是否可以分层标记matplotlib(pyplot)条形图? [英] Is it possible to hierarchically label a matplotlib (pyplot) bar plot?

问题描述

我设法使用matplotlib.pyplot制作了以下条形图.

I've managed to make the following bar plot using matplotlib.pyplot.

该图来自汇总的PANDAS DataFrame，如下所示.请注意，条形图中的每个条形对应于mean列中的值.还请注意，值不为零，但当浮点非常小时，PANDAS将输出0和-0.

The plot comes from an aggregated PANDAS DataFrame, printed below. Note that each bar in the bar-plot corresponds to a value in the mean column. Also note that the values are not zero, but that PANDAS outputs 0 and -0 when floats are very small.

Group Local Global Attn  mean
ASub  LD    GD     Attn    -0
                   Dist    -0
            GS     Attn    -0
                   Dist    -0
      LS    GD     Attn    -0
                   Dist    -0
            GS     Attn    -0
                   Dist    -0
DSub  LD    GD     Attn    -0
                   Dist     0
            GS     Attn    -0
                   Dist    -0
      LS    GD     Attn    -0
                   Dist    -0
            GS     Attn    -0
                   Dist    -0

我想以与上述标签相对应的方式对条形图的x轴进行分层标记.换句话说，x轴的左半部分对应于ASub组. ASub组的左半部分对应于Local因子的LD级别，依此类推...

I would like to label the x-axis of my bar plot hierarchically, in a manner corresponding to the labels above. In other words, the left half of the x-axis corresponds to the ASub group. The left half of the ASub group corresponds to the LD level of the Local factor, and so on...

可以做到吗?

我想我可能应该确切说明我想要什么.我希望有几个标签，从最一般的(组)到最具体的(Attn)，类似于上面DataFrame左4列中的组织.

I think I should probably clarify exactly what I want. I'd like for there to be several labels, progressing from most general (Group) to most specific (Attn), similar to the organization on the left 4 columns of the DataFrame above.

推荐答案

在主机上已实现(我将随机数据用于均值")，请参见底部的图像. 如果您不想升级，也可以手动设置:

On master this is implemented (i used random data for 'mean'), see image at the bottom. If you prefer not to upgrade you can also set it manually:

In [143]: ax = df.plot(kind='bar')

In [144]: ax.set_xticklabels(['|'.join(t) for t in df.index])

 In [167]: ax = df.plot(kind='bar')

 In [168]: ax.set_xticklabels(df.index.format(names=False))

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