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Nested Dictionary to Multiindex Dataframe

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  • Last Updated : 24 Feb, 2021

Pandas DataFrame is a two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). A Data frame is a two-dimensional data structure, i.e., data is aligned in a tabular fashion in rows and columns. Pandas DataFrame consists of three principal components, the data, rows, and columns. A Multiindex Dataframe is a pandas dataframe having multi-level indexing or hierarchical indexing.

Pandas needs multi-index values as tuples, not as a nested dictionary. So, first, we need to convert the nested index values into tuples.

Example #1:

Python3




# Import module
import pandas as pd
  
# Nested dictionary to convert it
# into multiindex dataframe
nested_dict = {'A': {'a': [1, 2, 3,
                           4, 5],
                     'b': [6, 7, 8,
                           9, 10]},
  
               'B': {'a': [11, 12, 13,
                           14, 15],
                     'b': [16, 17, 18,
                           19, 20]}}
  
reformed_dict = {}
for outerKey, innerDict in nested_dict.items():
    for innerKey, values in innerDict.items():
        reformed_dict[(outerKey,
                       innerKey)] = values
  
# Multiindex dataframe
reformed_dict

Output:

Notice that in the reformed_dict, index values are in the tuple. Now to convert reformed_dict into multiindex dataframe, we can use pd.DataFrame() method.

Python3




multiIndex_df = pd.DataFrame(reformed_dict)
multiIndex_df

Output:

Here in the output, we can see the hierarchical index/ multi index for the column.

Example #2:

Python3




# Import module
import pandas as pd
  
# Nested dictionary to convert it into multiindex dataframe
nested_dict = {'India': {'State': ['Maharashtra', 'West Bengal',
                                   'Uttar Pradesh', 'Bihar', 'Karnataka'],
                         'Capital': ['Mumbai', 'Kolkata', 'Lucknow',
                                     'Patna', 'Bengaluru']},
  
               'America': {'State': ['California', 'Florida', 'Georgia',
                                     'Massachusetts', 'New York'],
                           'Capital': ['Sacramento', 'Tallahassee', 'Atlanta',
                                       'Boston', 'Albany']}}
  
reformed_dict = {}
for outerKey, innerDict in nested_dict.items():
    for innerKey, values in innerDict.items():
        reformed_dict[(outerKey, innerKey)] = values
  
# Display multiindex dataframe
multiIndex_df = pd.DataFrame(reformed_dict)
multiIndex_df

Output:


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