移动 Matplotlib 1.5.1 中的现有图例 [英] Move existing legend in Matplotlib 1.5.1

问题描述

我尝试了上一个问题的答案，但无济于事Matplotlib 1.5.1.

I tried answers from a previous question to no avail in Matplotlib 1.5.1.

我有一个海洋生物:

import seaborn as sns
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
tips = sns.load_dataset("tips")
g = sns.jointplot("total_bill", "tip",  data = tips[["total_bill", "tip"]].applymap(lambda x : -np.log10(x)))

这不起作用:

g.ax_joint.legend(loc = 'lower right')

还有这个:

plt.legend(bbox_to_anchor=(1.05, 1), loc=4, borderaxespad=0.)

/usr/local/lib/python3.4/dist-packages/matplotlib/axes/_axes.py:520: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.
  warnings.warn("No labelled objects found. "

在这种情况下，如何将现有图例定位到右下方?

What is the way to locate an existing legend to lower right in this case?

目前不是一个优雅的解决方案:

Not an elegant solution for now is:

ll = g.ax_joint.get_legend().get_texts()[0]._text
g.ax_joint.get_legend().remove()
g.ax_joint.text( -12, -12, ll,  fontsize=14)

不过，我相信应该有更好的方法.

However, I believe there should be a better way.

推荐答案

没有简单的方法(使用像 'lower right' 这样的字符串)来重新定位我知道的现有图例.

There is no easy (using strings like 'lower right') way to relocate an existing legend that I am aware of.

要获取现有图例的句柄，您可以使用 legend.legendHandles().对于标签，legend.get_texts() 会给你 Text 对象.为了检索实际标签，您最好使用 .get_text() 而不是私有属性 _text.

To get the handles of an existing legend you can use legend.legendHandles(). For the labels, legend.get_texts() will give you the Text objects. In order to retrieve the actual labels you'd better use .get_text() instead of the private attribute _text.

以下内容会将现有图例的句柄和标签复制到新图例中.不会复制图例的其他属性.

The following will copy the handles and labels of an existing legend to a new one. Other properties of the legend won't be copied.

legend = g.ax_joint.get_legend()
labels = (x.get_text() for x in legend.get_texts())
g.ax_joint.legend(legend.legendHandles, labels, loc='lower right')

我之前建议使用 ax.get_legend_handles_labels() 但这将在轴中搜索，而不是图例，在这种情况下没有用.

I previously suggested using ax.get_legend_handles_labels() but this will search in the axes, not the legend, and is not useful in this case.

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