Hi all, A couple of times I've been confused by numpy.where(), and I think part of it comes from the docstring. Searching my gmail archive seems to indicate I'm not the only one bitten by this. Compare: In [14]: pdoc numpy.where Class Docstring: where(condition, | x, y) The result is shaped like condition and has elements of x and y where condition is respectively true or false. If x or y are not given, then it is equivalent to condition.nonzero(). To group the indices by element, rather than dimension, use transpose(where(condition, | x, y)) instead. This always results in a 2d array, with a row of indices for each element that satisfies the condition. with (b is just any array): In [17]: pdoc b.nonzero Class Docstring: a.nonzero() returns a tuple of arrays Returns a tuple of arrays, one for each dimension of a, containing the indices of the non-zero elements in that dimension. The corresponding non-zero values can be obtained with a[a.nonzero()]. To group the indices by element, rather than dimension, use transpose(a.nonzero()) instead. The result of this is always a 2d array, with a row for each non-zero element.; The sentence "The result is shaped like condition" in the where() docstring is misleading, since the behavior is really that of nonzero(). Where() *always* returns a tuple, not an array shaped like condition. If this were more clearly explained, along with a simple example for the usual case that seems to trip everyone: In [21]: a=arange(10) In [22]: N.where(a>5) Out[22]: (array([6, 7, 8, 9]),) In [23]: N.where(a>5)[0] Out[23]: array([6, 7, 8, 9]) I think we'd get a lot less confusion. Or am I missing something, or just being dense (quite likely)? Cheers, f