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
You could do -
ar.reshape(ar.shape[0],-1)
That second input to reshape : -1 takes care of the number of elements for the second axis. Thus, for a 2D input case, it does no change. For a 1D input case, it creates a 2D array with all elements being "pushed" to the first axis because of ar.shape[0], which was the total number of elements.
Sample runs
1D Case :
In [87]: ar
Out[87]: array([ 0.80203158, 0.25762844, 0.67039516, 0.31021513, 0.80701097])
In [88]: ar.reshape(ar.shape[0],-1)
Out[88]:
array([[ 0.80203158],
[ 0.25762844],
[ 0.67039516],
[ 0.31021513],
[ 0.80701097]])
2D Case :
In [82]: ar
Out[82]:
array([[ 0.37684126, 0.16973899, 0.82157815, 0.38958523],
[ 0.39728524, 0.03952238, 0.04153052, 0.82009233],
[ 0.38748174, 0.51377738, 0.40365096, 0.74823535]])
In [83]: ar.reshape(ar.shape[0],-1)
Out[83]:
array([[ 0.37684126, 0.16973899, 0.82157815, 0.38958523],
[ 0.39728524, 0.03952238, 0.04153052, 0.82009233],
[ 0.38748174, 0.51377738, 0.40365096, 0.74823535]])