关于#python#的问题：import matplotlib.pyplot as plt

 
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
 
def main():
    matplotlib.rcParams['font.family'] = 'SimHei'
    stuScore = np.loadtxt('student_score.csv', delimiter=',')  # 读入成绩文件,返回数组
    sumEach = np.sum(stuScore[:, 1:], axis=1)  # 返回每个学生3门课程总分
    avgEach = np.average(stuScore[:, 1:], axis=0)  # 返回每个学生每门课程平均分
    # 取出各科成绩
    mathScore = stuScore[:, 1]
    engScore = stuScore[:, 2]
    pythonScore = stuScore[:, 3]
    # Performanceanalysis(avgEach, stuScore, sumEach)
    while True:
        print("""成绩分析与可视化系统  
 1: 基本信息显示      
 2: 成绩分析          
 3: 可视化         
 4: 退出系统""")
        operation = input("请输入你的操作")
        if operation.isdigit():
            operation = int(operation)
            if operation == 1:
                print(" 学号  高数  英语  python")
                for i in stuScore:
                    print(f"{int(i[0])} {i[1]} {i[2]} {i[3]}")
            elif operation == 2:
                Performanceanalysis(avgEach, stuScore, sumEach)
            elif operation == 3:
                # name= input("请输入课程名")
                # if name=='xxx':
                # 由于不清楚你的课程名是啥,你这里自己填 if elif else结构就可以
                Highnumberhistogram(mathScore)
                Englishhistogram(engScore)
                Scorehistogram(pythonScore)
            elif operation == 4:
                import sys
                sys.exit(0)
            else:
                print("输入错误,请重新输入")
 
def Performanceanalysis(avgEach, stuScore, sumEach):
    # 返回最高分和最低分
    maxMath = np.max(stuScore[:, 1])
    maxEng = np.max(stuScore[:, 2])
    maxPython = np.max(stuScore[:, 3])
    minMath = np.max(stuScore[:, 1])
    minEng = np.max(stuScore[:, 2])
    minPython = np.max(stuScore[:, 3])
    print("个人总分情况是：")
    print(sumEach)
    print("个人平均分情况是：")
    print(avgEach)
    print("班级每门课程最高分：")
    print(maxMath, maxEng, maxPython)
    print("班级每门课程最低分：")
    print(minMath, minEng, minPython)
 
def Highnumberhistogram(mathScore):
    # 绘制高数直方图
    plt.suptitle("成绩分布直方图")
    plt.subplot(3, 1, 1)
    plt.hist(mathScore, bins=10, range=(0, 100), color='red')  # 0-100分,分成10段
    plt.xlabel("高数成绩分数段")  # 设置x轴标签
    plt.ylabel("人数")  # 设置y轴标签
    plt.xlim(0, 100)  # 设置x轴区间
    plt.xticks([0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100])  # 设置x轴刻度
    plt.yticks([0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100])  # 设置y轴刻度
    # plt.grid()
    plt.show()
 
def Englishhistogram(engScore):
    # 绘制英语直方图
    plt.subplot(3, 1, 2)
    plt.hist(engScore, bins=10, range=(0, 100), color='blue')  # 0-100分,分成10段
    plt.xlabel("英语成绩分数段")  # 设置x轴标签
    plt.ylabel("人数")  # 设置y轴标签
    plt.xlim(0, 10)  # 设置x轴区间
    plt.xticks([0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100])  # 设置x轴刻度
    plt.yticks([0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100])  # 设置y轴刻度
    # plt.grid()
    plt.show()
 
def Scorehistogram(pythonScore):
    # 绘制python直方图
    plt.suptitle("成绩分布直方图")
    plt.subplot(3, 1, 3)
    plt.hist(pythonScore, bins=10, range=(0, 100), color='green')  # 0-100分,分成10段
    plt.xlabel("Python成绩分数段")  # 设置x轴标签
    plt.ylabel("人数")  # 设置y轴标签
    plt.xlim(0, 100)  # 设置x轴区间
    plt.xticks([0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100])  # 设置x轴刻度
    plt.yticks([0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100])  # 设置y轴刻度
    # plt.grid()
    plt.show()
 
if __name__ == '__main__':
    main()

在这个python成绩后面画一个python成绩的饼图

文件发我？

import matplotlib.pyplot as plt
pythonScore=[12,34,43,34,11,67,89,24,99,93,24,43,65,73,99,34]
a=[i*10 for i in  range(11)]
pythonScore.extend(a)
pythonScore.sort()
values=[]
for i in range(10):
    b=pythonScore.index(i*10+10)-pythonScore.index(i*10)
    values.append(b)
labels=['0-10','10-20','20-30','30-40','40-50','50-60','60-70','70-80','80-90','90-100']
plt.pie(values,labels=labels,autopct="%1.1f%%",shadow=False,startangle=90)
plt.show()

import pandas as pd
df = pd.DataFrame()
pythonScore = [10,20,60,21,3,55,88,99,11,21,44,77,66,55]
df['python成绩'] = pythonScore
y=pd.cut(df['python成绩'],bins=[0,10,20,30,40,50,60,70,80,90,100],\
         labels=['0-10','10-20','20-30','30-40','40-50','50-60','60-70','70-80','80-90','90-100'])#分区间
a=y.value_counts()#统计区间人数

import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif']=['SimHei']
a.plot(kind='pie',title='python成绩统计图')

def Scorehistogram(pythonScore):
    # 绘制python直方图
    plt.suptitle("成绩分布直方图")
    plt.subplot(3, 1, 3)
    
    duan =(pythonScore // 10).astype(np.int32)
    res = np.bincount(duan)
    
    plt.subplot(1, 2, 1)
    plt.hist(pythonScore, bins=10, range=(0, 100), color='green')  # 0-100分,分成10段
    plt.xlabel("Python成绩分数段")  # 设置x轴标签
    plt.ylabel("人数")  # 设置y轴标签
    plt.xlim(0, 100)  # 设置x轴区间
    plt.xticks([0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100])  # 设置x轴刻度
    plt.yticks([0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100])  # 设置y轴刻度
    plt.grid()
    
    plt.subplot(1, 2, 2)
    plt.pie(res, labels=[str(i - 10) +'-'+ str(i) for i in range(10, 101, 10)], autopct="%.2f%%")
    plt.title("Python成绩分段饼图")
    plt.grid()
    plt.show()

def ScoreCircle(Scores):
    # 定义等级
    level = {90:0, 80:0, 70:0, 60:0, 50:0}
    for score in Scores:
        if score >= 90:
            level[90] += 1
        elif score >=80 and score < 90:
            level[80] += 1
        elif score >= 70 and score < 80:
            level[70] += 1
        elif score >= 60 and score < 70:
            level[60] += 1
        else:
            level[50] += 1

    # print([level[90], level[80], level[70], level[60], level[50]])

    labels = '优秀', '良', '中', '及格', '不及格'  # 标签
    fracs = [level[90], level[80], level[70], level[60], level[50]]  # 数值
    plt.axes(aspect=1)  # 显示为正圆
    explode = [0, 0, 0, 0, 0]  # 各数值远离圆点的距离
    plt.pie(x=fracs, labels=labels, autopct='%.0f%%',  # 显示数值，格式查阅百度
            explode=explode, shadow=True)  # 爆炸显示，有阴影
    plt.show()