How to Json_Normalize a Column with Nans

• This question is specific to columns of data in a pandas.DataFrame
• This question depends on if the values in the columns are str, dict, or list type.
• This question addresses dealing with the NaN values, when df.dropna().reset_index(drop=True) isn't a valid option.

Case 1

• With a column of str type, the values in the column must be converted to dict type, with ast.literal_eval, before using .json_normalize.

import numpy as np
import pandas as pd
from ast import literal_eval

df = pd.DataFrame({'col_str': ['{"a": "46", "b": "3", "c": "12"}', '{"b": "2", "c": "7"}', '{"c": "11"}', np.NaN]})

                            col_str
0  {"a": "46", "b": "3", "c": "12"}
1              {"b": "2", "c": "7"}
2                       {"c": "11"}
3                               NaN

type(df.iloc[0, 0])
[out]: str

df.col_str.apply(literal_eval)

Error:

df.col_str.apply(literal_eval) results in ValueError: malformed node or string: nan

Case 2

• With a column of dict type, use pandas.json_normalize to convert keys to column headers and values to rows

df = pd.DataFrame({'col_dict': [{"a": "46", "b": "3", "c": "12"}, {"b": "2", "c": "7"}, {"c": "11"}, np.NaN]})

                           col_dict
0  {'a': '46', 'b': '3', 'c': '12'}
1              {'b': '2', 'c': '7'}
2                       {'c': '11'}
3                               NaN

type(df.iloc[0, 0])
[out]: dict

pd.json_normalize(df.col_dict)

Error:

pd.json_normalize(df.col_dict) results in AttributeError: 'float' object has no attribute 'items'