Python 如何检查日期是否在周范围内?
因此,如果退出日期在一周范围内,我需要返回布尔值True或false,这样我就可以继续对给定退出日期的预订值求和Python 如何检查日期是否在周范围内?,python,pandas,dataframe,datetime,Python,Pandas,Dataframe,Datetime,因此,如果退出日期在一周范围内,我需要返回布尔值True或false,这样我就可以继续对给定退出日期的预订值求和 week_start week_end 0 2019-05-01 2019-05-08 1 2019-05-08 2019-05-15 2 2019-05-15 2019-05-22 3 2019-05-22 2019-05-29 4 2019-05-29 2019-06-05 week_start datetime64[ns
week_start week_end
0 2019-05-01 2019-05-08
1 2019-05-08 2019-05-15
2 2019-05-15 2019-05-22
3 2019-05-22 2019-05-29
4 2019-05-29 2019-06-05
week_start datetime64[ns]
week_end datetime64[ns]
以下退出日期是否在上述给定的周范围内(如果是的话)以及总预订价值^^
Exit_date = [ '2019-05-19','2019-05-27', '2019-05-26', '2019-05-28', '2019-05-27','2019-05-27', '2019-05-22', '2019-05-18', '2019-05-25', '2019-05-25', '2019-05-17', ' 2019-05-25']
booking_cost = ['113.3250','68.3250', '62.4900','80.9917', '79.9900', '41.6600', '50.8250','41.6600', '50.8250','68.3200','68.3200','114.9920']
data ={'Exit_date': Exit_date, 'booking_cost': booking_cost}
Exit_df = pd.DataFrame(data, columns=['Exit_date','booking_cost' ]) # exit date date frame
Exit_df['Exit_date'] = pd.to_datetime(Exit_df['Exit_date'])
print(Exit_df.head())
因此,如果退出日期在一周范围内,我将要计算预订价值的总和。。。像这样的
Total_Sales =0
if daterange_df['week_start'] <= parking_master['Exit_Date'] <= daterange_df['week_end'] :
Total_Sales=+Exit_df['Booking Cost']
print(Total_Sales)
总销售额=0
如果daterange_df['week_start']用于列表理解,对于计数True
s-匹配Exit_Date
值:
z = zip(daterange_df['week_start'], daterange_df['week_end'])
daterange_df['count'] = [parking_master['Exit_Date'].between(s, e).sum() for s, e in z]
print (daterange_df)
week_start week_end count
0 2019-05-01 2019-05-08 0
1 2019-05-08 2019-05-15 0
2 2019-05-15 2019-05-22 4
3 2019-05-22 2019-05-29 12
4 2019-05-29 2019-06-05 0
谢谢,杰兹-我需要对实例的预订价值求和,我该怎么做?
z = zip(daterange_df['week_start'], daterange_df['week_end'])
daterange_df['count'] = [parking_master['Exit_Date'].between(s, e).sum() for s, e in z]
print (daterange_df)
week_start week_end count
0 2019-05-01 2019-05-08 0
1 2019-05-08 2019-05-15 0
2 2019-05-15 2019-05-22 4
3 2019-05-22 2019-05-29 12
4 2019-05-29 2019-06-05 0