python实战之实现excel读取、统计、写入的示例讲解


            背景

图像领域内的一个国内会议快要召开了，要发各种邀请邮件，之后要录入、统计邮件回复（参会还是不参会等）。如此重要的任务，老师就托付给我了。ps: 统计回复邮件的时候，能知道谁参会或谁不参会。

而我主要的任务，除了录入邮件回复，就是统计理事和普通会员的参会情况了（参会的、不参会的、没回复的）。录入邮件回复信息没办法只能人工操作，但如果统计也要人工的话，那工作量就太大了（比如在上百人的列表中搜索另外上百人在不在此列表中！！），于是就想到了用python来帮忙，花两天时间不断修改，写了6个版本。。。

摘要

version_1 基本实现了excel读取、统计、显示功能，但问题也有不少，像显示出来后还要自已复制、粘贴到excel表，而且set中还有nan这样的bug。

version_2 相比较version_1而言，此版本用set代替list，可以自动去重。

version_3 解决了set中出现nan的bug，而且还加入的excel写入的功能，但一次只能写入一张表，所以要运行两次才能写入两张表（sheet）。

version_4 的改进在于将version_3中写入两张表格的操作，集成在一个程序里，只需要运行一次便可写入两张表，但也总是会写入两张表，万一你只想写入一张表呢？？

version_5 相对之前版本的最大改进在于将程序模块化，更具可读性了; 对修复set中出现nan的方法也进行了改进和简化; 而且可以自由控制写入多少张表了。

version_final 相比较version_5，修复了一个bug，之前需要先验知识，现在更通用一点（prep函数取代了set2list函数）。

version_1

基本实现了excel读取、统计、显示功能，但问题也有不少，像显示出来后还要自已复制、粘贴到excel表，而且set中还有nan这样的值。





#version_1

import os

import numpy as np

import pandas as pd

os.chdir('C:\\Users\\dell\\Desktop\\0711任务')

print(os.getcwd())

data = pd.read_excel('for_python.xlsx','Sheet2')

return_set = set(data['回执名单'])

demand_set = set(data['理事名单'])

answer_list = []

unanswer_list = []

for each in demand_set:

 if each in return_set:

 answer_list.append(each)

 else:

 unanswer_list.append(each)

notattend_set = set(data['回执名单'][-15:])

nt = []

for each in notattend_set:

 if each in answer_list:

 nt.append(each)

def disp(ll, cap, num = True):

 print(cap)

 if num:

 for i, each in enumerate(ll):

  print(i+1,each)

 else:

 for each in enumerate(ll):

  print(each)

disp(answer_list,'\n理事回执名单')

disp(unanswer_list,'\n理事未回执名单')

disp(nt,'\n理事回执说不参加名单')



version_2

相比较上一个版本，此版本用set代替list，可以自动去重。





#version_2

import os

import numpy as np

import pandas as pd

os.chdir('C:\\Users\\dell\\Desktop\\0711任务')

print(os.getcwd())

data = pd.read_excel('for_python.xlsx','Sheet2')

return_set = set(data['回执名单'])

demand_set = set(data['理事名单'])

answer_set = set([]) #理事回执名单

unanswer_set = set([]) #理事未回执名单

for each in demand_set:

 if each in return_set:

 answer_set.add(each)

 else:

 unanswer_set.add(each)

notattend_set = set(data['回执名单'][-17:])

nt = set([]) #理事回执说不参加名单

for each in notattend_set:

 if each in answer_set:

 nt.add(each)

ans_att_set = answer_set - nt #理事回执参加名单

def disp(ss, cap, num = False):

 print(cap)

 if num:

 for i, each in enumerate(ss):

  print(i+1,each)

 else:

 for each in ss:

  print(each)

