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    <p>You can pass your <code>inArr</code> to a pandas Dataframe:</p> <pre><code>df = pd.DataFrame(inArr, columns=['time', 'value']) </code></pre> <ul> <li><em>num seconds between each successive pair of timestamps:</em> <code>df['time'].diff()</code></li> <li><em>median delay</em>: <code>df['time'].diff().median()</code></li> <li><em>creates an array of the correct size</em> (I think that's taken care of?)</li> <li><em>presumes the first time period starts at half the median value before the first timestamp (putting the measurement in the middle of the time period)</em>; I don't know what you mean here</li> <li>averages values that happen to go into the same time bucket</li> </ul> <p>For several of these problems it may make since to convert your seconds to <code>datetime</code> and set it as the index:</p> <pre><code>In [39]: df['time'] = pd.to_datetime(df['time'], unit='s') In [41]: df = df.set_index('time') In [42]: df Out[42]: value time 2013-12-30 20:27:22 12.3 2013-12-30 20:27:42 11.1 2013-12-30 20:28:01 12.8 </code></pre> <p>Then to handle multiple values in the same time, use <code>groupby</code>.</p> <pre><code>In [49]: df.groupby(level='time').mean() Out[49]: value time 2013-12-30 20:27:22 12.3 2013-12-30 20:27:42 11.1 2013-12-30 20:28:01 12.8 </code></pre> <p>It's the same since there aren't any dupes.</p> <p>Not sure what you mean about the last two.</p> <p>And your desired output seems to contradict what you wanted earlier. You values with the same timestamp should be averaged, and now you want them all? Maybe clear that up a bit.</p>
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    1. POHow to Convert (timestamp, value) array to timeseries
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