Equivalent of Matlab 'ismember' in numpy (Python)?

If months is sorted, use np.searchsorted. Otherwise, sort and then use np.searchsorted:

import numpy as np
quarters = np.array([200712, 200803, 200806, 200809, 200812, 200903])
months = np.arange(200801, 200813)
loc = np.searchsorted(months, quarters)

np.searchsorted returns the insertion position. If there is a possibility that your data is not even in the right range, you might want to have a check afterwards:

valid = (quarters <= months.max()) & (quarters >= months.min())
loc = loc[valid]

This is a O(N log N) solution. If this is still a big deal in your programme in terms of run time, you might just do this one subroutine in C(++) using a hashing scheme, which would be O(N) (as well as avoiding some constant factors, of course).

I think you can redesign the original MATLAB code sample you give so that it doesn't use the ISMEMBER function. This may speed up the MATLAB code and make it easier to reimplement in Python if you still want to:

quarters = [200712 200803 200806 200809 200812 200903];
gdp_q = [10.1 10.5 11.1 11.8 10.9 10.3];

monthStart = 200801;              %# Starting month value
monthEnd = 200812;                %# Ending month value
nMonths = monthEnd-monthStart+1;  %# Number of months
gdp_m = NaN(1,nMonths);           %# Initialize gdp_m

quarters = quarters-monthStart+1;  %# Shift quarter values so they can be
                                   %#   used as indices into gdp_m
index = (quarters >= 1) & (quarters <= nMonths);  %# Logical index of quarters
                                                  %#   within month range
gdp_m(quarters(index)) = gdp_q(index);  %# Move values from gdp_q to gdp_m