Load multiple files into single dataset

[HYPERTONE]

I can't figure out an efficient way to do this.

Given a folder of ~20 .csv files, what is the most efficient way to iterate and load all files into single dataset?

Current method requires specifying a variable for each file to load, then using concat rows:

fileName1 = rt.load_csv_as_dataset(r"C:\IndianaJones\Antidote_to_poison1.csv")
fileName2 = rt.load_csv_as_dataset(r"C:\IndianaJones\Antidote_to_poison2.csv")

ds = rt.Dataset.concat_rows([fileName1, fileName2])

Is there a more efficient way to save Indiana Jones and load all these files more efficiently?

[HYPERTONE]

import glob
import os

path = r'C:\IndianaJones'

all_files = glob.glob(os.path.join(path, "Antidote_to_poison*.csv"))
ds= rt.Dataset.concat_rows([rt.load_csv_as_dataset(f) for f in all_files)

That was easy.