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First of all, sorry if this has been already answered, I have been searching for a while and did not find anything.

In short, I'm trying to create a programe to automate some tasks I have. In one, I have a loop on days, between a start date and an end date. For each iteration, I'm trying to get the lag in months between the current date and the start date. So I tried to use the euclidean division, which seemed appropriate. Bottom line is, it works for almost all dates except a few, for instance:

import datetime as dt
start=dt.date(2022, 7, 1)
end=dt.date(2023, 7, 1)
average_days=365/12

lag1=(end-start).days/average_days
lag2=(end-start).days//average_days

For these dates in particular, lag1 gives me 12 while lag2 gives me 11. Where does this difference come from exactly, and how can I work around this issue to continue using the euclidean division ?

Thank you

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    What do you mean by "euclidean division"? Commented Mar 2, 2022 at 15:49
  • I mean getting the quotient of the division, which is what I do in the definition of lag2 Commented Mar 2, 2022 at 15:54
  • It's a bit weird, but I think the problem comes from float precision. If you look at the rest 365%average_days it gives 30.416666666666654, which is slightly lower than average_days at 30.416666666666668 Commented Mar 2, 2022 at 15:58
  • For reference, Python refers to this operation as "floor division". Commented Mar 2, 2022 at 15:58

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This is answered here:

Dividing a Number by a Decimal Using the Integer Division Operator

In short, 365/12 is a float and is not exact after a certain number of digits. And could be a tiny bit larger than what you would expect. So when you divide 365 by this number it will be 11.99999... And // is floor division which truncates the decimals, resulting in 11.

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To correct this problem, I have a solution but it's a bit hacky (and there might be better ways): test if np.isclose(365%average_days, average_days), and when it is, add 1 to lag2
Best practice in general would be to flag/vote to close a question as a duplicate.

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