Delaying computation with Dask is a useful technique for processing large datasets that don't fit in memory. Dask is a parallel computing library for Python that allows you to scale computations across multiple cores or even multiple machines.
Here's an example of how you can use Dask to delay computation on a large dataset:
import dask.dataframe as dd# Load data into a Dask DataFramedf = dd.read_csv('large_data.csv')# Define a computation graph that calculates the sum of a columnsum_column = df['column_name'].sum()# Delay computation by calling the compute methodresult = sum_column.compute()# Print the resultprint(result) |
In this example, we first load a large dataset into a Dask DataFrame using the dd.read_csv function. This creates a distributed DataFrame that can be processed in parallel across multiple cores or machines.
We then define a computation graph that calculates the sum of a column in the DataFrame by using the sum() method on the desired column. However, instead of computing the result immediately, we delay the computation by storing it in the sum_column variable.
To actually compute the result, we call the compute() method on the delayed computation graph. This triggers the computation across all available cores or machines and returns the final result.
By delaying computation in this way, we can efficiently process large datasets without having to load all the data into memory at once. Dask automatically partitions the data into smaller chunks that can be processed in parallel, allowing us to scale computations across multiple cores or machines for improved performance.