---

name: configuration-extensions description: "Configure Koalas options, plotting, custom accessors, optional MLflow integration, migration, and performance settings." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

configuration-extensions

Use this sub-skill when a Koalas task involves package-level configuration, plotting backends, optional plotting or MLflow dependencies, custom accessors/extensions, .koalas or .spark accessor orientation, PySpark 3.2+ migration, or performance settings controlled by options.

Route here for

• ks.options, ks.get_option, ks.set_option, ks.reset_option, and ks.option_context.
• Option selection for performance, display, default indexes, operations on different frames, and plotting.
• Plotting backend selection (plotly, matplotlib, or a custom module) and row/sample controls.
• Optional dependency diagnosis for plotly, matplotlib, and mlflow; use scripts/koalas_optional_dependency_check.py.
• Custom extension decorators: register_dataframe_accessor, register_series_accessor, and register_index_accessor.
• Orientation between built-in .koalas and .spark accessors without deep-diving into their routed API areas.
• databricks.koalas.mlflow.load_model and PythonModelWrapper usage.
• Maintenance-mode guidance and migration toward pyspark.pandas on PySpark 3.2+.

Route elsewhere

• General DataFrame, Series, Index, pandas-like manipulation, conversion, and indexing: ../core-dataframes/SKILL.md.
• Spark IO, SQL, table/file storage, JDBC, and detailed Spark interoperability: ../spark-io-sql/SKILL.md.
• apply, transform, batch UDF, groupby, rolling, and expanding workflows: ../apply-groupby-window/SKILL.md.

Operating checklist

1. Check whether the runtime is legacy Koalas or PySpark 3.2+; for PySpark 3.2+, prefer the built-in pandas API on Spark (import pyspark.pandas as ps) unless the user must keep legacy Koalas.
2. Use lower-case dotted option names exactly as listed in the option catalog; unknown or mistyped option names raise an option error.
3. Use option_context for temporary settings, especially plotting backend, compute.default_index_type, and compute.ops_on_diff_frames.
4. For slow default-index or large-plan symptoms, inspect with kdf.spark.explain(...), prefer explicit index columns or distributed default indexes, and consider checkpointing.
5. For plotting or MLflow failures, diagnose the single optional dependency needed before installing broad extras.
6. For detailed recipes and failure recovery, read references/configuration-plotting-extensions.md and references/troubleshooting.md.