HaHafeng
f4f1d09837
Major features: 1. Pivot transformation enhancements: - Add option to keep unselected columns with 3 aggregation methods - Maintain original column order after pivot (aligned with source file) - Preserve pivot value order (first appearance order) 2. NA handling across 4 core functions: - Recode: Support keep/map/drop for NA values - Filter: Already supports is_null/not_null operators - Binning: Support keep/label/assign for NA values (fix nan display) - Conditional: Add is_null/not_null operators 3. UI improvements: - Enable column header tooltips with custom header component - Add closeable alert for 50-row preview - Fix page scrollbar issues Modified files: Python: pivot.py, recode.py, binning.py, conditional.py, main.py Backend: SessionController, QuickActionController, QuickActionService Frontend: PivotDialog, RecodeDialog, BinningDialog, ConditionalDialog, DataGrid, index Status: Ready for testing
117 lines
3.8 KiB
Python
117 lines
3.8 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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import numpy as np
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from typing import Dict, Any, Optional, Literal
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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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na_handling: Literal['keep', 'map', 'drop'] = 'keep',
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na_value: Any = 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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na_handling: NA值处理方式
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- 'keep': 保持为NA（默认）
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- 'map': 映射为指定值
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- 'drop': 删除包含NA的行
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na_value: 当na_handling='map'时，NA映射到的值
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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', None]})
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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', na_handling='map', na_value=0)
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>>> result['性别_编码'].tolist()
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[1, 2, 1, 0]
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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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# ✨ 统计原始NA数量
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original_na_count = result[column].isna().sum()
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# ✨ 优化：如果是创建新列，插入到原列旁边
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if create_new_column:
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original_col_index = result.columns.get_loc(column)
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result.insert(original_col_index + 1, target_column, result[column].map(mapping))
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else:
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# 覆盖原列
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result[target_column] = result[column].map(mapping)
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# ✨ 处理NA值
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if original_na_count > 0:
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na_mask = result[column].isna()
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if na_handling == 'keep':
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# 保持为NA（已经是NA，无需操作）
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print(f'📊 NA处理：保持为NA（{original_na_count}个）')
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elif na_handling == 'map':
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# 映射为指定值
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result.loc[na_mask, target_column] = na_value
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print(f'📊 NA处理：映射为 {na_value}（{original_na_count}个）')
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elif na_handling == 'drop':
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# 删除包含NA的行
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rows_before = len(result)
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result = result[~na_mask].copy()
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rows_after = len(result)
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print(f'📊 NA处理：删除包含NA的行（删除{rows_before - rows_after}行）')
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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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