Attributing values to a correlation matrix in Python without iterating

I have a table of loans with the features country and sector.

I need to define a correlation matrix using the following rules:

• if 2 loans have the same sector and country they are correlated by 35%
• if 2 loans have the same country they are correlated by 25%
• if 2 loans have the same sector they are correlated by 25%
• otherwise they are correlated by 5%

I created it by iteration. Is there a smarter way to do it?
Thanks

corr = np.zeros((10,10))
for i in range(df.shape[0]):
    for j in range(df.shape[0]):
        if df.loan[i] == df.loan[j]:
            corr[i,j] = 1
        else:
            if (df.sector[i] == df.sector[j]) and (df.country[i] == df.country[j]):
                corr[i,j] = 0.6
            else:
                if (df.sector[i] == df.sector[j]) or (df.country[i] == df.country[j]):
                    corr[i,j]=0.4
                else:
                    corr[i,j]=0.2

import numpy as np
import pandas as pd

s="""# LOAN	SECTOR	COUNTRY
Loan 1	food	germany
Loan 2	telecom	italy
Loan 3	auto	japan
Loan 4	food	japan
Loan 5	telecom	germany
Loan 6	auto	italy
Loan 7	auto	italy"""

rows = s.split('\n')
data = [r.split('\t') for r in rows[1:]]
header = rows[0].split('\t')
df = pd.DataFrame(data, columns=header)

n = len(df)
cols = df['# LOAN']
cmat1 = pd.DataFrame(np.full((n, n), 0), columns=cols, index=cols)
cmat2 = pd.DataFrame(np.full((n, n), 0), columns=cols, index=cols)
for sec in df.SECTOR.unique():
    idx = df.SECTOR == sec
    cmat1.iloc[idx, idx] = 1
for cou in df.COUNTRY.unique():
    idx = df.COUNTRY == cou
    cmat2.iloc[idx, idx] = 1

cmat = cmat1 + cmat2
cmat = cmat.replace(0, 0.05)
cmat = cmat.replace(1, 0.25)
cmat = cmat.replace(2, 0.35)
np.fill_diagonal(cmat.values, 1)

print(cmat)

prints

# LOAN  Loan 1  Loan 2  Loan 3  Loan 4  Loan 5  Loan 6  Loan 7
# LOAN                                                        
Loan 1    1.00    0.05    0.05    0.25    0.25    0.05    0.05
Loan 2    0.05    1.00    0.05    0.05    0.25    0.25    0.25
Loan 3    0.05    0.05    1.00    0.25    0.05    0.25    0.25
Loan 4    0.25    0.05    0.25    1.00    0.05    0.05    0.05
Loan 5    0.25    0.25    0.05    0.05    1.00    0.05    0.05
Loan 6    0.05    0.25    0.25    0.05    0.05    1.00    0.35
Loan 7    0.05    0.25    0.25    0.05    0.05    0.35    1.00