Explanation:
The correct answer is that a Python function can return a DataFrame. This is achieved by using the spark.read.table method to read the Delta table into a DataFrame, which can then be returned by the function. For example:
def get_source_dataframe(tablename):
df = spark.read.table(tablename)
return df
df = get_source_dataframe('test_table')
def get_source_dataframe(tablename):
df = spark.read.table(tablename)
return df
df = get_source_dataframe('test_table')
This code snippet demonstrates how a DataFrame is returned and assigned to a Python variable without causing an out-of-memory error, as no action is triggered until the DataFrame is actually used.
Ultimate access to all questions.
Which of the following statements accurately describes how to write a Python function that reads data from a Delta table and returns a DataFrame?
A
Writing an SQL UDF is the correct approach to return a DataFrame.
B
A Python function cannot return a DataFrame under any circumstances.
C
A Python function can indeed return a DataFrame, facilitating data manipulation and analysis.
D
Attempting to return a DataFrame from a Python function will inevitably lead to an out-of-memory error due to the data volume.
E
An SQL UDF that returns tabular data is the only viable solution for this task.
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