Converter: Jws To Csv

To flatten these into CSV columns (e.g., user.id , permissions.0 ), you can use pandas.json_normalize() instead of the direct DataFrame constructor.

Extend the script to handle JWE (encrypted tokens) or add signature validation columns. Happy data wrangling. Have you built a similar converter for a different token format? Let me know in the comments.

Opening a raw .log file full of base64url-encoded strings isn’t practical. But dropping that data into a CSV? Now you can sort, filter, and pivot. jws to csv converter

for token in tokens: if not token.strip(): continue payload = decode_jws_payload(token) # If no fields specified, take all top-level keys if fields_of_interest is None: rows.append(payload) else: filtered = field: payload.get(field, None) for field in fields_of_interest rows.append(filtered)

eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIxMjMiLCJyb2xlIjoidXNlciIsImV4cCI6MTczNTY4OTAwMH0.signature1 eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiI0NTYiLCJyb2xlIjoiYWRtaW4iLCJleHAiOjE3MzU2ODkwMDB9.signature2 python jws_to_csv.py tokens.txt output.csv --fields sub,role To flatten these into CSV columns (e

Once you have the CSV, the world opens up – pivot tables, duplicate detection, expiration audits, and even machine learning on claim patterns.

Replace the row-building section with:

Do not trust the claims from an unverified JWS in a security context. For analysis, it’s fine. For access control, always verify the signature. Real-World Example Input ( tokens.txt ):