> 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
