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@nabenabe0928 Could you review this PR?

The PR description could be better by the following revisions:

Describe how they look differently

In my eyes, the difference was not that obvious.
If I understand the difference correctly, each plot includes only a part of categories, making the dots only on the left side or the bottom of each plot in matplotlib.

Claim which figure is for matplotlib

In the first figure, it is not obvious which figure shows the result obtained by matplotlib, so it would be much more friendly with the guide.

Add a code block for smoother PR reviews

It is not that hard to add a code block, but it significantly makes reviews easier.

import optuna
import matplotlib.pyplot as plt


def objective(trial: optuna.Trial) -> float:
    category = trial.suggest_categorical("category", ["foo", "bar"])
    if category == "foo":
        return (trial.suggest_float("x1", 0, 10) - 2) ** 2
    else:
        return -((trial.suggest_float("x2", -10, 0) + 5) ** 2)


study = optuna.create_study()
study.optimize(objective, n_trials=50)
fig = optuna.visualization.plot_contour(study)
fig.show()
optuna.visualization.matplotlib.plot_contour(study)
plt.show()

I fixed comments.
Could you please review it?

@fusawa-yugo Could you add a unittest for this change?

@HideakiImamura
I discussed with @c-bata, and I think that translating the expected inputs and outputs of this private function into unit tests is not straightforward in this case.
Therefore, I feel if it might be reasonable to omit adding unit tests this time.