A Dask Array is an abstraction of the NumPy n-dimensional array, processed in parallel and partitioned into multiple sub-arrays. These small arrays can be on local or distributed remote machines. Dask Arrays can compute large-sized arraysby utilizing all the available cores in the system. They can be applied to statistics, optimization, bioinformatics, business domains, environmental science, and many more fields. They also support lots of NumPy operations, such as arithmetic and scalar operations, aggregation operations, matrices, and linear algebra operations. However, they do not support unknown shapes. Also, the tolist and sort operations are difficult to perform in parallel. Let's understand how Dask Arrays decompose data into a NumPy array and execute them in parallel by taking a look at the following diagram:
As we can see, there are multiple blocks of different shapes, all of which represent NumPy arrays. These arrays form a Dask Array and can be executed on...
