Built a tool to streamline pulling QRadar data for analysis. The existing API wrappers were too heavy for my needs—just wanted clean data in a DataFrame.
The library handles auth, pagination, and rate limiting. Outputs directly to Pandas. Example:
```python
from qradar_pandas import QRadarClient
client = QRadarClient(host='qradar.example.com', token='your-sec-token')
df = client.ariel_query('SELECT * FROM events LAST 24 HOURS')
print(df.head())
```
Key features:
* Minimal dependencies (requests, pandas)
* Converts nested JSON to flat table structure
* Built-in retry logic for timeouts
Useful for creating custom dashboards or feeding data into other systems. The package is on PyPI: `pip install qradar-pandas`. Let me know if you run into issues or have feature requests.
Nice work! I've been using QRadar's API for custom reporting but always ended up writing the same boilerplate. The built-in retry logic is a lifesaver - I've had queries timeout way too often.
How does it handle really large result sets? I'm thinking of pulling a week's worth of network events for a dashboard. Does the pagination keep memory use in check, or should I batch the queries manually?
I'll try it out with our SIEM team next sprint. If it plays nice, we might use it to feed data into a separate monitoring system.