如何在 Python 中的 Matplotlib 中绘制嵌套饼图？

Question

I have a problem about drawing a nested pie graph in Matplotlib in Python. I wrote some codes to handle with this process but I have an issue related with design and label

I'd like to draw a kind of this nested pie graph. (from the uppermost layer of the nested to its innermost is SEX, ALIGN with covering their counts)

Here is my dataframe which is shown below.

ALIGN   SEX count
2   Bad Characters  Male Characters 1542
5   Good Characters Male Characters 1419
3   Good Characters Female Characters   714
0   Bad Characters  Female Characters   419
8   Neutral Characters  Male Characters 254
6   Neutral Characters  Female Characters   138
1   Bad Characters  Genderless Characters   9
4   Good Characters Genderless Characters   4
7   Neutral Characters  Genderless Characters   3
9   Reformed Criminals  Male Characters 2

Here is my code snippets related with showing nested pie graph which is shown below.

fig, ax = plt.subplots(figsize=(24,12))
size = 0.3

ax.pie(dc_df_ALIGN_SEX.groupby('SEX')['count'].sum(), radius=1,
       labels=dc_df_ALIGN_SEX['SEX'].drop_duplicates(),
       autopct='%1.1f%%',
       wedgeprops=dict(width=size, edgecolor='w'))

ax.pie(dc_df_ALIGN_SEX['count'], radius=1-size, labels = dc_df_ALIGN_SEX["ALIGN"],
       wedgeprops=dict(width=size, edgecolor='w'))

ax.set(aspect="equal", title='Pie plot with `ax.pie`')
plt.show()

How can I design 4 row and 4 column and put each one in each slot and showing labels in legend area?

Answer 1

Since the question has been changed, I'm posting a new answer.

First, I slightly simplified your DataFrame:

import pandas as pd

df = pd.DataFrame([['Bad', 'Male', 1542],
                   ['Good', 'Male', 1419],
                   ['Good', 'Female', 714],
                   ['Bad', 'Female', 419],
                   ['Neutral', 'Male', 254],
                   ['Neutral', 'Female', 138], 
                   ['Bad', 'Genderless', 9], 
                   ['Good', 'Genderless', 4],
                   ['Neutral', 'Genderless', 3], 
                   ['Reformed', 'Male', 2]])
df.columns = ['ALIGN', 'SEX', 'n']

For the numbers in the outer ring, we can use a simple groupby , as you did:

outer = df.groupby('SEX').sum()

But for the numbers in the inner ring, we need to group by both categorical variables, which results in a MultiIndex:

inner = df.groupby(['SEX', 'ALIGN']).sum()
inner

                     n
SEX         ALIGN   
Female      Bad      419
            Good     714
            Neutral  138
Genderless  Bad        9
            Good       4
            Neutral    3
Male        Bad     1542
            Good    1419
            Neutral  254
            Reformed   2

We can extract the appropriate labels from the MultiIndex with its get_level_values() method:

inner_labels = inner.index.get_level_values(1)

Now you can turn the above values into one-dimensional arrays and plug them into your plot calls:

import matplotlib.pyplot as plt
import numpy as np

fig, ax = plt.subplots(figsize=(24,12))
size = 0.3

ax.pie(outer.values.flatten(), radius=1,
       labels=outer.index,
       autopct='%1.1f%%',
       wedgeprops=dict(width=size, edgecolor='w'))

ax.pie(inner.values.flatten(), radius=1-size, 
       labels = inner_labels,
       wedgeprops=dict(width=size, edgecolor='w'))

ax.set(aspect="equal", title='Pie plot with `ax.pie`')
plt.show()

Answer 2

You define the function percentage_growth(l) in a way that supposes its argument l to be a list (or some other one-dimensional object). But then (to assign colors ) you call this function on dc_df_ALIGN_SEX , which is apparently your DataFrame. So the function (in the first iteration of its loop) tries to evaluate dc_df_ALIGN_SEX[0] , which throws the key error, because that is not a proper way to index the DataFrame.

Perhaps you want to do something like percentage_growth(dc_df_ALIGN_SEX['count']) instead?