Debug and fix pandas test suite failures under the cudf.pandas compatibility layer. Use when given pytest node IDs of failing pandas tests that need to be fixed for cudf.pandas compatibility.
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name
debug-cudf-pandas
description
Debug and fix pandas test suite failures under the cudf.pandas compatibility layer. Use when given pytest node IDs of failing pandas tests that need to be fixed for cudf.pandas compatibility.
Debug cudf.pandas Compatibility Failures
Overview
cudf.pandas is a zero-code-change GPU accelerator for pandas. It intercepts import pandas via a Python MetaPathFinder (python/cudf/cudf/pandas/module_accelerator.py) and wraps all pandas objects and functions in proxy types that try the cudf (GPU) path first, falling back to pandas (CPU) if cudf raises an exception.
When the pandas test suite is run with -p cudf.pandas, test failures indicate one of:
A cudf implementation bug — cudf's DataFrame/Series/Column/Index behavior differs from pandas for an edge case
A proxy/dispatch bug — the wrapping/unwrapping mechanism doesn't correctly handle a type or operation
A missing proxy registration — a pandas type or return value has no registered cudf equivalent
A to/from_pandas conversion bug — data is corrupted or lost when converting between cudf and pandas objects
A test setup bug — the testing scripts or the pandas testing plugin introduce an issue
A dependency/environment gap — the test requires a package (e.g. xlsxwriter) that pandas CI has but our test environment lacks, causing a different code path to execute
A pandas bug — rarely, the expected behavior in the pandas test itself is wrong
Your job is to find the root cause and implement the fix.
Unacceptable Fix Patterns
The following patterns are prohibited regardless of whether they make a test pass. Stop immediately and ask the user if the only apparent solution falls into one of these categories.
Esoteric/test-specific special cases: Do not implement narrow, brittle logic targeting only the specific failing node IDs (e.g. special-casing a single dtype/shape combination). If a test is failing, find the general API contract mismatch and fix it at that level.
Pandas CPU fallback as a fix: Typically you should not make tests pass by simply raising an exception inside cudf to force CPU fallback. Falling back to pandas/CPU is only an acceptable fix if attempting to fix the bug leads to discovering that large features must be implemented in cudf to support the change (e.g. a completely new class).
Private pandas APIs: Do not import or call any symbol from pandas.core, pandas.compat, or any underscored pandas module (e.g. pandas._libs.tslibs.parsing). These are explicitly unstable per the pandas API policy. Use public pandas APIs or write equivalent local logic instead.
PyArrow as a CPU execution backend: Do not route GPU operations through pyarrow.compute on CPU as a substitute for cudf/libcudf semantics. Arrow is an interchange format; it is not an acceptable execution backend for cudf operations.
Returning pandas objects from cudf APIs: cudf public methods (Series, Index, DataFrame operations and accessors) must return cudf-native objects, not pd.Series, pd.Index, or pd.DataFrame. Use _return_or_inplace and the existing cudf container reconstruction helpers.
Diverging from pandas to pass a test: The goal is to match pandas behavior exactly. Do not implement proxy overrides that suppress exceptions or alter behavior that vanilla pandas exhibits. If pandas raises an error in a given scenario, cudf.pandas should raise the same error. A fix that makes cudf.pandas behave differently from pandas — even if it makes a test pass — is wrong.
Prerequisites
Before starting, verify you are at the repository root. All commands in this skill assume the working directory is the cudf repository root.
Setting up and refreshing the test checkout. The test harness lives in pandas-testing/pandas-tests/. The runner performs first-time setup (cloning pandas, copying the test tree, rewriting imports) only when the relevant directories are missing; once they exist it runs against them as-is and never refreshes them. The xfail/skip markers are applied by a pytest plugin loaded fresh on every run (-p cudf.pandas.scripts.pandas-testing-plugin), not by appending to the pandas conftest.py, so repeated runs do not accumulate duplicate hook registrations.
