I have a very complex nested JSON file and I used the following script to see how the data is looking.

From Python I write the dataframe into Alteryx:

from ayx import Package,Alteryx
import pandas as pd
import json
from flatten_json import flatten
from pandas.io.json import json_normalize
def load_json_file(file_path):
    """
    Loads a JSON file and returns the data as a Python object.

    Args:
        file_path (str): The path to the JSON file.

    Returns:
        dict or list: The data loaded from the JSON file, or None if an error occurs.
    """
    try:
        with open(file_path, 'r') as file:
            data = json.load(file)
            return data
    except FileNotFoundError:
        print(f"Error: File not found at {file_path}")
        return None
    except json.JSONDecodeError:
        print(f"Error: Invalid JSON format in {file_path}")
        return None
    except Exception as e:
        print(f"An unexpected error occurred: {e}")
        return None

# Example usage:
file_path = r'FileName1.json'
data = load_json_file(file_path)
flat_json = flatten(data)
df = pd.DataFrame([flat_json])
print(df1)
Alteryx.write(df1,1)

This generates 1 row with around 15,000 columns into alteryx. Every single data value is treated as a column.

Y want the rows and columns just like a SQL table. For example, there is dict groups; the data values should be rows and the column names as column names.

Any guidance?