if value < else 'mediumif value < else 'high'white_wine['quality_label'pd categorical(white_wine['quality_label']categories=['low''medium''high']merge red and white wine datasets wines pd concat([red_winewhite_wine]re-shuffle records just to randomize data points wines wines sample(frac= random_state= reset_index(drop=trueour objective in future sections would be to predict wine_type and quality_label based on other features in the wines dataset let' now try to understand more about our dataset and its features understanding dataset features the wines dataframe we obtained in the previous section is our final dataset we will be using for our analysis and modeling we will also be using the red_wine and white_wine dataframes where necessary for basic exploratory analysis and visualizations let' start by looking at the total number of data samples we are dealing with and also the different features in our dataset in [ ]print(white_wine shapered_wine shapeprint(wines info()( ( rangeindex entries to data columns (total columns)fixed acidity non-null float volatile acidity non-null float citric acid non-null float residual sugar non-null float chlorides non-null float free sulfur dioxide non-null float total sulfur dioxide non-null float density non-null float ph non-null float sulphates non-null float alcohol non-null float quality non-null int wine_type non-null object quality_label non-null category dtypescategory( )float ( )int ( )object( memory usage kb this information tells us that we have white wine data points and red wine data points the merged dataset contains total of data points and we also get an idea of numeric and categorical attributes let' take peek at our dataset to see some sample data points in [ ]wines head(