使用bokeh或matplotlib的Pandas DataFrame中的分层饼图/甜甜圈图

问题描述：

我有以下pandas DataFrame("A"是最后一列的标题；其余的列是组合的层次结构索引):

I have the following pandas DataFrame ("A" is the last column's header; the rest of columns are a combined hierarchical index):

    A
kingdom      phylum            class             order                family                        genus              species             
No blast hit                                                                                                                           2496
k__Archaea   p__Euryarchaeota  c__Thermoplasmata o__E2                f__[Methanomassiliicoccaceae] g__vadinCA11       s__                6
k__Bacteria  p__               c__               o__                  f__                           g__                s__                5
             p__Actinobacteria c__Acidimicrobiia o__Acidimicrobiales  f__                           g__                s__                0
                               c__Actinobacteria o__Actinomycetales   f__Corynebacteriaceae         g__Corynebacterium s__stationis       2
                                                                      f__Micrococcaceae             g__Arthrobacter    s__                8
                                                 o__Bifidobacteriales f__Bifidobacteriaceae         g__Bifidobacterium s__              506
                                                                                                                       s__animalis       48
                               c__Coriobacteriia o__Coriobacteriales  f__Coriobacteriaceae          g__                s__              734
                                                                                                    g__Collinsella     s__aerofaciens     3

(带有CSV数据的此处)

我想在饼图/甜甜圈图中作图，其中每个同心圆都是一个级别(王国，门等)，并根据该级别的A列总和进行划分，所以我以类似的结尾为此，但有我的数据:

I want to plot in a pie/donut chart , where each concentric circle is a level (kingdom, phylum, etc.) and is divided according to the sum of the column A for that level, so I end with something similar to this, but with my data:

磁盘使用情况图表

我研究了matplotlib和bokeh，但是到目前为止，我发现的最相似的东西是使用不推荐使用的图表的bokeh Donut图表示例，我不知道如何推断两个以上的水平.

I've looked into matplotlib and bokeh, but the most similar thing I've found so far is the bokeh Donut chart example, using a deprecated chart, which I don't know how to extrapolate for more than 2 levels.

答

我不知道是否有任何预定义的方法可以执行此操作，但是可以使用groupby和重叠的饼图构造自己的图形.我构建了以下脚本来获取您的数据并获得至少与您指定的内容类似的内容.

I don't know if there is anything pre-defined that does this, but it's possible to construct your own using groupby and overlapping pie plots. I constructed the following script to take your data and get something at least similar to what you specified.

请注意，groupby调用(用于计算每个级别的总数)必须已关闭排序功能，才能正确排列内容.您的数据集也非常不均匀，因此为了说明起见，我只是制作了一些随机数据以稍微分散结果图表.

Note that the groupby calls (which are used to calculate the totals at each level) must have sorting turned off for things to line up correctly. Your dataset is also very non-uniform, so I just made some random data to spread out the resulting chart a bit for the sake of illustration.

您可能需要调整颜色和标签位置，但这可能是一个开始.

You'll probably have to tweak colors and label positions, but it may be a start.

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

df = pd.read_csv('species.csv')
df = df.dropna() # Drop the "no hits" line
df['A'] = np.random.rand(len(df)) * 100 + 1

# Do the summing to get the values for each layer
def nested_pie(df):

    cols = df.columns.tolist()
    outd = {}
    gb = df.groupby(cols[0], sort=False).sum()
    outd[0] = {'names':gb.index.values, 'values':gb.values}
    for lev in range(1,7):
        gb = df.groupby(cols[:(lev+1)], sort=False).sum()
        outd[lev] = {'names':gb.index.levels[lev][gb.index.labels[lev]].tolist(),
                     'values':gb.values}
    return outd

outd = nested_pie(df)
diff = 1/7.0

# This first pie chart fill the plot, it's the lowest level
plt.pie(outd[6]['values'], labels=outd[6]['names'], labeldistance=0.9,
        colors=plt.style.library['bmh']['axes.color_cycle'])
ax = plt.gca()
# For each successive plot, change the max radius so that they overlay
for i in np.arange(5,-1,-1):
    ax.pie(outd[i]['values'], labels=outd[i]['names'], 
           radius=np.float(i+1)/7.0, labeldistance=((2*(i+1)-1)/14.0)/((i+1)/7.0),
           colors=plt.style.library['bmh']['axes.color_cycle'])
ax.set_aspect('equal')

从调用到random()的模块略有变化，这将产生如下图:

Modulo slight changes from the call to random(), this yields a plot like this:

在您的真实数据上，它看起来像这样:

On your real data it looks like this:

使用bokeh或matplotlib的Pandas DataFrame中的分层饼图/甜甜圈图

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