> match(v1, v2) => returns a boolean array of length len(v1) indicating > whether element i in v1 is in v2.
You want numpy.in1d (and friends, probably, like numpy.unique and the others that are all collected in numpy.lib.arraysetops...) Definition: numpy.in1d(ar1, ar2, assume_unique=False) Docstring: Test whether each element of a 1D array is also present in a second array. Returns a boolean array the same length as `ar1` that is True where an element of `ar1` is in `ar2` and False otherwise. Parameters ---------- ar1 : array_like, shape (M,) Input array. ar2 : array_like The values against which to test each value of `ar1`. assume_unique : bool, optional If True, the input arrays are both assumed to be unique, which can speed up the calculation. Default is False. Returns ------- mask : ndarray of bools, shape(M,) The values `ar1[mask]` are in `ar2`. See Also -------- numpy.lib.arraysetops : Module with a number of other functions for performing set operations on arrays. _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion