带有 Qt5Agg 后端的 matplotlib 返回空刻度标签 [英] matplotlib with Qt5Agg backend returns empty ticklabels

问题描述

在下面的代码片段中，我想在绘图的所有Y轴刻度标签上附加一个百分号:

 将matplotlib导入为mpl将熊猫作为pd导入从 matplotlib 导入 pyplot 作为 plt打印mpl .__ version__，mpl.get_backend()df = pd.DataFrame({'a': [10, 40], 'b': [20, 30]})ax = df.plot(种类='bar'，title ='百分比图')plt.draw()ax.set_yticklabels([x.get_yticklabels()中的x的'x.get_text()+'％')ax.get_figure().savefig('test.png', bbox_inches='tight')

在 python 2.7.13 + matplotlib 1.5.3 中，使用后端 Qt5Agg，ax.get_yticklabels() 返回空的 Text 对象列表，结果如下输出图片:

以上代码段在python 2.6.9 + matplotlib 1.4.2 + Qt4Agg后端以及python 2.6.6 + matplotlib 1.3.1 + TkAgg后端下均能正常工作.

可能与:

无需绘制任何东西，该解决方案也独立于后端.

In the following code snippet, I want to append a percentage sign to all Y axis tick labels of a plot:

import matplotlib as mpl
import pandas as pd
from matplotlib import pyplot as plt
print mpl.__version__, mpl.get_backend()

df = pd.DataFrame({'a': [10, 40], 'b': [20, 30]})
ax = df.plot(kind='bar', title='Plot of Percentage')
plt.draw()
ax.set_yticklabels([x.get_text() + '%' for x in ax.get_yticklabels()])
ax.get_figure().savefig('test.png', bbox_inches='tight')

In python 2.7.13 + matplotlib 1.5.3, using backend Qt5Agg, ax.get_yticklabels() returns a list of empty Text objects, resulting in the following output image:

The above code snippet works correctly under python 2.6.9 + matplotlib 1.4.2 + Qt4Agg backend and under python 2.6.6 + matplotlib 1.3.1 + TkAgg backend.

Possibly related to: https://github.com/matplotlib/matplotlib/issues/6103/

解决方案

The ticklabels are not actually filled until the canvas is completely drawn. Since the ticklabels are determined by a matplotlib.ticker.***Formatter, the best solution is of course not trying to change the ticklabels themselves but to use a convenient Formatter.

Here a FuncFormatter seems a good choice.

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker

df = pd.DataFrame({'a': [10, 40], 'b': [20, 30]})
ax = df.plot(kind='bar', title='Plot of Percentage')

func = lambda x, pos: "{} %".format(x)
ax.yaxis.set_major_formatter(matplotlib.ticker.FuncFormatter(func))
plt.show()

There is no need to draw anything and this solution is also independent on backends.

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