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Explanation: Pandas is a powerful library that primarily deals with data manipulation and analysis . It provides data structures like DataFrame (tabular data) and Series (one-dimensional data), making it excellent for tasks such as cleaning, transforming, filtering, and aggregating data. Pandas is built on top of NumPy and offers additional capabilities for handling structured data such as missing values, time series, and categorical data. NumPy , on the other hand, is designed for numerical computing . It is used to perform efficient operations on large datasets stored as arrays or matrices, enabling operations like linear algebra, statistical calculations, and other complex numerical tasks. NumPy arrays are much more efficient for numerical data than native Python lists due to their support for vectorized operations.
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Match the following lists.
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