Dask Examples

These examples show how to use Dask in a variety of situations.

First, there are some high level examples about various Dask APIs like arrays, dataframes, and futures, then there are more in-depth examples about particular features or use cases.

You can run these examples in a live session here:

Basic Examples:

• Dask Arrays
• Dask Bags
• Dask DataFrames
• Custom Workloads with Dask Delayed
• Custom Workloads with Futures
• Dask for Machine Learning
• Xarray with Dask Arrays

Dataframes:

• DataFrames: Read and Write Data
• DataFrames: Groupby

Machine Learning:

• Scale Scikit-Learn for Small Data Problems
• Score and Predict Large Datasets
• Train Models on Large Datasets
• Incrementally Train Large Datasets
• Scale XGBoost
• Use Voting Classifiers
• Automate Machine Learning with TPOT
• Generalized Linear Models
• Singular Value Decomposition

Applications:

• Analyze web-hosted JSON data
• Async/Await and Non-Blocking Execution
• Embarrassingly parallel Workloads
• Image Processing
• ETL Pipelines with Prefect
• Reading and manipulating tiled GeoTIFF datasets
• Stencil Computations with Numba

User Surveys:

• 2019 Dask User Survey Results