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What is the idiomatic way of converting a pandas DateTimeIndex to (an iterable of) Unix Time? This is probably not the way to go:
[time.mktime(t.timetuple()) for t in my_data_frame.index.to_pydatetime()] 
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DateTime to Unix timestamp in UTC Timezone In the time module, the timegm function returns a Unix timestamp. The timetuple() function of the datetime class returns the datetime's properties as a named tuple. To obtain the Unix timestamp, use print(UTC).
To convert the index of a DataFrame to DatetimeIndex , use Pandas' to_datetime(~) method.
To get the current epoch/UNIX timestamp in Python: Import the time module. Call the time. time() function.
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 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 If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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