Because the runner never refreshes an existing checkout, do not rely on its contents being current, and be aware that any edit you make under pandas-testing/ persists into every subsequent run. Never modify the vendored pandas test files (the tests/**.py tree — see the "never modify the pandas test files" rule below); a stray edit there will silently follow you and can derail your investigation. The one sanctioned in-place edit is the temporary xfail_strict flip in pandas-tests/pyproject.toml described in Step 0 — revert it (or delete the checkout) once you are done so it does not linger. Delete the checkout whenever it may be stale. In particular, if you change python/cudf/cudf/pandas/scripts/run-pandas-tests.sh in a way that affects what it places in pandas-testing/, the existing checkout will not reflect that change — delete it before re-running so the runner rebuilds it. The runner uses two independent guards: it clones pandas into pandas-testing/pandas/ only if that directory is missing, and copies the test tree into pandas-testing/pandas-tests/ only if that is missing. So removing just pandas-tests/ re-copies a clean test tree from the existing clone, while picking up a new pandas version (the clone is pinned to the tag matching the installed pandas) requires removing the whole pandas-testing/ so the clone is refetched:
rm -rf pandas-testing/pandas-tests/ # re-copy a clean test tree from the existing clonerm -rf pandas-testing/ # full reset, e.g. after a pandas version change or a change to run-pandas-tests.sh
The cudf Python package is almost entirely pure Python. For inplace installs (e.g. pip install -e .), changes to .py files take effect immediately — no rebuild is needed. For non-inplace installs (e.g. ./build.sh), you must either reinstall or copy changed files to site-packages.
Input Format
Input is one or more pytest node IDs, relative to the pandas-tests/ directory inside the test harness:
Node IDs with parameters like [Float64-False-False-first] target a specific parametrized case. When multiple node IDs are provided, check whether they share a root cause before attempting separate fixes.
Step 0 — Update pandas-testing-plugin.py
The file python/cudf/cudf/pandas/scripts/pandas-testing-plugin.py contains three dictionaries that gate how tests are handled:
NODEIDS_THAT_FAIL — tests marked xfail (expected to fail). Keys are alphabetically sorted.
NODEIDS_TO_SKIP — tests marked skip (not run at all). Keys are alphabetically sorted.
The pandas-tests harness runs with xfail_strict = false (set in the vendored pandas-tests/pyproject.toml to tolerate flaky XPASSes — NVIDIA/cudf#22681). A test listed in NODEIDS_THAT_FAIL that now passes is therefore reported as XPASSwithout failing the run, so a stale entry will not flag itself. You must change the false to true yourself before testing your fix; otherwise the test reports XPASS instead of a genuine PASSED and the dead marker lingers silently. Do not commit this change in any commit.
On the first run the script clones the matching pandas version and copies the test tree (subsequent runs reuse it). It runs pytest with both -p cudf.pandas and the marker plugin -p cudf.pandas.scripts.pandas-testing-plugin, and automatically applies -m "not slow and not single_cpu and not db and not network" and --disable-warnings. Substitute your actual node ID.
If the test passes: the xfail entry was stale. Commit only the pandas-testing-plugin.py change and stop.
If the test fails: read the failure output carefully — the assertion message tells you the exact behavioral difference. Proceed to Step 2.
Which pandas API is being exercised (method name, class, module)
What the test asserts — this is the exact behavior cudf must match
Whether parameters narrow the scope (e.g. only Float64 dtype, only skipna=False)
The assertion error output from Step 1 tells you what cudf produced vs. what was expected. Use both together to understand the gap.
Step 3 — Diagnose Root Cause
Always check cudf's direct behavior first. Do not jump to proxy investigation.
3a. Direct cudf test (most important step)
Write a minimal script that exercises the API using cudf directly, without going through cudf.pandas. This is strictly an example — adapt the structure to match what the failing test actually does (comparisons, exception checks, dtype validation, etc.):
import cudf
import pandas as pd
# Mirror the test setup
pdf = pd.DataFrame({"value": [1.0, None, 2.0, 3.0]}, dtype="Float64")
cdf = cudf.from_pandas(pdf)
pd_result = pdf.groupby(level=0).first(skipna=False)
cudf_result = cdf.groupby(level=0).first(skipna=False)
print("pandas:", pd_result)
print("cudf: ", cudf_result)
print("match:", pd_result.equals(cudf_result.to_pandas()))
For tests that assert exceptions are raised, structure your script to verify both cudf and pandas raise the same exception type and message.
