python excel转为markdown(包含合并行/列填充)


import openpyxl
import openpyxl.worksheet
import openpyxl.worksheet.worksheet
import pandas as pd


def unmerge_and_fill_cells(worksheet: openpyxl.worksheet.worksheet.Worksheet):
    all_merged_cell_ranges = list(worksheet.merged_cells.ranges)
    for merged_cell_range in all_merged_cell_ranges:
        merged_cell = merged_cell_range.start_cell
        worksheet.unmerge_cells(range_string=merged_cell_range.coord)
        for row_index, col_index in merged_cell_range.cells:
            cell = worksheet.cell(row=row_index, column=col_index)
            cell.value = merged_cell.value
            
def sheet_to_dataframe(sheet):
    # 读取sheet最大范围的单元格数据
    data = []
    for row in sheet.iter_rows(values_only=True):
        data.append(list(row))
    df = pd.DataFrame(data)
    return df



def unmerge_cell_and_convert_to_markdown(filename):
    # excel分开合并单元格并填充,然后转为markdown
    wb = openpyxl.load_workbook(filename)
    result = {}
    for sheet_name in wb.sheetnames:
        sheet = wb[sheet_name]
        unmerge_and_fill_cells(sheet)
        df = sheet_to_dataframe(sheet)
        # 转为markdown格式,index=False去掉行号
        md = df.to_markdown(index=False, tablefmt="github")
        result[sheet_name] = md

    return result



if __name__ == '__main__':
    markdown_dict = unmerge_cell_and_convert_to_markdown("example.xlsx")
    for sheet, md_text in markdown_dict.items():
        # print(f"### Sheet: {sheet}\n")
        with open(file="t.md", mode="w", encoding="utf8") as f:
            f.write(md_text)

        # print("\n\n")

``` def get_5_workdays_back(end_date=None): """计算从结束日期倒推5个工作日的起始日期""" end_date = pd.Timestamp.now().normalize() if end_date is None else end_date count = 0 current_date = end_date # 复用原有节假日配置 holidays = [ # 元旦(不调休) '2025-01-01', # 周三 # 春节(调休2天) '2025-01-28', '2025-01-29', '2025-01-30', '2025-01-31', '2025-02-01', '2025-02-02', '2025-02-03', '2025-02-04', # 1.28(除夕)-2.4 # 清明节(不调休) '2025-04-04', '2025-04-05', '2025-04-06', # 周五-周日 # 劳动节(调休1天) '2025-05-01', '2025-05-02', '2025-05-03', '2025-05-04', '2025-05-05', # 周四-周一 # 端午节(不调休) '2025-05-31', '2025-06-01', '2025-06-02', # 周六-周一 # 中秋节+国庆节(调休2天) '2025-10-01', '2025-10-02', '2025-10-03', '2025-10-04', '2025-10-05', '2025-10-06', '2025-10-07', '2025-10-08' # 周三-下周三 ] holiday_dates = pd.to_datetime(holidays) # 新增调休工作日表(换为日期式) workdays_adjustment = [ '2025-01-26', # 周日补春节 '2025-02-08', # 周六补春节 '2025-04-27', # 周日补劳动节 '2025-09-28', # 周日补国庆 '2025-10-11' # 周六补国庆 ] adjustment_dates = pd.to_datetime(workdays_adjustment) # 换日期式 while count < 5: current_date -= pd.Timedelta(days=1) # 判断是否为有效工作日 is_workday = ( (current_date.weekday() < 5 or current_date in adjustment_dates) and current_date not in holiday_dates ) if is_workday: count += 1 return current_date, end_date # 获取处理后的数据 df = getchaoshi() # 计算时间范围 start_date, end_date = get_5_workdays_back() date_mask = (df['收案时间'] >= start_date) & (df['收案时间'] <= end_date) weekly_cases = df[date_mask].copy() # 计算上周时间范围 last_week_start, _ = get_5_workdays_back(start_date) date_mask_last_week = (df['收案时间'] >= last_week_start) & (df['收案时间'] < start_date) last_weekly_cases = df[date_mask_last_week].copy() # 分组统计上周数据 last_week_stats = last_weekly_cases.groupby('组别')['案件状态'].count().reset_index() last_week_stats.columns = ['组别', '上周总案件数'] # 新增:统计上周已完成案件(根据结案时间) date_mask_last_week_closed = (df['结案时间'] >= last_week_start) & (df['结案时间'] < start_date) last_week_closed_cases = df[date_mask_last_week_closed].copy() # 分组统计上周已完成数据 last_week_closed_stats = last_week_closed_cases.groupby('组别')['案件状态'].count().reset_index() last_week_closed_stats.columns = ['组别', '上周已完成情况'] # 添加状态分类 weekly_cases['状态分类'] = weekly_cases['案件状态'].apply( lambda x: '已完成' if x in ['办结', '发件'] else '正在处理' ) # 分组统计 result = weekly_cases.groupby('组别').agg( 总案件数=('案件状态', 'count'), 已完成=('状态分类', lambda x: (x == '已完成').sum()), 处理中=('状态分类', lambda x: (x == '正在处理').sum()), 超时案件数=('是否超时', lambda x: (x == '是').sum()), 缓办案件数=('是否缓办', lambda x: (x == '是').sum()) # 新增行 ).reset_index() # # 并上周数据 # result = result.merge(last_week_stats, on='组别', how='left').fillna(0) # 并三组数据(原代码基础上增加新) result = result.merge(last_week_stats, on='组别', how='left') result = result.merge(last_week_closed_stats, on='组别', how='left').fillna(0) # 新增:添加总计行(核心修改点) total_row = { '组别': '总计', '总案件数': result['总案件数'].sum(), '已完成': result['已完成'].sum(), '处理中': result['处理中'].sum(), '超时案件数': result['超时案件数'].sum(), '缓办案件数': result['缓办案件数'].sum(), '上周总案件数': result['上周总案件数'].sum(), '上周已完成情况': result['上周已完成情况'].sum() # 新增 } result = pd.concat([result, pd.DataFrame([total_row])], ignore_index=True) # 结果展示样例 print(result.to_markdown(index=False)) # 创建Excel写入对象(注意:需要安装openpyxl) with ExcelWriter('GCB案件统计结果t4.xlsx', engine='openpyxl') as writer: # 将result写入Sheet1 result.to_excel(writer, sheet_name='按周统计情况', index=False) # 将原始数据写入Sheet2 weekly_cases.to_excel(writer, sheet_name='本周案件', index=False) # 将原始数据写入Sheet3 df.to_excel(writer, sheet_name='所有案件', index=False)```输出的结果表“按周统计情况”,添加四,一为“较上周记录新增册数”,计算为:总案件数-上周总案件数;二为“较上周记录结案册数”,计算为:已完成-上周已完成情况;三为”新增册数百分比“,计算为:较上周记录新增册数/上周总案件数,百分数表达;四为”完成率百分比“=较上周记录结案册数/上周总案件数,百分数表达。
03-28
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