title: Initialize Multiple Numpy Arrays (Multiple Assignment) - Like MATLAB deal()
body: <p>I was unable to find anything describing how to do this, which leads to be believe I'm not doing this in the proper idiomatic Python way.  Advice on the 'proper' Python way to do this would also be appreciated.</p>
  
  <p>I have a bunch of variables for a datalogger I'm writing (arbitrary logging length, with a known maximum length).  In MATLAB, I would initialize them all as 1-D arrays of zeros of length n, n bigger than the number of entries I would ever see, assign each individual element <code>variable(measurement_no) = data_point</code> in the logging loop, and trim off the extraneous zeros when the measurement was over.  The initialization would look like this:</p>
  
  <pre><code>[dData gData cTotalEnergy cResFinal etc] = deal(zeros(n,1));
  </code></pre>
  
  <p>Is there a way to do this in Python/NumPy so I don't either have to put each variable on its own line:</p>
  
  <pre><code>dData = np.zeros(n)
  gData = np.zeros(n)
  etc.
  </code></pre>
  
  <p>I would also prefer not just make one big matrix, because keeping track of which column is which variable is unpleasant.  Perhaps the solution is to make the <code>(length x numvars)</code> matrix, and assign the column slices out to individual variables?</p>
  
  <p>EDIT:  Assume I'm going to have a lot of vectors of the same length by the time this is over; e.g., my post-processing takes each log file, calculates a bunch of separate metrics (>50), stores them, and repeats until the logs are all processed.  Then I generate histograms, means/maxes/sigmas/etc. for all the various metrics I computed.  Since initializing 50+ vectors is clearly not easy in Python, what's the best (cleanest code and decent performance) way of doing this?</p>