Save to a temporary file (e.g. test_debug.py) and run:
python test_debug.py
Results:
cudf result differs from pandas → cudf implementation bug → go to Step 4a
cudf raises an exception → missing feature or bug → evaluate scope; may need user input if the feature is large. Note: this may be OK if the test is verifying that an exception should be raised.
cudf result matches pandas → proxy/dispatch bug → go to Step 4b
cudf result matches pandas AND the test still fails → check if vanilla pandas (without cudf.pandas) also fails → go to Step 3d
Classify the root cause before writing any fix. Ask yourself: Is this a specific method/keyword handling bug? A broad dtype casting mismatch affecting many operations? A proxy/wrapping issue? A missing cudf capability? For broad issues, the fix should be applied at the shared/base layer, not patched per individual method. If the only apparent fix is test-shaped (i.e. it looks like it exists to make exactly these node IDs pass), step back and re-examine the general API contract.
3b. Environment variable diagnostics (run through cudf.pandas)
Use these env vars to trace what is happening at the proxy layer:
Variable
Effect
CUDF_PANDAS_FAIL_ON_FALLBACK=1
Raises instead of silently falling back — shows exactly which operation triggers fallback
LOG_FAST_FALLBACK=1
Logs every fallback with function name and exception
CUDF_PANDAS_DEBUGGING=1
Runs both cudf and pandas paths in parallel, warns on result divergence
Fallback is a diagnostic signal, not a fix. If CUDF_PANDAS_FAIL_ON_FALLBACK=1 causes the test to fail, routing the operation to pandas CPU is not acceptable as a final solution unless adding GPU support would require implementing large, entirely unsupported features. Bug fixes for cudf's behavior (e.g. to add support for a particular dtype to a function) are the expected path.
3c. Standalone instrumented script
For deeper investigation, copy the relevant test body into a temporary script (e.g. test_debug.py), add print statements or assertions at intermediate steps, then run through the proxy layer or try to write matching pandas code to see the differences in behaviors and fix them:
python -m cudf.pandas test_debug.py
This gives you full control to narrow down exactly where the divergence begins.
If the test also fails under vanilla pandas, the issue is NOT a cudf bug. Common causes:
Missing dependency: pandas CI has a package installed (e.g. xlsxwriter, lxml, odfpy) that changes code path selection. Check pandas' ci/deps/ YAML files to see what they install.
Version mismatch: the installed version of a third-party library differs from what pandas CI uses.
Pandas test bug: the test itself is broken (e.g. relies on side effects of other packages being present).
Resolution for dependency gaps: add the missing package to dependencies.yaml under the test_cudf_pandas_pandas_tests section (for conda environments that don't use pip extras), then run rapids-dependency-file-generator to propagate. See Step 4c.
Resolution for pandas bugs: xfail the test with an explanation string that describes why it's a pandas/upstream issue, and optionally write up a bug report for upstream.
Step 4a — Fix a cudf Implementation Bug
The fix must be tightly scoped and try to keep it minimal. Fixing one edge case can break another test. Do not refactor surrounding code while fixing, and do not add mode.pandas_compatible guards (ask the user first).
Classify the failure mode before writing a fix: is it a specific issue (e.g. one method handles a keyword incorrectly) or a broad failure mode (e.g. dtype casting inconsistency that affects many operations)? For broad issues, consider whether the fix should be applied at a shared/base level rather than patching individual methods.
Common fix locations:
What fails
Where to look
DataFrame method
python/cudf/cudf/core/dataframe.py
Series method
python/cudf/cudf/core/series.py
Index operation
python/cudf/cudf/core/index.py
Shared DataFrame/Series method
python/cudf/cudf/core/indexed_frame.py
Column-level operation
python/cudf/cudf/core/column/*.py
GroupBy operation
python/cudf/cudf/core/groupby/
IO operation
python/cudf/cudf/io/
Note: mode.pandas_compatible is automatically set to True when cudf.pandas is active. Account for this in any conditional logic, but do not add new guards for it without explicit user approval.
