Python: minimize function in respect to i-th variable

I have a function func(x) where the argument is a vector of length n. I would like to minimize it in respect to i-th component of x while keeping the other components fixed. So to express it as a function of a single component I would do something like

import numpy as np
from scipy.optimize import minimize_scalar

def func(x):
    #do some calculations
    return function_value

def func_i(x_i, x0, i):
    x = np.copy(x0)
    x[i] = x_i
    return func(x)

res = minimize_scalar(func_i, args=(x0, i))

Is there a more efficient way of doing this? This kind of calculations will be done repeatedly, cycling over variables, and I worry that x = np.copy(x0), x[i] = x_i slow down the calculation. (The problem emerges in the context of Gibbs sampling, so minimizing in respect to all the variables simultaneously is not what I want.)

One possible go faster option rather than a full copy of x0 is to require the function to put x0 back into the state it found it after making the evaluation. IOW func_i becomes:

 def func_i(x_i, x0, i)
     temp = x0[i]
     x0[i] = x_i
     result = func(x0)
     x0[i] = temp
     return result

This avoids the copy to x but requires 3 extra assignments instead. I don't know the length of your array x0 but I'd hazard a guess that it will be faster than a copy for lengths where n is large.