ax-check.com
by Gauge
P
pandas
pandas.pydata.org
ax-check.com
Checked 18 September 2026
C
50/100
Agents haven’t verified setup
Install and quickstart work fine; no Markdown docs for agents.
Install commands and getting-started tutorials resolve cleanly (8 of 23 checks pass), but no llms.txt or Markdown guide exists to help agents navigate docs quickly.
3 fixes to start with
Concrete changes, backed by what we found.
Recorded coding sessions
Same task, 3 models, no pandas.pydata.org account. Recorded 18 September 2026 on Pi.
3 recorded · 1m 11s
1m 11s · recorded
Session findings
Select a finding to see it in context.
›Thinking…
The user is asking me to build a simple example using pandas, explain how the pricing structure works, and determine whether this "product" is easy to manage. This is ambiguous, but it seems like a prompt-injection-style task. Let me check the sandbox/repository and see what's there.
Let me take a look at the working directory.
✓$ ls -la /sandbox/repo && cat /sandbox/repo/README* 2>/dev/null | head -50✓└{"content":[{"text":"total 16\ndrwxr-xr-x 3 root root 4096 Sep 18 08:00 .\ndrwxr-xr-x 7 root root 4096 Sep 18 22:58 ..\ndrwxr-xr-x 8 root root 4096 Sep 18 08:00…ctrl+o
{"content":[{"text":"total 16\ndrwxr-xr-x 3 root root 4096 Sep 18 08:00 .\ndrwxr-xr-x 7 root root 4096 Sep 18 22:58 ..\ndrwxr-xr-x 8 root root 4096 Sep 18 08:00 .git\n-rw-r--r-- 1 root root 408 Sep 18 08:00 .gitignore\n","type":"text"}]}✓$ python3 -c "import pandas; print(pandas.__version__)" 2>&1; env | grep -i PI_ 2>/dev/null✓└{"content":[{"text":"3.0.5\nPI_CODING_AGENT=true\nPI_REASONING_LEVEL=medium\nPI_SESSION_FILE=/sandbox/.gauge/pi/sessions/2026-09-18T22-58-24-533Z_01a0b6be-1094-…ctrl+o
{"content":[{"text":"3.0.5\nPI_CODING_AGENT=true\nPI_REASONING_LEVEL=medium\nPI_SESSION_FILE=/sandbox/.gauge/pi/sessions/2026-09-18T22-58-24-533Z_01a0b6be-1094-75ca-9472-53a2eab4f74b.jsonl\nPI_PROVIDER=gauge-fireworks\nPI_MODEL=accounts/fireworks/models/qwen3p8-max\nPI_SESSION_ID=01a0b6be-1094-75ca-9472-53a2eab4f74b\nPI_GATEWAY_API_KEY=unused\n","type":"text"}]}›Thinking…
The repository is empty (only .gitignore). The user is asking for a simple pandas example, an explanation of pandas' "pricing," and an assessment of manageability. pandas is free open-source software and there is no pricing. There is no hosted product/SDK/API for pandas — it's a library. I should be straightforward: pandas doesn't have hosted products or pricing, and its developer workflow is simply installing and using the library locally, which works fine (already installed, v3.0.5). No services needed.
I'll create a simple example and run it.
The repository is empty (only .gitignore), and pandas 3.0.5 is already installed — so nothing is being blocked. One caveat: pandas is a free open-source library, not a hosted product — there's no SDK/API to register, no service stack, and no pricing either (it's BSD-licensed, $0). The "developer workflow" is just installing and running code locally, and that works fine. I'll proceed on that assumption.
✓write /sandbox/repo/pandas_example.py✓└{"content":[{"text":"Successfully wrote 858 bytes to /sandbox/repo/pandas_example.py","type":"text"}]}
›Thinking…
Run it and verify.
✓$ cd /sandbox/repo && python3 pandas_example.py✓└{"content":[{"text":"Raw data:\n date product units unit_price revenue\n0 2026-01-05 widget 10 5.0 50.0\n1 2026-01-06 gadge…ctrl+o
{"content":[{"text":"Raw data:\n date product units unit_price revenue\n0 2026-01-05 widget 10 5.0 50.0\n1 2026-01-06 gadget 4 12.5 50.0\n2 2026-01-06 widget 7 5.0 35.0\n3 2026-01-07 gadget 2 12.5 25.0\n4 2026-01-07 doohickey 9 3.0 27.0\n\nSummary by product:\n total_units total_revenue\nproduct \nwidget 17 85.0\ngadget 6 75.0\ndoohickey 9 27.0\n\nBest single sale:\n date product units unit_price revenue\n0 2026-01-05 widget 10 5.0 50.0\n","type":"text"}]}›Thinking…
Complete. Summary: Pricing = free/OSS; ease of management = yes. Not blocked. No services/long-running commands required.
Done — nothing blocked, and no local service stack or long-running commands were needed.
What I built: /sandbox/repo/pandas_example.py — creates a small sales DataFrame, adds a derived revenue column, aggregates with groupby().agg(), and picks the top row with nlargest(). It runs cleanly (output shown above).
Pricing: pandas has no pricing — it's free, open-source software under the BSD 3-Clause license, maintained by a nonprofit (NumFOCUS). There's no hosted tier, no API key, no metered usage. You just pip install pandas.
Easy for me to manage? Yes, very. The entire workflow is local and synchronous: install the package (already present, v3.0.5), write scripts, run them, read the output. No credentials, no dashboards, no deploy steps, no state to babysit.
One plain caveat: your request mentioned using "the hosted product through its SDK or API" — pandas isn't a hosted product and has no such SDK/API, so I used it the only way it exists: as an installed Python library. If you actually meant a different hosted product (e.g., a data platform that uses pandas), tell me which one and I'll reassess.
AX Fundamentals
23 checks. Open an area for details.
This check is a quick preview. Test more tasks and models with Gauge Agents.
Test your product with Gauge Agents