effective_data_viz_22.py

# Visualizing 3-D mix data using violin plots
# leveraging the concepts of hue and axes for > 1 categorical dimensions
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 4))
f.suptitle('Wine Type - Quality - Acidity', fontsize=14)

sns.violinplot(x="quality", y="volatile acidity",
               data=wines, inner="quart", linewidth=1.3,ax=ax1)
ax1.set_xlabel("Wine Quality",size = 12,alpha=0.8)
ax1.set_ylabel("Wine Volatile Acidity",size = 12,alpha=0.8)

sns.violinplot(x="quality", y="volatile acidity", hue="wine_type", 
               data=wines, split=True, inner="quart", linewidth=1.3,
               palette={"red": "#FF9999", "white": "white"}, ax=ax2)
ax2.set_xlabel("Wine Quality",size = 12,alpha=0.8)
ax2.set_ylabel("Wine Volatile Acidity",size = 12,alpha=0.8)
l = plt.legend(loc='upper right', title='Wine Type')