title: Recommended use of Numpy's structured arrays
body: <p>I want to save 1D arrays into each entry of another 1D array. Essentially I have a list of grid points (1D array) for which each I would like to save a 1D array containing information specific to the grid point. (Imagine for each of a list of cities in a country, saving the demographic age distribution from age 1 to 100.)</p>
  <p>While I can think of many ways to theoretically implement this, I am not sure which is the most efficient and in general recommended.</p>
  <p>I could save the data in a matrix where each row is a country and the columns specify the age group. That would work and I can implement it.</p>
  <p>Alternatively I could save the information as a structured array where the list of countries is an array and each entry is an array with the age distribution. Intuitively I prefer this option, but I don't know whether it's actually recommended. My program runs many thousand iterations, so speed is more important than prettiness of the code.
  The other problem I have with this is that I need to initialise that array dynamically (I hope I'm using that word correctly). I have a for-loop iterating through the list of points and saving the results for each point. However, since my list of points varies based on the init values, I can't initialise it like so <code>data = np.array([(0, 0, 0, 0, 0), (0, 0, 0, 0, 0), (0, 0, 0, 0, 0), (0, 0, 0, 0, 0)])</code> because it won't always be a 4x5 array.</p>
  <p>Any ideas on what would be the best way to do this?</p>