title: Timeseries average with python
body: <p>I looked at Panda and it might not be easy of use for my purpose.
  Probably there is a python toolkit out there and I'm not aware of.
  Could you tell me what package could easily deal with situations like the one described below?</p>
  
  <p>I have a series of 2D numpy arrays of instant values of a meteorological variable (for a a geographical area), sampled at hours 0, 6, 12, ..., 96,... for a specific date.</p>
  
  <p>I have it as a python dictionary:</p>
  
  <pre><code>values[0]:[[3, 2,...,9, 5][6, 7, ..., 6, 7]]
  ...
  ...
  values[96]:[[2, 2,...,8, 5][6, 7, ..., 5, 6]]
  </code></pre>
  
  <p>I need to average on a configurable aggregation step. For example, for an aggregation step of 24 hours I would obtain 4 daily averages:</p>
  
  <pre><code>values_avg24h=average(values, aggr_step=24)
  values_avg24h[24]=[[...][...]]
  values_avg24h[48]=[[...][...]]
  values_avg24h[72]=[[...][...]]
  values_avg24h[96]=[[...][...]]
  </code></pre>
  
  <p>The time resolution can change. Also, original values can be averaged as well but on a different aggregation step. A specific existing package could solve all of that. Anyway, even a smart solution to this simple problem would be appreciated.</p>