Pandas, on the other hand, offers a suite of datetime functions specifically designed for data analysis. These functions are highly optimized for performance and work seamlessly with pandas ...

Pandas allows you to define custom aggregation functions to be used with pivot tables. To create a custom aggregator, you first define a Python function that specifies the aggregation operation.

Python isn’t too dissimilar, as we can rely on the inbuilt len function, which can be combined with Pandas’ loc[] to access a specific row of data within a column: len(df['Title'].loc[0]) ...