Python: Effective Techniques for Managing Dates in DataFrame
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In data analysis and time series processing, working with date columns is essential for extracting meaningful insights from datasets. Understanding how to extract specific date components, calculate time differences, set date columns as indexes, and convert date formats are key skills for data analysts and data scientists. In this article, we will explore common scenarios and methods for handling table dates in Python using the `pandas` library.