omega-ml
  • Introduction
  • User Guide
    • Getting started
    • Generative AI
    • Classic ML
      • Tutorial for classic ML
        • Loading datasets for training
        • Training models
        • Evaluating models
        • Deploying models
        • Adding business logic to models
        • Serving models for real-time predictions
        • Tracking and monitoring models
      • Working with models
      • Working with datasets
        • Storing and retrieving data
        • Filtering Data
        • Large, Out of Core-sized DataFrames
    • Task Automation
    • Deployment and Operations
    • Command-line interface
    • Advanced features
  • Deploying omega-ml
  • Extending omega-ml
  • Reference
  • Support
  • Changes
omega-ml
  • User Guide
  • Classic ML
  • Tutorial for classic ML
  • Working with datasets
  • Large, Out of Core-sized DataFrames
  • View page source

Large, Out of Core-sized DataFrames¶

  • Working with MDataFrame
    • What is MDataFrame?
    • Concepts
    • Storing data as an MDataFrame
    • Getting an MDataFrame
    • Executing a query
    • Persisting the result of a query
    • Slicing
    • Filtering
    • Aggregation and transformation
    • In-database transformations
    • Parallel transformations
    • Customized chunking
    • Lazy evaluation
    • What won’t work
  • MDataFrame Operations
    • Selection
    • Filtering data
    • Ordering operations
    • Aggregation
  • Aggregation Framework
    • Standard Groupby aggregation
    • Motivating example
    • Math operations
    • Datetime Operators
    • String Operators
    • Cached operations
    • Complex operations
  • Debugging
    • Understanding the actual MongoDB query
    • Explaining the access path
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