If the bug is not in cudf core, move to cudf.pandas-specific fixes.
Prohibited in cudf implementation fixes:
Do not call pandas._libs.* or any other private/underscored pandas module. Use public pandas APIs (pd.Timestamp, pd.to_datetime, pd.DateOffset, etc.) and write equivalent local logic if needed.
Do not convert to pyarrow and use pyarrow.compute as a substitute for libcudf behavior. If libcudf doesn't support the exact semantics, consult the user before adding a pyarrow fallback.
Do not construct and return pd.Series(...), pd.Index(...), or pd.DataFrame(...) from cudf public methods. Use _return_or_inplace, _from_column, or other cudf container reconstruction helpers so that return types remain cudf-native.
Step 4b — Fix a Proxy/Dispatch Bug
Only reach this step after Step 3a has confirmed that cudf itself is correct. Some of the common cases you should consider at this stage:
Most common cause: a pandas object in a particular state is not round-tripping correctly through cudf's conversion APIs. cudf.from_pandas() and <object>.to_pandas() are culprits here. To debug this case, set up the standalone instrumented script as described in step 3c. Then instrument the code to perform direct modifications and testing of the proxy state, for instance by accessing the _fsproxy_fast and _fsproxy_slow attributes of the relevant objects and calling cudf.from_pandas and <object>.to_pandas to see if information is being lost or corrupted in one of the conversions.
Next most common cause: a pandas return type has no registered cudf proxy. Check python/cudf/cudf/pandas/_wrappers/pandas.py — this file registers which pandas types map to which cudf types using make_final_proxy_type() and related functions. If a new pandas type needs wrapping, add the registration here.
fast_slow_proxy.py and module_accelerator.py are core infrastructure files. Fix them only if you believe the bug is in one of them.
Important constraint for proxy fixes
Never make cudf.pandas diverge from pandas to pass a test. If your proposed proxy fix would cause cudf.pandas to behave differently from vanilla pandas (e.g. suppressing an exception that pandas raises, or returning a different value), that fix is wrong — even if it makes the test pass. The test may be broken, or the issue may be an environment/dependency gap rather than a proxy bug. Always verify vanilla pandas behavior first (Step 3d).
Step 4c — Fix a Dependency or Environment Gap
Only reach this step if Step 3d confirmed the test also fails under vanilla pandas due to a missing package or version mismatch.
Identify the missing dependency. Check what pandas CI installs by examining their CI config files (available in pandas-testing/pandas/ci/deps/). Common culprits: xlsxwriter, lxml, odfpy, python-calamine, pyxlsb.
Add to dependencies.yaml under the test_cudf_pandas_pandas_tests section. This group provides packages that pandas CI has installed (via pip extras like pandas[excel]) but conda environments need listed explicitly:
# Additional dependencies for running the pandas test suite under cudf.pandas.# Unlike test_python_pandas_cudf (which uses pip extras like pandas[excel]),# conda environments need these listed explicitly.test_cudf_pandas_pandas_tests:common:-output_types: [conda]
packages:-<new-package>
Regenerate dependency files:
rapids-dependency-file-generator
This propagates the change to python/cudf/pyproject.toml and any other generated files.
If the test still fails even with the dependency present (e.g. the test has a genuine pandas/upstream bug that happens regardless), xfail it with an explanation:
"tests/io/excel/test_openpyxl.py::test_name": "<root cause explanation>",
The explanation string in the xfail dict should describe the root cause (e.g. "openpyxl limitation", "pandas test bug: assumes xlsxwriter present"), not just the error message.
Expected: test still passes. If this fails, the fix works by falling back to pandas rather than actually fixing cudf — that is not acceptable.
Exception: some tests intentionally validate fallback behavior. If FAIL_ON_FALLBACK causes this test to fail but the test logic requires fallback, skip this check for that specific test and note the justification.
