What are cuDF's primary capabilities?

cuDF's primary capabilities are centered around efficient dataframe operations, which are crucial for handling large datasets. It is designed to accelerate the processing of data by providing a GPU-accelerated framework that mimics the functionality of Pandas, but with significantly improved performance for larger datasets. With cuDF, users can perform tasks such as filtering, grouping, joining, and aggregating data much faster than traditional CPU-based dataframes.

The framework leverages the computational power of GPUs to enhance performance, making it particularly well-suited for data science and machine learning tasks where large volumes of data are common. This capability allows for real-time data analysis and manipulation, which is essential in environments that require quick insights from big data.

While other options may represent important functionalities in their respective domains, they do not describe cuDF’s core purpose or capabilities. For instance, word representation and masking are more relevant to natural language processing, graph processing pertains to specialized graph computation frameworks, and fleet routing workflows are specific to logistics and operational management. Thus, cuDF's focus on dataframe operations distinctly positions it within the landscape of data manipulation tools.