Python脚本开发文件初始化
This commit is contained in:
655
BOMCompare/BOMCompare for Merge V1.py
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655
BOMCompare/BOMCompare for Merge V1.py
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import pandas as pd
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import tkinter as tk
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from tkinter import filedialog
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from datetime import datetime
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import os
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from typing import Dict, List, Tuple, Optional
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class BOMComparator:
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"""BOM文件差异对比器"""
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def __init__(self):
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self.file1_path = ""
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self.file2_path = ""
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self.file1_sheets = []
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self.file2_sheets = []
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self.common_sheets = []
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self.differences = {}
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self.file1_name = ""
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self.file2_name = ""
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self.columns_to_exclude = ['检查信息', '检查状态', '校验信息'] # 要排除的列名
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def select_file(self, title: str) -> str:
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"""手动选择文件"""
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root = tk.Tk()
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root.withdraw()
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file_path = filedialog.askopenfilename(
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title=title,
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filetypes=[("Excel files", "*.xlsx"), ("All files", "*.*")]
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)
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root.destroy()
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return file_path
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def find_valid_sheets(self, file_path: str) -> List[str]:
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"""参考附件3的方式查找有效的sheet"""
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valid_sheets = []
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try:
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xl_file = pd.ExcelFile(file_path)
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for sheet_name in xl_file.sheet_names:
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try:
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# 尝试读取sheet，检查是否包含BOM数据
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df = pd.read_excel(file_path, sheet_name=sheet_name, nrows=10)
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# 检查是否包含BOM相关列（参考附件结构）
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required_columns = ['Partnumber', 'Purchase_Code', 'MF_PN', 'Description']
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found_columns = [col for col in df.columns if col in required_columns]
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if len(found_columns) >= 2: # 至少找到2个关键列
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# 检查是否有实际数据（不只是表头）
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if len(df) > 1:
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valid_sheets.append(sheet_name)
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except Exception as e:
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continue
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except Exception as e:
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print(f"读取文件 {file_path} 时出错: {e}")
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return valid_sheets
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def get_common_sheets(self) -> List[str]:
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"""获取两个文件的共同工作表"""
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if not self.file1_sheets or not self.file2_sheets:
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return []
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# 标准化工作表名称（去除空格和特殊字符）
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file1_clean = [self.standardize_sheet_name(sheet) for sheet in self.file1_sheets]
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file2_clean = [self.standardize_sheet_name(sheet) for sheet in self.file2_sheets]
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# 找出共同的工作表
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common_sheets = []
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for sheet1 in self.file1_sheets:
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clean_sheet1 = self.standardize_sheet_name(sheet1)
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for sheet2 in self.file2_sheets:
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clean_sheet2 = self.standardize_sheet_name(sheet2)
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if clean_sheet1 == clean_sheet2:
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common_sheets.append(sheet1)
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break
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return common_sheets
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def standardize_sheet_name(self, sheet_name: str) -> str:
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"""标准化工作表名称，便于比较"""
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return str(sheet_name).strip().lower().replace(' ', '_').replace('-', '_')
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def load_bom_data(self, file_path: str, sheet_name: str) -> pd.DataFrame:
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"""加载BOM数据"""
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try:
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df = pd.read_excel(file_path, sheet_name=sheet_name)
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# 清理数据：去除空行和空列
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df = df.dropna(how='all').dropna(axis=1, how='all')
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# 清理列名
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df.columns = df.columns.str.strip()
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return df
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except Exception as e:
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print(f"加载sheet {sheet_name} 时出错: {e}")
