in [ ]cs_df[cs_df['stockcode'==cat_des_df stockcode value_counts()[cat_des_df stockcode value_counts()> reset_index()['index'][ ]]['description'unique(out[ ]array(['mistletoe heart wreath cream''mistletoe heart wreath white''mistletoe heart wreath cream''?''had been put aside'nan]dtype=objectthis gives the multiple descriptions for one of those items and we witness the simple ways in which data quality can be corrupted in any dataset simple spelling mistake can end up in reducing data quality and an erroneous analysis in an enterprise-level scenariodedicated people work toward restoring data quality manually over time since the intent of this section is to focus on customer segmentationwe will be skipping this tedious activity for now let' now verify the sanctity of the quantity and unitprice attributesas those are the attributes we will be using in our analysis in [ ]cs_df quantity describe(out[ ]count mean std min - max namequantitydtypefloat in [ ]cs_df unitprice describe(out[ ]count mean std min - max nameunitpricedtypefloat we can observe from the preceding output that both of these attributes are having negative valueswhich may mean that we may have some return transactions in our data also this scenario is quite common for any retailer but we need to handle these before we proceed to our analysis these are some of the data quality issues we found in our dataset in the real worldthe datasets are generally messy and have considerable data quality issuesso it is always good practice to explicitly verify information at hand before performing any kind of analysis we encourage you to try and find similar issues with any dataset which you might want to analyze in the future