To read multiple text files using Dask, you can use the dask.bag.read_text function. This function takes a glob string or a list of file paths as input and creates a Dask bag that contains the lines of all the text files. Each line in each file will be treated as a separate partition, and Dask will process them in parallel.
For example, you can create a Dask bag from all the text files in a directory like this:
import dask.bag as dbmy_bag = db.read_text('/path/to/my/files/*.txt') |
This will create a Dask bag that contains the lines of all the text files in the /path/to/my/files directory that end with .txt.
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 lines that contain a certain string:
filtered_bag = my_bag.filter(lambda line: 'target_string' in line) |
This will create a new Dask bag that contains only the lines that match the specified filter. You can also use the map method to apply a function to each line in the bag, and the reduce method to aggregate the lines in the bag using a given function.
Overall, Dask bags provide a flexible and efficient way to work with multiple text files in a parallelized manner. They can be especially useful when dealing with large text datasets that cannot be easily loaded into memory.