Efficiency Leave data on the cluster Use larger tasks Adjust between Threads and Processes Don’t go distributed Efficiency Parallel computing done well is responsive and re...

Funding Funding Dask receives generous funding and support from the following sources: The time and effort of numerous open source contributors The DARPA XData program The ...

Data Structures Indexing and selecting data Interpolating data Computation GroupBy: split-apply-combine Reshaping and reorganizing data Combining data Time series data We...

Installation Required dependencies Optional dependencies For netCDF and IO For accelerating xarray For parallel computing For plotting Instructions Testing Performance Monit...

API Asynchronous methods Client Future Other Adaptive API Client Client ([address, loop, timeout, …]) Connect to and submit computation to a Dask cluster Client.call_st...

xarray: N-D labeled arrays and datasets in Python Documentation See also Get in touch NumFOCUS History License xarray: N-D labeled arrays and datasets in Python xarray (...

Pandas ecosystem Statistics and machine learning Statsmodels sklearn-pandas Featuretools Visualization Altair Bokeh seaborn yhat/ggpy IPython Vega Plotly QtPandas IDE I...

Managing Memory Creating Futures Persisting Collections Difference with dask.compute Clearing data Aggressively Clearing Data Resilience Advanced techniques Managing Mem...

Educational Resources Educational Resources This is the curated list of resources for learning Dask: Tutorials Examples Presentations

SQLFlow Syntax Extension: TO RUN Data Transformation The Challenge Syntax Extension Run Any Program TO RUN Semantics The SELECT Prefix The INTO Suffix The Context Run a Py...