heyong.fu
292 lines
9.0 KiB
Python
292 lines
9.0 KiB
Python
def process_student_data(student_info):
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"""
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处理学生数据，进行类型转换和验证
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参数:
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student_info: 字典，包含学生信息
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格式: {"name": "张三", "age": "20", "score": "85.5", "is_passed": "true"}
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返回:
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处理后的字典，所有值都转换为正确的Python数据类型
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如果数据无效，返回None
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要求:
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- name: 转换为字符串，去除首尾空格
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- age: 转换为整数，范围1-100
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- score: 转换为浮点数，范围0.0-100.0
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- is_passed: 转换为布尔值（"true"/"false"字符串转为True/False）
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- 如果任何转换失败或超出范围，返回None
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"""
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try:
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required_fields = ["name", "age", "score", "is_passed"]
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# 校验是否字典中都存在字段
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for field in required_fields:
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if field not in student_info:
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return None
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# 将姓名取出收尾空格
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name = str(student_info["name"]).strip()
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if not name:
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return None
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# 将年龄转为整数，并检查范围
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try:
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age = int(student_info["age"])
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if not (1 <= age <= 100):
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return None
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except (ValueError, TypeError):
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return None
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# 分数转为浮点型
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try:
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score = float(student_info["score"])
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if not (0.0 < score < 100.0):
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return None
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except (ValueError, TypeError):
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return None
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# 处理is_passed
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is_passed = str(student_info["is_passed"]).lower()
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if is_passed == "true":
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is_passed = True
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elif is_passed == "false":
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is_passed = False
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else:
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is_passed = None
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return {"name": name, "age": age, "score": score, "is_passed": is_passed}
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except Exception:
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return None
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def calculate_and_format_grades(students):
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"""
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计算学生成绩等级并格式化输出
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参数:
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students: 学生信息列表，每个元素是字典
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格式: [{"name": "张三", "score": 85.5}, ...]
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返回:
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格式化后的成绩报告字符串
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要求:
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- 根据分数计算等级：>=90为A，>=80为B，>=70为C，>=60为D，<60为F
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- 计算平均分、最高分、最低分
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- 统计各等级人数
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- 使用f-string格式化输出，保留2位小数
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- 输出格式要美观，包含表格样式
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"""
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if not students:
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return "没有学生数据"
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def get_grade(score):
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if score >= 90:
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return "A"
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elif score >= 80:
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return "B"
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elif score >= 70:
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return "C"
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elif score >= 60:
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return "D"
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else:
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return "F"
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student_with_grade = []
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# 学生添加等级
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for student in students:
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score = student["score"]
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grade = get_grade(score)
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# 字段用**来进行解包
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student_with_grade.append({**student, "grade": grade})
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# 计算平均分
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# 列表式推导
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scores = [s["score"] for s in student_with_grade]
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# 计算平均分
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average_score = sum(scores) / len(scores)
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# 最高分
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max_score = max(scores)
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# 最低分
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min_score = min(scores)
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# 统计每个等级的人数
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grade_counts = {}
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for student in student_with_grade:
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grade = student["grade"]
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grade_counts[grade] = grade_counts.get(grade, 0) + 1
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# 输出结果
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result = "成绩报告\n"
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# 分割线
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result += "=" * 42 + "\n"
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# 添加表头
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result += f"{'学生姓名':<10}{'分数':<8}{'等级':<4}\n"
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result += "=" * 42 + "\n"
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# 格式化输出每个学生的成绩
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for student in student_with_grade:
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result += (
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f"{student['name']:<10}{student['score']:<8.2f}{student['grade']:<4}\n"
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)
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# 分割线
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result += "=" * 42 + "\n"
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# 平均分
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result += f"平均分：{average_score}\n"
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# 最高分
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result += f"最高分{max_score}\n"
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# 最低分
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result += f"最低分{min_score}\n"
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# 等级分布
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grade_dist = ",".join(
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[f"{grade}:{count}人" for grade, count in grade_counts.items()]
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)
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result += f"等级分布{grade_dist}\n"
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return result
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def analyze_student_performance(students, filters=None):
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"""
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分析学生表现数据
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参数:
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students: 学生信息列表
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filters: 筛选条件字典，可选参数
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格式: {"min_score": 80, "max_score": 95, "grade": "A"}
