variant herewe apply different aggregation functions on two different attributes the agg(function takes dictionary as input containing attributes as keys and aggregation functions as values (see figure - figure - groupby with different aggregation functions for different attributes variant herewe do combination of variants and we apply multiple aggregations on the price field while applying only single one on quantity_purchased again dictionary is passedas shown in the snippet the output is shown in figure - figure - groupby with showcasing complex operation apart from groupby(based summarizationother functions such as pivot()pivot_table()stack()unstack()crosstab()and melt(provide capabilities to reshape the pandas dataframe as per requirements complete description of these methods with examples is available as part of pandas documentation at to go through the same data visualization data science is type of storytelling that involves data as its lead character as data science practitioners we work with loads of data which undergo processingwranglingand analysis day in and day out for various use cases augmenting this storytelling with visual aspects like chartsgraphsmaps and so on not just helps in improving the understanding of data (and in turn the use case/business problembut also provides opportunities to find hidden patterns and potential insights