How to Create a Pandas DataFrame with Hierarchical Indexing from a Nested Dictionary?

Constructing a Pandas DataFrame from Nested Dictionary Values with Hierarchical Indexing

Consider a nested dictionary, user_dict, where the first level keys represent UserIds, the second level keys are Categories, and the third level keys are various Attributes. The goal is to construct a pandas DataFrame with a hierarchical index using the values in the third level.

To achieve this, we need to reshape the dictionary's keys into tuples representing the hierarchical index. Using pd.DataFrame.from_dict, we can create a DataFrame with the correct index structure:

user_dict = {12: {'Category 1': {'att_1': 1, 'att_2': 'whatever'},
                  'Category 2': {'att_1': 23, 'att_2': 'another'}},
             15: {'Category 1': {'att_1': 10, 'att_2': 'foo'},
                  'Category 2': {'att_1': 30, 'att_2': 'bar'}}}

pd.DataFrame.from_dict({(i,j): user_dict[i][j] 
                           for i in user_dict.keys() 
                           for j in user_dict[i].keys()},
                       orient='index')


               att_1     att_2
12 Category 1      1  whatever
   Category 2     23   another
15 Category 1     10       foo
   Category 2     30       bar

An alternative approach involves concatenating individual DataFrames created from each dictionary entry:

user_ids = []
frames = []

for user_id, d in user_dict.iteritems():
    user_ids.append(user_id)
    frames.append(pd.DataFrame.from_dict(d, orient='index'))

pd.concat(frames, keys=user_ids)

               att_1     att_2
12 Category 1      1  whatever
   Category 2     23   another
15 Category 1     10       foo
   Category 2     30       bar

Both methods effectively construct a DataFrame with a hierarchical index using the values from the nested dictionary's third level.

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