ray.data.Dataset.drop_columns#

Dataset.drop_columns(cols: List[str], *, compute: str | None = None, concurrency: int | Tuple[int, int] | None = None, **ray_remote_args) → Dataset[source]#

Drop one or more columns from the dataset.

Examples

>>> import ray
>>> ds = ray.data.read_parquet("s3://anonymous@ray-example-data/iris.parquet")
>>> ds.schema()
Column        Type
------        ----
sepal.length  double
sepal.width   double
petal.length  double
petal.width   double
variety       string
>>> ds.drop_columns(["variety"]).schema()
Column        Type
------        ----
sepal.length  double
sepal.width   double
petal.length  double
petal.width   double

Time complexity: O(dataset size / parallelism)

Parameters:

  • cols – Names of the columns to drop. If any name does not exist, an exception is raised. Column names must be unique.

  • compute – This argument is deprecated. Use concurrency argument.

  • concurrency – The number of Ray workers to use concurrently. For a fixed-sized worker pool of size n, specify concurrency=n. For an autoscaling worker pool from m to n workers, specify concurrency=(m, n).

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