HaHafeng
691dc2bc98
Summary: - Created Node.js backend Docker image build guide - Updated deployment progress overview with backend status - Updated system status documentation Backend build achievements: - Fixed 200+ TypeScript compilation errors (200+ to 0) - Completed Prisma reverse sync (32 models from RDS) - Manually added 30+ Prisma relation fields - Successfully built Docker image (838MB) - Pushed image to ACR (v1.0 + latest tags) Documentation updates: - Added 10-Node.js后端-Docker镜像构建手册.md - Updated 00-部署进度总览.md with backend deployment status - Updated 00-系统当前状态与开发指南.md with latest progress - Fixed date format (2024 -> 2025) Next steps: - Deploy Node.js backend to SAE - Configure environment variables - Test end-to-end functionality Status: Backend Docker image ready for SAE deployment
102 lines
2.3 KiB
Python
102 lines
2.3 KiB
Python
"""
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数值映射（重编码）操作
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将分类变量的原始值映射为新值（如：男→1，女→2）。
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"""
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import pandas as pd
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from typing import Dict, Any, Optional
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def apply_recode(
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df: pd.DataFrame,
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column: str,
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mapping: Dict[Any, Any],
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create_new_column: bool = True,
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new_column_name: Optional[str] = None
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) -> pd.DataFrame:
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"""
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应用数值映射
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Args:
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df: 输入数据框
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column: 要重编码的列名
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mapping: 映射字典，如 {'男': 1, '女': 2}
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create_new_column: 是否创建新列（True）或覆盖原列（False）
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new_column_name: 新列名（create_new_column=True时使用）
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Returns:
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重编码后的数据框
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Examples:
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>>> df = pd.DataFrame({'性别': ['男', '女', '男', '女']})
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>>> mapping = {'男': 1, '女': 2}
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>>> result = apply_recode(df, '性别', mapping, True, '性别_编码')
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>>> result['性别_编码'].tolist()
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[1, 2, 1, 2]
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"""
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if df.empty:
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return df
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# 验证列是否存在
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if column not in df.columns:
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raise KeyError(f"列 '{column}' 不存在")
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if not mapping:
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raise ValueError('映射字典不能为空')
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# 确定目标列名
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if create_new_column:
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target_column = new_column_name or f'{column}_编码'
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else:
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target_column = column
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# 创建结果数据框（避免修改原数据）
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result = df.copy()
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# 应用映射
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result[target_column] = result[column].map(mapping)
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# 统计结果
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mapped_count = result[target_column].notna().sum()
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unmapped_count = result[target_column].isna().sum()
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total_count = len(result)
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print(f'映射完成: {mapped_count} 个值成功映射')
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if unmapped_count > 0:
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print(f'警告: {unmapped_count} 个值未找到对应映射')
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# 找出未映射的唯一值
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unmapped_mask = result[target_column].isna()
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unmapped_values = result.loc[unmapped_mask, column].unique()
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print(f'未映射的值: {list(unmapped_values)[:10]}') # 最多显示10个
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# 映射成功率
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success_rate = (mapped_count / total_count * 100) if total_count > 0 else 0
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print(f'映射成功率: {success_rate:.1f}%')
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return result
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