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2013-12-30T21:44:24.063
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2013-12-30T21:56:33.907
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Here's a sketch Create your input series <pre><code>In [24]: x = zip(pd.date_range('20130101',periods=1000000,freq='s').asi8/1000000000,np.random.randn(1000000)) In [49]: x[0] Out[49]: (1356998400, 1.2809949462375376) </code></pre> Create the frame <pre><code>In [25]: df = DataFrame(x,columns=['time','value']) </code></pre> Make the dates a bit random (to simulate some data) <pre><code>In [26]: df['time1'] = df['time'] + np.random.randint(0,10,size=1000000) </code></pre> Convert the epoch seconds to datetime64[ns] dtype <pre><code>In [29]: df['time2'] = pd.to_datetime(df['time1'],unit='s') </code></pre> Difference the series (to create timedeltas) <pre><code>In [32]: df['diff'] = df['time2'].diff() </code></pre> Looks like this <pre><code>In [50]: df Out[50]: time value time1 time2 diff 0 1356998400 -0.269644 1356998405 2013-01-01 00:00:05 NaT 1 1356998401 -0.924337 1356998401 2013-01-01 00:00:01 -00:00:04 2 1356998402 0.952466 1356998410 2013-01-01 00:00:10 00:00:09 3 1356998403 0.604783 1356998411 2013-01-01 00:00:11 00:00:01 4 1356998404 0.140927 1356998407 2013-01-01 00:00:07 -00:00:04 5 1356998405 -0.083861 1356998414 2013-01-01 00:00:14 00:00:07 6 1356998406 1.287110 1356998412 2013-01-01 00:00:12 -00:00:02 7 1356998407 0.539957 1356998414 2013-01-01 00:00:14 00:00:02 8 1356998408 0.337780 1356998412 2013-01-01 00:00:12 -00:00:02 9 1356998409 -0.368456 1356998410 2013-01-01 00:00:10 -00:00:02 10 1356998410 -0.355176 1356998414 2013-01-01 00:00:14 00:00:04 11 1356998411 -2.912447 1356998417 2013-01-01 00:00:17 00:00:03 12 1356998412 -0.003209 1356998418 2013-01-01 00:00:18 00:00:01 13 1356998413 0.122424 1356998414 2013-01-01 00:00:14 -00:00:04 14 1356998414 0.121545 1356998421 2013-01-01 00:00:21 00:00:07 15 1356998415 -0.838947 1356998417 2013-01-01 00:00:17 -00:00:04 16 1356998416 0.329681 1356998419 2013-01-01 00:00:19 00:00:02 17 1356998417 -1.071963 1356998418 2013-01-01 00:00:18 -00:00:01 18 1356998418 1.090762 1356998424 2013-01-01 00:00:24 00:00:06 19 1356998419 1.740093 1356998428 2013-01-01 00:00:28 00:00:04 20 1356998420 1.480837 1356998428 2013-01-01 00:00:28 00:00:00 21 1356998421 0.118806 1356998427 2013-01-01 00:00:27 -00:00:01 22 1356998422 -0.935749 1356998427 2013-01-01 00:00:27 00:00:00 </code></pre> Calc median <pre><code>In [34]: df['diff'].median() Out[34]: 0 00:00:01 dtype: timedelta64[ns] </code></pre> Calc mean <pre><code>In [35]: df['diff'].mean() Out[35]: 0 00:00:00.999996 dtype: timedelta64[ns] </code></pre> Should get you started
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1. POHow to Convert (timestamp, value) array to timeseries
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1. USJeff
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1. COfirst, why not `df['diff'] = df['time2'].diff()`? second, why preserve old data, i.e. introduce those time1, time2, and not simply overwrite `df['time']`?
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2. CO@alko yep ...``diff`` is better hear; I was trying to 'illustrate' so didn't overwrite the columns
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