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name: io-interoperability description: "Use Modin readers, writers, conversion APIs, glob I/O, SQL partitioning, and partition handoff safely." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Modin I/O and interoperability

Use this sub-skill when a task needs nontrivial Modin I/O, conversion between Modin and other dataframe systems, experimental multi-file glob I/O, distributed SQL reading, custom text parsing, dataframe interchange, Ray/Dask conversion, or direct partition handoff.

Start here

1. Read references/io-reference.md for stable and experimental readers/writers, async read mode, glob APIs, distributed SQL parameters, and parser dependency caveats.
2. Read references/interoperability.md for pandas, NumPy, Arrow, dataframe-interchange, Ray, Dask, from_map, unwrap_partitions, and from_partitions conversions.
3. Read references/troubleshooting.md when a format falls back to pandas, a glob API is unavailable for the selected engine, a conversion requires Ray/Dask, or a parser dependency is missing.
4. Run scripts/interop_smoke.py to validate small pandas/NumPy/interchange/Arrow conversions, with optional Ray/Dask checks.
5. Run scripts/io_glob_smoke.py to validate local experimental CSV glob I/O and optional JSON/parquet glob round-trips.

Routing boundaries

This sub-skill owns:

• Stable modin.pandas readers/writers beyond the simplest read_csv starter path.
• MODIN_ASYNC_READ_MODE consequences for read_csv, read_fwf, read_table, and read_custom_text.
• Experimental modin.experimental.pandas glob readers/writers and read_custom_text.
• Distributed SQL partitioning concepts: partition_column, lower_bound, upper_bound, and max_sessions.
• Conversion APIs in modin.pandas.io: from_arrow, from_dataframe, from_ray, from_dask, from_map, to_pandas, to_numpy, to_ray, and to_dask.
• Low-level partition APIs in modin.distributed.dataframe.pandas: unwrap_partitions and from_partitions.

Route ordinary pandas-compatible operations to ../core-pandas-api/SKILL.md, engine/resource setup to ../engines-configuration/SKILL.md, and experimental frontends such as XGBoost, Batch Pipeline, spreadsheet, Modin NumPy, or Modin Polars to ../advanced-extensions/SKILL.md.

Operating pattern

1. Choose and verify the engine first. Experimental glob APIs are Ray/Dask/Unidist-oriented; Ray is the default local smoke route.
2. Validate a tiny local fixture before using remote object stores, SQL databases, or many-file glob patterns.
3. Keep credentials out of code and logs. Use secure runtime configuration for cloud storage and SQL engines.
4. Materialize with to_pandas() or to_numpy() only on bounded data.
5. For Arrow/Ray/Dask conversion, verify the package dependency and engine compatibility before converting large objects.
6. Use direct partition APIs only when a task truly needs partition ownership; otherwise prefer public readers and conversion helpers.