如何用空列表值替换 Pandas 列 NaN 值？

Question

Given a json string of records where the "schema" for each record is not consistent (eg each record does not have the full set of "columns"):

s = """[{"a": 3, "b":[]}, {"a": 4, "b": [4]}, {"a": 5}]"""

A pandas DataFrame can be constructed from this string:

import pandas as pd
import json

json_df = pd.DataFrame.from_records(json.loads(s))

Which results in

   a    b
0  3   []
1  4  [4]
2  5  NaN

How can all NaN instances of a pandas Series column be filled with empty list values? The expected resulting DataFrame would be:

   a    b
0  3   []
1  4  [4]
2  5   []

I have tried the following; none of which worked:

json_df[json_df.b.isna()] = [[]]*json_df[json_df.b.isna()].shape[0]

from itertools import repeat
json_df[json_df.b.isna()] = repeat([], json_df[json_df.b.isna()].shape[0])

import numpy as np
json_df[json_df.b.isna()] = np.repeat([], json_df[json_df.b.isna()].shape[0])

Thank you in advance for your consideration and response.

Answer 1

first find the nan and replace by the same shape of data

json_df.loc[json_df.b.isnull(), 'b'] = json_df.loc[json_df.b.isnull(), 'b'].apply(lambda x: [])


    a   b
0   3   []
1   4   [4]
2   4   []