ray.data.Dataset.select_columns#

Dataset.select_columns(cols: List[str], *, compute: Optional[Union[str, ray.data._internal.compute.ComputeStrategy]] = None, **ray_remote_args) → ray.data.dataset.Dataset[source]#

Select one or more columns from the dataset.

All input columns used to select need to be in the schema of the dataset.

Examples

>>> import ray
>>> # Create a dataset with 3 columns
>>> ds = ray.data.from_items([{"col1": i, "col2": i+1, "col3": i+2}
...      for i in range(10)])
>>> # Select only "col1" and "col2" columns.
>>> ds = ds.select_columns(cols=["col1", "col2"])
>>> ds
MapBatches(<lambda>)
+- Dataset(
      num_blocks=10,
      num_rows=10,
      schema={col1: int64, col2: int64, col3: int64}
   )

Time complexity: O(dataset size / parallelism)

Parameters

  • cols – Names of the columns to select. If any name is not included in the dataset schema, an exception will be raised.

  • compute – The compute strategy, either “tasks” (default) to use Ray tasks, ray.data.ActorPoolStrategy(size=n) to use a fixed-size actor pool, or ray.data.ActorPoolStrategy(min_size=m, max_size=n) for an autoscaling actor pool.

  • ray_remote_args – Additional resource requirements to request from ray (e.g., num_gpus=1 to request GPUs for the map tasks).