For AI agents: a documentation index is available at the root level at /llms.txt and /llms-full.txt. Append /llms.txt to any URL for a page-level index, or .md for the markdown version of any page.
  • Getting Started
    • Welcome
    • Contributing
  • Concepts
    • Columns
    • Seed Datasets
    • Agent Rollout Ingestion
    • Custom Columns
    • Validators
    • Processors
    • Person Sampling
    • Traces
    • Architecture & Performance
    • Deployment Options
    • Security
  • Tutorials
    • Overview
    • The Basics
    • Structured Outputs, Jinja Expressions, and Conditional Generation
    • Seeding with an External Dataset
    • Providing Images as Context
    • Generating Images
    • Image-to-Image Editing
  • Recipes
    • Recipe Cards
  • Plugins
    • Overview
    • Example Plugin
    • FileSystemSeedReader Plugins
    • Discover
  • Code Reference
    • Overview
      • Overview
      • seed_readers
      • processors
      • mcp
      • column_generators
      • Seed Reader API
      • Processor API
        • Base
        • Drop Columns
        • Registry
        • Schema Transform
      • MCP Runtime API
      • Column Generator API
  • Dev Notes
    • Overview
    • Prompt Sensitivity
    • Retriever SDG Toolkit
    • Have It Your Way
    • VLM Long Document Understanding
    • Push Datasets to Hugging Face Hub
    • Text-to-SQL for Nemotron Super
    • Async All the Way Down
    • Owning the Model Stack
NVIDIANVIDIA
Developer-friendly docs for your API
Privacy Policy | Manage My Privacy | Do Not Sell or Share My Data | Terms of Service | Accessibility | Corporate Policies | Product Security | Contact

Copyright © 2026, NVIDIA Corporation.

LogoLogoNeMo Data Designer
On this page
  • Module Contents
  • Classes
  • Data
  • API
Code ReferenceEngine Extension APIProcessor API

data_designer.engine.processing.processors.drop_columns

||View as Markdown|
Previous

Base

Next

Registry

Module Contents

Classes

NameDescription
DropColumnsProcessorDrops specified columns from the dataset after each batch.

Data

logger

API

1logger = getLogger(...)
1class data_designer.engine.processing.processors.drop_columns.DropColumnsProcessor(
2 config: data_designer.engine.configurable_task.TaskConfigT,
3 resource_provider: data_designer.engine.resources.resource_provider.ResourceProvider
4)

Bases: data_designer.engine.processing.processors.base.Processor[data_designer.config.processors.DropColumnsProcessorConfig]

Drops specified columns from the dataset after each batch.

1_resolve_columns(available: pandas.Index) -> list[str]

Expand column_names entries (including glob patterns) against available columns.

1process_after_batch(
2 data: pandas.DataFrame,
3 *,
4 current_batch_number: int | None
5) -> pandas.DataFrame
1_save_dropped_columns(
2 data: pandas.DataFrame,
3 resolved: list[str],
4 current_batch_number: int
5) -> None