Which Python library is used to handle structured data in tabular form?

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BlackTom AI · Accepted Answer

When considering which Python library is used to handle structured data in tabular form, we should evaluate each option.
Option 1: Scikit-learn. This library is primarily focused on machine learning algorithms, model selection, and evaluation tools. It does not specialize in managing tabular data structures itself, though it can operate on data that you prepare with other tools.
Option 2: Pandas. This library is specifically designed for data manipulation and analysis, with robust support for tabular data via DataFrame and Series structures. It excels at handling structured data in rows and columns, performing filtering, aggregation, joins, and reshaping.
Option 3: NumPy. NumPy provides efficient numerical array objects and operations, but it is more about homogeneous multi-dimensional arrays rather than labeled tabular data with row/column semantics. It lacks the richer table-oriented features found in Pandas.
Option 4: Matplotlib. This is a plotting library used for creating visualizations. While it can visualize data that resides in other structures, it is not a tool for managing or manipulating tabular data itself.

In summary, the library best suited for tabular, structured data manipulation is Pandas due to its DataFrame construct and extensive data-handling capabilities, while the other libraries serve different primary purposes such as machine learning, numerical computing, or plotting.

Introduction to Machine Learning & AI - DAT-5329 - BMBAN2 In-Class Knowledge Check #1 (Remotely Proctored)

Which Python library is used to handle structured data in tabular form?

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