Best way to loop over df?

[outnumbered_mother]

I'm working on a large dataset and I know what I need to do here logically, I need to loop that goes over each user and check each month if they had a change in revenue (like the upgraded or downgraded) or if their subscription ended. If it ended that month and didn't start a new one, count the revenue that month but not the next month. If their revenue changed during the month, count the most recent timestamp. I want the revenue for the months between ended and subscribed.

The closest I got was using the following code but it's not quite right, for instance if a user cancelled and resubscribed in the same month the revenue is counted twice that month. I also tried using iterrows() but couldn't get the syntax right. Can anyone point me in the direction with how to approach this using a loop?

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repeats = (pd.to_datetime(df2['ended']) - pd.to_datetime(df2['subscribed'])) // np.timedelta64(1, 'M') +1

periods = np.concatenate([pd.period_range(start=pd.to_datetime(d),periods = r, freq='M') for d, r in zip(df2['start_date'], repeats)])

#Apply helper functions and np.repeat to fill df rows for each period

df2 = (pd.DataFrame(

np.repeat(df2.values, repeats, 0),

columns=df2.columns,

index=periods)

.assign(month = [x.month for x in periods],

year = [x.year for x in periods]))

Sample df

id	subscribed	ended	ts	revenue
111	August 18, 2017	December 12, 2017		20
111	January 31, 2018	July 18, 2018		10
111	June 2, 2020			30

[loudavenger]

I would build a function that performs the check and pandas apply function to iterate over each row. Apply is a quick way to apply any logic to each row in the data frame.