutil.relativedelta.relativedelta(days=1)#减1天datetime12 = datetime7 - dateutil.relativedelta.relativedelta(months=1)#减一个月datetime13 = datetime7 - dateutil.relativedelta.relativedelta(years=1)#减一年print('字符串格式为时间:',datetime7)print('second2:',datetime8)print('minute2:',datetime9)print('hour2:',datetime10)print('day2:',datetime11)print('month2:',datetime12)print('year:',datetime13) |
2.案例：
处理未来12个月
1 2 3 4 5 6 7 8 9 10 11 12 | def get_dynamic_year(i): today=datetime.datetime.now() tmp = today + dateutil.relativedelta.relativedelta(months=i) return tmp.yeardef get_dynamic_month(i): today=datetime.datetime.now() tmp = today + dateutil.relativedelta.relativedelta(months=i) return tmp.monthpd_insert["year"] = [get_dynamic_year(i) for i in range(1,13)]pd_insert["month"] = [ get_dynamic_month(i) for i in range(1,13) ] |
处理未来7天的日期
1 2 3 4 5 6 7 8 9 | """ pandas实在是太变态了，用row.name的方式获取行索引 把每一天都加上 """ data_date = today + datetime.timedelta(days=i+1) return data_date.strftime("%Y-%m-%d")pd_insert['forecast_date'] = today.strftime("%Y-%m-%d")pd_insert['data_date'] = pd_insert.apply( lambda x: get_date(x.name, x['forecast_date']), axis=1) |
总结
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