In Dask, dask.bag provides a way to work with large datasets in parallel using functional programming concepts. dask.bag provides a map method that takes a function and applies it to each element of the bag in parallel.
Here's an example of how to use dask.bag.map to square a list of numbers:
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 squares a numberdef square(x): return x ** 2# Use map to apply the square function to each element of the bag in parallelsquared_numbers = b.map(square)# Compute the result and print itprint(squared_numbers.compute()) |
This will output [1, 4, 9, 16, 25], which is the result of applying the square function to each element of the b bag using the map method.
Using dask.bag.map is particularly useful when working with large datasets, as it allows you to apply a transformation to each element 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.