图例仅在与 pandas 一起绘图时显示一个标签 [英] Legend only shows one label when plotting with pandas

问题描述

我有两个希望以单个图形绘制的Pandas DataFrame.我正在使用IPython笔记本.

I have two Pandas DataFrames that I'm hoping to plot in single figure. I'm using IPython notebook.

我希望图例显示两个DataFrame的标签，但是到目前为止，我只能显示后一个.同样，关于如何以更明智的方式编写代码的任何建议也将受到赞赏.我是这一切的新手，并不真正了解面向对象的绘图.

I would like the legend to show the label for both of the DataFrames, but so far I've been able to get only the latter one to show. Also any suggestions as to how to go about writing the code in a more sensible way would be appreciated. I'm new to all this and don't really understand object oriented plotting.

%pylab inline
import pandas as pd

#creating data

prng = pd.period_range('1/1/2011', '1/1/2012', freq='M')
var=pd.DataFrame(randn(len(prng)),index=prng,columns=['total'])
shares=pd.DataFrame(randn(len(prng)),index=index,columns=['average'])

#plotting

ax=var.total.plot(label='Variance')
ax=shares.average.plot(secondary_y=True,label='Average Age')
ax.left_ax.set_ylabel('Variance of log wages')
ax.right_ax.set_ylabel('Average age')
plt.legend(loc='upper center')
plt.title('Wage Variance and Mean Age')
plt.show()

推荐答案

这确实有点令人困惑.我认为可以归结为Matplotlib如何处理辅助轴.熊猫可能将ax.twinx()称为将辅助轴叠加在第一个轴上的某个地方，但这实际上是一个单独的轴.因此，也请使用单独的行&标签和单独的图例.调用plt.legend()仅适用于一个轴(活动轴)，在您的示例中为第二个轴.

This is indeed a bit confusing. I think it boils down to how Matplotlib handles the secondary axes. Pandas probably calls ax.twinx() somewhere which superimposes a secondary axes on the first one, but this is actually a separate axes. Therefore also with separate lines & labels and a separate legend. Calling plt.legend() only applies to one of the axes (the active one) which in your example is the second axes.

幸运的是，熊猫确实存储了两个轴，因此您可以从两个轴中捕获所有线对象，然后将它们自己传递给.legend()命令.给定您的示例数据:

Pandas fortunately does store both axes, so you can grab all line objects from both of them and pass them to the .legend() command yourself. Given your example data:

您可以完全按照自己的方式进行绘制:

You can plot exactly as you did:

ax = var.total.plot(label='Variance')
ax = shares.average.plot(secondary_y=True, label='Average Age')

ax.set_ylabel('Variance of log wages')
ax.right_ax.set_ylabel('Average age')

两个轴对象都可以使用ax(左轴)和ax.right_ax使用，因此您可以从中获取线对象. Matplotlib的.get_lines()返回一个列表，因此您可以通过简单的加法合并它们.

Both axes objects are available with ax (left axe) and ax.right_ax, so you can grab the line objects from them. Matplotlib's .get_lines() return a list so you can merge them by simple addition.

lines = ax.get_lines() + ax.right_ax.get_lines()

行对象具有label属性，可用于读取标签并将标签传递给.legend()命令.

The line objects have a label property which can be used to read and pass the label to the .legend() command.

ax.legend(lines, [l.get_label() for l in lines], loc='upper center')

其余图:

ax.set_title('Wage Variance and Mean Age')
plt.show()

如果您更严格地分开Pandas(数据)和Matplotlib(绘图)部分，可能会减少混乱，因此避免使用Pandas内置绘图(无论如何都只包装Matplotlib):

It might be less confusing if you separate the Pandas (data) and the Matplotlib (plotting) parts more strictly, so avoid using the Pandas build-in plotting (which only wraps Matplotlib anyway):

fig, ax = plt.subplots()

ax.plot(var.index.to_datetime(), var.total, 'b', label='Variance')
ax.set_ylabel('Variance of log wages')

ax2 = ax.twinx()
ax2.plot(shares.index.to_datetime(), shares.average, 'g' , label='Average Age')
ax2.set_ylabel('Average age')

lines = ax.get_lines() + ax2.get_lines()
ax.legend(lines, [line.get_label() for line in lines], loc='upper center')

ax.set_title('Wage Variance and Mean Age')
plt.show()

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