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return pd.DataFrame()
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def should_compare_column(self, column_name: str) -> bool:
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"""判断是否应该对比该列（排除检查信息类列）"""
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exclude_keywords = ['检查', '校验', '状态', '备注', 'comment', 'check']
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column_lower = str(column_name).lower()
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# 检查是否在排除列表中
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if column_name in self.columns_to_exclude:
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return False
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# 检查是否包含排除关键词
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for keyword in exclude_keywords:
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if keyword in column_lower:
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return False
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return True
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def get_columns_to_compare(self, df1: pd.DataFrame, df2: pd.DataFrame) -> List[str]:
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"""获取需要对比的列名（排除检查信息类列）"""
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common_columns = list(set(df1.columns).intersection(set(df2.columns)))
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# 过滤掉不需要对比的列
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columns_to_compare = [col for col in common_columns if self.should_compare_column(col)]
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return columns_to_compare
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def compare_dataframes(self, df1: pd.DataFrame, df2: pd.DataFrame, sheet_name1: str, sheet_name2: str) -> Dict:
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"""对比两个DataFrame的差异（排除检查信息类列）"""
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differences = {
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'sheet_names': f"{sheet_name1} vs {sheet_name2}",
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'added_rows': [],
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'removed_rows': [],
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'modified_rows': [],
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'columns_comparison': {},
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'summary': {
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'total_rows_df1': len(df1),
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'total_rows_df2': len(df2),
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'added_count': 0,
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'removed_count': 0,
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'modified_count': 0
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},
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'original_dfs': {
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'df1': df1.copy(),
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'df2': df2.copy()
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}
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}
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# 确定关键列用于行匹配
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key_columns = self.identify_key_columns(df1, df2)
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if not key_columns:
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differences['error'] = "无法确定用于对比的关键列"
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return differences
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try:
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# 设置索引
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df1_indexed = df1.set_index(key_columns)
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df2_indexed = df2.set_index(key_columns)
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# 获取需要对比的列（排除检查信息类列）
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columns_to_compare = self.get_columns_to_compare(df1, df2)
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# 找出新增的行
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new_indexes = df2_indexed.index.difference(df1_indexed.index)
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if len(new_indexes) > 0:
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differences['added_rows'] = df2_indexed.loc[new_indexes].reset_index().to_dict('records')
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differences['summary']['added_count'] = len(new_indexes)
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# 找出删除的行
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removed_indexes = df1_indexed.index.difference(df2_indexed.index)
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if len(removed_indexes) > 0:
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differences['removed_rows'] = df1_indexed.loc[removed_indexes].reset_index().to_dict('records')
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differences['summary']['removed_count'] = len(removed_indexes)
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# 找出共同的行并进行详细对比（排除检查信息类列）
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common_indexes = df1_indexed.index.intersection(df2_indexed.index)
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for idx in common_indexes:
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row1 = df1_indexed.loc[idx]
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row2 = df2_indexed.loc[idx]
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# 检查每列的值是否相同（只对比需要比较的列）
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modified_cols = {}
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for col in columns_to_compare:
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if col in df1_indexed.columns and col in df2_indexed.columns:
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val1 = row1[col]
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val2 = row2[col]
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# 处理NaN值的比较
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if pd.isna(val1) and pd.isna(val2):
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continue
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elif pd.isna(val1) or pd.isna(val2) or str(val1) != str(val2):
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modified_cols[col] = {
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'old_value': val1,
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'new_value': val2
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}
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if modified_cols:
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# 获取完整的行数据以显示所有需要的列
