I have a dataframe like this:

Subject_id    Subject    Score    
Subject_1        Math        5                 
Subject_1    Language        4                 
Subject_1       Music        8
Subject_2        Math        8                 
Subject_2    Language        3                 
Subject_2       Music        9

And I want to convert it into a dictionary, grouping by subject_id

{'Subject_1': {'Math': 5,'Language': 4,'Music': 8},
{'Subject_2': {'Math': 8,'Language': 3,'Music': 9}
}

If I would have only one Subject, then I could so:

my_dict['Subject_1'] = dict(zip(df['Subject'],df['Score']))

But since I have several subjects the list of keys repeats, so I cannot use directly a zip.

Dataframes has a .to_dict('index') method but I need to be able to group by a certain column when creating the dictionary.

How could I achieve that?

Thanks.

Use groupby with custom lambda function and last convert output Series to_dict:

d = (df.groupby('Subject_id').apply(lambda x: dict(zip(x['Subject'],x['Score']))).to_dict())print (d)
{'Subject_2': {'Math': 8, 'Music': 9, 'Language': 3}, 'Subject_1': {'Math': 5, 'Music': 8, 'Language': 4}}

Detail:

print (df.groupby('Subject_id').apply(lambda x: dict(zip(x['Subject'],x['Score']))))Subject_id
Subject_1    {'Math': 5, 'Music': 8, 'Language': 4}
Subject_2    {'Math': 8, 'Music': 9, 'Language': 3}
dtype: object