pandas
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Runssource: GitHubPythonBSD-3-Clausecommit 63651d6717fa
Python, BSD-3-Clause licensed. The project labels itself: alignment, data analysis, data science, flexible, pandas and python.
pandas runs. An Argusic agent installed it in 14 minutes and hit 6 errors and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
pandas 0+untagged.1.g63651d6 builds from source with Meson and runs on Python 3.12; 12675 tests pass covering DataFrame methods, CSV parsing, datetime indexes, generic operations, and dtypes; DataFrame, groupby, merge, and C-parser CSV all produce correct results.
What the agent ended up with on a clean machine, in its own words. How this is measured
Measured by Argusic on a fresh machine every time. Every number links to its evidence. Argusic Score 100 of 100 (the mean of this project's run scores).
At a glance
Subject data from GitHub, linked at the top of this page. Test data by Argusic (CC BY 4.0); every number links to a run page with the full log, the recording, and their sha256 hashes.
What was measured
From the run that decided this verdict. Every line is a recorded value, not an estimate.
What went wrong, in the agent's own words
The agent's own words, worst first, never rewritten into advice.
- Cython requires python3 dependency for link testing - missing Python.h and libpython3.128 minutes
- cannot find -lpython3.12 / libpython3.12.a cannot make shared object2 minutes
- fatal error: x86_64-linux-gnu/python3.12/pyconfig.h not found1 minute
- externally-managed-environment blocked pip installs (PEP 668)0.5 minutes
- Could not find ninja version 1.8.2 or newer0.5 minutes
- ZoneInfoNotFoundError 'No time zone found with key US/Pacific' (tzdata-legacy missing)0.5 minutes
- Install time
- 14 minutes
- Cold machine to finish
- 15 minutes
- Errors hit and fixed
- 6 hit, 6 fixed with no human help
- How the result was proved
- python -m pytest pandas/tests/frame/methods/test_astype.py pandas/tests/frame/test_constructors.py pandas/tests/frame/test_reductions.py pandas/tests/frame/test_validate.py pandas/tests/io/parser/test_parse_dates.py pandas/tests/io/parser/test_header.py pandas/tests/io/parser/test_index_col.py pandas/tests/generic/ pandas/tests/dtypes/ --no-header returned 12675 passed, 1987 skipped, 31 xfailed in 12.82s
- Model tokens used
- 329,342
- Exact commit tested
- 63651d6717fa
- Container image
- sha256:cdd920bce783
What was not measured
The other half, so you can see exactly how far this evidence goes. A gap is printed as a gap and never filled in.
- Peak GPU memory
- this ran on a CPU machine, so there was no GPU to sample
- Behaviour over a long run
- each attempt uses the smallest documented settings and a fixed time budget, so this measures whether it runs, not how it holds up under load
The end of the recording
The last lines of the terminal session that decided this verdict, exactly as they were recorded.
exec /bin/sh -lc '# Final comprehensive test run . ~/venv/bin/activate && cd /work/repo && python -m pytest pandas/tests/frame/methods/test_astype.p… succeeded in 14921ms: 0.01s call pandas/tests/frame/methods/test_astype.py::TestAstype::test_astype_dict_like[Series] 0.01s call pandas/tests/generic/test_duplicate_labels.py::test_pickle (7 durations < 0.005s hidden. Use -vv to show these durations.) 12675 passed, 1987 skipped, 31 xfailed in 12.82s Pandas is fully built from source and verified. Here's the report: tokens used 329,342 Pandas is fully built from source and verified. Here's the report:
Replay the whole session, every command from a clean machine to this point.
How it was tested
One agent, one container, no human help. It installs the project the way its own documentation says to, runs it, and fixes what breaks. Everything below is recorded as it happened: the terminal session, the log and the exact commit. The full method.
Strengths and limits
Measured facts, not opinions. How this is written.
What went well
- Reached a running state on a clean machine, with the session recorded.
- Installed in 14 minutes.
- Recovered from all 6 errors without a human stepping in, which says the failures are documented well enough to solve.
- Ran without a GPU, so it does not need one to start.
- Licensed BSD-3-Clause, as reported by its host.
What did not, or is not known
- Measured once so far. A single attempt shows it can run, not that it runs reliably.
What it is a reasonable choice for
- Trying it on a laptop or a small server: it reached a running state without a GPU.
Run history
Topics (from GitHub)
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/pandas)Questions
- Does pandas run?
- Yes. pandas runs. Argusic installed and launched it on a clean machine in 14 minutes, hitting 6 errors on the way, and recorded the session.
- How did Argusic test pandas?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit 63651d6717fa. One attempt is recorded, and the full method is on the methodology page.
- What did it cost to test pandas?
- The run that produced this verdict cost $0.13: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does pandas take to install?
- 14 minutes in the run that decided this verdict, from a clean machine to a running state. That is one measured attempt, not an average.
- Does pandas need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing pandas?
- 6 things broke in the recorded run, and 6 were fixed without human help. Each one, and the time it cost, is listed on this page.
- Where is the evidence for pandas?
- The recorded run is on this page, each linking to its full log and terminal recording, stored with a sha256 fingerprint so it cannot be quietly altered.