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full_row_data = self.get_full_row_data_for_display(df1, df2, idx, key_columns)
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differences['modified_rows'].append({
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'key_values': dict(zip(key_columns, idx)) if isinstance(idx, tuple) else {key_columns[0]: idx},
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'modified_columns': modified_cols,
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'full_row_data': full_row_data
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})
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differences['summary']['modified_count'] += 1
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# 列级对比（包含所有列，用于统计）
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common_columns = set(df1.columns).intersection(set(df2.columns))
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df1_only_columns = set(df1.columns).difference(set(df2.columns))
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df2_only_columns = set(df2.columns).difference(set(df1.columns))
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# 计算实际参与对比的列
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compared_columns = set(columns_to_compare)
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excluded_columns = common_columns - compared_columns
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differences['columns_comparison'] = {
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'common_columns': list(common_columns),
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'compared_columns': list(compared_columns),
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'excluded_columns': list(excluded_columns),
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'file1_only_columns': list(df1_only_columns),
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'file2_only_columns': list(df2_only_columns)
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}
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except Exception as e:
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differences['error'] = f"对比过程中出错: {str(e)}"
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return differences
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def get_full_row_data_for_display(self, df1: pd.DataFrame, df2: pd.DataFrame, idx, key_columns: List[str]) -> Dict:
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"""获取完整的行数据用于显示"""
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display_data = {}
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# 获取两个文件中的对应行数据
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row1_data = self.extract_row_data(df1, idx, key_columns)
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row2_data = self.extract_row_data(df2, idx, key_columns)
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# 定义需要显示的列（排除检查信息类列）
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display_columns = ['Purchase_Code', 'MF_PN', 'Description', 'Part Type', 'MF_NAME', 'PCB_Footprint', '合计']
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# 过滤掉检查信息类列
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display_columns = [col for col in display_columns if self.should_compare_column(col)]
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for col in display_columns:
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val1 = row1_data.get(col, '')
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val2 = row2_data.get(col, '')
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# 格式化显示：有差异显示原值->新值，无差异显示原值
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if pd.isna(val1) or val1 == '':
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display_value = val2
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elif pd.isna(val2) or val2 == '':
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display_value = val1
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elif str(val1) != str(val2):
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display_value = f"{val1} -> {val2}"
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else:
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display_value = val1
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display_data[col] = display_value
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# 添加文件来源信息
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display_data['_from_file1'] = row1_data
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display_data['_from_file2'] = row2_data
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return display_data
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def extract_row_data(self, df: pd.DataFrame, idx, key_columns: List[str]) -> Dict:
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"""从DataFrame中提取指定行的数据"""
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row_data = {}
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try:
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if isinstance(idx, tuple):
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# 多列索引的情况
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mask = pd.Series(True, index=df.index)
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for i, key in enumerate(key_columns):
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mask = mask & (df[key] == idx[i])
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if mask.any():
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original_row = df[mask].iloc[0]
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for col in df.columns:
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row_data[col] = original_row[col]
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else:
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# 单列索引的情况
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matching_rows = df[df[key_columns[0]] == idx]
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if len(matching_rows) > 0:
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original_row = matching_rows.iloc[0]
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for col in df.columns:
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row_data[col] = original_row[col]
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except Exception as e:
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pass
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return row_data
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def format_value_display(self, value1, value2):
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"""格式化值的显示：有差异显示原值->新值，无差异显示原值"""
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if pd.isna(value1) or value1 == '':
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return value2
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elif pd.isna(value2) or value2 == '':