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返回:
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分析结果字典，包含：
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{
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"total_count": 总人数,
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"filtered_count": 筛选后人数,
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"average_score": 平均分,
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"grade_distribution": 等级分布,
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"top_performers": 前3名学生,
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"pass_rate": 及格率
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}
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要求:
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- 支持按分数范围筛选
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- 支持按等级筛选
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- 计算及格率（>=60分）
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- 找出前3名高分学生
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- 处理边界情况（空数据、无效筛选条件等）
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"""
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# 如果没有筛选数据，返回默认值
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if not filters:
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return {
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"total_count": 0,
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"filtered_count": 0,
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"average_score": 0.0,
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"grade_distribution": {},
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"top_performers": [],
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"pass_rate": 0.0,
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}
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# 学生总人数
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total_count = len(students)
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# 拷贝一份数据
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filter_students = students.copy()
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# 获取当前学生等级
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def get_grade(score):
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if score >= 90:
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return "A"
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elif score >= 80:
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return "B"
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elif score >= 70:
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return "C"
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elif score >= 60:
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return "D"
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else:
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return "F"
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# 过滤大于最小分数的学生的学生数据
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if "min_score" in filters:
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filter_students = [
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s for s in filter_students if s["score"] >= filters["min_score"]
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]
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# 过滤小于最大分数分的学生数据
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if "max_score" in filters:
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filter_students = [
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s for s in filter_students if s["score"] <= filters["max_score"]
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]
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# 等级筛选
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if "grade" in filters:
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filter_grade = filters["grade"]
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filter_students = [
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s for s in filter_students if get_grade(s["score"]) == filter_grade
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]
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# 筛选后的学生数量
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filter_count = len(filter_students)
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# 计算平均分
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if filter_students:
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average_score = sum(s["score"] for s in filter_students) / filter_count
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else:
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average_score = 0.0
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# 统计筛选后的各个等级的人数
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grade_distribution = {}
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for student in filter_students:
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grade = get_grade(student["score"])
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grade_distribution[grade] = grade_distribution.get(grade, 0) + 1
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# 针对学生的分数进行排序
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sorted_students = sorted(filter_students, key=lambda x: x["score"], reverse=True)
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# 取前三名
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top_performers = sorted_students[:3]
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# 筛选出及格人数
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pass_count = sum(1 for s in filter_students if s["score"] >= 60)
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# 计算出及格率
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pass_rate = pass_count / len(filter_students) * 100 if pass_count > 0 else 0.0
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return {
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"total_count": total_count,
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"filter_count": filter_count,
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"average_score": average_score,
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"grade_distribution": grade_distribution,
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"top_performers": top_performers,
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"pass_rate": pass_rate,
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}
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# 测试数据
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test_students = [
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{"name": " 张三 ", "age": "20", "score": "85.5", "is_passed": "true"},
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{"name": "李四", "age": "19", "score": "92.0", "is_passed": "true"},
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{"name": "王五", "age": "21", "score": "78.3", "is_passed": "false"},
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{"name": "赵六", "age": "22", "score": "65.7", "is_passed": "true"},
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{"name": "钱七", "age": "20", "score": "45.2", "is_passed": "false"},
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]
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# 筛选条件测试
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test_filters = [{"min_score": 80, "max_score": 95}, {"grade": "A"}, {"min_score": 60}]
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# 格式化学生数据
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process_students = []
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for i, student in enumerate(test_students):
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processed = process_student_data(student)
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if processed:
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process_students.append(processed)
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print(f"学生{i}:{processed}")
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else:
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print(f"第{i+1}个学生数据无效")
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# 计算学生成绩与格式化
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if process_students:
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grade_report = calculate_and_format_grades(process_students)
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print(grade_report)
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# 测试数据筛选和统计分析
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print("3. 数据筛选与统计:")
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# 准备三组不同的筛选条件
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test_filters = [{"min_score": 80}, {"grade": "A"}, {"min_score": 60, "max_score": 90}]
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# 遍历筛选条件，输出分析结果
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for i, filter_condition in enumerate(test_filters, 1):
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print(f"筛选条件{i}:{filter_condition}")
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result = analyze_student_performance(process_students, filter_condition)
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print(f"总人数{result['total_count']}，筛选后的人数{result['filter_count']}")
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print(f"平均分{result['average_score']:.2f},及格率：{result['pass_rate']}")
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print(f"等级分布{result['grade_distribution']}")
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print(f"前三名{[s['name'] for s in result['top_performers']]}")
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