#disp(answer_set,'\n理事回执名单')

disp(ans_att_set,'\n理事回执说参加名单')

disp(nt,'\n理事回执说不参加名单')

disp(unanswer_set,'\n理事未回执名单')

print(len(ans_att_set),len(nt),len(unanswer_set))





version_3

此版本解决了set中出现nan的bug，而且还加入的excel写入的功能，但一次只能写入一张表，所以要运行两次才能写入两张表（sheet）。

step_1





import os

import numpy as np

import pandas as pd

os.chdir('C:\\Users\\dell\\Desktop')

print('work_directory: ', os.getcwd())

data = pd.read_excel('理事与会员名单.xlsx','理事与会员名单')

#1.载入excel，得到三个名单

ans_attend_set = set(data['回执参加']) #回执参会名单

N = len(ans_attend_set)

ans_notatt_idx = [i for i in range(N) if type(data['回执不参加'][i]) == np.float][0]

ans_notatt_set = set(data['回执不参加'][:ans_notatt_idx])#回执不参会名单

concil_idx = [i for i in range(N) if type(data['理事名单'][i]) == np.float][0]

concil_set = set(data['理事名单'][:concil_idx])  #理事名单

#2.统计理事参会情况

concil_attend_set = set([]) #理事回执参会名单

concil_notatt_set = set([]) #理事回执不参会名单

concil_notans_set = set([]) #理事未回执名单

for each in concil_set:

 if each in ans_attend_set:

 concil_attend_set.add(each)

 elif each in ans_notatt_set:

 concil_notatt_set.add(each)

 else:

 concil_notans_set.add(each)

#3. 显示结果

def disp(ss, cap, num = True):

 #ss: 名单集合

 #cap: 开头描述

 print(cap,'({})'.format(len(ss)))

 for i in range(np.ceil(len(ss)/5).astype(int)):

 pre = i * 5

 nex = (i+1) * 5

 #调整显示格式

 dd = ''

 for each in list(ss)[pre:nex]:

  if len(each) == 2:

  dd = dd + ' ' + each

  elif len(each) == 3:

  dd = dd + ' ' + each

  else:

  dd = dd + '' + each

 print('{:3.0f} -{:3.0f} {}'.format(i*5+1,(i+1)*5,dd))

disp(concil_attend_set,'\n参会理事')

disp(concil_notatt_set,'\n不参会理事')

disp(concil_notans_set,'\n未回执理事')

#4. 将理事参会情况，写入excel

df = pd.DataFrame(list(concil_attend_set),columns = ['参会理事'])

df['']=pd.DataFrame([''])

df['序号1'] = pd.DataFrame(np.arange(len(concil_notatt_set))+1)

df['不参会理事'] = pd.DataFrame(list(concil_notatt_set))

df['_']=pd.DataFrame([''])

df['序号2'] = pd.DataFrame(np.arange(len(concil_notans_set))+1)

df['未回执理事'] = pd.DataFrame(list(concil_notans_set))

df.index = df.index + 1

df.to_excel('理事和会员回执统计.xlsx', sheet_name='理事回执统计')

print('\n\n写入excel成功~~')



step_2





import os

import numpy as np

import pandas as pd

os.chdir('C:\\Users\\dell\\Desktop')

print('work_directory: ', os.getcwd())

data = pd.read_excel('理事与会员名单.xlsx','理事与会员名单')

#1.载入excel，得到三个名单

ans_attend_set = set(data['回执参加']) #回执参会名单

N = len(ans_attend_set)

ans_notatt_idx = [i for i in range(N) if type(data['回执不参加'][i]) == np.float][0]

ans_notatt_set = set(data['回执不参加'][:ans_notatt_idx])#回执不参会名单

mem_idx = [i for i in range(N) if type(data['被推荐人'][i]) == np.float][0]

mem_set = set(data['被推荐人'][:mem_idx])  #被推荐为会员代表名单

#2.统计会员参会情况

mem_attend_set = set([]) #回执参会会员

mem_notatt_set = set([]) #回执不参会会员

mem_notans_set = set([]) #未回执会员

for each in mem_set:

 if each in ans_attend_set:

 mem_attend_set.add(each)

 elif each in ans_notatt_set:

 mem_notatt_set.add(each)

 else:

 mem_notans_set.add(each)

#3. 显示结果

def disp(ss, cap, num = True):

