Dask is a flexible parallel computing library for analytic computing in Python. It enables efficient parallelization of Python code, making it possible to work with large datasets that do not fit into memory.
To connect with Dask, you need to install the Dask package first. You can do this by running the following command in your command prompt or terminal:
pip install dask |
Once you have installed Dask, you can import the library into your Python code:
import dask |
Dask supports multiple schedulers to execute tasks in parallel. The most common scheduler is the Dask distributed scheduler, which allows you to run Dask on a cluster of computers. To use the distributed scheduler, you need to install the distributed package:
pip install distributed |
You can then create a Dask distributed cluster using the following code:
from dask.distributed import Clientclient = Client() |
This will create a local Dask cluster using all available CPUs. You can then submit tasks to the cluster using the client object. For example, you can create a Dask DataFrame and perform operations on it using the following code:
import dask.dataframe as dddf = dd.read_csv('large_file.csv')result = df.groupby('column').mean().compute() |
In this example, the dd.read_csv() function creates a Dask DataFrame by lazily loading the data from a CSV file. The groupby() and mean() functions are then called to perform a grouped mean operation on the data. The compute() method then triggers the actual computation of the result and returns it as a Pandas DataFrame.
Note that Dask is a powerful library that can significantly speed up your code by enabling parallel execution. However, it requires some understanding of parallel computing concepts and careful design of your code to take full advantage of its capabilities.