需求
原始表格:
想在Total列中对每日的Amount进行汇总,然后对Date和Total进行合并居中,效果如下:
思路
遍历Excel行,从第一个非空Date列开始,到下个非空Date列,对Amount列进行求和,结果赋给第一个非空Data列对应行的Total列。
代码
- import os
- import openpyxl
- from openpyxl.styles import Border, Side, PatternFill, Font, GradientFill, Alignment
- def 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 sum
- def 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 True
- def total_amount(worksheet):
- """ 对某sheet的A、E列合并居中,并对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 当前为空行(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 + 1
- def main():
- # 根据情况修改代码
- in_file_name = 'In.xlsx'
- processing_sheet = 'Sheet1'
- path_name = 'D:\\Users\\Desktop\\Temp'
- 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()
说明
本功能用到了openpyxl模块,更多Excel操作请见官网
本代码不支持Excel中间有空行的情况,最后有空行无影响
f'A{sum_row_start}'这样的代码用到了f-string功能,若python版本低于3.6,需改为'A'+str(sum_row_start)或其它形式
补充:Python3 Pandas DataFrame 对某一列求和
在操作pandas的DataFrame的时候,常常会遇到某些列是字符串,某一些列是数值的情况,如果直接使用df_obj.apply(sum)往往会出错
使用如下方式即可对其中某一列进行求和
- dataf_test1['diff'].sum() // diff为要求和的列
以上为个人经验,希望能给大家一个参考,也希望大家多多支持我们。如有错误或未考虑完全的地方,望不吝赐教。
原文链接:https://blog.csdn.net/helloword4217/article/details/88945745