使用Pandas MultiIndex对matplotlib条形图中的标签进行分组

问题描述

我有一个带有MultiIndex的pandas DataFrame:

I have a pandas DataFrame with a MultiIndex:

group   subgroup    obs_1    obs_2
GroupA  Elem1       4        0
        Elem2       34       2
        Elem3       0        10
GroupB  Elem4       5        21

，依此类推.如此SO问题所述，这是实际上在matplotlib中是可行的，但我宁愿(如果可能)使用我已经知道层次结构的事实(这要归功于MultiIndex).当前正在发生的事情是索引显示为元组.

and so on. As noted in this SO question this is actually doable in matplotlib, but I'd rather (if possible) use the fact that I already know the hierarchy (thanks to the MultiIndex). Currently what's happening is that the index is shown as a tuple.

这有可能吗?

推荐答案

如果MultiIndex中只有两个级别，我相信以下内容会更容易:

If you have just two levels in the MultiIndex, I believe the following will be easier:

plt.figure()
ax = plt.gca()
DF.plot(kind='bar', ax=ax)
plt.grid(True, 'both')
minor_XT = ax.get_xaxis().get_majorticklocs()
DF['XT_V'] = minor_XT
major_XT = DF.groupby(by=DF.index.get_level_values(0)).first()['XT_V'].tolist()
DF.__delitem__('XT_V')
ax.set_xticks(minor_XT, minor=True)
ax.set_xticklabels(DF.index.get_level_values(1), minor=True)
ax.tick_params(which='major', pad=15)
_ = plt.xticks(major_XT, (DF.index.get_level_values(0)).unique(), rotation=0)

还有一些涉及，但更通用的解决方案(与您拥有多少级无关):

And a bit of involving, but more general solution (doesn't matter how many levels you have):

def cvt_MIdx_tcklab(df):
    Midx_ar = np.array(df.index.tolist())
    Blank_ar = Midx_ar.copy()
    col_idx = np.arange(Midx_ar.shape[0])
    for i in range(Midx_ar.shape[1]):
        val,idx = np.unique(Midx_ar[:, i], return_index=True)
        Blank_ar[idx, i] = val
        idx=~np.in1d(col_idx, idx)
        Blank_ar[idx, i]=''
    return map('\n'.join, np.fliplr(Blank_ar))

plt.figure()
ax = plt.gca()
DF.plot(kind='bar', ax=ax)
ax.set_xticklabels(cvt_MIdx_tcklab(DF), rotation=0)

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