---

name: tensor-dataframe-core description: "Routes Mars tensor, DataFrame, session, eager-mode, and local CPU execution requests to the verified array-and-table workflow guidance." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Tensor and DataFrame Core

Use this sub-skill for the everyday Mars workflows that look like NumPy, pandas, and a local session: new_session, execute, fetch, stop_server, tensor creation and arithmetic, DataFrame and Series creation, selection, groupby, and small file-backed IO.

Trigger phrases

• "How do I start a local Mars session?"
• "How do I run a small tensor or DataFrame computation?"
• "Why does execute() return a Mars object?"
• "How do I switch eager mode on or off?"
• "How do I read or write a small CSV, HDF5, or Parquet file?"
• "How do I convert between Mars tensors, DataFrames, NumPy, and pandas?"

What belongs here

• Local CPU usage and session lifecycle.
• Core tensor and DataFrame constructors, math, reductions, indexing, and reshaping.
• mars.config.options and option_context for eager mode and related settings.
• Tiny safe IO examples that do not need external services or credentials.

What stays elsewhere

• Remote DAGs, logs, or script execution -> remote-and-scripts.
• Learn estimators and integrations -> learn-and-integrations.
• Ray, GPU, Kubernetes, YARN, or CLI help -> deployment-and-backends.

Read these bundled files

• references/api-reference.md for the verified API surface and representative signatures.
• references/workflows.md for the step-by-step local compute path.
• references/troubleshooting.md for install/import, session, IO, and path-shadowing failures.
• scripts/check_tensor_dataframe.py for a tiny CPU smoke that future agents can run without reopening the source repo.

Minimal route

1. Import mars, mars.tensor as mt, and mars.dataframe as md.
2. Create a local session with mars.new_session() when you want repeatable execution.
3. Build a tiny tensor or DataFrame.
4. Call .execute() and then .fetch() only when you want the concrete value.
5. Use mars.stop_server() after smoke runs that create a session.

Common decisions

• Use .execute() rather than .to_numpy() or .to_pandas() for large values.
• Use option_context({'eager_mode': True}) only when you want immediate execution for a short debugging session.
• Treat groupby, indexing, and IO as part of the same local workflow, not as separate skills.
• If mars.dataframe import fails because a shadowed ray module is visible, fix the environment first instead of rewriting the workflow.

Quality bar

A future agent should be able to answer the usual local Mars questions from this sub-skill alone: start a session, run a small tensor or DataFrame example, understand the execution model, choose between execute and fetch, and recover from the most common environment and IO issues.