How to pull a key from a dict (pandas series) to its own row?

Question

Here is my example data with two fields where the last one [outbreak] is a pandas series.

Start:

Goal (Excel mock-up):

Reproduction Code:

import pandas as pd
import json

d = {'report_id': [100, 101], 'outbreak': [
    '{"outbreak_100":{"name":"Chris","disease":"A-Pox"},"outbreak_101":{"name":"Stacy","disease": "H-Pox"}}', 
    '{"outbreak_200":{"name":"Brandon","disease":"C-Pox"},"outbreak_201":{"name":"Karen","disease": "G-Pox"},"outbreak_202":{"name":"Tim","disease": "Z-Pox"}}']}

df = pd.DataFrame(data=d)
print(type(df['outbreak']))
display(df)

#Ignore
df = pd.json_normalize(df['outbreak'].apply(json.loads), max_level=0)
display(df)

Attempts: I thought about using json_normalize() which would convert every [outbreak_id] to its own field and then use pandas.wide_to_long() to get my final output. It works in testing but my concern is that my actual production data is so long and nested that it ends up generating hundred of thousands of fields before pivoting. That does not sounds good to me and why I also hope to avoid loop iterations.

I also thought about using df = df.explode('outbreak') but I am getting a KeyError: 0

Perhaps someone has a better idea than I do? Thank you.

Answer 1

You can try with ast convert to dict format , then we do conversion

import ast 
out = df.pop('outbreak').map(ast.literal_eval).apply(pd.Series).stack().reset_index(level=1).join(df)
out.columns = ['outbreak_id','outbreak_value','report_id']
Out[157]: 
        level_1                                        0  report_id
0  outbreak_100    {'name': 'Chris', 'disease': 'A-Pox'}        100
0  outbreak_101    {'name': 'Stacy', 'disease': 'H-Pox'}        100
1  outbreak_200  {'name': 'Brandon', 'disease': 'C-Pox'}        101
1  outbreak_201    {'name': 'Karen', 'disease': 'G-Pox'}        101
1  outbreak_202      {'name': 'Tim', 'disease': 'Z-Pox'}        101

Answer 2

One way to do this is to convert the json for each outbreak into a dictionary, make a list of all the dictionary key/value pairs and then explode that list and convert the values into the two desired columns:

df['outbreak'] = df['outbreak'].apply(lambda v:json.loads(v).items())
df = df.explode('outbreak')
df[['outbreak_id', 'outbreak_value']] = pd.DataFrame(df.pop('outbreak').tolist(), index=df.index)

Output (for your sample data):

   report_id   outbreak_id                           outbreak_value
0        100  outbreak_100    {'name': 'Chris', 'disease': 'A-Pox'}
0        100  outbreak_101    {'name': 'Stacy', 'disease': 'H-Pox'}
1        101  outbreak_200  {'name': 'Brandon', 'disease': 'C-Pox'}
1        101  outbreak_201    {'name': 'Karen', 'disease': 'G-Pox'}
1        101  outbreak_202      {'name': 'Tim', 'disease': 'Z-Pox'}

Note: if the outbreak values are already dicts, not JSON, change the first line of this code to:

df['outbreak'] = df['outbreak'].apply(dict.items)