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 iterate over the columns, in the other case I cannot.

How do you solve elegantly that problem? To avoid littering my code with if statements checking for the dimensionality, I use this function:

def reshape_to_vect(ar):     if len(ar.shape) == 1:       return ar.reshape(ar.shape[0],1)     return ar 

However, this feels inelegant and costly. Is there a better solution?

Answer 1

The simplest way:

ar.reshape(-1, 1) 

Answer 2

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]])