How to do chain computation using Dask?
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How to do chain computation using Dask?

How to do chain computation using Dask?

This recipe helps you do chain computation using Dask

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Recipe Objective.

How to do chain computation using Dask.

In dask we have compute() method or dask.compute() function. This function blocks the computation until the previous is finished, moving directly from a lazy dask collection to a solid information in local memory.

Step 1- Importing Libraries.

import dask df = dask.datasets.timeseries()

Step 2- Reading Files.

df = dd.read_csv('data/2000.csv', parse_dates=['timestamp']) df ``` timestamp id name x y npartitions=10 datetime64[ns] int64 object float64 float64 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ```

Whenever we have to operate a dataframe and then compute it through some chain of operations we have to go through this way. This code is very efficient to memory

df.groupby('name').x.mean().compute()

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