 #ss: 名单集合

 #cap: 开头描述

 print(cap,'({})'.format(len(ss)))

 for i in range(np.ceil(len(ss)/5).astype(int)):

 pre = i * 5

 nex = (i+1) * 5

 #调整显示格式

 dd = ''

 for each in list(ss)[pre:nex]:

  if len(each) == 2:

  dd = dd + ' ' + each

  elif len(each) == 3:

  dd = dd + ' ' + each

  else:

  dd = dd + '' + each

 print('{:3.0f} -{:3.0f} {}'.format(i*5+1,(i+1)*5,dd))

disp(mem_attend_set,'\n参会会员')

disp(mem_notatt_set,'\n不参会会员')

disp(mem_notans_set,'\n未回执会员')

#4. 将会员参会情况，写入excel

if len(mem_attend_set) > len(mem_notans_set):

 print('#1')

 L = len(mem_attend_set)

 mem_notans_list = list(mem_notans_set)

 mem_notans_list.extend([''] * (L - len(mem_notans_set)))

 mem_attend_list = list(mem_attend_set)

else:

 print('#2')

 L = len(mem_notans_set)

 mem_attend_list = list(mem_attend_set)

 mem_attend_list.extend([''] * (L - len(mem_attend_set)))

 mem_notans_list = list(mem_notans_set) 

df = pd.DataFrame(mem_attend_list,columns = ['参会会员'])

df['']=pd.DataFrame([''])

if len(mem_notatt_set) == 0:

 df['序号1'] = np.NaN

 df['不参会会员'] = np.NaN

else:

 df['序号1'] = pd.DataFrame(np.arange(len(mem_notatt_set))+1)

 df['不参会会员'] = pd.DataFrame(list(mem_notatt_set))

df['_']=pd.DataFrame([''])

df['序号2'] = pd.DataFrame(np.arange(len(mem_notans_set))+1)

df['未回执会员'] = pd.DataFrame(mem_notans_list)

df.index = df.index + 1

df0 = pd.read_excel('理事和会员回执统计.xlsx',sheet_name='理事回执统计')

writer = pd.ExcelWriter('理事和会员回执统计.xlsx')

df0.to_excel(writer, sheet_name='理事回执统计')

df.to_excel(writer, sheet_name='会员回执统计')

writer.save()

print('\n\n写入excel成功~~')



version_4

version_4的改进在于将version_3中写入两张表格的操作，集成在一个程序里，只需要运行一次便可写入两张表，也总是会写入两张表。问题是要是你只想写入一张表呢？？





import os

import numpy as np

import pandas as pd

os.chdir('C:\\Users\\dell\\Desktop')

print('work_directory: ', os.getcwd())

loadfile_sheet = ['理事与会员名单.xlsx','理事与会员名单']

columns = ['回执参加','回执不参加','理事','会员']

savefile_sheet = ['理事和会员回执统计.xlsx','理事回执统计','会员回执统计']

display = [1,1]

def main(loadfile_sheet,columns,savefile_sheet,display):

 #1. 载入excel，得到名单

 data = pd.read_excel(loadfile_sheet[0],loadfile_sheet[1])

 def first_nan_index(pd):

 for i, each in enumerate(pd):

  if type(each) == np.float:

  return i

 return i

 idx = first_nan_index(data[columns[0]])

 ans_attend_set = set(data[columns[0]][:idx])#回执参会名单

 idx = first_nan_index(data[columns[1]])

 ans_notatt_set = set(data[columns[1]][:idx])#回执不参会名单

 idx = first_nan_index(data[columns[2]])

 concil_set = set(data[columns[2]][:idx])#理事名单

 idx = first_nan_index(data[columns[3]])

 mem_set = set(data[columns[3]][:idx])#会员名单

 #2. 统计参会情况

 concil_attend_set = set([]) #回执参会理事

 concil_notatt_set = set([]) #回执不参会理事

 concil_notans_set = set([]) #未回执理事

 for each in concil_set:

 if each in ans_attend_set:

  concil_attend_set.add(each)

 elif each in ans_notatt_set:

  concil_notatt_set.add(each)

 else:

  concil_notans_set.add(each)

 mem_attend_set = set([]) #回执参会会员

 mem_notatt_set = set([]) #回执不参会会员

 mem_notans_set = set([]) #未回执会员

 for each in mem_set:

 if each in ans_attend_set:

  mem_attend_set.add(each)

 elif each in ans_notatt_set:

  mem_notatt_set.add(each)

 else:

  mem_notans_set.add(each)

