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AIclinicalresearch/python-microservice/operations/recode.py
HaHafeng bdfca32305 docs(iit): REDCap对接技术方案完成与模块状态更新
- 新增《REDCap对接技术方案与实施指南》(1070行)
  - 确定DET+REST API技术方案(不使用External Module)
  - 完整RedcapAdapter/WebhookController/SyncManager代码设计
  - Day 2详细实施步骤与验收标准
- 更新《IIT Manager Agent模块当前状态与开发指南》
  - 记录REDCap本地环境部署完成(15.8.0)
  - 记录对接方案确定过程与技术决策
  - 更新Day 2工作计划(6个阶段详细清单)
  - 整体进度18%(Day 1完成+REDCap环境就绪)
- REDCap环境准备完成
  - 测试项目test0102(PID 16)创建成功
  - DET功能源码验证通过
  - 本地Docker环境稳定运行

技术方案:
- 实时触发: Data Entry Trigger (0秒延迟)
- 数据拉取: REST API exportRecords (增量同步)
- 轮询补充: pg-boss定时任务 (每30分钟)
- 可靠性: Webhook幂等性 + 轮询补充机制
2026-01-02 14:30:38 +08:00

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"""
数值映射(重编码)操作
将分类变量的原始值映射为新值男→1女→2
"""
import pandas as pd
from typing import Dict, Any, Optional
def apply_recode(
df: pd.DataFrame,
column: str,
mapping: Dict[Any, Any],
create_new_column: bool = True,
new_column_name: Optional[str] = None
) -> pd.DataFrame:
"""
应用数值映射
Args:
df: 输入数据框
column: 要重编码的列名
mapping: 映射字典,如 {'': 1, '': 2}
create_new_column: 是否创建新列True或覆盖原列False
new_column_name: 新列名create_new_column=True时使用
Returns:
重编码后的数据框
Examples:
>>> df = pd.DataFrame({'性别': ['', '', '', '']})
>>> mapping = {'': 1, '': 2}
>>> result = apply_recode(df, '性别', mapping, True, '性别_编码')
>>> result['性别_编码'].tolist()
[1, 2, 1, 2]
"""
if df.empty:
return df
# 验证列是否存在
if column not in df.columns:
raise KeyError(f"'{column}' 不存在")
if not mapping:
raise ValueError('映射字典不能为空')
# 确定目标列名
if create_new_column:
target_column = new_column_name or f'{column}_编码'
else:
target_column = column
# 创建结果数据框(避免修改原数据)
result = df.copy()
# 应用映射
result[target_column] = result[column].map(mapping)
# 统计结果
mapped_count = result[target_column].notna().sum()
unmapped_count = result[target_column].isna().sum()
total_count = len(result)
print(f'映射完成: {mapped_count} 个值成功映射')
if unmapped_count > 0:
print(f'警告: {unmapped_count} 个值未找到对应映射')
# 找出未映射的唯一值
unmapped_mask = result[target_column].isna()
unmapped_values = result.loc[unmapped_mask, column].unique()
print(f'未映射的值: {list(unmapped_values)[:10]}') # 最多显示10个
# 映射成功率
success_rate = (mapped_count / total_count * 100) if total_count > 0 else 0
print(f'映射成功率: {success_rate:.1f}%')
return result