How to loop the days from frequency until the end of the month for Python?-CodePudding

Let's imagine this is my table (better view here in

Using the above example, it's supposed to loop to the next week, but I can't figure it out. I used the while-loop before the for-loop but I ran into an error. For instance, in the example, the next_schedule for weekly frequency is supposed to be 2022-11-20 22:15:00, not 2022-11-13 22:15:00 again, and continually through the following weeks until the end_date.

Secondly, how do make the next_schedule into separate lines instead of all in 1 cell?

Thank you so much!

CodePudding user response：

I updated your code to suit the solution you seek.

The way you inserted the next schedule column is for adding a row not a column
You need to convert the column in a format when it can be added to timedelta output

I hope this solves your problem

from calendar import mdays, calendar
from datetime import datetime as dt, timedelta
from datetime import date
from dateutil.relativedelta import relativedelta
from dateutil.rrule import rrule, DAILY
from dateutil.parser import parse

df1 = pd.read_csv("Downloads/Frequency Loop - Sheet1.csv")
df1['next_schedule'] = ""
#convert start & end_excution_date format into the same date format
df1["start_execution_date"] = df1["start_execution_date"].apply(lambda x: dt.strptime(x, "%Y-%m-%d %H:%M:%S"))
df1["end_month"] = df1["end_month"].apply(lambda x: dt.strptime(parse(x).strftime('%Y-%m-%d %H:%M:%S'), "%Y-%m-%d %H:%M:%S"))
for frequency in df1['frequency']:
    predict = []
    start_time = df1.loc[df1.frequency==frequency,'start_execution_date'].values[0]
    end_date = df1.loc[df1.frequency==frequency,'end_month'].values[0]
    next_date = start_time
    if frequency == 'Daily':
        while(next_date<end_date):
            next_date = next_date   pd.Timedelta(days=1)
            predict.append(next_date)
    elif frequency == 'Weekly':
        while(next_date<=end_date):
            next_date = next_date   pd.Timedelta(weeks=1)
            predict.append(next_date)
    elif frequency == 'Every 2 Weeks':
        while(next_date<=end_date):
            next_date = next_date   pd.Timedelta(weeks=2)
            predict.append(next_date)
    elif frequency == 'Monthly':
        while(next_date<=end_date):
            next_date = next_date   pd.Timedelta(weeks=4)
            predict.append(next_date)
    elif frequency == 'Every 1 Months':
        while(next_date<=end_date):
            next_date = next_date   pd.Timedelta(weeks=4)
            predict.append(next_date)
    elif frequency == 'Every 3 Months':
        while(next_date<=end_date):
            next_date = next_date   pd.Timedelta(weeks=12)
            predict.append(next_date)
    elif frequency == 'Every 6 Months':
        while(next_date<=end_date):
            next_date = next_date   pd.Timedelta(weeks=24)
            predict.append(next_date)
    else:
        predict.append(df1.loc[df1.frequency==frequency,'start_execution_date'].values[0] )
    df1.loc[df1.frequency==frequency,'next_schedule'] = " ".join([str(pd.to_datetime(i)) for i in predict])

df1

output:

    index        frequency start_execution_date  end_month  \
0       0           Weekly  2022-11-06 22:15:00 2023-07-02   
1       1            Daily  2022-11-06 22:15:00 2023-07-02   
2       2          Monthly  2022-11-06 22:15:00 2023-07-02   
3       3               ??  2022-11-06 22:15:00 2023-07-02   
4       4             Once  2022-11-06 21:00:00 2023-07-02   
5       5   Every 1 months  2022-11-06 21:00:00 2023-07-02   
6       6  Every 12 months  2022-11-06 21:00:00 2023-07-02   
7       7   Every 3 months  2022-11-06 21:00:00 2023-07-02   
8       8      SQL Startup  2021-07-29 12:38:01 2023-07-02   
9       9    Every 2 weeks  2022-11-10 12:30:00 2023-07-02   
10     10   Every 6 months  2022-11-10 12:30:00 2023-07-02   

                                        next_schedule  
0   2022-11-13 22:15:00 2022-11-20 22:15:00 2022-1...  
1   2022-11-07 22:15:00 2022-11-08 22:15:00 2022-1...  
2   2022-12-04 22:15:00 2023-01-01 22:15:00 2023-0...  
3                                 2022-11-06 22:15:00  
4                                 2022-11-06 21:00:00  
5                                 2022-11-06 21:00:00  
6                                 2022-11-06 21:00:00  
7                                 2022-11-06 21:00:00  
8                                 2021-07-29 12:38:01  
9                                 2022-11-10 12:30:00  
10                                2022-11-10 12:30:00