You can use a bool index array that you can produce using np.in1d.

You can index a np.ndarray along any axis you want using for example an array of bools indicating whether an element should be included. Since you want to index along axis=0, meaning you want to choose from the outest index, you need to have 1D np.array whose length is the number of rows. Each of its elements will indicate whether the row should be included.

A fast way to get this is to use np.in1d on the second column of a. You get all elements of that column by a[:, 1]. Now you have a 1D np.array whose elements should be checked against your filter. Thats what np.in1d is for.

So the complete code would look like:

import numpy as np

a = np.asarray([[2,'a'],[3,'b'],[4,'c'],[5,'d']])
filter = np.asarray(['a','c'])
a[np.in1d(a[:, 1], filter)]

or in a longer form:

import numpy as np

a = np.asarray([[2,'a'],[3,'b'],[4,'c'],[5,'d']])
filter = np.asarray(['a','c'])
mask = np.in1d(a[:, 1], filter)
a[mask]

You should perform the condition only over the first column:

x_displayed = xy_dat[((xy_dat[:,0] > min) & (xy_dat[:,0] < max))]

What we do here is constructing a view where we only take into account the first column with xy_dat[:,0]. By now checking if this 1d is between bounds, we construct a 1D boolean array of the rows we should retain, and now we make a selection of these rows by using it as item in the xy_dat[..] parameter.

you can use multiple filters in a slice, something like this:

x = np.arange(90.).reshape(30, 3)
#set the first 10 rows of cols 1,2 to be zero
x[0:10, 0:2] = 0.0
x[(x[:,0] == 0.) & (x[:,1] == 0.) & (x[:,2] > 10)]
#should give only a few rows
array([[  0.,   0.,  11.],
       [  0.,   0.,  14.],
       [  0.,   0.,  17.],
       [  0.,   0.,  20.],
       [  0.,   0.,  23.],
       [  0.,   0.,  26.],
       [  0.,   0.,  29.]])