pandas datetime to unix timestamp seconds

Question

From the official documentation of pandas.to_datetime we can say,

unit : string, default ‘ns’

unit of the arg (D,s,ms,us,ns)

Answer 1

I think you misunderstood what the argument is for. The purpose of origin='unix' is to convert an integer timestamp to datetime, not the other way.

pd.to_datetime(1.547559e+09, unit='s', origin='unix') 
# Timestamp('2019-01-15 13:30:00')

Conversely, you can get the timestamp by converting to integer (to get nanoseconds) and divide by 10⁹.

pd.to_datetime(['2019-01-15 13:30:00']).astype(int) / 10**9
# Float64Index([1547559000.0], dtype='float64')

---

Update

Pandas docs recommend using the following method:

# create test data
dates = pd.to_datetime(['2019-01-15 13:30:00'])

# calculate unix datetime
(dates - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s')

[out]:
Int64Index([1547559000], dtype='int64')

Not as fast as the method shown above, but this makes no assumption about how pandas internally stores its datetime objects.

Answer 2

In case you are accessing a particular datetime64 object from the dataframe, chances are that pandas will return a Timestamp object which is essentially how pandas stores datetime64 objects.

You can use pd.Timestamp.to_datetime64() method of the pd.Timestamp object to convert it to numpy.datetime64 object with ns precision.