Python成绩排名解读

本文将从数据统计、数据分析、数据可视化等多个方面进行阐述Python成绩排名的相关内容。

一、数据统计

在进行数据分析之前，首先需要对数据进行统计和整理。下面是对Python成绩的一个示例数据。

students = [
    {"name": "Tom", "age": 18, "gender": "male", "score": {"Python": 80, "math": 90, "English": 70}},
    {"name": "Jack", "age": 19, "gender": "male", "score": {"Python": 90, "math": 85, "English": 80}},
    {"name": "Alice", "age": 17, "gender": "female", "score": {"Python": 95, "math": 84, "English": 88}},
    {"name": "Lily", "age": 18, "gender": "female", "score": {"Python": 83, "math": 91, "English": 85}},
    {"name": "Bob", "age": 19, "gender": "male", "score": {"Python": 78, "math": 80, "English": 75}}
]

以上数据表示了5名学生的姓名、年龄、性别和Python、数学、英语三科的成绩。

下面是对Python成绩进行统计的代码，计算Python平均分和Python成绩最高的学生。

python_scores = []
for student in students:
    python_scores.append(student['score']['Python'])
print("Python平均分:", sum(python_scores)/len(python_scores))

max_python_score = max(python_scores)
max_python_student = ''
for student in students:
    if student['score']['Python'] == max_python_score:
        max_python_student = student['name']
print("Python成绩最高的学生:", max_python_student)

以上代码的输出结果为：

Python平均分: 85.2
Python成绩最高的学生: Alice

二、数据分析

对以上数据进行统计之后，可以进行更深入的数据分析。下面是对Python成绩进行分析的一些示例代码。

1. Python成绩分布

首先可以绘制Python成绩的分布图，以便更好地了解Python成绩的分布情况。

import matplotlib.pyplot as plt

python_scores = []
for student in students:
    python_scores.append(student['score']['Python'])
    
plt.hist(python_scores, bins=5, color='steelblue', edgecolor='k', alpha=0.6)
plt.xlabel('score')
plt.ylabel('frequency')
plt.title('Python score distribution')
plt.show()

以上代码会生成一个Python成绩的分布图。

2. 不同性别Python成绩对比

然后可以对不同性别的Python成绩进行对比，以便更好地了解各性别学生的学习情况。

male_scores = []
female_scores = []
for student in students:
    if student['gender'] == 'male':
        male_scores.append(student['score']['Python'])
    elif student['gender'] == 'female':
        female_scores.append(student['score']['Python'])
        
plt.boxplot([male_scores, female_scores], labels=['male', 'female'], 
            patch_artist=True, boxprops=dict(facecolor='steelblue', color='k'), 
            showmeans=True, meanprops=dict(marker='D', markeredgecolor='black'))
plt.title('Python score comparison of different genders')
plt.show()

以上代码会生成一个不同性别Python成绩的对比图。

三、数据可视化

最后需要对数据进行可视化处理，以便更好地展示分析的结果。下面是一个基于Django框架的Web应用示例，可以更加直观地展示Python成绩排名和各项数据分析结果。

1. 安装Django

首先需要安装Django，可以使用以下命令进行安装。

pip install django

2. 编写Django应用

在Django项目中创建一个名为'score'的应用，该应用需要完成以下功能。

读取并显示Python成绩的数据表格。
显示Python成绩的平均分和成绩最高的学生。
显示不同性别Python成绩的对比图。

下面是'score'应用中的views.py代码。

from django.shortcuts import render
import matplotlib.pyplot as plt

# 示例数据
students = [
    {"name": "Tom", "age": 18, "gender": "male", "score": {"Python": 80, "math": 90, "English": 70}},
    {"name": "Jack", "age": 19, "gender": "male", "score": {"Python": 90, "math": 85, "English": 80}},
    {"name": "Alice", "age": 17, "gender": "female", "score": {"Python": 95, "math": 84, "English": 88}},
    {"name": "Lily", "age": 18, "gender": "female", "score": {"Python": 83, "math": 91, "English": 85}},
    {"name": "Bob", "age": 19, "gender": "male", "score": {"Python": 78, "math": 80, "English": 75}}
]

def index(request):
    # 统计Python成绩
    python_scores = []
    for student in students:
        python_scores.append(student['score']['Python'])
    avg_python_score = round(sum(python_scores)/len(python_scores), 2)
    
    # Python成绩最高的学生
    max_python_score = max(python_scores)
    max_python_student = ''
    for student in students:
        if student['score']['Python'] == max_python_score:
            max_python_student = student['name']
    
    # 不同性别Python成绩对比
    male_scores = []
    female_scores = []
    for student in students:
        if student['gender'] == 'male':
            male_scores.append(student['score']['Python'])
        elif student['gender'] == 'female':
            female_scores.append(student['score']['Python'])
    fig, ax = plt.subplots()
    ax.boxplot([male_scores, female_scores])
    ax.set_xticklabels(['Male', 'Female'])
    
    # 数据传递到模板
    context = {
        'python_scores': python_scores,
        'avg_python_score': avg_python_score,
        'max_python_score': max_python_score,
        'max_python_student': max_python_student,
        'gender_scores': ax
    }
    return render(request, 'score/index.html', context=context)

下面是'score'应用中的index.html模板代码。





    
    Score Dashboard
    


    
            {% for student in students %}
            
            {% endfor %}
        
        
            Name
            Age
            Gender
            Python
            Math
            English
        
        
                {{ student.name }}
                {{ student.age }}
                {{ student.gender }}
                {{ student.score.Python }}
                {{ student.score.math }}
                {{ student.score.English }}
            
    
    

    Average Python score: {{ avg_python_score }}
    Max Python score: {{ max_python_score }}, Student: {{ max_python_student }}
    
        {{ gender_scores|safe }}