How to create a multi-level nested dictionary from a pandas dataframe?

Question

I am trying to create a multi-level nested dictionary from a pandas dataframe - In the below example I want to retrieve for every postal code, the sum of salary for each sex and age combination. The output must be a dictionary as presented in the Expected output comment.

from typing import NamedTuple, Sequence, Tuple

import pandas as pd

data = [
    ["tom", 22, "ab 11", "M", 5555],
    ["Rob", 22, "ab 11", "M", 9999],
    ["nick", 33, "ab 22", "M", 3333],
    ["juli", 18, "ab 11", "F", 2222],
]
people = pd.DataFrame(data, columns=["Name", "Age", "PostalCode", "Sex", "Salary"])

d = (
    people.groupby(["PostalCode", "Sex", "Age"])["Salary"]
    .apply(sum)
    .to_dict()
)

print(d)

# Expected output
print({"ab 11": {("M", 22): 15554, ("F", 18): 2222}, "ab 22": {("M", 33): 3333}})

Andy L. · Accepted Answer · 2019-11-25 10:52:56Z

2

Just change your solution a little and use additional dict comprehension

df = (
    people.groupby(["PostalCode", "Sex", "Age"])["Salary"]
          .sum()
          .unstack(0)
    )

d =  {col: df[col].dropna().to_dict() for col in df}

print(d)

Out[40]:
{'ab 11': {('F', 18): 2222.0, ('M', 22): 15554.0},
 'ab 22': {('M', 33): 3333.0}}

answered Nov 25, 2019 at 10:52

Andy L.

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