Answer-first summary for fast verification
Answer: Yes
The solution meets the goal because: 1) The path pattern '/data/*/*.csv' correctly matches all the required CSV files in both the 2018 and 2019 subdirectories, 2) Dataset.Tabular.from_delimited_files() creates a tabular dataset from multiple delimited files, and 3) to_pandas_dataframe() loads all the data from the dataset into a single pandas DataFrame. The community discussion shows conflicting opinions, but the most recent and upvoted comments (including one with 9 upvotes) confirm that the solution is correct based on current Azure ML documentation. Earlier comments suggesting 'No' appear to be based on outdated versions where the code might have created a FileDataset instead of a TabularDataset, but the current implementation correctly creates a TabularDataset that supports to_pandas_dataframe().
Author: LeetQuiz Editorial Team
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You create an Azure Machine Learning datastore containing the following files:
All files have the following format:
id,f1,f2,l
1,1,2,0
2,1,1,1
3,2,1,0
4,2,2,1
id,f1,f2,l
1,1,2,0
2,1,1,1
3,2,1,0
4,2,2,1
You run the following code:
from azureml.core import Dataset, Datastore, Workspace
ws = Workspace.from_config()
datastore = Datastore.get(ws, 'workspaceblobstore')
dataset = Dataset.Tabular.from_delimited_files(path=(datastore, '/data/*/*.csv'))
training_data = dataset.to_pandas_dataframe()
from azureml.core import Dataset, Datastore, Workspace
ws = Workspace.from_config()
datastore = Datastore.get(ws, 'workspaceblobstore')
dataset = Dataset.Tabular.from_delimited_files(path=(datastore, '/data/*/*.csv'))
training_data = dataset.to_pandas_dataframe()
You need to create a dataset named training_data that loads the data from all files into a single DataFrame.
Solution: Run the following code:
from azureml.core import Dataset, Datastore, Workspace
ws = Workspace.from_config()
datastore = Datastore.get(ws, 'workspaceblobstore')
dataset = Dataset.Tabular.from_delimited_files(path=(datastore, '/data/*/*.csv'))
training_data = dataset.to_pandas_dataframe()
from azureml.core import Dataset, Datastore, Workspace
ws = Workspace.from_config()
datastore = Datastore.get(ws, 'workspaceblobstore')
dataset = Dataset.Tabular.from_delimited_files(path=(datastore, '/data/*/*.csv'))
training_data = dataset.to_pandas_dataframe()
Does the solution meet the goal?
A
Yes
B
No
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