How to convert integer timestamp into a datetime
Question:
I have a data file containing timestamps like “1331856000000”. Unfortunately, I don’t have a lot of documentation for the format, so I’m not sure how the timestamp is formatted. I’ve tried Python’s standard datetime.fromordinal() and datetime.fromtimestamp() and a few others, but nothing matches. I’m pretty sure that particular number corresponds to the current date (e.g. 2012-3-16), but not much more.
How do I convert this number to a datetime?
Answers:
datetime.datetime.fromtimestamp() is correct, except you are probably having timestamp in miliseconds (like in JavaScript), but fromtimestamp() expects Unix timestamp, in seconds.
Do it like that:
>>> import datetime
>>> your_timestamp = 1331856000000
>>> date = datetime.datetime.fromtimestamp(your_timestamp / 1e3)
and the result is:
>>> date
datetime.datetime(2012, 3, 16, 1, 0)
Does it answer your question?
EDIT: jfs correctly suggested in a now-deleted comment to use true division by 1e3 (float 1000). The difference is significant, if you would like to get precise results, thus I changed my answer. The difference results from the default behaviour of Python 2.x, which always returns int when dividing (using / operator) int by int (this is called floor division). By replacing the divisor 1000 (being an int) with the 1e3 divisor (being representation of 1000 as float) or with float(1000) (or 1000. etc.), the division becomes true division. Python 2.x returns float when dividing int by float, float by int, float by float etc. And when there is some fractional part in the timestamp passed to fromtimestamp() method, this method’s result also contains information about that fractional part (as the number of microseconds).
Alternatively, you can use pandas.to_datetime and choose the units for yourself together with the timezone. That avoids all the comments and problems mentioned in the previous answer:
import pandas as pd
pd.to_datetime(int('1331856000000'), utc=True, unit='ms')
# Timestamp('2012-03-16 00:00:00+0000', tz='UTC')
Similar to @Tadek answer
If the timestamps are in UTC timezone (a common way to store dates) you should use
datetime.datetime.utcfromtimestamp()
If you use fromtimestamp it will assume the date is represented in your local timezone
I have a data file containing timestamps like “1331856000000”. Unfortunately, I don’t have a lot of documentation for the format, so I’m not sure how the timestamp is formatted. I’ve tried Python’s standard datetime.fromordinal() and datetime.fromtimestamp() and a few others, but nothing matches. I’m pretty sure that particular number corresponds to the current date (e.g. 2012-3-16), but not much more.
How do I convert this number to a datetime?
datetime.datetime.fromtimestamp() is correct, except you are probably having timestamp in miliseconds (like in JavaScript), but fromtimestamp() expects Unix timestamp, in seconds.
Do it like that:
>>> import datetime
>>> your_timestamp = 1331856000000
>>> date = datetime.datetime.fromtimestamp(your_timestamp / 1e3)
and the result is:
>>> date
datetime.datetime(2012, 3, 16, 1, 0)
Does it answer your question?
EDIT: jfs correctly suggested in a now-deleted comment to use true division by 1e3 (float 1000). The difference is significant, if you would like to get precise results, thus I changed my answer. The difference results from the default behaviour of Python 2.x, which always returns int when dividing (using / operator) int by int (this is called floor division). By replacing the divisor 1000 (being an int) with the 1e3 divisor (being representation of 1000 as float) or with float(1000) (or 1000. etc.), the division becomes true division. Python 2.x returns float when dividing int by float, float by int, float by float etc. And when there is some fractional part in the timestamp passed to fromtimestamp() method, this method’s result also contains information about that fractional part (as the number of microseconds).
Alternatively, you can use pandas.to_datetime and choose the units for yourself together with the timezone. That avoids all the comments and problems mentioned in the previous answer:
import pandas as pd
pd.to_datetime(int('1331856000000'), utc=True, unit='ms')
# Timestamp('2012-03-16 00:00:00+0000', tz='UTC')
Similar to @Tadek answer
If the timestamps are in UTC timezone (a common way to store dates) you should use
datetime.datetime.utcfromtimestamp()
If you use fromtimestamp it will assume the date is represented in your local timezone