In Dask, dask.bag provides a way to work with large datasets in parallel using functional programming concepts. dask.bag provides a filter method that takes a function and applies it to each element of the bag in parallel, returning a new bag containing only the elements for which the function returns True.
Here's an example of how to use dask.bag.filter to select only the even numbers from a list:
import dask.bag as db# Define a list of numbersnumbers = [1, 2, 3, 4, 5]# Create a dask bag from the listb = db.from_sequence(numbers)# Define a function that tests if a number is evendef is_even(x): return x % 2 == 0# Use filter to select only the even numbers from the bag in paralleleven_numbers = b.filter(is_even)# Compute the result and print itprint(even_numbers.compute()) |
This will output [2, 4], which is the result of applying the is_even function to each element of the b bag using the filter method.
Using dask.bag.filter is particularly useful when working with large datasets, as it allows you to filter elements of the dataset in parallel, rather than iterating over each element individually. This can greatly improve the performance of your code on multi-core or distributed systems.