sales_gender = current_employees.groupby("gender")['amount'].mean()

male_college = current_employees.loc[(current_employees['gender'] == 0)]

female_college = current_employees.loc[(current_employees['gender'] == 1)]

male_college = male_college.groupby("college")["gender"].count()

male_college = male_college.tolist()

female_college = female_college.groupby("college")["gender"].count()
female_college = female_college.tolist()

gender_college = male_college + female_college
gender_college

gender = ["Male","Female"]

gender_college_label = [ "Male Degree","Female No Degree", "Female Degree","Male No Degree",]

plt.figure(1)
plt.figure(figsize=(20, 8))

size = 0.3
plt.rcParams['font.size'] = 9.0
cmap = plt.colormaps["tab20c"]
outer_colors = cmap(np.arange(3)*4)
inner_colors = cmap([1, 2, 5, 6, 9, 10])

plt.pie(sales_gender, radius=1, colors=outer_colors, wedgeprops=dict(width=size, edgecolor='w'), 
    labels=gender, autopct='%1.1f%%', pctdistance=(1-size/2), textprops={'fontsize': 12})

plt.pie(gender_college, radius=1-size, colors=inner_colors, wedgeprops=dict(width=size, edgecolor='w'), 
    labels=gender_college, labeldistance=0.7, pctdistance=(1-size/2), textprops={'fontsize': 12})

plt.title('Sales Performance by Gender & Number of Gender with degrees or not',fontsize=12, fontweight='bold')

plt.legend(handles[1:], gender_college_label, loc=(0.9, 0.1))

plt.show()

字符串
这是我使用matplotlib创建嵌套饼图的代码，图像是输出

的数据
我希望这两个饼图是相互内联，使例子36和9将完全符合橙子49.6%的女性和33和8将完全符合50.4%的男性
这是可能的，而不使用任何其他库，并保持解决方案尽可能简单？