Numpy reshape 1d to 2d array with 1 column

Question

In numpy the dimensions of the resulting array vary at run time. There is often confusion between a 1d array and a 2d array with 1 column. In one case I can ite

Answer 1

To avoid the need to reshape in the first place, if you slice a row / column with a list, or a "running" slice, you will get a 2D array with one row / column

import numpy as np
x = np.array(np.random.normal(size=(4,4)))
print x, '\n'

Result:
[[ 0.01360395  1.12130368  0.95429414  0.56827029]
 [-0.66592215  1.04852182  0.20588886  0.37623406]
 [ 0.9440652   0.69157556  0.8252977  -0.53993904]
 [ 0.6437994   0.32704783  0.52523173  0.8320762 ]] 

y = x[:,[0]]
print y, 'col vector \n'
Result:
[[ 0.01360395]
 [-0.66592215]
 [ 0.9440652 ]
 [ 0.6437994 ]] col vector 


y = x[[0],:]
print y, 'row vector \n'

Result:
[[ 0.01360395  1.12130368  0.95429414  0.56827029]] row vector 

# Slice with "running" index on a column
y = x[:,0:1]
print y, '\n'

Result:
[[ 0.01360395]
 [-0.66592215]
 [ 0.9440652 ]
 [ 0.6437994 ]] 

Instead if you use a single number for choosing the row/column, it will result in a 1D array, which is the root cause of your issue:

y = x[:,0]
print y, '\n'

Result:
[ 0.01360395 -0.66592215  0.9440652   0.6437994 ]