effective_data_viz_1.py

white_wine = pd.read_csv('winequality-white.csv', sep=';')
red_wine = pd.read_csv('winequality-red.csv', sep=';')

# store wine type as an attribute
red_wine['wine_type'] = 'red'   
white_wine['wine_type'] = 'white'

# bucket wine quality scores into qualitative quality labels
red_wine['quality_label'] = red_wine['quality'].apply(lambda value: 'low' 
                                                          if value <= 5 else 'medium' 
                                                              if value <= 7 else 'high')
red_wine['quality_label'] = pd.Categorical(red_wine['quality_label'], 
                                           categories=['low', 'medium', 'high'])
white_wine['quality_label'] = white_wine['quality'].apply(lambda value: 'low' 
                                                              if value <= 5 else 'medium' 
                                                                  if value <= 7 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_wine, white_wine])

# re-shuffle records just to randomize data points
wines = wines.sample(frac=1, random_state=42).reset_index(drop=True)