302 lines
12 KiB
Python
302 lines
12 KiB
Python
import tkinter as tk
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from tkinter import ttk, messagebox
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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import os
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# 定义常量
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SUBJECTS = ['语文', '数学', '英语', '物理', '化学', '生物']
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# 设置绘图字体
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plt.rcParams['font.sans-serif'] = ['SimHei'] # 这里假设Windows环境，Linux/Mac可能需要更改
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plt.rcParams['axes.unicode_minus'] = False
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class GradeSystemApp:
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def __init__(self, root):
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self.root = root
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self.root.title("学生成绩分析系统 V2.0")
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self.root.geometry("800x600")
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# 数据存储列表
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self.student_data_list = []
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self.setup_ui()
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def setup_ui(self):
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# --- 顶部：录入区域 ---
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input_frame = tk.LabelFrame(self.root, text="成绩录入", padx=10, pady=10)
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input_frame.pack(fill="x", padx=10, pady=5)
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# 学号录入
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tk.Label(input_frame, text="学号:").grid(row=0, column=0, padx=5, pady=5)
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self.entry_id = tk.Entry(input_frame, width=15)
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self.entry_id.grid(row=0, column=1, padx=5, pady=5)
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# 各科目录入框字典
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self.score_entries = {}
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row = 1
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col = 0
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for i, subj in enumerate(SUBJECTS):
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tk.Label(input_frame, text=f"{subj}:").grid(row=row, column=col * 2, padx=5, pady=5)
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entry = tk.Entry(input_frame, width=10)
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entry.grid(row=row, column=col * 2 + 1, padx=5, pady=5)
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self.score_entries[subj] = entry
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# 每行显示3个科目，换行
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col += 1
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if col > 2:
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col = 0
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row += 1
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# 按钮区域
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btn_frame = tk.Frame(input_frame)
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btn_frame.grid(row=row + 1, column=0, columnspan=6, pady=10)
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tk.Button(btn_frame, text="添加学生", command=self.add_student, bg="#e1f5fe", width=12).pack(side=tk.LEFT,
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padx=10)
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tk.Button(btn_frame, text="清空输入框", command=self.clear_entries, width=12).pack(side=tk.LEFT, padx=10)
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tk.Button(btn_frame, text="生成分析报告", command=self.generate_report, bg="#4caf50", fg="white",
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width=15).pack(side=tk.LEFT, padx=10)
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# --- 中部：数据展示区域 (Treeview) ---
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list_frame = tk.LabelFrame(self.root, text="已录入学生列表 (可选中并删除)", padx=10, pady=10)
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list_frame.pack(fill="both", expand=True, padx=10, pady=5)
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columns = ['学号'] + SUBJECTS
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self.tree = ttk.Treeview(list_frame, columns=columns, show='headings', height=10)
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# 设置表头和列宽
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for col in columns:
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self.tree.heading(col, text=col)
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self.tree.column(col, width=80, anchor='center')
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# 添加滚动条
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scrollbar = ttk.Scrollbar(list_frame, orient=tk.VERTICAL, command=self.tree.yview)
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self.tree.configure(yscroll=scrollbar.set)
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self.tree.pack(side=tk.LEFT, fill="both", expand=True)
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scrollbar.pack(side=tk.RIGHT, fill="y")
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# 绑定删除事件（双击或按钮，这里用按钮）
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del_btn = tk.Button(list_frame, text="删除选中行", command=self.delete_selected, bg="#ffcdd2")
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del_btn.pack(side=tk.BOTTOM, fill="x", pady=5)
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def add_student(self):
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stu_id = self.entry_id.get().strip()
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if not stu_id:
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messagebox.showwarning("提示", "请输入学号！")
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return
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# 检查ID是否重复
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for data in self.student_data_list:
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if data['ID'] == stu_id:
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messagebox.showerror("错误", f"学号 {stu_id} 已存在！")
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return
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# 获取分数
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row_data = {'ID': stu_id}
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display_values = [stu_id]
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try:
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for subj in SUBJECTS:
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val = self.score_entries[subj].get().strip()
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if not val:
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messagebox.showwarning("提示", f"请输入 {subj} 成绩！")
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return
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score = float(val)
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if score < 0 or score > 150: # 简单校验
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messagebox.showwarning("提示", f"{subj} 成绩不合理，请检查！")
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return
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row_data[subj] = score
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display_values.append(score)
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# 保存数据
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self.student_data_list.append(row_data)
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# 更新界面表格
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self.tree.insert('', tk.END, values=display_values)
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# 清空输入并将焦点回到学号框
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self.clear_entries()
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self.entry_id.focus_set()
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except ValueError:
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messagebox.showerror("错误", "成绩必须是数字！")
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def delete_selected(self):
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selected_item = self.tree.selection()
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if not selected_item:
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return
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for item in selected_item:
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values = self.tree.item(item, 'values')
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stu_id = values[0] # 学号是第一列
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# 从数据源删除
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self.student_data_list = [d for d in self.student_data_list if d['ID'] != stu_id]
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# 从UI删除
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self.tree.delete(item)
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def clear_entries(self):
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self.entry_id.delete(0, tk.END)
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for entry in self.score_entries.values():
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entry.delete(0, tk.END)
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def generate_report(self):
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if not self.student_data_list:
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messagebox.showinfo("提示", "当前没有数据，无法生成报告。")
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return
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try:
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# 转换为DataFrame
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df = pd.DataFrame(self.student_data_list)
