HaHafeng
decff0bb1f
Summary: - Successfully deployed complete system to Aliyun SAE (2025-12-25) - All services running: Python microservice + Node.js backend + Frontend Nginx + CLB - Public access available at http://8.140.53.236/ Major Achievements: 1. Python microservice deployed (v1.0, internal IP: 172.17.173.66:8000) 2. Node.js backend deployed (v1.3, internal IP: 172.17.173.73:3001) - Fixed 4 critical issues: bash path, config directory, pino-pretty, ES Module 3. Frontend Nginx deployed (v1.0, internal IP: 172.17.173.72:80) 4. CLB load balancer configured (public IP: 8.140.53.236) New Documentation (9 docs): - 11-Node.js backend SAE deployment config checklist (21 env vars) - 12-Node.js backend SAE deployment operation manual - 13-Node.js backend image fix record (config directory) - 14-Node.js backend pino-pretty fix - 15-Node.js backend deployment success summary - 16-Frontend Nginx deployment success summary - 17-Complete deployment practical manual 2025 edition (1800 lines) - 18-Deployment documentation usage guide - 19-Daily update quick operation manual (670 lines) Key Fixes: - Environment variable name correction: EXTRACTION_SERVICE_URL (not PYTHON_SERVICE_URL) - Dockerfile fix: added COPY config ./config - Logger configuration: conditional pino-pretty for dev only - Health check fix: ES Module compatibility (require -> import) Updated Files: - System status document updated with full deployment info - Deployment progress overview updated with latest IPs - All 3 Docker services' Dockerfiles and configs refined Verification: - All health checks passed - Tool C 7 features working correctly - Literature screening module functional - Response time < 1 second BREAKING CHANGE: Node.js backend internal IP changed from 172.17.173.71 to 172.17.173.73 Closes #deployment-milestone
103 lines
2.3 KiB
Python
103 lines
2.3 KiB
Python
"""
|
||
数值映射（重编码）操作
|
||
|
||
将分类变量的原始值映射为新值（如：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 {'男': 1, '女': 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': ['男', '女', '男', '女']})
|
||
>>> mapping = {'男': 1, '女': 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"列 '{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
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|