Efficient way to add a singleton dimension to a NumPy vector so that slice assignments work

Question

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)?

 # 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)?

Answer 1

In the most general case, the easiest way to add extra dimensions to an array is by using the keyword None when indexing at the position to add the extra dimension. For example

my_array = numpy.array([1,2,3,4])

my_array[None, :] # shape 1x4

my_array[:, None] # shape 4x1

Answer 2

Why not simply add square brackets?

>> my_list
[1, 2, 3, 4]
>>> numpy.asarray([my_list])
array([[1, 2, 3, 4]])
>>> numpy.asarray([my_list]).shape
(1, 4)

.. wait, on second thought, why is your slice assignment failing? It shouldn't:

>>> my_list = [1,2,3,4]
>>> d = numpy.ones((3,4))
>>> d
array([[ 1.,  1.,  1.,  1.],
       [ 1.,  1.,  1.,  1.],
       [ 1.,  1.,  1.,  1.]])
>>> d[0,:] = my_list
>>> d[1,:] = numpy.asarray(my_list)
>>> d[2,:] = numpy.asarray([my_list])
>>> d
array([[ 1.,  2.,  3.,  4.],
       [ 1.,  2.,  3.,  4.],
       [ 1.,  2.,  3.,  4.]])

even:

>>> d[1,:] = (3*numpy.asarray(my_list)).T
>>> d
array([[  1.,   2.,   3.,   4.],
       [  3.,   6.,   9.,  12.],
       [  1.,   2.,   3.,   4.]])

Answer 3

import numpy as np
a = np.random.random(10)
sel = np.at_least2d(a)[idx]