Hands-On Data Analysis with Pandas

Annotations

We will often find the need to annotate specific points in our visualizations either to point out events, such as the days on which Facebook's stock price dropped due to certain news stories breaking or labeling values that are important for comparisons. For example, let's use the plt.annotate() function to label the support and resistance:

>>> ax = fb.close.plot(title='FB Closing Price 2018', figsize=(15, 3))>>> ax.axhspan(support, resistance, alpha=0.2)>>> plt.annotate(...     f'support\n(${support:,.2f})',...     xy=('2018-12-31', support),...     xytext=('2019-01-21', support),...     arrowprops={'arrowstyle' : '->'}... )>>> plt.annotate(...     f'resistance\n(${resistance:,.2f})',...     xy=('2018-12-23', resistance)... )>>> plt.ylabel('price ($)') ...