Utilize array input for numpy mgrid function instead of for-loop over array

Question

The code below creates a list of arrays of coordinates from a DataFrame using numpy mgrid function - is there a way of doing this without the for-loop and utilizing numpy arrays?

import pandas as pd
import numpy as np

x = np.random.randint(0,100,10)
y = np.random.randint(0,100,10)
z = np.random.randint(0,100,10)

x2 = x+10
y2 = y+10
z2 = z+10

num_x = np.random.randint(1,11,10)
num_y = np.random.randint(1,11,10)
data = [x,y,z,x2,y2,z2,num_x,num_y]

df = pd.DataFrame(data).T
df.columns = ['x','y','z','x2','y2','z2','num_x','num_y']

out=[]

for i in range(len(df)):
    grid = np.mgrid[df['x'].values[i]:df['x2'].values[i]:df['num_x'].values[i]*1j,df['y'].values[i]:df['y2'].values[i]:df['num_y'].values[i]*1j,df['z'].values[i]:df['z2'].values[i]:1j]
    grid2 = np.array([grid[0].flatten(),grid[1].flatten(),grid[2].flatten()]).T
    out.append(grid2)

I appreciate that a for loop may be necessary but wanted to check before giving up. Reason for asking; a couple of articles 1, 2 suggest that by avoiding for-loops, list comprehension and map function in favour of numpy functions, especially when the data is array like, can significantly reduce runtime.

I have tried to pass the x-data as an array to mgrid but it gives a TypeError;

np.mgrid[df['x'].values:df['x2'].values:df['num_x'].values*1j]

TypeError: only length-1 arrays can be converted to Python scalars

A related question suggests that this error is caused by non-numpy like data going into the numpy function, but everything is type numpy.ndarray. Does this mean that mgrid can't take array input? Thanks.