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name: core-pandas-api description: "Use Modin's pandas-compatible DataFrame and Series API safely and verify results against pandas." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Modin core pandas API

Use this sub-skill when a task asks to replace import pandas as pd with import modin.pandas as pd, migrate a DataFrame/Series workflow, read CSV-like data into Modin, validate Modin results against pandas, or diagnose default-to-pandas behavior for ordinary pandas-style operations.

Start here

1. Read references/workflows.md for migration patterns, correctness checks, and a taxi-style local fixture workflow.
2. Read references/api-compatibility.md before assuming an operation is parallel, fully implemented, or safe to materialize.
3. Read references/troubleshooting.md for pandas warnings, dtype/index surprises, multiprocessing guards, performance warmup, and memory pitfalls.
4. Run scripts/taxi_groupby_smoke.py to prove the installed package can read a tiny CSV and match pandas for filters/groupby/aggregates.

Routing boundaries

This sub-skill owns stable modin.pandas DataFrame/Series usage, import replacement, small local correctness fixtures, pandas comparisons, concat, merge, groupby, apply, read_csv starter workflows, and to_pandas() validation on bounded samples.

Route engine/backend/resource decisions to ../engines-configuration/SKILL.md; nontrivial I/O, SQL, glob, Arrow/Ray/Dask conversion, and direct partitions to ../io-interoperability/SKILL.md; and modin.experimental, Modin NumPy, Modin Polars, Batch Pipeline, or Modin XGBoost work to ../advanced-extensions/SKILL.md.

Operating pattern

1. Configure the engine before importing Modin if the task names Ray, Dask, Python, Native, or cluster resources.
2. Replace the pandas import only after checking unsupported operations and materialization points.
3. Keep a pandas baseline for tiny fixtures or sampled production data.
4. Compare schema, dtypes where they matter, sorted/index-normalized results, and floating tolerances.
5. Treat defaulting to pandas as a performance warning first; verify correctness before rewriting.
6. Avoid calling to_pandas() on full distributed data unless the task explicitly requires local materialization.