Replace <module_directory> with the directory containing your test (e.g. tests/groupby/). Any new failures that are not already listed in pandas-testing-plugin.py must be investigated before committing.
d. Add unit tests (where appropriate):
If your fix changes cudf behavior, add a unit test in cudf classic or cudf.pandas depending on where the fix lives. When adding tests:
Prefer adding to existing test parameterizations if a related test already exists
If no related test exists, write a test that compares cudf output to pandas (using assert_eq or assert_exceptions_equal)
Place cudf classic tests alongside existing tests in python/cudf/cudf/tests/
Place cudf.pandas-specific tests in python/cudf/cudf_pandas_tests/
Step 6 — Commit
Stage only the intended files — never anything from pandas-testing/:
git add python/cudf/cudf/ # source fix (if applicable)
git add python/cudf/cudf/pandas/scripts/pandas-testing-plugin.py # xfail removal
git status # verify nothing from pandas-testing/ is staged
git commit -m "fix(cudf.pandas): <short description>
Fixes the following failing pandas tests:
- tests/groupby/test_reductions.py::test_first_last_skipna[Float64-False-False-first]"
If many node IDs share the same root cause, summarize them in the commit message rather than listing every parametrized variant. Commit message should convey what was wrong and what was fixed.
STOP Conditions
Stop immediately and report findings to the user when any of the following apply:
The fix requires modifying .pyx, .cu, .cuh, or CMakeLists.txt files — this requires compilation, which is outside the scope of this skill
Investigation reveals the behavioral divergence is intentional — cudf was deliberately designed to differ from pandas
You have been through 3 or more investigation cycles without converging on a fix
The fix would add a new mode.pandas_compatible conditional
For intentional divergence: stop and ask the user. In most cases, the goal is to make cudf match pandas. Only when the divergence has significant performance implications should mode.pandas_compatible be used — and even then, sparingly. cudf already overuses this flag; the default behavior should agree with pandas.
Important Notes
mode.pandas_compatible is automatically set to True when cudf.pandas is active. This is done at the end of python/cudf/cudf/pandas/_wrappers/pandas.py.
cudf Python is almost entirely pure Python — for inplace installs, changes to .py files take effect immediately without rebuilding.
The xfail/skip markers are applied by a pytest plugin (-p cudf.pandas.scripts.pandas-testing-plugin) loaded fresh on every run, not by appending to the pandas conftest.py. The runner sets up pandas-testing/ only when it is missing and never refreshes it afterward, so if you modify python/cudf/cudf/pandas/scripts/run-pandas-tests.sh in a way that changes the pandas-testing/ directory, delete it before re-running the script. Deleting only pandas-tests/ re-copies the test tree from the existing clone; to pick up a new pandas version delete the whole pandas-testing/, since the pandas/ clone is guarded separately and is not refetched by removing pandas-tests/ alone.
Keys in all three pandas-testing-plugin.py dictionaries must remain in alphabetical order.
Never write comments that explain what old code was replaced — write comments about what the code does and why.
Never modify the pandas test files themselves — fix cudf, not pandas.
Never fix the testing APIs (like assert_frame_equal, assert_series_equal) — fix the actual APIs that produce wrong results.
First see if the problem is in cudf classic and fix it there; if not, then move over to cudf.pandas.
The pandas-tests harness runs with xfail_strict = false (vendored pandas-tests/pyproject.toml, to tolerate flaky XPASSes — issue #22681), so a stale NODEIDS_THAT_FAIL entry that now passes shows up as a non-failing XPASS and won't flag itself. While testing your fix, flip xfail_strict to true in that file so the XPASS surfaces (do not commit that change) and remove the stale entry so the test reports a genuine PASSED.
When a fix requires adding a test dependency, update dependencies.yaml (under test_cudf_pandas_pandas_tests for conda environments) and run rapids-dependency-file-generator to propagate. Never manually edit the generated pyproject.toml entries marked as auto-generated.
Always verify vanilla pandas behavior before implementing proxy-layer fixes. If the test also fails without cudf.pandas, the problem is upstream or environmental, not a cudf bug.
xfail explanation strings should describe the root cause ("openpyxl limitation", "pandas test assumes xlsxwriter is installed"), not just the error type ("AssertionError", "IndexError").
