rows = [] for entry in har_data['log']['entries']: row = 'timestamp': entry['startedDateTime'], 'url': entry['request']['url'], 'method': entry['request']['method'], 'status': entry['response']['status'], 'duration_ms': entry['time'], 'size_bytes': entry['response']['content'].get('size', 0)
The complexity arises here: a HAR file is deeply nested. The root object contains a log property, which contains an entries array (each entry is a single HTTP request/response). The user must navigate the Power Query Editor to expand the log.entries table. This expansion is non-trivial; columns like request.headers or response.cookies contain nested records or lists. The analyst must selectively expand only the needed fields—such as startedDateTime , request.url , response.status , time (duration), and response.content.size —while choosing to "ignore" deeply nested arrays to avoid column explosion. Once flattened, the data is loaded into an Excel worksheet. This method is powerful but requires a moderate understanding of JSON structures. convert har file to excel
python har_to_excel.py