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return value1
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elif str(value1) != str(value2):
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return f"{value1} -> {value2}"
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else:
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return value1
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def get_modified_columns_summary(self, modified_columns: Dict) -> str:
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"""获取修改列的概要汇总"""
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if not modified_columns:
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return "无修改"
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modified_list = list(modified_columns.keys())
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# 如果修改列数量较少，直接显示
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if len(modified_list) <= 3:
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return ", ".join(modified_list)
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else:
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# 数量较多时显示前3个加省略号
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return ", ".join(modified_list[:3]) + f"...等{len(modified_list)}列"
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def identify_key_columns(self, df1: pd.DataFrame, df2: pd.DataFrame) -> List[str]:
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"""识别用于行匹配的关键列"""
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# 优先使用Partnumber作为关键列
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potential_keys = ['Partnumber', 'Purchase_Code', 'MF_PN']
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for key in potential_keys:
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if key in df1.columns and key in df2.columns:
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# 检查该列是否适合作为关键列（不应有过多重复值）
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df1_dup_rate = df1[key].duplicated().sum() / len(df1)
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df2_dup_rate = df2[key].duplicated().sum() / len(df2)
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if df1_dup_rate < 0.1 and df2_dup_rate < 0.1: # 允许少量重复
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return [key]
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# 如果没有单一关键列，尝试组合
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for key_combo in [['Partnumber', 'MF_PN'], ['Purchase_Code', 'MF_PN']]:
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if all(col in df1.columns for col in key_combo) and all(col in df2.columns for col in key_combo):
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return key_combo
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# 最后尝试使用所有找到的共同列
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common_cols = list(set(df1.columns).intersection(set(df2.columns)))
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if common_cols:
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return common_cols[:2] # 最多使用前两列
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return []
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def generate_output_filename(self) -> str:
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"""生成输出文件名，以两个文件的有效sheet名称开头"""
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if not self.file1_sheets or not self.file2_sheets:
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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return f"BOM差异报告_{timestamp}.xlsx"
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# 使用第一个文件第一个sheet和第二个文件第一个sheet
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file1_sheet_name = str(self.file1_sheets[0]) if self.file1_sheets else "File1"
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file2_sheet_name = str(self.file2_sheets[0]) if self.file2_sheets else "File2"
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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# 清理sheet名称中的特殊字符
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clean_sheet1 = self.clean_filename(file1_sheet_name)
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clean_sheet2 = self.clean_filename(file2_sheet_name)
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filename = f"{clean_sheet1}_vs_{clean_sheet2}_差异报告_{timestamp}.xlsx"
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return filename
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def clean_filename(self, filename: str) -> str:
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"""清理文件名中的特殊字符"""
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filename = str(filename)
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# 移除Windows文件名中不允许的字符
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invalid_chars = '<>:"/\\|?*'
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for char in invalid_chars:
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filename = filename.replace(char, '_')
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# 移除多余的空格和特殊字符
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filename = filename.replace(' ', '_')
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filename = filename.replace('\t', '_')
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filename = filename.replace('\n', '_')
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# 限制文件名长度
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if len(filename) > 50:
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filename = filename[:50]
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return filename
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def clean_sheet_name(self, sheet_name: str, max_length: int = 25) -> str:
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"""清理工作表名称，确保符合Excel工作表名称限制"""
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sheet_name = str(sheet_name)
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# 移除Excel工作表名称中不允许的字符
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invalid_chars = '[]:*?/\\'
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for char in invalid_chars:
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sheet_name = sheet_name.replace(char, '_')
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# 限制工作表名称长度（Excel限制为31个字符）
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if len(sheet_name) > max_length:
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sheet_name = sheet_name[:max_length]
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return sheet_name
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def get_output_directory(self) -> str:
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"""获取输出目录（第二个文件所在目录）"""
