Plotting with matplotlib

General

1. Import matplotlib.pyplot using the standard convention in the Python community.

Line Plots

1. State the procedure to generate a line plot approximation to a function’s graph on a given interval using numpy and matplotlib.
2. Generate a line plot approximation to a provided function using numpy and matplotlib.

Interactive Plots

1. Use the magic command %matplotlib to toggle static in-line, interactive in-line, and pop-out figures in Jupyter notebooks.

Modifying and Decorating Plots

1. Modify the horizontal and vertical limits on a matplotlib figure using the plt.*lim() functions.
2. Add horizontal and vertical labels to a matplotlib figure using the plt.*label() functions.
3. Add a legend to a matplotlib figure using the plt.legend() function.

Saving Plots

1. Save a matplotlib figure using plt.savefig().

Programmatically Constructing Plots

1. Generate a specified matplotlib figure programmatically using programming concepts from earlier in the course (e.g. conditional statements, loops, formatted strings, etc.).
2. Use np.zeros() and np.ones() to generate accumulator arrays for sums and products, respectively.
3. Programmatically generate figures with the graphs of Taylor polynomials for a given function.

Scatter Plots

1. Generate scatter plots using matplotlib.