How to Clean Weird JSON Data in Pandas with a One‑Liner

Problem

A user posted a Pandas data‑processing issue where a column contains JSON strings with an unexpected structure; some entries have an empty tblActors list, causing downstream analysis to fail.

Solution

The provided code maps each row to a cleaned value, inserting a default {'id': -1} when tblActors is empty, otherwise returning the original list:

df['text'] = df['text'].map(lambda x: {'id': -1} if json.loads(x).get('tblActors') == [] else json.loads(x).get('tblActors'))

Conclusion

This one‑line transformation simplifies the data‑cleaning step, allowing the dataset to be processed without additional manual checks, and demonstrates how Pandas combined with json.loads can efficiently handle irregular JSON data.