Using data from pythons pandas dataframes to sample from normal distributions

I'm trying to sample from a normal distribution using means and standard deviations that are stored in pandas DataFrames.

For example:

means= numpy.arange(10)
means=means.reshape(5,2)

produces:

   0  1
0  0  1
1  2  3
2  4  5
3  6  7
4  8  9

and:

sts=numpy.arange(10,20)
sts=sts.reshape(5,2)

produces:

    0   1
0  10  11
1  12  13
2  14  15
3  16  17
4  18  19

How would I produce another pandas dataframe with the same shape but with values sampled from the normal distribution using the corresponding means and standard deviations.

i.e. position 0,0 of this new dataframe would sample from a normal distribution with mean=0 and standard deviation=10, and so on.

My function so far:

    def make_distributions(self):
        num_data_points,num_species= self.means.shape
        samples=[]
        for i,j in zip(self.means,self.stds):
            for k,l in  zip(self.means[i],self.stds[j]):
                samples.append( numpy.random.normal(k,l,self.n) )

will sample from the distributions for me but I'm having difficulty putting the data back into the same shaped dataframe as the mean and standard deviation dfs. Does anybody have any suggestions as to how to do this?

Thanks in advance.