# Summary

## Summary

In this chapter, you learned how to create plots using Python and Matplotlib. You learned what Matplotlib is and why problem solvers should learn how to use Matplotlib. Matplotlib installation was shown at the start of the chapter. Then, you learned how to build line plots and save plots as image files. You learned how to customize plots by including axis label, titles, and legends on your plots. You also learned how to add annotations to plots.

The types of plots detailed in this chapter are shown in the table below.

Chart Type Matplotlib method
line plot ax.plot(x,y)
multi-line plot ax.plot(x,y) ax.plot(x,z)
bar graph ax.bar(x_pos, heights)
pie chart ax.pie(sizes, labels=[labels])
bar graphs with error bars ax.bar(x_pos, heights, yerr=[error])
line plot with error bars ax.errorbar(x, y, xerr= , yerr= )
histogram ax.hist(data, n_bins)
box plot ax.boxplot([data list])
violin plot ax.violinplot([data list])
scatter plot ax.scatter(x_points, y_points)
plot annotations ax.annotate('text',xy=loc,xy_coords= )
subplots fig, (ax1,ax2,ax3) = plt.subplots(1,3)
plot styles plt.style.use('style')
2D contour plot ax.contour(X, Y, Z)
2D filled contour plot ax.contourf(X, Y, Z)
color bars fig.colorbar(cf, ax=ax)
color maps mycmap = plt.get_cmap('map')
quiver plot ax.quiver(x_pos, y_pos, x_dir, y_dir)
stream plot ax.streamplot(x,y,x_d,y_d, density= )
3D surface plot ax.plot_surface(X, Y, Z)
3D wireframe plot ax.plot_wireframe(X,Y,Z)

### Key Terms and Concepts

plot

dpi

invoke

library

parameters

RGBA

object

attribute

object-oriented programming

method

image resolution

error bars

box plot

violin plot

histogram

annotation

reference frame

contour plot

quiver plot

stream plot

field

wire frame plot

projection