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# Scatter Plot
plt.scatter(wines['sulphates'], wines['alcohol'],
            alpha=0.4, edgecolors='w')

plt.xlabel('Sulphates')
plt.ylabel('Alcohol')
plt.title('Wine Sulphates - Alcohol Content',y=1.05)


# Joint Plot

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# Using subplots or facets along with Bar Plots
fig = plt.figure(figsize = (10, 4))
title = fig.suptitle("Wine Type - Quality", fontsize=14)
fig.subplots_adjust(top=0.85, wspace=0.3)
# red wine - wine quality
ax1 = fig.add_subplot(1,2, 1)
ax1.set_title("Red Wine")
ax1.set_xlabel("Quality")
ax1.set_ylabel("Frequency") 
rw_q = red_wine['quality'].value_counts()

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# Multi-bar Plot
cp = sns.countplot(x="quality", hue="wine_type", data=wines, 
                   palette={"red": "#FF9999", "white": "#FFE888"})

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# facets with histograms
fig = plt.figure(figsize = (10,4))
title = fig.suptitle("Sulphates Content in Wine", fontsize=14)
fig.subplots_adjust(top=0.85, wspace=0.3)

ax1 = fig.add_subplot(1,2, 1)
ax1.set_title("Red Wine")
ax1.set_xlabel("Sulphates")
ax1.set_ylabel("Frequency") 
ax1.set_ylim([0, 1200])

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# Using multiple Histograms 
fig = plt.figure(figsize = (6, 4))
title = fig.suptitle("Sulphates Content in Wine", fontsize=14)
fig.subplots_adjust(top=0.85, wspace=0.3)
ax = fig.add_subplot(1,1, 1)
ax.set_xlabel("Sulphates")
ax.set_ylabel("Frequency") 

g = sns.FacetGrid(wines, hue='wine_type', palette={"red": "r", "white": "y"})
g.map(sns.distplot, 'sulphates', kde=False, bins=15, ax=ax)

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# Box Plots
f, (ax) = plt.subplots(1, 1, figsize=(12, 4))
f.suptitle('Wine Quality - Alcohol Content', fontsize=14)

sns.boxplot(x="quality", y="alcohol", data=wines,  ax=ax)
ax.set_xlabel("Wine Quality",size = 12,alpha=0.8)
ax.set_ylabel("Wine Alcohol %",size = 12,alpha=0.8)

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# Violin Plots
f, (ax) = plt.subplots(1, 1, figsize=(12, 4))
f.suptitle('Wine Quality - Sulphates Content', fontsize=14)

sns.violinplot(x="quality", y="sulphates", data=wines,  ax=ax)
ax.set_xlabel("Wine Quality",size = 12,alpha=0.8)
ax.set_ylabel("Wine Sulphates",size = 12,alpha=0.8)

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# Scatter Plot with Hue for visualizing data in 3-D
cols = ['density', 'residual sugar', 'total sulfur dioxide', 'fixed acidity', 'wine_type']
pp = sns.pairplot(wines[cols], hue='wine_type', size=1.8, aspect=1.8, 
                  palette={"red": "#FF9999", "white": "#FFE888"},
                  plot_kws=dict(edgecolor="black", linewidth=0.5))
fig = pp.fig 
fig.subplots_adjust(top=0.93, wspace=0.3)
t = fig.suptitle('Wine Attributes Pairwise Plots', fontsize=14)

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# Visualizing 3-D numeric data with Scatter Plots
# length, breadth and depth
fig = plt.figure(figsize=(8, 6))
ax = fig.add_subplot(111, projection='3d')

xs = wines['residual sugar']
ys = wines['fixed acidity']
zs = wines['alcohol']
ax.scatter(xs, ys, zs, s=50, alpha=0.6, edgecolors='w')

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# Visualizing 3-D numeric data with a bubble chart
# length, breadth and size
plt.scatter(wines['fixed acidity'], wines['alcohol'], s=wines['residual sugar']*25, 
            alpha=0.4, edgecolors='w')

plt.xlabel('Fixed Acidity')
plt.ylabel('Alcohol')
plt.title('Wine Alcohol Content - Fixed Acidity - Residual Sugar',y=1.05)