# Multi-level groupby sub-population percentages

Let's consider the following dataframe:

``````df = {'Location': ['A','A','B','B','C','C','A','C','A'],
'Gender'['M','M','F','M','M','F','M','M','M'],
'Edu'['N','N','Y','Y','Y','N','Y','Y','Y'],
'Access1': [1,0,1,0,1,0,1,1,1], 'Access2': [1,1,1,0,0,1,0,0,1] }
df = pd.DataFrame(data=d, dtype=np.int8)
``````

Output from dataframe:

``````   Access1  Access2 Edu Gender Location
0        1        1   N      M        A
1        0        1   N      M        A
2        1        1   Y      F        B
3        0        0   Y      M        B
4        1        0   Y      M        C
5        0        1   N      F        C
6        1        0   Y      M        A
7        1        0   Y      M        C
8        1        1   Y      M        A
``````

Then I am using groupby to analyse the frequencies in df

``````D0=df.groupby(['Location','Gender','Edu']).sum()
((D0/ D0.groupby(level = [0]).transform(sum))*100).round(3).astype(str) + '%'
``````

Output:

``````                     Access1  Access2
Location Gender Edu
A        M      N    33.333%  66.667%
Y    66.667%  33.333%
B        F      Y     100.0%   100.0%
M      Y       0.0%     0.0%
C        F      N       0.0%   100.0%
M      Y     100.0%     0.0%
``````

From this output, I infer that 33.3% of uneducated men in location A with Access to service 1 (=Access1) is the result of considering 3 people in location A having access to service 1, of which 1 uneducated man has access to it (=1/3).

Yet, wish to get a different output. I would like to consider a total of 4 men in location A as my 100%. 50% of this group of men are uneducated. Out of that 50% of uneducated men, 25% have access to service 1. So, the percentage I would like to see in the table is 25% (total of uneducated men in area A accessing service 1). Is groupby the right way to get there, and what would be the best way to measure the % of Access to service 1 while considering a disaggregation from the total population of reference per location?

I believe need divide `D0` by first level of MultiIndex mapped by `a` Series:

``````D0=df.groupby(['Location','Gender','Edu']).sum()

a = df['Location'].value_counts()
#alternative
#a = df.groupby(['Location']).size()
print (a)
A    4
C    3
B    2
Name: Location, dtype: int64

df1 = D0.div(D0.index.get_level_values(0).map(a.get), axis=0)
print (df1)
Access1   Access2
Location Gender Edu
A        M      N    0.250000  0.500000
Y    0.500000  0.250000
B        F      Y    0.500000  0.500000
M      Y    0.000000  0.000000
C        F      N    0.000000  0.333333
M      Y    0.666667  0.000000
``````

Detail:

``````print (D0.index.get_level_values(0).map(a.get))
Int64Index([4, 4, 2, 2, 3, 3], dtype='int64', name='Location')
``````