Generating a list of numbers based on a constraint-step procedure

I'm trying to generate a list of 8 numbers with the following for code:

import numpy as np
import pandas as pd

n2 = np.random.uniform(0.1, 1.5)
c2 = np.random.uniform(4,14)
c3 = np.random.uniform(0.1,2.9)
ic4 = np.random.uniform(0.01,1)
nc4 = np.random.uniform(0.01,1)
ic5 = np.random.uniform(0,0.2)
nc5 = np.random.uniform(0,0.01)

comp_list = []

for i in range(1):
    if n2/c2 <= 0.11:
        comp_list.append(c2)
        comp_list.append(n2)
    if c3/c2 <= 0.26:
        comp_list.append(c3)
    if ic4/c3 <= 0.27:
        comp_list.append(ic4)
    if nc4/ic4 <= 1:
        comp_list.append(nc4)
    if ic5/nc4 <= 0.06:
        comp_list.append(ic5)
    if nc5/ic5 <= 0.25:
        comp_list.append(nc5)

    sum = n2+c2+c3+ic4+nc4+ic5+nc5
    c1 = 100-sum
    comp_list.append(c1)

df = pd.Series(comp_list)

print df

However, when i run the code, the amount of numbers outputted is not consistent and can range from 3 to 5. for example, 1 run would give me:

0     1.560
1     0.251
2     0.008
3     86.665

a second run would give me:

0     12.929
1     1.015
2     2.126
3     0.093
4     0.0025
5     83.376

I have no idea why the output is not consistent.

Maybe I need to iterate through a random distribution until all the if statements are satisfied? or am I missing something obvious?