coffee male gender female figure boxplots of quantitative feature 'coffeeas function of the levels of categorical feature 'gendernote that we used different"notched"style boxplot this time further reading the focus in this book is on the mathematical and statistical analysis of dataand for the rest of the book we assume that the data is available in suitable form for analysis howevera large part of practical data science involves the cleaning of datathat isputting it into form that is amenable to analysis with standard software packages standard python modules such as numpy and pandas can be used to reformat rowsrename columnsremove faulty outliersmerge rowsand so on mckinneythe creator of pandasgives many practical case studies in [ effective data visualization techniques are beautifully illustrated in [ exercises before you attempt these exercisesmake sure you have up-to-date versions of the relevant python packagesspecifically matplotlibpandasand seaborn an easy way to ensure this is to update packages via the anaconda navigatoras explained in appendix visit the uci repository the data and download the mushroom data set agaricus-lepiota data using pandasread the data into dataframe called mushroomvia read_csv (ahow many features are in this data set(bwhat are the initial names and types of the features(crename the first feature (index to 'edibilityand the sixth feature (index to 'odor[hintthe column names in pandas are immutableso individual columns cannot be modified directly however it is possible to assign the entire column names list via mushroom columns newcols