the data for each feature (corresponding to its specific namecan be accessed by using python' slicing notation [for examplethe object iris['sepal length'contains the sepal lengths the first three rows of the abalone data set from the uci repository can be found as followsabalone head ( herethe missing headers have been assigned according to the order of the natural numbers the names should correspond to sexlengthdiameterheightwhole weightshucked weightviscera weightshell weightand ringsas described in the file with the name abalone names on the uci website we can manually add the names of the features to the dataframe by reassigning the columns attributeas inabalone columns ['sex ''length ''diameter ''height ''whole weight ','shucked weight ''viscera weight ''shell weight ''rings ' structuring features according to type we can generally classify features as either quantitative or qualitative quantitative features possess "numerical quantity"such as heightagenumber of birthsetc and can either be continuous or discrete continuous quantitative features take values in continuous range of possible valuessuch as heightvoltageor crop yieldsuch features capture the idea that measurements can always be made more precisely discrete quantitative features have countable number of possibilitiessuch as count in contrastqualitative features do not have numerical meaningbut their possible values can be divided into fixed number of categoriessuch as { ,ffor gender or {blueblackbrowngreenfor eye color for this reason such features are also called categorical simple rule of thumb isif it does not make sense to average the datait is categorical for exampleit does not make sense to average eye colors of course it is still possible to represent categorical data with numberssuch as blue black brownbut such numbers carry no quantitative meaning categorical features are often called factors when manipulatingsummarizingand displaying datait is important to correctly specify the type of the variables (featureswe illustrate this using the nutrition_elderly data set from [ ]which contains the results of study involving nutritional measurements of thirteen features (columnsfor elderly individuals (rowsthe data set can be obtained fromexcel files can be read directly into pandas via the read_excel methodquantitative qualitative categorical factors