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df.set_index('ID', inplace=True)
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# 确保保存报告的文件夹存在
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report_dir = "学生成绩报告单"
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if not os.path.exists(report_dir):
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os.makedirs(report_dir)
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# ========================
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# 1. 学生维度分析
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# ========================
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student_stats = df.copy()
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student_stats['总分'] = student_stats[SUBJECTS].sum(axis=1)
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student_stats['平均分'] = student_stats[SUBJECTS].mean(axis=1).round(1)
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student_stats['成绩波动(标准差)'] = student_stats[SUBJECTS].std(axis=1).round(2)
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def get_best_worst(row):
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scores = row[SUBJECTS]
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best_s = scores.idxmax()
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worst_s = scores.idxmin()
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return pd.Series([f"{best_s}({scores[best_s]})", f"{worst_s}({scores[worst_s]})"])
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student_stats[['最好科目', '最差科目']] = student_stats.apply(get_best_worst, axis=1)
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# 排名
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student_stats = student_stats.sort_values(by='总分', ascending=False)
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student_stats.insert(0, '排名', range(1, len(student_stats) + 1))
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# 导出 CSV 1
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student_stats.to_csv('1_学生总成绩排名表.csv', encoding='utf-8-sig')
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# ========================
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# 2. 科目维度分析
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# ========================
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subject_stats_list = []
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bins = range(0, 101, 10) # 0-10, ..., 90-100 (不含101，需要特殊处理)
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for subj in SUBJECTS:
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s_data = df[subj]
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excellent = (s_data >= 90).sum()
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passing = (s_data >= 60).sum()
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count = len(s_data)
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# 分布统计
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dist_str = []
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for i in range(0, 100, 10):
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if i == 90:
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# 90-100 (包含100)
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c = ((s_data >= 90) & (s_data <= 200)).sum() # 稍微放大上限容错
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dist_str.append(f"90分以上:{c}人")
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else:
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c = ((s_data >= i) & (s_data < i + 10)).sum()
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dist_str.append(f"{i}-{i + 9}:{c}人")
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stats = {
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'科目': subj,
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'最高分': s_data.max(),
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'最低分': s_data.min(),
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'平均分': round(s_data.mean(), 1),
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'优秀率': f"{excellent / count:.1%}",
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'及格率': f"{passing / count:.1%}",
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'分数分布': " | ".join(dist_str),
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'_std': s_data.std() # 内部使用，导出时删除
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}
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subject_stats_list.append(stats)
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sub_df = pd.DataFrame(subject_stats_list)
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# 导出 CSV 2
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sub_df.drop(columns=['_std']).to_csv('2_科目统计分析表.csv', index=False, encoding='utf-8-sig')
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# ========================
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# 3. 生成每个学生的图片
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# ========================
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# 准备科目平均分字典和标准差字典，用于绘图对比
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sub_avg_map = dict(zip(sub_df['科目'], sub_df['平均分']))
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sub_std_map = dict(zip(sub_df['科目'], sub_df['_std']))
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for student_id, row in df.iterrows():
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# 获取该生统计信息
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info = student_stats.loc[student_id]
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fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 12))
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fig.suptitle(f'学生成绩报告: {student_id} (第{info["排名"]}名)', fontsize=16, fontweight='bold')
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# A. 柱状图
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scores = [row[s] for s in SUBJECTS]
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avgs = [sub_avg_map[s] for s in SUBJECTS]
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x = np.arange(len(SUBJECTS))
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width = 0.35
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r1 = ax1.bar(x - width / 2, scores, width, label='个人得分', color='#4CAF50')
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r2 = ax1.bar(x + width / 2, avgs, width, label='班级平均', color='#2196F3', alpha=0.6)
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ax1.set_ylabel('分数')
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ax1.set_title('各科得分与班级平均分对比')
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ax1.set_xticks(x)
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ax1.set_xticklabels(SUBJECTS)
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ax1.legend()
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ax1.bar_label(r1, padding=3)
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ax1.bar_label(r2, padding=3, fmt='%.1f')
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# B. 异常检测与文本
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anomalies = []
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for s in SUBJECTS:
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mu = sub_avg_map[s]
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sigma = sub_std_map[s]
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val = row[s]
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if sigma > 0: # 避免除0
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if val > mu + 2 * sigma:
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anomalies.append(f"★ {s}: {val}分 (显著高于平均 {mu}，表现极其优异)")
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elif val < mu - 2 * sigma:
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anomalies.append(f"⚠ {s}: {val}分 (显著低于平均 {mu}，需重点帮扶)")
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text_c = f"--- 综合评价 ---\n"
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text_c += f"总分: {info['总分']}\n"
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text_c += f"平均分: {info['平均分']}\n"
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text_c += f"发挥稳定性(标准差): {info['成绩波动(标准差)']} (越低越稳)\n"
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text_c += f"最好科目: {info['最好科目']}\n"
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text_c += f"最差科目: {info['最差科目']}\n\n"
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text_c += f"--- 异常预警 (超出平均分±2个标准差) ---\n"
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if anomalies:
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text_c += "\n".join(anomalies)
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else:
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text_c += "各科成绩均在正常波动范围内。"
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ax2.text(0.05, 0.95, text_c, transform=ax2.transAxes, fontsize=12, verticalalignment='top',
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linespacing=1.8)
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ax2.axis('off')
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output_path = os.path.join(report_dir, f"{student_id}_分析报告.png")
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plt.tight_layout()
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plt.savefig(output_path)
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plt.close(fig)
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messagebox.showinfo("成功", f"分析完成！\n\n已生成表格文件和 {len(df)} 张学生报告图片。\n请查看程序所在目录。")
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except Exception as e:
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messagebox.showerror("运行错误", f"分析过程中发生错误:\n{str(e)}")
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if __name__ == "__main__":
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root = tk.Tk()
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app = GradeSystemApp(root)
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root.mainloop() |