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Answer: result = pandas_spark_df.join(other_pandas_spark_df, on='id', how='inner')
To perform a distributed join operation between two Pandas on Spark DataFrames, you can use the 'join()' method, specifying the common column 'id' and the type of join (e.g., 'inner'). The correct code snippet is shown in option A, which demonstrates the distributed join operation using Pandas API on Spark. There is no need to convert the DataFrames to Spark DataFrames or back to Pandas on Spark DataFrames, as the join operation is inherently distributed when using Pandas API on Spark.
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Given a Pandas on Spark DataFrame named 'pandas_spark_df', write a code snippet that demonstrates how to perform a distributed join operation with another Pandas on Spark DataFrame named 'other_pandas_spark_df' on a common column named 'id'.
A
result = pandas_spark_df.join(other_pandas_spark_df, on='id', how='inner')
B
result = pandas_spark_df.toSpark().join(other_pandas_spark_df.toSpark(), on='id', how='inner')
C
result = pandas_spark_df.join(other_pandas_spark_df.toSpark(), on='id', how='inner')
D
result = pandas_spark_df.toSpark().join(other_pandas_spark_df, on='id', how='inner').toPandasAPI()