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return os.path.dirname(self.file2_path)
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def generate_difference_report(self) -> str:
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"""生成差异报告Excel文件"""
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if not self.differences:
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return "没有发现差异"
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# 生成输出文件名和路径
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output_filename = self.generate_output_filename()
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output_directory = self.get_output_directory()
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output_path = os.path.join(output_directory, output_filename)
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try:
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with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
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# 创建总摘要表
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summary_data = []
|
||||
for diff_key, differences in self.differences.items():
|
||||
if 'error' not in differences:
|
||||
columns_comparison = differences.get('columns_comparison', {})
|
||||
excluded_count = len(columns_comparison.get('excluded_columns', []))
|
||||
|
||||
summary_data.append([
|
||||
differences.get('sheet_names', diff_key),
|
||||
differences['summary']['total_rows_df1'],
|
||||
differences['summary']['total_rows_df2'],
|
||||
differences['summary']['added_count'],
|
||||
differences['summary']['removed_count'],
|
||||
differences['summary']['modified_count'],
|
||||
excluded_count
|
||||
])
|
||||
|
||||
if summary_data:
|
||||
summary_df = pd.DataFrame(summary_data, columns=[
|
||||
'工作表对比', '文件1行数', '文件2行数', '新增行数', '删除行数', '修改行数', '排除列数'
|
||||
])
|
||||
summary_df.to_excel(writer, sheet_name='对比摘要', index=False)
|
||||
|
||||
# 为每个对比创建详细报告
|
||||
for diff_key, differences in self.differences.items():
|
||||
sheet_key = self.clean_sheet_name(diff_key.replace('vs', '_vs_'))
|
||||
|
||||
if 'error' in differences:
|
||||
# 如果有错误，创建错误报告
|
||||
error_df = pd.DataFrame([['错误信息', differences['error']]])
|
||||
error_df.to_excel(writer, sheet_name=f"{sheet_key}_错误", index=False, header=False)
|
||||
continue
|
||||
|
||||
# 汇总表 - 包含列对比的详细信息
|
||||
summary_data = []
|
||||
summary_data.append(["对比项", "数量"])
|
||||
summary_data.append(["文件1总行数", differences['summary']['total_rows_df1']])
|
||||
summary_data.append(["文件2总行数", differences['summary']['total_rows_df2']])
|
||||
summary_data.append(["新增行数", differences['summary']['added_count']])
|
||||
summary_data.append(["删除行数", differences['summary']['removed_count']])
|
||||
summary_data.append(["修改行数", differences['summary']['modified_count']])
|
||||
summary_data.append(["共同列数", len(differences['columns_comparison']['common_columns'])])
|
||||
summary_data.append(["实际对比列数", len(differences['columns_comparison']['compared_columns'])])
|
||||
summary_data.append(["排除列数", len(differences['columns_comparison']['excluded_columns'])])
|
||||
summary_data.append(["文件1特有列", len(differences['columns_comparison']['file1_only_columns'])])
|
||||
summary_data.append(["文件2特有列", len(differences['columns_comparison']['file2_only_columns'])])
|
||||
|
||||
# 添加排除列详情
|
||||
excluded_cols = differences['columns_comparison'].get('excluded_columns', [])
|
||||
if excluded_cols:
|
||||
summary_data.append(["", ""])
|
||||
summary_data.append(["排除的列", "（检查信息类列不参与对比）"])
|
||||
for col in excluded_cols:
|
||||
summary_data.append(["", f"- {col}"])
|
||||
|
||||
pd.DataFrame(summary_data).to_excel(
|
||||
writer,
|
||||
sheet_name=f"{sheet_key}_汇总",
|
||||
index=False,
|
||||
header=False
|
||||
)
|
||||
|
||||
# 新增行详情
|
||||
if differences['added_rows']:
|
||||
pd.DataFrame(differences['added_rows']).to_excel(
|
||||
writer,
|
||||
sheet_name=f"{sheet_key}_新增行",
|
||||
index=False
|
||||
)
|
||||
|
||||
# 删除行详情
|
||||
if differences['removed_rows']:
|
||||
pd.DataFrame(differences['removed_rows']).to_excel(
|
||||
writer,
|
||||
sheet_name=f"{sheet_key}_删除行",
|
||||
index=False
|
||||
)
|
||||
|
||||
# 修改行详情 - 优化后的显示格式（排除检查信息列）
|
||||
if differences['modified_rows']:
|
||||
modified_data = []
|
||||
|
||||
for mod_row in differences['modified_rows']:
|
||||
# 创建基础记录
|
||||
record = {
|
||||
**mod_row['key_values'], # 关键列（如Partnumber）
|
||||
'修改列': self.get_modified_columns_summary(mod_row['modified_columns'])
|
||||
}
|
||||
|
||||
# 添加所有需要显示的列（排除检查信息类列）
|
||||
display_data = mod_row.get('full_row_data', {})
|
||||
|
||||
# 获取需要显示的列
|
||||
display_columns = list(display_data.keys())
|
||||
display_columns = [col for col in display_columns if
|
||||
not col.startswith('_') and self.should_compare_column(col)]
|
||||
|
||||
for col in display_columns:
|
||||
record[col] = display_data.get(col, '')
|
||||
|
||||
# 添加详细的修改信息（只包括参与对比的列）
|
||||
for col, values in mod_row['modified_columns'].items():
|
||||
if self.should_compare_column(col):
|
||||
record[f'详细_{col}'] = f"{values['old_value']} -> {values['new_value']}"
|
||||
|
||||
modified_data.append(record)
|
||||
|
||||
if modified_data:
|
||||
modified_df = pd.DataFrame(modified_data)
|
||||
|
||||
# 重新排列列的顺序，让重要信息在前
|
||||
column_order = list(mod_row['key_values'].keys()) + ['修改列']
|
||||
|
||||
# 添加其他显示列
|
||||
other_columns = [col for col in modified_df.columns
|
||||
if col not in column_order and not col.startswith('详细_')]
|
||||
column_order.extend(other_columns)
|
||||
|
||||
# 添加详细修改信息列
|
||||
detailed_cols = [col for col in modified_df.columns if col.startswith('详细_')]
|
||||
column_order.extend(detailed_cols)
|
||||
|
||||
# 确保所有列都存在
|
||||
existing_columns = [col for col in column_order if col in modified_df.columns]
|
||||
modified_df = modified_df[existing_columns]
|
||||
|
||||
modified_df.to_excel(
|
||||
writer,
|
||||
sheet_name=f"{sheet_key}_修改行",
|
||||
index=False
|
||||
)
|
||||
|
||||
return output_path
|
||||
|
||||
except Exception as e:
|
||||
print(f"生成报告时出错: {e}")
|
||||
return ""
|
||||
|
||||
def run_comparison(self):
|
||||
"""执行完整的BOM对比流程"""
|
||||
print("=== BOM文件差异对比工具 ===")
|
||||
print("注意：检查信息类列（如'检查信息'）将不参与修改行对比")
|
||||
|
||||
# 1. 选择第一份文件
|
||||
print("\n步骤1: 选择第一份Excel文件")
|
||||
self.file1_path = self.select_file("选择第一份BOM Excel文件")
|
||||
if not self.file1_path:
|
||||
print("未选择文件，程序退出")
|
||||
return
|
||||
|
||||
self.file1_name = os.path.basename(self.file1_path)
|
||||
|
||||
# 2. 选择第二份文件
|
||||
print("\n步骤2: 选择第二份Excel文件")
|
||||
self.file2_path = self.select_file("选择第二份BOM Excel文件")
|
||||
if not self.file2_path:
|
||||
print("未选择文件，程序退出")
|
||||
return
|
||||
|
||||
self.file2_name = os.path.basename(self.file2_path)
|
||||
|
||||
print(f"\n文件1: {self.file1_name}")
|
||||
print(f"文件2: {self.file2_name}")
|
||||
|
||||
# 3. 查找有效sheet
|
||||
print("\n步骤3: 查找有效的工作表...")
