pandas group by custom column

Question

For example, I have a following df:

score	var1
0.465	jack, jones, phil
0.712	don, sam, bob
0.112	jones, alex, sam

I want to round column 'score' in such a way, so I would have for example a mean score of jones in such a way

name	0.1 (score)	0.5
jones	1	1
jack	0	1
alex	0	0

second and third column, are the rounded score, so if we have max score of 1, then we will have 10 column, and as shown in table above, it shows how much jones appeared with the rounded score of 0.1 and etc

I have a code written to similiar question as this, but instead of a score I just have boolean target column with 0 and 1. however, I cant figure how to do it in this case this is the code:

out=(df
 .assign(cr_social_vector=df['var_1'].str.split(',\s*'),
         dev_var=np.where(df['target'].eq(1), 'dev_var_1', 'dev_var_0'),
         value=1
        )
 .explode('cr_social_vector')
 .pivot_table(index=['var_1', 'var_2'], columns='target', values='value',
              aggfunc='sum', fill_value=0)
 .reset_index().rename_axis(columns=None)
 .groupby(['var_1'], as_index=False)
 .agg({'target_1': 'sum', 'target_0': 'sum', 'var_2': 'last'})
)

Answer 1

You can try

out_ = (df.assign(score=df['score'].round(1),
                var1=df['var1'].str.split(', '))
       .explode('var1'))

out = pd.crosstab(out_['var1'], out_['score'])

print(out_)

   score   var1
0    0.5   jack
0    0.5  jones
0    0.5   phil
1    0.7    don
1    0.7    sam
1    0.7    bob
2    0.1  jones
2    0.1   alex
2    0.1    sam


print(out)

score  0.1  0.5  0.7
var1
alex     1    0    0
bob      0    0    1
don      0    0    1
jack     0    1    0
jones    1    1    0
phil     0    1    0
sam      1    0    1

Answer 2

You can use pivot_table

import pandas as pd

df = pd.DataFrame({'score':[0.465,0.712, 0.112],'var1':[['jack', 'jones', 'phil'],['don', 'sam', 'bob'],['jones', 'alex', 'sam']]})
 
df = df.round(decimals=1).explode("var1").pivot_table(index="var1",columns="score", aggfunc="size", fill_value=0).reset_index()
df

Output: