Once you have the CSV, the world opens up – pivot tables, duplicate detection, expiration audits, and even machine learning on claim patterns.
If you work with JWT (JSON Web Tokens) or JWS (JSON Web Signatures) in logging, analytics, or batch processing, you’ve likely run into the same headache: how do you analyze hundreds or thousands of these tokens in a human-readable way?
To flatten these into CSV columns (e.g., user.id , permissions.0 ), you can use pandas.json_normalize() instead of the direct DataFrame constructor. jws to csv converter
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.
from pandas import json_normalize normalized = json_normalize(payload) rows.append(normalized.iloc[0].to_dict()) What About Invalid or Expired Signatures? A pure converter doesn’t need to verify the signature – it just decodes the payload. However, you may want to add a signature_valid column using a cryptographic library (e.g., cryptography or jwt with verification disabled first, then verified separately). Once you have the CSV, the world opens
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.
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 ): Opening a raw
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)