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:
array bag dataframe delayed futures machine-learning sql xarray resilience
dataframes/01-data-access dataframes/02-groupby dataframes/03-from-pandas-to-dask dataframes/04-reading-messy-data-into-dataframes
machine-learning/blockwise-ensemble machine-learning/scale-scikit-learn machine-learning/parallel-prediction machine-learning/torch-prediction machine-learning/training-on-large-datasets machine-learning/incremental machine-learning/text-vectorization machine-learning/hyperparam-opt.ipynb machine-learning/xgboost machine-learning/voting-classifier machine-learning/tpot machine-learning/glm machine-learning/svd
applications/json-data-on-the-web applications/async-await applications/async-web-server applications/embarrassingly-parallel applications/evolving-workflows applications/image-processing applications/prefect-etl applications/stencils-with-numba applications/forecasting-with-prophet
surveys/2021.ipynb surveys/2020.ipynb surveys/2019.ipynb