Answer-first summary for fast verification
Answer: Yes
The solution meets the goal because the code correctly creates a TabularDataset from multiple file paths using Dataset.Tabular.from_delimited_files(paths), where 'paths' is a list of tuples specifying the datastore and file patterns for both 2018 and 2019 data. This method supports wildcards (e.g., 'data/2018/*.csv') to include all matching files, and the resulting dataset can be converted to a single pandas DataFrame with to_pandas_dataframe(). Community discussion strongly supports this, with high upvotes for comments confirming the code works (e.g., PakE's comment with 18 upvotes, brendal89's reference to similar examples with 11 upvotes). While one comment (Thornehead) suggests data cleansing is needed first, the question's goal is solely to load all data into a DataFrame, not to preprocess it, making the solution valid.
Author: LeetQuiz Editorial Team
Ultimate access to all questions.
You create an Azure Machine Learning datastore containing files in the following paths:
All files have the format: id,f1,f2,l 1,1,2,0 2,1,1,1 3,2,1,0 4,2,2,1
You run the provided code.
You need to create a dataset named training_data that loads the data from all files into a single DataFrame using the provided code.
Solution: Run the provided code.
Does the solution meet the goal?
A
Yes
B
No
No comments yet.