Plotting guidelines

Here we recommend the input arguments, return value, and behavior of the plot method of a class.


  1. Convenience to interactive users. This is the highest priority. Compared to being called in a batch script as a library (for composing more complicated plots or other purposes), the plot method is mainly used in interactive sessions like ipython, jupyter, colab, PyCharm, and python interpreter.
  2. Plot is customizable. The plot should be customizable by the user after plot returns. This is important because user may need to change the look for presentation, paper, or just the style they prefer. One plot style does not fit all.
  3. No unnecessary messages in interactive sessions. It should not produce any warning/error messages in normal usages in an interactive sessions. See #1890 for an example of such message.
  4. No popups during tests. It should not produce any pop-up windows during tests.


The plot method must produce a plot when there is no arguments in an interactive session. The recommended way to achieve that is illustrated in the example below.

from typing import Any, List, Optional
import matplotlib.pyplot as plt

class Foo:
    def plot(self, ax: Optional[plt.Axes]=None, **plot_kwargs: Any) -> plt.Axes:
        show_plot = not ax
        if not ax:
            fig, ax = plt.subplots(1, 1)  # or your favorite figure setup
        # Call methods of the ax instance like ax.plot to plot on it.
        if show_plot:
        return ax

This plot method works in 2 modes: memory mode and interactive mode, signalled by the presence of the ax argument. When present, the method is instructed to plot on the provided ax instance in memory. No plot is shown on the screen. When absent, the code is in interactive mode, and it creates a figure and shows it.

The returned ax instance can be used to further customize the plot if the user wants to. Note that if we were to call instead of, the customizations on the returned ax does not show up on subsequent call to

To satisfy requirement number 4, unit test codes should create an ax object and pass it into the plot method like the following example.

def test_foo_plot():
    # make a Foo instance foo
    figure, ax = plt.subplots(1, 1)
    # assert on the content of ax here if necessary.

This does not produce a pop-up window because is not called.

Classes that produce multi-axes plot

Some classes contain complicated data and plotting on a single ax is not sufficient. The plot method of such a class should take an optional axes argument that is a list of plt.Axes instances.

class Foo:
    def plot(self, axes: Optional[List[plt.Axes]]=None,
             **plot_kwargs: Any) -> List[plt.Axes]:
        show_plot = not axes
        if not axes:
            fig, axes = plt.subplots(1, 2)  # or your favorite figure setup
        elif len(axes) != 2:  # your required number of axes
            raise ValueError('your error message')
        # Call methods of the axes[i] objects to plot on it.
        if show_plot:
        return axes

The reason we don't recommend passing a plt.Figure argument is that, the plot method has no information on which plt.Axes objects to plot on if there are more plt.Axes in the figure than what the method needs. The caller is responsible for passing in correct number of Axes instances.

The plot method can be tested similarly.

PyCharm issue

As of this writing in October 2019, running a script calling a plot method in PyCharm does not pop up a window with the figure. A call to is needed to show it. We believe this is a PyCharm-specific issue because the same code works in Python interpreter.


  • Issue #1890 "Plotting code should not call show"
  • PR #2097
  • PR #2286