You have a Parquet file named Customers.parquet uploaded to a Fabric lakehouse and you want to load it into a pandas DataFrame using PySpark in a notebook. Which code should you run?

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Multiple Choice

You have a Parquet file named Customers.parquet uploaded to a Fabric lakehouse and you want to load it into a pandas DataFrame using PySpark in a notebook. Which code should you run?

Explanation:
Loading a Parquet file into a pandas DataFrame requires bringing the pandas library into scope and then using its read_parquet function with the correct file path. The best approach imports pandas with the conventional alias and calls the read_parquet function through that alias, pointing to the file in the default lakehouse Files location: import pandas as pd; df = pd.read_parquet("/lakehouse/default/Files/Customers.parquet"). This ensures pandas is actually available in the notebook and the path matches where the file resides in Fabric lakehouse. Why the other forms don’t fit: they either omit the import (so pandas or pd isn’t defined) or use an incorrect path (which would fail to locate the file). The combination of a proper import and the correct path is what makes this the right approach.

Loading a Parquet file into a pandas DataFrame requires bringing the pandas library into scope and then using its read_parquet function with the correct file path. The best approach imports pandas with the conventional alias and calls the read_parquet function through that alias, pointing to the file in the default lakehouse Files location: import pandas as pd; df = pd.read_parquet("/lakehouse/default/Files/Customers.parquet"). This ensures pandas is actually available in the notebook and the path matches where the file resides in Fabric lakehouse.

Why the other forms don’t fit: they either omit the import (so pandas or pd isn’t defined) or use an incorrect path (which would fail to locate the file). The combination of a proper import and the correct path is what makes this the right approach.

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