Answer-first summary for fast verification
Answer: `compute.ops_on_diff_frames` - Allows operations between different DataFrames when enabled.
The correct answer is **C. `compute.ops_on_diff_frames`**. This configuration option in Pandas API on Spark controls whether operations between different DataFrames are allowed. It's disabled by default to prevent potentially expensive operations that could degrade performance, especially with large datasets. Enabling it permits such operations when necessary for your analysis. **How to Enable Operations:** 1. Import: `from pyspark.pandas import config` 2. Set Option: `config.set_option('compute.ops_on_diff_frames', True)` **Caution:** Use this option carefully, as enabling operations between different DataFrames can lead to performance issues. Consider alternatives like joining DataFrames or using explicit Spark transformations for better performance.
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When working with two different DataFrames in Pandas API on Spark, you encounter an error related to expensive operations. Which configuration option can you enable to permit operations between these DataFrames?
A
display.max_rows - Controls the number of rows displayed in output, not operations between DataFrames.
B
compute.default_index_type - Determines the default index type for DataFrames, unrelated to operations between them.
C
compute.ops_on_diff_frames - Allows operations between different DataFrames when enabled.
D
compute.shortcut_limit - Sets a row limit for certain computations, not for DataFrame operations.
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