dipanjanS

# Visualizing 3-D categorical data using bar plots
# leveraging the concepts of hue and facets
fc = sns.factorplot(x="quality", hue="wine_type", col="quality_label", 
                    data=wines, kind="count",
                    palette={"red": "#FF9999", "white": "#FFE888"})

dipanjanS

# Visualizing 3-D mix data using scatter plots
# leveraging the concepts of hue for categorical dimension
jp = sns.pairplot(wines, x_vars=["sulphates"], y_vars=["alcohol"], size=4.5,
                  hue="wine_type", palette={"red": "#FF9999", "white": "#FFE888"},
                  plot_kws=dict(edgecolor="k", linewidth=0.5))
                  
# we can also view relationships\correlations as needed                  
lp = sns.lmplot(x='sulphates', y='alcohol', hue='wine_type', 
                palette={"red": "#FF9999", "white": "#FFE888"},
                data=wines, fit_reg=True, legend=True,

dipanjanS

# Visualizing 3-D mix data using kernel density plots
# leveraging the concepts of hue for categorical dimension
ax = sns.kdeplot(white_wine['sulphates'], white_wine['alcohol'],
                  cmap="YlOrBr", shade=True, shade_lowest=False)
ax = sns.kdeplot(red_wine['sulphates'], red_wine['alcohol'],
                  cmap="Reds", shade=True, shade_lowest=False)

dipanjanS

# 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)

dipanjanS

# Visualizing 3-D mix data using box 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 - Alcohol Content', fontsize=14)

sns.boxplot(x="quality", y="alcohol", hue="wine_type",
               data=wines, palette={"red": "#FF9999", "white": "white"}, ax=ax1)
ax1.set_xlabel("Wine Quality",size = 12,alpha=0.8)
ax1.set_ylabel("Wine Alcohol %",size = 12,alpha=0.8)

dipanjanS

# Visualizing 4-D mix data using scatter plots
# leveraging the concepts of hue and depth
fig = plt.figure(figsize=(8, 6))
t = fig.suptitle('Wine Residual Sugar - Alcohol Content - Acidity - Type', fontsize=14)
ax = fig.add_subplot(111, projection='3d')

xs = list(wines['residual sugar'])
ys = list(wines['alcohol'])
zs = list(wines['fixed acidity'])
data_points = [(x, y, z) for x, y, z in zip(xs, ys, zs)]

dipanjanS

# Visualizing 4-D mix data using bubble plots
# leveraging the concepts of hue and size
size = wines['residual sugar']*25
fill_colors = ['#FF9999' if wt=='red' else '#FFE888' for wt in list(wines['wine_type'])]
edge_colors = ['red' if wt=='red' else 'orange' for wt in list(wines['wine_type'])]

plt.scatter(wines['fixed acidity'], wines['alcohol'], s=size, 
            alpha=0.4, color=fill_colors, edgecolors=edge_colors)

plt.xlabel('Fixed Acidity')

dipanjanS

# Visualizing 4-D mix data using scatter plots
# leveraging the concepts of hue and facets for > 1 categorical attributes
g = sns.FacetGrid(wines, col="wine_type", hue='quality_label', 
                  col_order=['red', 'white'], hue_order=['low', 'medium', 'high'],
                  aspect=1.2, size=3.5, palette=sns.light_palette('navy', 4)[1:])
g.map(plt.scatter, "volatile acidity", "alcohol", alpha=0.9, 
      edgecolor='white', linewidth=0.5, s=100)
fig = g.fig 
fig.subplots_adjust(top=0.8, wspace=0.3)
fig.suptitle('Wine Type - Alcohol - Quality - Acidity', fontsize=14)

dipanjanS

# Visualizing 4-D mix data using scatter plots
# leveraging the concepts of hue and facets for > 1 categorical attributes
g = sns.FacetGrid(wines, col="wine_type", hue='quality_label', 
                  col_order=['red', 'white'], hue_order=['low', 'medium', 'high'],
                  aspect=1.2, size=3.5, palette=sns.light_palette('green', 4)[1:])
g.map(plt.scatter, "volatile acidity", "total sulfur dioxide", alpha=0.9, 
      edgecolor='white', linewidth=0.5, s=100)
fig = g.fig 
fig.subplots_adjust(top=0.8, wspace=0.3)
fig.suptitle('Wine Type - Sulfur Dioxide - Acidity - Quality', fontsize=14)

dipanjanS

# Visualizing 5-D mix data using bubble charts
# leveraging the concepts of hue, size and depth
fig = plt.figure(figsize=(8, 6))
ax = fig.add_subplot(111, projection='3d')
t = fig.suptitle('Wine Residual Sugar - Alcohol Content - Acidity - Total Sulfur Dioxide - Type', fontsize=14)

xs = list(wines['residual sugar'])
ys = list(wines['alcohol'])
zs = list(wines['fixed acidity'])
data_points = [(x, y, z) for x, y, z in zip(xs, ys, zs)]