pandas datetime to unix timestamp seconds

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')

Here are some options:

Option 1: integer division

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")

Pros:

• super fast

Cons:

• makes assumptions about how pandas internally stores dates

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Option 2: recommended by pandas

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")

Pros:

• “idiomatic”, recommended by the library

Cons:

• unweildy
• not as performant as integer division

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Option 3: pd.Timestamp

If you have a single date string, you can use pd.Timestamp as shown in the other answer:

pd.Timestamp('2019-01-15 13:30:00').timestamp()
# 1547559000.0

If you have to cooerce multiple datetimes (where pd.to_datetime is your only option), you can initialize and map:

pd.to_datetime(['2019-01-15 13:30:00']).map(pd.Timestamp.timestamp)
# Float64Index([1547559000.0], dtype="float64")

Pros:

• best method for a single datetime string
• easy to remember

Cons:

• not as performant as integer division