id1  id2  weights
0   a    2a   144.0
1   a    2b   52.5
2   a    2c   2.0
3   a    2d   1.0
4   a    2e   1.0
5   b    2a   2.0
6   b    2e   1.0
7   b    2f   1.0
8   b    2b   1.0
9   b    2c   0.008

2a    2b   2c   2d   2e   2f
2a  1     0.5  0.7  0.2  0.1  0.3
2b  0.5   1    0.6  0.4  0.3  0.4
2c  0.7   0.6  1    0.1  0.4  0.2
2d  0.2   0.4  0.1  1    0.8  0.7
2e  0.1   0.3  0.4  0.8  1    0.8
2f  0.3   0.4  0.2  0.7  0.8  1

ids = df.id1.unique()
output = pd.DataFrame(columns = mat.columns,index = ids)
for id in ids:
    df_slice = df.loc[df.id1 == id]
    to_normalize = df_slice.weights.sum()
    temp = mat.loc[df_slice.id2]
    for art in df_slice.id2:
        temp.loc[art] *= df_slice.ix[df_slice.id2 == art,'weights'].values[0]
        temp.loc[art] /= (1.*to_normalize)
    output.loc[id] = temp.sum()

2a          2b          2c          2d          2e          2f
a   0.857606    0.630424    0.672319    0.258354    0.163342    0.329676
b   0.580192    0.540096    0.520767    0.459425    0.459904    0.559425

import io

import pandas as pd


raw_df = io.StringIO("""
  id1  id2  weights
0   a    2a   144.0
1   a    2b   52.5
2   a    2c   2.0
3   a    2d   1.0
4   a    2e   1.0
5   b    2a   2.0
6   b    2e   1.0
7   b    2f   1.0
8   b    2b   1.0
9   b    2c   0.008
""")
df = pd.read_csv(raw_df, delim_whitespace=True)

raw_mat = io.StringIO("""
    2a    2b   2c   2d   2e   2f
2a  1     0.5  0.7  0.2  0.1  0.3
2b  0.5   1    0.6  0.4  0.3  0.4
2c  0.7   0.6  1    0.1  0.4  0.2
2d  0.2   0.4  0.1  1    0.8  0.7
2e  0.1   0.3  0.4  0.8  1    0.8
2f  0.3   0.4  0.2  0.7  0.8  1
""")
mat = pd.read_csv(raw_mat, delim_whitespace=True)


df['norm'] = df.groupby('id1')['weights'].transform('sum')

m = pd.merge(df, mat, left_on='id2', right_index=True)
m[mat.index] = m[mat.index].multiply(m['weights'] / m['norm'], axis=0)

output = m.groupby('id1')[mat.index].sum()
output.columns.name = 'id2'
print(output)

id2        2a        2b        2c        2d        2e        2f
id1
a    0.857606  0.630424  0.672319  0.258354  0.163342  0.329676
b    0.580192  0.540096  0.520767  0.459425  0.459904  0.559425

disease  symptom  frequence
0   d1       s1       Very frequent (99-80%)
1   d1       s2       Very frequent (99-80%)
2   d2       s1       Frequent (79-30%)
3   d2       s3       Very frequent (99-80%)
4   d3       s2       Occasional (29-5%
5   d4       s1       Very frequent (99-80%)
6   d4       s2       Frequent (79-30%)
7   d4       s3       Occasional (29-5%
8   d5       s3       Occasional (29-5%
9   d5       s4      Very frequent (99-80%)

s1  s2  s3  s4  s5 s6
d1  1   1   0   0   0  0
d2  1   0   1   0   0  0
d3  0   1   1   1   1  1
d4  1   0   1   0   0  0
d5  0   0   1   1   0  0

import pandas as pd
import numpy as np
import io

    data = pd.read_csv("disease_sym_frq_list.csv", sep="[;,]", engine='python')
    data

    dat_mat= io.StringIO("""data

    """)
    mat = pd.read_csv(dat_mat, delim_whitespace=True)

    data['norm'] = data.groupby('Disease')['Frequence'].transform('sum')

    m = pd.merge(data, mat, left_on='Symptom', right_index=True)
    m[mat.index] = m[mat.index].multiply(m['Frequence'] / m['norm'], axis=0)

    output = m.groupby('Disease')[mat.index].sum()
    output.columns.name = 'Symptom'
    print(output)

Empty DataFrame
Columns: []
Index: []