python - I have a gaussian function with two independent discrete variables. How do I create a matrix of all possible values? -

basically have this:

from scip.stats import norm import pandas pd  r = pd.series([1, 2, 3]) k = pd.series([0.2, 0.3, 0.4, 0.5]) x = 2  mean = x + k variance = k  # i'm feeding gaussian function 2 vectors.  # i'd matrix of possible combinations. quickly.  values = norm.pdf(r, mean, variance) 

so i'm giving function norm.pdf 2 vectors of data, , i'd (3x4) matrix returned me looks like:

values(1, 0.2)    values(1, 0.3)    values(1, 0.4)    values(1, 0.5) values(2, 0.2)                                             ... values(3, 0.2)                                             ... values(4, 0.2)     ...........       ...........      values(4, 0.5) 

i know iterate on items in arrays, takes lot of time, , plan on scaling quite bit. i'd take advantage of numpy's speed. i've tried vectorizing, fails. ideas? thanks!!!

you can apply pdf each element of r , automatically put results in matrix using:

r.apply(lambda x: pd.series(norm.pdf(x, mean, variance), index=k)) 

if return series apply results automatically unpacked columns. output:

            0.2       0.3       0.4       0.5 0  3.037941e-08  0.000111  0.002182  0.008864 1  1.209854e+00  0.806569  0.604927  0.483941 2  6.691511e-04  0.087406  0.323794  0.483941