the few lines from the dataset gives us information about the attributes of the datasetsas shown in figure - figure - sample transactions from the retail transactions dataset the attributes of the dataset are easily identifiable from their names we know right away what each of these fields might mean for the sake of completenesswe include the description of each column hereinvoicenoa unique identifier for the invoice an invoice number shared across rows means that those transactions were performed in single invoice (multiple purchasesstockcodeidentifier for items contained in an invoice descriptiontextual description of each of the stock item quantitythe quantity of the item purchased invoicedatedate of purchase unitpricevalue of each item customerididentifier for customer making the purchase countrycountry of customer let' analyze this data and first determine which are the top countries the retailer is shipping its items toand how are the volumes of sales for those countries in [ ]cs_df country value_counts(reset_index(head( = out[ ]index country united kingdom germany france eire spain netherlands belgium switzerland portugal australia this shows us that the bulk of ordering is taking place in its home country only which is not surprising we also notice the odd country name eirewhich is little concerning but quick web search indicates that it is just an old name for irelandso no harm doneinterestinglyaustralia is also in the top-ten list of sales by country