原始表格：

想在Total列中對(duì)每日的Amount進(jìn)行匯總，然后對(duì)Date和Total進(jìn)行合并居中，效果如下：

遍歷Excel行，從第一個(gè)非空Date列開(kāi)始，到下個(gè)非空Date列，對(duì)Amount列進(jìn)行求和，結(jié)果賦給第一個(gè)非空Data列對(duì)應(yīng)行的Total列。

import osimport openpyxlfrom openpyxl.styles import Border, Side, PatternFill, Font, GradientFill, Alignmentdef range_sum(worksheet,start,end): sum = 0 for row in worksheet[start:end]: for cell in row: if cell.value != None:sum += cell.value return sumdef is_blank_row(worksheet,row_num,max_col=None): if not max_col: max_col = worksheet.max_column for cell in worksheet[row_num][:max_col]: if cell.value: return False return Truedef total_amount(worksheet): ''' 對(duì)某sheet的A、E列合并居中，并對(duì)E列求和 ''' ws = worksheet row, max_row = 4, ws.max_row while row < ws.max_row: sum_row_start, sum_row_end = row, row for working_row in range(row + 1,max_row + 2): if (not is_blank_row(worksheet, working_row-1) # 上一行有值 and (ws[f’A{working_row}’].value or is_blank_row(worksheet, working_row))): # A列有值 或 當(dāng)前為空行（最后一次合并） # 求和 sum_row_end = working_row - 1 ws[f’E{sum_row_start}’] = range_sum(ws,f’C{sum_row_start}’,f’C{sum_row_end}’) # 合并居中 ws[f’E{sum_row_start}’].alignment = Alignment(horizontal='center', vertical='center') ws[f’A{sum_row_start}’].alignment = Alignment(horizontal='center', vertical='center') ws.merge_cells(f’E{sum_row_start}:E{sum_row_end}’) ws.merge_cells(f’A{sum_row_start}:A{sum_row_end}’) break row = sum_row_end + 1def main(): # 根據(jù)情況修改代碼 in_file_name = ’In.xlsx’ processing_sheet = ’Sheet1’ path_name = ’D:UsersDesktopTemp’ out_file_name = ’Out.xlsx’ wb = openpyxl.load_workbook(filename=os.path.join(path_name,in_file_name)) total_amount(wb[processing_sheet]) wb.save(os.path.join(path_name,out_file_name))if __name__==’__main__’: main()說(shuō)明

本功能用到了openpyxl模塊，更多Excel操作請(qǐng)見(jiàn)官網(wǎng)

本代碼不支持Excel中間有空行的情況，最后有空行無(wú)影響

f’A{sum_row_start}’這樣的代碼用到了f-string功能，若python版本低于3.6，需改為’A’+str(sum_row_start)或其它形式

補(bǔ)充：Python3 Pandas DataFrame 對(duì)某一列求和

在操作pandas的DataFrame的時(shí)候，常常會(huì)遇到某些列是字符串，某一些列是數(shù)值的情況，如果直接使用df_obj.apply(sum)往往會(huì)出錯(cuò)

使用如下方式即可對(duì)其中某一列進(jìn)行求和

dataf_test1[’diff’].sum() // diff為要求和的列

以上為個(gè)人經(jīng)驗(yàn)，希望能給大家一個(gè)參考，也希望大家多多支持好吧啦網(wǎng)。如有錯(cuò)誤或未考慮完全的地方，望不吝賜教。