Answer-first summary for fast verification
Answer: The former is distributed, and the latter operates on a single machine.
The correct answer is **C**. Here's why: - **pandas-on-Spark DataFrame**: - **Distributed**: Data is partitioned across multiple nodes in a Spark cluster, enabling scalable processing of large datasets. - **Scalable**: Capable of handling datasets too large for a single machine's memory. - **Spark-based**: Utilizes Spark's distributed engine for efficient operations. - **Pandas-like API**: Offers a familiar interface for those accustomed to pandas. - **pandas DataFrame**: - **Single-Machine**: Data is processed in memory on a single machine, suitable for smaller datasets. - **Stand-alone**: Operates independently of distributed systems like Spark. - **Versatile**: Widely used for a variety of data analysis tasks. In summary, opt for **pandas-on-Spark** when dealing with large datasets requiring distributed processing, and choose **pandas** for smaller datasets or when leveraging its extensive feature set.
Author: LeetQuiz Editorial Team
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What distinguishes a pandas-on-Spark DataFrame from a pandas DataFrame?
A
The former operates on a single machine, while the latter is distributed.
B
They are fundamentally the same in terms of distribution.
C
The former is distributed, and the latter operates on a single machine.
D
The former lacks the advanced functionalities found in the latter.
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