Convert pandas DateTimeIndex to Unix Time?

As DatetimeIndex is ndarray under the hood, you can do the conversion without a comprehension (much faster).

In [1]: import numpy as np

In [2]: import pandas as pd

In [3]: from datetime import datetime

In [4]: dates = [datetime(2012, 5, 1), datetime(2012, 5, 2), datetime(2012, 5, 3)]
   ...: index = pd.DatetimeIndex(dates)
   ...: 
In [5]: index.astype(np.int64)
Out[5]: array([1335830400000000000, 1335916800000000000, 1336003200000000000], 
        dtype=int64)

In [6]: index.astype(np.int64) // 10**9
Out[6]: array([1335830400, 1335916800, 1336003200], dtype=int64)

%timeit [t.value // 10 ** 9 for t in index]
10000 loops, best of 3: 119 us per loop

%timeit index.astype(np.int64) // 10**9
100000 loops, best of 3: 18.4 us per loop

Complementing the other answers: //10**9 will do a flooring divide, which gives full past seconds rather than the nearest value in seconds. A simple way to get more reasonable rounding, if that is desired, is to add 5*10**8 - 1 before doing the flooring divide.

A summary of other answers:

df['<time_col>'].astype(np.int64) // 10**9

If you want to keep the milliseconds divide by 10**6 instead

Note: Timestamp is just unix time with nanoseconds (so divide it by 10**9):

[t.value // 10 ** 9 for t in tsframe.index]

For example:

In [1]: t = pd.Timestamp('2000-02-11 00:00:00')

In [2]: t
Out[2]: <Timestamp: 2000-02-11 00:00:00>

In [3]: t.value
Out[3]: 950227200000000000L

In [4]: time.mktime(t.timetuple())
Out[4]: 950227200.0

As @root points out it's faster to extract the array of values directly:

tsframe.index.astype(np.int64) // 10 ** 9