Python实现：从TXT文件计算学生加权平均分并生成字典

1. 定义科目分数权重 #

先明确每个科目内各分数的对应权重（按分数顺序匹配）：

subject_weights = {
    'Math': [0.05, 0.10, 0.15, 0.70],
    'Physics': [0.05, 0.10, 0.15, 0.70],
    'Chemistry': [0.05, 0.10, 0.15, 0.70],
    'Biology': [0.05, 0.10, 0.15, 0.70],
    'Literature': [0.05, 0.10, 0.10, 0.15, 0.60],
    'Language': [0.05, 0.10, 0.10, 0.15, 0.60],
    'History': [0.05, 0.10, 0.10, 0.15, 0.60],
    'Geography': [0.05, 0.10, 0.10, 0.15, 0.60]
}

2. 读取文件并计算加权平均分 #

编写函数处理TXT文件，逐行解析学生数据并计算各科加权平均分：

def calculate_weighted_averages(file_path):
    result = {}
    # 科目顺序与权重字典对应
    subjects = ['Math', 'Physics', 'Chemistry', 'Biology', 'Literature', 'Language', 'History', 'Geography']
    
    with open(file_path, 'r', encoding='utf-8') as f:
        next(f)  # 跳过表头行
        for line in f:
            line = line.strip()
            if not line:
                continue
            # 分割每行数据并去除多余空格
            parts = [p.strip() for p in line.split(';')]
            student_id = parts[0]
            student_scores = {}
            
            for idx, subject in enumerate(subjects):
                # 将分数字符串转为整数列表
                scores = list(map(int, parts[idx+1].split(',')))
                # 计算加权平均分并保留两位小数
                weighted_avg = sum(score * weight for score, weight in zip(scores, subject_weights[subject]))
                student_scores[subject] = round(weighted_avg, 2)
            
            result[f'Student {student_id}'] = student_scores
    return result

3. 调用函数并输出结果 #

替换为你的TXT文件路径后运行：

file_path = 'students_scores.txt'
final_result = calculate_weighted_averages(file_path)
print(final_result)

输出示例 #

运行后会生成符合要求的字典格式，示例片段：

{
    'Student 1': {'Math': 7.6, 'Physics': 5.85, 'Chemistry': 7.15, 'Biology': 6.15, 'Literature': 7.7, 'Language': 6.8, 'History': 7.75, 'Geography': 6.9},
    'Student 2': {'Math': 6.7, 'Physics': 4.85, 'Chemistry': 7.7, 'Biology': 4.15, 'Literature': 5.7, 'Language': 6.85, 'History': 7.15, 'Geography': 5.1}
}

内容的提问来源于stack exchange，提问作者Marcus