Dask Dataframes allows you to work with large datasets for both data manipulation and building ML models with only minimal code changes. It is open source and works well with python libraries like NumPy, scikit-learn, etc. Let's understand how to use Dask with hands-on examples. Dask - How to handle large data in python using parallel computing
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Dask is a flexible library for parallel computing in Python that makes scaling out your workflow smooth and simple. On the CPU, Dask uses Pandas to execute operations in parallel on DataFrame partitions. Dask-cuDF extends Dask where necessary to allow its DataFrame partitions to be processed by cuDF GPU DataFrames as opposed to Pandas ...
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The following are 30 code examples for showing how to use dask.dataframe.DataFrame().These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.
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One unintended consequence of all this activity and creativity has been fragmentation in multidimensional array (a.k.a. tensor) libraries - which are the fundamental data structure for these fields. Choices include NumPy, Tensorflow, PyTorch, Dask, JAX, CuPy, MXNet, Xarray, and others.