At bit OT, but I am new to numpy. The help for np.where says:

Returns
    -------
    out : ndarray or tuple of ndarrays
        If both `x` and `y` are specified, the output array contains
        elements of `x` where `condition` is True, and elements from
        `y` elsewhere.

        If only `condition` is given, return the tuple
        ``condition.nonzero()``, the indices where `condition` is True.

However, I don't see any case that it returns an ndarray (it always seems to return a tuple of ndarrys). It seems to me for the case where only 'condition' is given it should return just the ndarry, eg (using this case discussed above):

In [44]: np.where(i==0)
Out[44]: (array([8, 9]),)

This should just return the ndarray and not the tuple of ndarrays. In what case does it only return the ndarray?

Thanks,

Bob

On Wed, Apr 17, 2013 at 4:34 AM, Todd <toddrjen@gmail.com> wrote:

The data type:
x in ndarray and x[ i ]--> int64
type(f) --> ' list '
type( f[ 0 ] ) --> ' tuple '
type( f[ 0][0] ) --> 'ndarray'
type( f[ 0 ][ 0 ][ 0] ) --> 'int64'

How do you think to avoid diversity if data type in this example? I think it is not necessary to get diverse dtype as well as more than 1D array..

That is why I suggested this approach was better ( note the that this is where()[0] instead of just where() as it was in my first example):

x,i=numpy.unique(y, return_inverse=True)
f=[numpy.where(i==ind)[0] for ind in range(len(x))]

type(f) --> list
type(f[0]) --> ndarray

type(f[0][0]) is meaningless since it is just a single element in an array. It must be an int type of some sort of since indices have to be int types. x will be the same dtype as your input array.

You could conceivably change the type of f[0] to a list, but why would you want to? One of the big advantages of python is that usually it doesn't matter what the type is. In this case, a numpy ndarray will work the same as a list in most cases where you would want to use these sorts of indices. It is possibly to change the ndarray to a list, but unless there is a specific reason you need to use lists so then it is better not to.

You cannot change the list to an ndarray because the elements of the list are different lengths. ndarray doesn't support that.

_______________________________________________
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion