figure - univariate plots depicting feature distributions for the wine quality dataset the power of packages like matplotlib and pandas enable you to easily plot variable distributions as depicted in figure - using minimal code do you notice any interesting patterns across the two wine typeslet' take the feature named residual sugar and plot the distributions across data pertaining to red and white wine samples fig plt figure(figsize ( , )title fig suptitle("residual sugar content in wine"fontsize= fig subplots_adjust(top= wspace= ax fig add_subplot( , ax set_title("red wine"ax set_xlabel("residual sugar"ax set_ylabel("frequency"ax set_ylim([ ]ax text( '$\mu$='+str(round(red_wine['residual sugar'mean(), ))fontsize= r_freqr_binsr_patches ax hist(red_wine['residual sugar']color='red'bins= edgecolor='black'linewidth= ax fig add_subplot( , ax set_title("white wine"ax set_xlabel("residual sugar"ax set_ylabel("frequency"ax set_ylim([ ]ax text( '$\mu$='+str(round(white_wine['residual sugar'mean(), ))fontsize= w_freqw_binsw_patches ax hist(white_wine['residual sugar']color='white'bins= edgecolor='black'linewidth=