title: Converting nested lists of data into multidimensional Numpy arrays
body: <p>In the code below I am building data up in a nested list.  After the for loop what I would like is to cast it into a multidimensional Numpy array as neatly as possible.  However, when I do the array conversion on it, it only seems to convert the outer list into an array.  Even worse when I continue downward I wind up with dataPoints as shape <code>(100L,)</code>...so an array of lists where each list is my data (obviously I wanted a <code>(100,3)</code>).  I have tried fooling with <code>numpy.asanyarray()</code> also but I can't seem to work it out.  I would really like a 3d array from my 3d list from the outset if that is possible.  If not, how can I get the array of lists into a 2d array without having to iterate and convert them all?</p>
  
  <p>Edit:  I am also open to better way of structuring the data from the outset if it makes processing easier.  However, it is coming over a serial port and the size is not known beforehand.</p>
  
  <pre><code>import numpy as np
  import time
  
  data = []
  for _i in range(100):   #build some list of lists
      d = [np.random.rand(), np.random.rand(), np.random.rand()]
      data.append([d,time.clock()])
  
  dataArray = np.array(data)  #now I have an array of lists of a list(of data) and a time
  dataPoints = dataArray[:,0] #this is the data in an array of lists
  </code></pre>