你需要重复外环的颜色，每个颜色中包含的元素数。Pandas的groupby(...).count()可以用来计数，np.repeat可以用来创建一个重复的数组。
如果您同时按公司名称和销售量排序，则外圈将考虑该顺序。
由于一些主机的销量非常小，名称会重叠，使情节混乱。您可能需要过滤掉它们。

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

data = [['PlayStation 2', 'Home', 'Sony', 2000, 155000000], ['Nintendo DS', 'Handheld', 'Nintendo', 2004, 154002000], ['Nintendo Switch', 'Hybrid', 'Nintendo', 2017, 122550000], ['Game Boy', 'Handheld', 'Nintendo', 1989, 118690000], ['PlayStation 4', 'Home', 'Sony', 2013, 117200000], ['PlayStation', 'Home', 'Sony', 1994, 102490000], ['Wii', 'Home', 'Nintendo', 2006, 101630000], ['PlayStation 3', 'Home', 'Sony', 2006, 87400000], ['Xbox 360', 'Home', 'Microsoft', 2005, 84000000], ['PlayStation Portable', 'Handheld', 'Sony', 2004, 82000000], ['Game Boy Advance', 'Handheld', 'Nintendo', 2001, 81510000], ['Nintendo 3DS', 'Handheld', 'Nintendo', 2011, 75940000], ['NES', 'Home', 'Nintendo', 1983, 61910000], ['Xbox One', 'Home', 'Microsoft', 2013, 58500000], ['SNES', 'Home', 'Nintendo', 1990, 49100000], ['Nintendo 64', 'Home', 'Nintendo', 1996, 32930000], ['PlayStation 5', 'Home', 'Sony', 2020, 32100000], ['Xbox', 'Home', 'Microsoft', 2001, 24000000], ['GameCube', 'Home', 'Nintendo', 2001, 21740000], ['Xbox Series X/S', 'Home', 'Microsoft', 2020, 18500000], ['PlayStation Vita', 'Handheld', 'Sony', 2011, 15000000], ['Wii U', 'Home', 'Nintendo', 2012, 13560000], ['SNES Classic', 'Dedicated', 'Nintendo', 2017, 5280000], ['NES Classic', 'Dedicated', 'Nintendo', 2016, 3560000]]
df_3 = pd.DataFrame(data=data, columns=['Console', 'Type', 'Company', 'Release', 'Units_sold'])

df_3 = df_3[df_3['Units_sold'] > 10000000] # optionally filter away very small values
df_3_outer = df_3.sort_values(['Company', 'Units_sold'])
df_3_outer = df_3_outer[['Console', 'Units_sold']]
df_3_inner = df_3.groupby(['Company'])['Units_sold'].sum().reset_index()
df_3_counts = df_3.groupby(['Company'])['Units_sold'].count()

fig, ax = plt.subplots(figsize=(12, 9))

inner_colors = ['#156EAF', '#DB2018', '#56B45B']
outer_colors_single = ['#5599CC', '#EA6727', '#83C143']
outer_colors = np.repeat(outer_colors_single, df_3_counts)
size = 0.3
r = 1

ax.pie(df_3_outer['Units_sold'], labels=df_3_outer['Console'], radius=r, colors=outer_colors,
       wedgeprops=dict(width=size, edgecolor='w'))
ax.pie(df_3_inner['Units_sold'], labels=df_3_inner['Company'], radius=r - size, colors=inner_colors,
       labeldistance=0.6,
       textprops=dict(color='w', fontsize=15))
plt.tight_layout()
plt.show()

要重新创建这个数字，你需要先根据公司名称，然后根据销售量对数据框进行排序，你可以用以下代码来点选：df_3_outer = df_3.sort_values(by=['Company','Units_sold']).reset_index()。然后你需要将公司对应的颜色与每个控制台相关联。完成后，你可以像在代码中一样绘制饼图。
完整代码如下：

import pandas as pd
import matplotlib.pyplot as plt

fig,ax=plt.subplots(figsize=(20,20))

df_3=pd.read_fwf('data.dat',columns=['Console','Type','Company','Release','Units_sold']) #load dataset
df_3_outer = df_3.sort_values(by=['Company','Units_sold']).reset_index() #Sort by company names and then units_sold
df_3_inner = df_3.groupby(['Company'])['Units_sold'].sum().reset_index()

inner_colors = ['#156EAF', '#DB2018', '#56B45B']
outer_colors =['#5599CC', '#EA6727', '#83C143']

#assign colors to consoles
o_colors=[]
for i in range(len(df_3_outer)):
  if df_3_outer['Company'].loc[i]=='Microsoft':
    o_colors.append(outer_colors[0])
  elif df_3_outer['Company'].loc[i]=='Nintendo':
    o_colors.append(outer_colors[1])
  else:
      o_colors.append(outer_colors[2])

#plot
size = 0.8
r = 2

ax.pie(df_3_outer['Units_sold'], labels=df_3_outer['Console'],radius=r, colors=o_colors,wedgeprops=dict(width=size, edgecolor='w'))
ax.pie(df_3_inner.Units_sold, labels=df_3_inner.Company, radius=r-size, colors=inner_colors,labeldistance=0.6,textprops=dict(color="w", fontsize=15))

plt.tight_layout()
plt.show()

重复使用numpy数组：

fig, ax = plt.subplots(figsize=(24, 12))
cmap = plt.get_cmap('tab20c')

c = df.groupby('Company')['Units_sold'].count().to_numpy()
df_console = df.groupby(['Company', 'Console'])['Units_sold'].sum()
df_company = df.groupby('Company')['Units_sold'].sum()

inner_colors = ['#156EAF', '#DB2018', '#56B45B']
outer_colors =['#5599CC', '#EA6727', '#83C143']
outer_colors = np.array(outer_colors).repeat(c)

ax.pie(df_console.to_numpy().flatten(), 
       radius=1, 
       labels=df_console.index.get_level_values(1), 
       colors=outer_colors,
       wedgeprops={'edgecolor':'w'});

ax.pie(df_company.to_numpy().flatten(), 
       radius=.7, 
       labels=df_company.index, 
       colors=inner_colors, 
       labeldistance=.3,
       wedgeprops={'edgecolor':'w'});

输出：