Pandas: solution with dynamically creating DataFrame from JSON with nested structure and achieving a flat format
I'm trying to debug I'm sure I'm missing something obvious here, but After trying multiple solutions online, I still can't figure this out... I'm working with a JSON dataset that has a nested structure, and I need to convert it into a pandas DataFrame while flattening the nested fields. The JSON looks like this: ```json [ { "id": 1, "name": "Alice", "address": { "city": "New York", "state": "NY" }, "orders": [ {"order_id": 101, "amount": 250}, {"order_id": 102, "amount": 150} ] }, { "id": 2, "name": "Bob", "address": { "city": "Los Angeles", "state": "CA" }, "orders": [ {"order_id": 103, "amount": 200} ] } ] ``` I want to achieve a flat DataFrame where each order is represented as a separate row with the associated user information. My intention is to get the output like this: | id | name | city | state | order_id | amount | |----|-------|-------------|-------|----------|--------| | 1 | Alice | New York | NY | 101 | 250 | | 1 | Alice | New York | NY | 102 | 150 | | 2 | Bob | Los Angeles | CA | 103 | 200 | I tried using the `json_normalize` function from pandas, but I'm running into issues when it comes to flattening the nested `orders` array. Here is what I have currently: ```python import pandas as pd import json # Sample JSON data json_data = '''[ {"id": 1, "name": "Alice", "address": {"city": "New York", "state": "NY"}, "orders": [{"order_id": 101, "amount": 250}, {"order_id": 102, "amount": 150}]}, {"id": 2, "name": "Bob", "address": {"city": "Los Angeles", "state": "CA"}, "orders": [{"order_id": 103, "amount": 200}]} ]''' # Convert JSON to DataFrame data = json.loads(json_data) # Attempting to flatten the DataFrame orders_df = pd.json_normalize(data, "orders", ["id", "name", "address.city", "address.state"]) ``` However, I'm getting the following behavior: ``` ValueError: Length of passed values is 2, index implies 3 ``` Also, I'm unsure if there is a more efficient way to achieve this without having to manually iterate through the orders. I'm using pandas version 1.3.3. Any help or suggestions on how to properly flatten this nested structure would be greatly appreciated! This is my first time working with Python latest. I'm open to any suggestions.