feat(dc): Complete Tool C quick action buttons Phase 1-2 - 7 functions
Summary: - Implement 7 quick action functions (filter, recode, binning, conditional, dropna, compute, pivot) - Refactor to pre-written Python functions architecture (stable and secure) - Add 7 Python operations modules with full type hints - Add 7 frontend Dialog components with user-friendly UI - Fix NaN serialization issues and auto type conversion - Update all related documentation Technical Details: - Python: operations/ module (filter.py, recode.py, binning.py, conditional.py, dropna.py, compute.py, pivot.py) - Backend: QuickActionService.ts with 7 execute methods - Frontend: 7 Dialog components with complete validation - Toolbar: Enable 7 quick action buttons Status: Phase 1-2 completed, basic testing passed, ready for further testing
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extraction_service/operations/compute.py
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extraction_service/operations/compute.py
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"""
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计算列 - 预写函数
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基于公式计算新列，支持数学运算和常用函数
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"""
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import pandas as pd
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import numpy as np
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import re
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from typing import Dict, Any
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# 允许的函数（安全白名单）
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ALLOWED_FUNCTIONS = {
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'abs': abs,
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'round': round,
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'sqrt': np.sqrt,
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'log': np.log,
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'log10': np.log10,
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'exp': np.exp,
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'sin': np.sin,
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'cos': np.cos,
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'tan': np.tan,
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'floor': np.floor,
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'ceil': np.ceil,
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'min': min,
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'max': max,
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}
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def validate_formula(formula: str, available_columns: list) -> tuple[bool, str]:
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"""
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验证公式安全性和正确性
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Args:
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formula: 公式字符串
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available_columns: 可用的列名列表
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Returns:
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(is_valid, error_message)
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"""
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# 检查是否为空
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if not formula or not formula.strip():
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return False, '公式不能为空'
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# 检查危险操作
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dangerous_patterns = [
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r'__', # 双下划线（Python内部属性）
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r'import\s', # import语句
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r'exec\s', # exec函数
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r'eval\s', # eval函数
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r'open\s*\(', # 文件操作
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r'compile\s*\(', # 编译函数
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r'globals\s*\(', # 全局变量
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r'locals\s*\(', # 局部变量
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r'__builtins__', # 内置函数
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]
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for pattern in dangerous_patterns:
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if re.search(pattern, formula, re.IGNORECASE):
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return False, f'公式包含不允许的操作: {pattern}'
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# 检查是否只包含允许的字符
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allowed_chars = r'[a-zA-Z0-9_\u4e00-\u9fa5\s\+\-\*/\(\)\.,\*\*]'
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if not re.match(f'^{allowed_chars}+$', formula):
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return False, '公式包含不允许的字符'
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return True, ''
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def compute_column(
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df: pd.DataFrame,
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new_column_name: str,
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formula: str
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) -> pd.DataFrame:
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"""
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基于公式计算新列
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Args:
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df: 输入数据框
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new_column_name: 新列名称
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formula: 计算公式
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- 支持列名引用（如：身高, 体重）
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- 支持运算符（+, -, *, /, **, %）
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- 支持函数（abs, round, sqrt, log, exp等）
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Returns:
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添加了新列的数据框
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示例:
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# BMI计算
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compute_column(df, 'BMI', '体重 / (身高/100)**2')
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# 年龄平方根
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compute_column(df, '年龄_sqrt', 'sqrt(年龄)')
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# 复杂公式
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compute_column(df, '综合得分', '(FMA*0.6 + ADL*0.4) / 100')
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"""
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result = df.copy()
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print(f'计算新列: {new_column_name}')
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print(f'公式: {formula}')
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print('')
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# 验证公式
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is_valid, error_msg = validate_formula(formula, list(result.columns))
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if not is_valid:
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raise ValueError(f'公式验证失败: {error_msg}')
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# 准备执行环境
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# 1. 添加数据框的列作为变量（自动转换数值类型）
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env = {}
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for col in result.columns:
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# 尝试将列转换为数值类型
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try:
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# 如果列可以转换为数值，就转换
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numeric_col = pd.to_numeric(result[col], errors='coerce')
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# 如果转换后不全是NaN，说明是数值列
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if not numeric_col.isna().all():
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env[col] = numeric_col
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print(f' 列 "{col}" 自动转换为数值类型')
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else:
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# 否则保持原样
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env[col] = result[col]
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except Exception:
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# 转换失败，保持原样
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env[col] = result[col]
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# 2. 添加允许的函数
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env.update(ALLOWED_FUNCTIONS)
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# 3. 添加numpy（用于数学运算）
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env['np'] = np
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try:
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# 执行公式计算
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result[new_column_name] = eval(formula, {"__builtins__": {}}, env)
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print(f'计算成功！')
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print(f'新列类型: {result[new_column_name].dtype}')
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print(f'新列前5个值:')
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# 安全打印（避免NaN/inf导致序列化错误）
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for idx, val in result[new_column_name].head().items():
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if pd.isna(val):
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print(f' [{idx}] None (NaN)')
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elif np.isinf(val):
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print(f' [{idx}] None (inf)')
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else:
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print(f' [{idx}] {val}')
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print('')
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# 统计结果
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if pd.api.types.is_numeric_dtype(result[new_column_name]):
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col_data = result[new_column_name]
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# 统计缺失值和无效值
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nan_count = col_data.isna().sum()
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inf_count = np.isinf(col_data.replace([np.nan], 0)).sum()
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print(f'统计信息:')
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# 只对有效值计算统计量
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valid_data = col_data.dropna().replace([np.inf, -np.inf], np.nan).dropna()
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if len(valid_data) > 0:
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print(f' 最小值: {valid_data.min():.2f}')
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print(f' 最大值: {valid_data.max():.2f}')
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print(f' 平均值: {valid_data.mean():.2f}')
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else:
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print(f' 没有有效的数值')
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if nan_count > 0:
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print(f' 缺失值(NaN): {nan_count} 个')
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if inf_count > 0:
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print(f' 无穷大值(inf): {inf_count} 个')
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else:
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print(f'非数值类型，跳过统计')
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return result
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except NameError as e:
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# 列名不存在
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missing_col = str(e).split("'")[1]
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raise ValueError(f'列 "{missing_col}" 不存在，请检查公式中的列名')
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except ZeroDivisionError:
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raise ValueError('除零错误：公式中存在除以0的情况')
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except Exception as e:
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raise ValueError(f'计算失败: {str(e)}')
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def get_formula_examples() -> list[Dict[str, str]]:
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"""
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获取公式示例
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Returns:
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示例列表
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"""
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return [
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{
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'name': 'BMI计算',
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'formula': '体重 / (身高/100)**2',
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'description': '体重指数（需要身高(cm)和体重(kg)列）'
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},
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{
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'name': '年龄分组',
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'formula': 'round(年龄 / 10) * 10',
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'description': '按10岁为一组（20, 30, 40...）'
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},
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{
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'name': '综合得分',
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'formula': '(FMA得分 * 0.6 + ADL得分 * 0.4)',
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'description': '加权平均分'
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},
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{
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'name': '变化率',
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'formula': '(随访值 - 基线值) / 基线值 * 100',
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'description': '计算变化百分比'
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},
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{
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'name': '对数转换',
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'formula': 'log(值 + 1)',
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'description': '对数变换（处理偏态分布）'
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},
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]
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