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I have the following dict:

nested_dict={4.0: {'high': 93.34, 'low': 91.53}, 7.0: {'high': 13.95, 'low': 13.88}, 9.0: {'high': 48.84, 'low': 48.36}} # ...continues

In the interest of full disclosure I want to map this dict to the index of a data frame, creating 2 new cols: 'high', and 'low'. After multiple attempts to map the nested dict to the index failed, the easiest solution seems to be to break the dict into 2, because that can easily be mapped:

high_dict={4.0:93.34, 7.0:13.95, 9.0: 48.84} # ...continues low_dict ={4.0:91.53, 7.0:13.88, 9.0: 48.36} # ditto

The rest is easy:

df['high']= df.index.map(high_dict) df['low'] = df.index.map(low_dict)

How do I split the above-nested dict into my desired 2 new dicts?

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  • Not sure what you mean Rodrigo - works fine for me Commented Oct 8, 2022 at 17:19

4 Answers 4

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You could use Python Dictionary Comprehension to achieve it in a pandas-independent way:

high_dict={key : value['high'] for key, value in nested_dict.items()} low_dict ={key : value['low'] for key, value in nested_dict.items()}
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You can achieve it by this way:

nested_dict={4.0: {'high': 93.34, 'low': 91.53}, 7.0: {'high': 13.95, 'low': 13.88}, 9.0: {'high': 48.84, 'low': 48.36}} high_dict = {} low_dict = {} for k, v in nested_dict.items(): high_dict[k] = v['high'] for k, v in nested_dict.items(): low_dict[k] = v['low'] print(high_dict) print(low_dict)
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As you have the tag, you can just use a transposition:

out = pd.DataFrame.from_dict(nested_dict, orient='index').to_dict()

output:

{'high': {4.0: 93.34, 7.0: 13.95, 9.0: 48.84}, 'low': {4.0: 91.53, 7.0: 13.88, 9.0: 48.36}}

If you need variables:

high_dict = out['high'] low_dict = out['low']

For a pure python solution with a single loop over the input dictionary:

high_dict = {} low_dict = {} mapper = {'high': high_dict, 'low': low_dict} for key, d in nested_dict.items(): for k, v in d.items(): if k in mapper: mapper[k][key] = v high_dict # {4.0: 93.34, 7.0: 13.95, 9.0: 48.84} low_dict # {4.0: 91.53, 7.0: 13.88, 9.0: 48.36}
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An interesting way to turn a nested dict into a df. For my application I dont even do the interim step in python but just map pd.DataFrame.from_dict(nested_dict, orient='index') directly via merge.
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df = pd.DataFrame(nested_dict).transpose().reset_index(names="key") high = df.set_index("key")["high"].to_dict() low = df.set_index("key")["low"].to_dict()
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