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

I asked about dtype because your example is puzzling.

I can make a structured array with 3 elements (1d) and 3 fields:

In [1]: A = np.ones((3,), dtype='i,i,i')
In [2]: A
Out[2]: 
array([(1, 1, 1), (1, 1, 1), (1, 1, 1)], 
      dtype=[('f0', '

I can access one field by name (adding brackets doesn't change things)

In [3]: A['f0'].shape
Out[3]: (3,)

but if I access 2 fields, I still get a 1d array

In [4]: A[['f0','f1']].shape
Out[4]: (3,)
In [5]: A[['f0','f1']]
Out[5]: 
array([(1, 1), (1, 1), (1, 1)], 
      dtype=[('f0', '

Actually those extra brackets do matter, if I look at values

In [22]: A['f0']
Out[22]: array([1, 1, 1], dtype=int32)
In [23]: A[['f0']]
Out[23]: 
array([(1,), (1,), (1,)], 
      dtype=[('f0', '

If the array is a simple 2d one, I still don't get your shapes

In [24]: A=np.ones((3,3),int)
In [25]: A[0].shape
Out[25]: (3,)
In [26]: A[[0]].shape
Out[26]: (1, 3)
In [27]: A[[0,1]].shape
Out[27]: (2, 3)

But as to question of making sure an array is 2d, regardless of whether the indexing returns 1d or 2, your function is basically ok

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

You could test ar.ndim instead of len(ar.shape). But either way it is not costly - that is, the execution time is minimal - no big array operations. reshape doesn't copy data (unless your strides are weird), so it is just the cost of creating a new array object with a shared data pointer.

Look at the code for np.atleast_2d; it tests for 0d and 1d. In the 1d case it returns result = ary[newaxis,:]. It adds the extra axis first, the more natural numpy location for adding an axis. You add it at the end.

ar.reshape(ar.shape[0],-1) is a clever way of bypassing the if test. In small timing tests it faster, but we are talking about microseconds, the effect of a function call layer.

np.column_stack is another function that creates column arrays if needed. It uses:

 if arr.ndim < 2:
        arr = array(arr, copy=False, subok=True, ndmin=2).T