使用 bokeh 或 matplotlib 来自 Pandas DataFrame 的分层饼图/甜甜圈图
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Hierarchic pie/donut chart from Pandas DataFrame using bokeh or matplotlib
提问by Adrià Cereto i Massagué
I have the following pandas DataFrame ("A" is the last column's header; the rest of columns are a combined hierarchical index):
我有以下 Pandas DataFrame（“A”是最后一列的标题；其余列是组合的分层索引）：
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
(a CSV with the data is available here)
（此处提供包含数据的 CSV 文件）
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:
我想绘制饼图/甜甜圈图，其中每个同心圆是一个级别（界、门等），并根据该级别 A 列的总和进行划分，因此我以类似的内容结束，但我的数据：
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.
我已经研究过 matplotlib 和 bokeh，但到目前为止我发现的最相似的东西是 bokeh Donut 图示例，使用的是已弃用的图表，我不知道如何推断超过 2 个级别。
回答by Ajean
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.
我不知道是否有任何预定义的东西可以做到这一点，但可以使用 groupby 和重叠饼图构建自己的。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相似的内容。
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.
请注意，groupby 调用（用于计算每个级别的总数）必须关闭排序，以便正确排列。你的数据集也非常不均匀，所以为了说明起见，我只是制作了一些随机数据来分散生成的图表。
You'll probably have to tweak colors and label positions, but it may be a start.
您可能需要调整颜色和标签位置，但这可能是一个开始。
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')
Modulo slight changes from the call to random(), this yields a plot like this:
从调用到 的模数略有变化random()，这会产生如下图：
On your real data it looks like this:
在您的真实数据上，它看起来像这样：
