Error when converting numpy.datetime64 to int

Question

I'm trying to convert np.datetime64 to int via int(np.datetime64(...)). Surprisingly sometimes it works and sometimes it doesn't depending on how was datetime created:

a = np.datetime64('2017-09-26T15:20:11.546205184')
int(a)
a = np.datetime64('2017-09-26')
int(a)

will results int:

1506439211546205184
TypeError: int() argument must be a string, a bytes-like object or a number, not 'datetime.date'

Is there a difference in how those dates are stored internally by numpy and then causing error when converting to int?

this error actually is not specific to dates; if your date/time string specifies any precision less than nanoseconds, you must also specify a unit for int(...) to work. I think this behavior ensures that the conversion to a serial date/time is unambiguous - if the unit of the input datetime is e.g. seconds, the result is seconds since the Unix epoch and so on. — FObersteiner
– FObersteiner, Commented Feb 18, 2022 at 14:45
However, using .astype(np.int64) will work in any case, and return Unix time in the precision you specified. E.g. np.datetime64('2017-09-26T15:20:11.546').astype(np.int64) will give you milliseconds since the epoch etc. — FObersteiner
– FObersteiner, Commented Feb 18, 2022 at 14:53

Park · Accepted Answer · 2022-02-19 03:19:57Z

3

The difference is whether it include time values, such as hours, minutes, and seconds.

When you try to convert datetime (or np.datetime64) to int (or np.int64), the value will be epoch time, which is a value of seconds from 1970-01-01 00:00:00 (utc).

(See epoch time calculator: https://www.epochconverter.com/)

However, if you try to convert "2017-09-26" to int, it is hard to calculate how many seconds from 1970-01-01 00:00:00 because the value does not include hour, minutes, seconds information and timezone information.

To make it convertable, you have to add time information, as follows:

a = np.datetime64('2017-09-26T00:00:00.000000000')
print(int(a)) # 1506384000000000000 --> This is an epoch time for 2017-09-26 00:00:00

a = np.datetime64('2017-09-26','us').astype(np.int64) # not int, use np.int64
print(a) # 1506384000000000 -> This is also a epoch time for 2017-09-26 00:00:00

In addition, please use astype(np.int64) instead of astype(int) to convert it to exact epoch time when your value is saved as datetime64. If you use int, this will return the number of days from 1970-01-01.

a = np.datetime64('2017-09-26T15:20:11.546205184').astype(int)
print(a) # 1072585728 -> not an epoch time, but days from 1970-01-01

a = np.datetime64('2017-09-26T15:20:11.546205184').astype(np.int64)
print(a) # 1506439211546205184 -> a correct epoch time of 2017-09-26 15:20:11 with miliseconds

edited with consideration of @FObersteiner 's comment, Thanks!

edited Feb 19, 2022 at 3:19

answered Feb 18, 2022 at 14:15

Park

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5 Comments

FObersteiner Over a year ago

"it is impossible to calculate" - well it is possible, but you have to make assumptions about the time and the time zone (which a bare date does not have). Interestingly, np.datetime64('2017-09-26').astype(np.int64) gives you the number of days since the Unix epoch 🙄

Park Over a year ago

@FObersteiner Oh, thank you for the comment. I edited the some wording as you commented. I did not know that it was the number of days. I thought the information was lost by converting int64 to int32. Thank you again :)

FObersteiner Over a year ago

you can check with np.datetime64('1970-01-01T00:00') + np.timedelta64(np.datetime64('2017-09-26').astype(np.int64), 'D') - and I think it is safe to assume that the unit of a date must be 'days', also considering that numpy's datetime assumes UTC by default and doesn't do weird local time stuff like native Python datetime.

Park Over a year ago

@FObersteiner Nice tips!

hburd Mar 6 at 17:09

1072585728 days would be 3 million years; that number is actually just the lower 32 bits of 1506439211546205184. And that number in milliseconds would be 47 million years - surely the units must be nanoseconds instead.

baovolamada · Accepted Answer · 2022-02-18 13:49:02Z

1

Try:

a = np.datetime64('2017-09-26','us').astype(np.int64)

answered Feb 18, 2022 at 13:49

baovolamada

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