the output shown in figure - clearly shows skewed and tailed distribution this information will be useful while using such attributes in our algorithms more will be clear when we work on actual use cases we can take this step further and try to visualize the price distribution on per week basis we do so by using the parameter by in the hist(function this parameter helps us group data based on the attribute mentionedas by and then generates subplot for each such grouping in our casewe group by purchase week as shown in the following snippet df[['price','purchase_week']hist(by='purchase_week,sharex=truethe output depicted in figure - showcases distribution of price on weekly basis with the highest bin clearly marked in different color figure - histograms on weekly basis pie charts one of the most commonly sought after questions while understanding the data or extracting insights is to know which type is contributing the most to visualize percentage distributionpie charts are best utilized for our datasetthe following snippet helps us visualize which user type purchased how much class_series df groupby('user_class'size(class_series name 'user class distributionclass_series plot pie(autopct=' 'plt title('user class share'plt show(