Matplotlib 3.0 Cookbook

How to do it...

Here are the steps to plot the required plots:

1. Plot a linear regression plot, with R² and the p-value annotation:

R, p = pearsonr(wine_quality['fixed acidity'], wine_quality.pH)g1 = sns.regplot(x='fixed acidity', y='pH', data=wine_quality,                  truncate=True, ci=99,                 marker='D', scatter_kws={'color': 'r'});textstr =  '$\mathrm{pearson}\hspace{0.5}\mathrm{R}^2=%.2f$\n$\mathrm{pval}=%.    2e$' % (R**2, p)props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)g1.text(0.55, 0.95, textstr, transform=ax.transAxes, fontsize=14,         va='top', bbox=props)plt.title('1. Linear Regression', size=15, color='g', weight='bold')

2. Plot a polynomial regression curve of order 2:

g2 = sns.regplot(x='fixed acidity', y='pH', data=wine_quality,  order=2, ...