To create a Dask bag from a sequence in Python, you can use the dask.bag.from_sequence function. This function takes a Python iterable as input and creates a Dask bag that contains the elements of the iterable. Each element in the bag will be treated as a separate partition, and Dask will process them in parallel.
For example, you can create a Dask bag from a list like this:
import dask.bag as dbmy_list = [1, 2, 3, 4, 5]my_bag = db.from_sequence(my_list) |
This will create a Dask bag that contains the elements of the my_list list.
You can also create a Dask bag from a generator expression, like this:
my_gen = (x**2 for x in range(10))my_bag = db.from_sequence(my_gen) |
This will create a Dask bag that contains the squares of the first 10 integers.
Once you have created a Dask bag, you can perform various operations on it, such as filtering, mapping, and reducing. For example, you can use the filter method to select only the elements that satisfy a certain condition:
filtered_bag = my_bag.filter(lambda x: x % 2 == 0) |
This will create a new Dask bag that contains only the even elements of the original bag. You can also use the map method to apply a function to each element in the bag, and the reduce method to aggregate the elements in the bag using a given function.
Overall, Dask bags provide a flexible and efficient way to work with sequences of data in a parallelized manner. They can be especially useful when dealing with large datasets that cannot be easily loaded into memory.