The article explains Pandas' aggregation capabilities, focusing on the 'agg' function and its versatile applications in data analysis. It provides detailed examples of using 'agg' with DataFrames and
Series objects, including grouped operations, multiple aggregations, and column-specific calculations.
Reasons to Read -- Learn:
how to perform complex data aggregations in Pandas using a single versatile method ('agg') instead of multiple separate operations, which can significantly improve your code efficiency.
how to combine groupby operations with different aggregation functions for each column, enabling you to analyze data patterns like calculating the sum of salaries while simultaneously finding the earliest hire date for each gender group.
through practical examples how to transform raw data into meaningful insights using 9 different aggregation patterns, complete with sample code and real-world scenarios using employee data.
publisher: @tubelwj
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