we'll look at the attributes quantity_purchased and purchase_week using box plots the following snippet generates the required plot for us df[['quantity_purchased','purchase_week']plot box(plt title('quantity and week value distribution'nowwe'll look at the plots generated (see figure - the bottom edge of the box in box plot marks the first quartilewhile the top one marks the third the line in the middle of the box marks the second quartile or the median the top and bottom whiskers extending from the box mark the range of values outliers are marked beyond the whisker boundaries in our examplefor quantity purchasedthe median is quite close to the middle of the box while the purchase week has it toward the bottom (clearly pointing out the skewness in the datayou are encouraged to read more about box plots for an in-depth understanding at courses//boxplot htm figure - box plots using pandas scatter plots scatter plots are another class of visualizations usually used to identify correlations or patterns between attributes like most visualization we have seen so farscatter plots are also available through the plot(interface of pandas to understand scatter plotswe first need to perform couple of steps of data wrangling to get our data into required shape we first encode the user_class with dummy encoding (as discussed in the previous sectionusing map(and then getting mean price and count of transactions on per week per user_class level using groupby(the following snippet helps us get our dataframe uclass_map {'new' 'existing' 'loyal_existing' ,'error': df['enc_uclass'df user_class map(uclass_map