title: Summing time series with slight variance in timestamps
body: <p>I imagine that I have several time series like following, from different &quot;sources&quot;:</p>
  <pre><code>       time     events
  0      1000    1080000
  1      2003    2122386
  2      3007    3043985
  3      4007    3872544
  4      5007    4853763
  </code></pre>
  <p>Here, an monotonic increasing count <em>events</em> is sampled every 1000 ms. The sampling is not <em>exact</em> so most of the timestamps vary from their ideal values by a few ms - e.g., the second point is at 2003 instead of 2000.</p>
  <p>I want to <em>sum</em> several of these time series: they will all be sampled at ~1000 ms but may not agree to the exact millsecond. E.g another time series could be:</p>
  <pre><code>       time     events
  0      1000    1070000
  1      2002    2122486
  2      3006    3063985
  3      4007    3872544
  4      5009    4853763
  </code></pre>
  <p>I'd like something <em>reasonable</em> in terms of the final result. For example the same number of rows as each of the input dataframes, with a timestamp column the same as the first, or average of the inputs times. As long as the inputs are smooth, the outputs should be too.</p>