You create an Azure Machine Learning datastore containing the following files:

• /data/2018/Q1.csv
• /data/2018/Q2.csv
• /data/2018/Q3.csv
• /data/2018/Q4.csv
• /data/2019/Q1.csv

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?*_