|
||||
self.file1_sheets = self.find_valid_sheets(self.file1_path)
|
||||
self.file2_sheets = self.find_valid_sheets(self.file2_path)
|
||||
|
||||
print(f"文件1的有效工作表: {self.file1_sheets}")
|
||||
print(f"文件2的有效工作表: {self.file2_sheets}")
|
||||
|
||||
if not self.file1_sheets or not self.file2_sheets:
|
||||
print("至少有一个文件没有有效的工作表，无法进行对比")
|
||||
return
|
||||
|
||||
# 4. 进行差异对比
|
||||
print("\n步骤4: 进行差异对比...")
|
||||
self.differences = {}
|
||||
|
||||
# 使用第一个文件第一个sheet和第二个文件第一个sheet进行对比
|
||||
sheet1 = self.file1_sheets[0]
|
||||
sheet2 = self.file2_sheets[0]
|
||||
|
||||
print(f"正在对比: {sheet1} (文件1) vs {sheet2} (文件2)")
|
||||
|
||||
df1 = self.load_bom_data(self.file1_path, sheet1)
|
||||
df2 = self.load_bom_data(self.file2_path, sheet2)
|
||||
|
||||
if df1.empty:
|
||||
print(f" ⚠ 文件1的工作表 {sheet1} 数据加载失败")
|
||||
return
|
||||
|
||||
if df2.empty:
|
||||
print(f" ⚠ 文件2的工作表 {sheet2} 数据加载失败")
|
||||
return
|
||||
|
||||
differences = self.compare_dataframes(df1, df2, sheet1, sheet2)
|
||||
comparison_key = f"{sheet1}_vs_{sheet2}"
|
||||
self.differences[comparison_key] = differences
|
||||
|
||||
if 'error' in differences:
|
||||
print(f" ⚠ 对比过程中出错: {differences['error']}")
|
||||
else:
|
||||
columns_comparison = differences.get('columns_comparison', {})
|
||||
excluded_count = len(columns_comparison.get('excluded_columns', []))
|
||||
|
||||
print(f" √ 完成对比:")
|
||||
print(f" 文件1行数: {differences['summary']['total_rows_df1']}")
|
||||
print(f" 文件2行数: {differences['summary']['total_rows_df2']}")
|
||||
print(f" 新增行数: {differences['summary']['added_count']}")
|
||||
print(f" 删除行数: {differences['summary']['removed_count']}")
|
||||
print(f" 修改行数: {differences['summary']['modified_count']}")
|
||||
print(f" 排除列数: {excluded_count} (检查信息类列不参与对比)")
|
||||
|
||||
# 5. 生成差异报告
|
||||
print("\n步骤5: 生成差异报告...")
|
||||
output_file = self.generate_difference_report()
|
||||
|
||||
if output_file and os.path.exists(output_file):
|
||||
print(f"\n=== 对比完成 ===")
|
||||
print(f"差异报告已生成: {os.path.basename(output_file)}")
|
||||
# print(f"文件位置: {output_file}")
|
||||
print(f"输出目录: {self.get_output_directory()}")
|
||||
else:
|
||||
print("未成功生成差异报告")
|
||||
|
||||
|
||||
def main():
|
||||
"""主函数"""
|
||||
comparator = BOMComparator()
|
||||
comparator.run_comparison()
|
||||
|
||||
input("\n按Enter键退出...")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user