title: Efficient way to add a singleton dimension to a NumPy vector so that slice assignments work
body: <p>In NumPy, how can you efficiently make a 1-D object into a 2-D object where the singleton dimension is inferred from the current object (i.e. a list should go to either a 1xlength or lengthx1 vector)?</p>
  
  <pre><code> # This comes from some other, unchangeable code that reads data files.
   my_list = [1,2,3,4]
  
   # What I want to do:
   my_numpy_array[some_index,:] = numpy.asarray(my_list)
  
   # The above doesn't work because of a broadcast error, so:
   my_numpy_array[some_index,:] = numpy.reshape(numpy.asarray(my_list),(1,len(my_list)))
  
   # How to do the above without the call to reshape?
   # Is there a way to directly convert a list, or vector, that doesn't have a
   # second dimension, into a 1 by length "array" (but really it's still a vector)?
  </code></pre>