Implementation:Apache Paimon Ray Dataset To Pandas

Knowledge Sources	Apache Paimon Ray Dataset API
Domains	Data_Lake, Distributed_Computing
Last Updated	2026-02-07 00:00 GMT

Overview

Wrapper documentation for collecting Ray Dataset results to local pandas DataFrames.

Description

ray.data.Dataset.to_pandas() collects all distributed data blocks to the driver node and concatenates them into a single pandas DataFrame. count() returns the total row count without materializing data. These are standard Ray Data methods used as the final step in Paimon distributed processing pipelines.

Usage

Call to_pandas() on a Ray Dataset after all distributed transformations are complete. Use count() when only the row count is needed, as it avoids the overhead of full data materialization.

Code Reference

Source Location

External Tool (Wrapper) - Ray Dataset API documentation

Signature

class Dataset:
    def to_pandas(self) -> pandas.DataFrame:
    def count(self) -> int:

Import

# Method on ray.data.Dataset (no separate import needed)
import ray.data

I/O Contract

Inputs

Name	Type	Required	Description
(self)	ray.data.Dataset	Yes	The distributed Ray Dataset to collect

Outputs

Name	Type	Description
to_pandas() return	pandas.DataFrame	All distributed data collected into a local DataFrame
count() return	int	Total row count across all distributed blocks

Usage Examples

Basic Usage

# Collect results to pandas DataFrame
df = aggregated.to_pandas()
print(df)

# Just count rows (without full materialization)
row_count = ray_dataset.count()
print(f"Total rows: {row_count}")

Related Pages

Implements Principle

• Principle:Apache_Paimon_Distributed_Result_Collection

Requires Environment

• Environment:Apache_Paimon_Optional_Extensions