fabric1.py

import json
import pandas as pd
import sempy.fabric as fabric


dataset_name = "<>"
workspace_name = "<>"

def custom_flatten(json_object, dataset_name, table_name=None, table_properties=None, column_name=None, flat_list=None):
    if flat_list is None:
        flat_list = []
    
    if table_properties is None:
        table_properties = {}
    
    if not table_name:  # Dataset level
        for k, v in json_object.items():
            if k == 'model':
                table_properties.update({f"dataset_{key}": value for key, value in v.items() if key != 'tables'})
                custom_flatten(v, dataset_name, table_properties=table_properties, flat_list=flat_list)
            elif k == 'tables':  # Table level
                for table in v:
                    custom_flatten(table, dataset_name, table_name=table['name'], table_properties=table_properties, flat_list=flat_list)
            elif k != 'name':
                table_properties[f"dataset_{k}"] = v
                
    elif not column_name:  # Table level
        for k, v in json_object.items():
            if k == 'columns':  # Column level
                for column in v:
                    custom_flatten(column, dataset_name, table_name, table_properties, column_name=column['name'], flat_list=flat_list)
            elif k != 'name':
                table_properties[f"table_{k}"] = v
                
    else:  # Column level
        column_properties = {f"column_{k}": v for k, v in json_object.items() if k != 'name'}
        row = {'dataset_name': dataset_name, 'table_name': table_name, **table_properties, 'column_name': column_name, **column_properties}
        flat_list.append(row)
        
    return flat_list

tmsl_data = json.loads(fabric.get_tmsl(workspace=workspace_name, dataset=dataset_name))

dataset_name = tmsl_data['name']

flat_data = custom_flatten(tmsl_data, dataset_name)

tmsl_df = pd.DataFrame(flat_data)

tmsl_df.head()