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vaex 4.0.0-alpha.10 documentation

  • What is Vaex?
  • Installation
  • Tutorials
    • Vaex introduction in 11 minutes
    • The escape hatch: apply
    • Machine Learning with vaex.ml
    • Jupyter integration: interactivity
  • Examples
  • Gallery
  • API
  • Datasets
  • FAQ

TutorialsΒΆ

  • Vaex introduction in 11 minutes
    • DataFrame
      • Columns
      • Virtual columns
      • Selections and filtering
    • Statistics on N-d grids
    • Getting your data in
    • Plotting
      • 1-D and 2-D
      • Selections for plotting
      • Advanced Plotting
      • Slices in a 3rd dimension
      • Visualization of smaller datasets
      • In control
      • Healpix (Plotting)
    • xarray suppport
    • Interactive widgets
    • Joining
    • Group-by
    • String processing
    • Propagation of uncertainties
    • Just-In-Time compilation
    • Parallel computations
    • Extending Vaex
      • Adding functions
      • Adding DataFrame accessors
  • The escape hatch: apply
    • When not to use apply
  • Machine Learning with vaex.ml
    • Preprocessing
      • Scaling of numerical features
      • Encoding of categorical features
    • Feature Engineering
      • KBinsDiscretizer
      • GroupBy Transformer
    • Dimensionality reduction
      • Principal Component Analysis
    • Clustering
      • K-Means
    • Supervised learning
      • Scikit-Learn example
      • XGBoost example
      • CatBoost example
    • State transfer - pipelines made easy
  • Jupyter integration: interactivity
    • Introduction
    • An interactive xarray DataArray display
    • Interactive plots
    • Selection widgets
    • Axis control widgets
    • A nice container
    • Interactive plots
    • Creating your own visualizations
      • ipyvolume example
      • plotly example
Installing Vaex introduction in 11 minutes

© Copyright 2014, Maarten A. Breddels.

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