Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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City Parking Violations & Citations Dataset (Free Sample)

This is a free sample with 2,070 rows. The full dataset has 14,825 rows across 3 tables.

Parking citation records for a simulated mid-size US city (population ~350K) over 2 years. 25,000 citations across 50 zones, with officer assignments, violation types, fine amounts, and payment status tracking.

Features location clustering (downtown hotspots), time-of-day patterns (meter violations peak 9-11 AM), repeat offenders, and two anomalies — a parking meter system failure causing a citation surge and a holiday enforcement blitz in December.

Ideal for: civic tech apps, GovTech dashboards, urban planning analytics, fine collection systems, and open data portal demos.

Sample tables

Table	Sample Rows
citations	2,000
officers	20
zones	50
Total	2,070

Full dataset

The complete dataset includes all tables with full row counts:

Table	Full Rows
citations	14,755
officers	20
zones	50
Total	14,825

Formats included: CSV, Parquet, SQLite

Get the full dataset on Gumroad

About

Generated by Mindweave Technologies -- realistic synthetic datasets for developers, QA teams, and data engineers.

Every dataset features:

Enforced foreign key relationships across all tables
Realistic statistical distributions (not uniform random)
Temporal patterns (seasonal, time-of-day, day-of-week)
Injected anomalies for ML training and anomaly detection
Deterministic generation (same seed = same output)

Browse all datasets: https://mindweavetech.gumroad.com

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