 #3. 是否显示中间结果 

 def disp(ss, cap, num = True):

 #ss: 名单集合

 #cap: 开头描述

 print(cap,'({})'.format(len(ss)))

 for i in range(np.ceil(len(ss)/5).astype(int)):

  pre = i * 5

  nex = (i+1) * 5

  #调整显示格式

  dd = ''

  for each in list(ss)[pre:nex]:

  if len(each) == 2:

   dd = dd + ' ' + each

  elif len(each) == 3:

   dd = dd + ' ' + each

  else:

   dd = dd + '' + each

  print('{:3.0f} -{:3.0f} {}'.format(i*5+1,(i+1)*5,dd))

 if display[0]:

 disp(concil_attend_set,'\n参会理事')

 disp(concil_notatt_set,'\n不参会理事')

 disp(concil_notans_set,'\n未回执理事')

 if display[1]:

 disp(mem_attend_set,'\n参会会员')

 disp(mem_notatt_set,'\n不参会会员')

 disp(mem_notans_set,'\n未回执会员')

 #4. 写入excel

 def trans_pd(df,ss,cap,i=1):

 if len(ss) == 0:

  df['序号{}'.format(i)] = np.NaN

  df[cap] = np.NaN

 else:

  df['序号{}'.format(i)] = pd.DataFrame(np.arange(len(ss))+1)

  df[cap] = pd.DataFrame(list(ss))

 df['_'*i]=pd.DataFrame([''])

 return df

 def set2list(mem_attend_set,mem_notans_set):

 if len(mem_attend_set) > len(mem_notans_set):

  L = len(mem_attend_set)

  mem_notans_list = list(mem_notans_set)

  mem_notans_list.extend([''] * (L - len(mem_notans_set)))

  mem_attend_list = list(mem_attend_set)

 else:

  L = len(mem_notans_set)

  mem_attend_list = list(mem_attend_set)

  mem_attend_list.extend([''] * (L - len(mem_attend_set)))

  mem_notans_list = list(mem_notans_set)

 return mem_attend_list,mem_notans_list

 mem_attend_list, mem_notans_list = set2list(mem_attend_set, mem_notans_set) 

 df1 = pd.DataFrame(mem_attend_list,columns = ['参会会员'])

 df1['']=pd.DataFrame([''])

 df1 = trans_pd(df1,mem_notatt_set,'不参会会员')

 df1 = trans_pd(df1,mem_notans_set,'未回执会员',2)

 df1.index = df1.index + 1

 concil_attend_list, concil_notans_list = set2list(concil_attend_set, concil_notans_set)

 df2 = pd.DataFrame(concil_attend_list,columns = ['参会理事'])

 df2['']=pd.DataFrame([''])

 df2 = trans_pd(df2,concil_notatt_set,'不参会理事')

 df2 = trans_pd(df2,concil_notans_list,'未回执理事',2)

 df2.index = df2.index + 1

 writer = pd.ExcelWriter(savefile_sheet[0])

 df2.to_excel(writer, sheet_name=savefile_sheet[1])

 df1.to_excel(writer, sheet_name=savefile_sheet[2])

 writer.save()

 print('\n\n写入excel成功~~')

if __name__ == '__main__':

 main(loadfile_sheet,columns,savefile_sheet,display)



version_5

version_5对修复set中出现nan的方法进行了改进和简化; 而且将程序模块化，更具可读性; 可以自由控制写入多少张表了。





import os

import numpy as np

import pandas as pd

os.chdir('C:\\Users\\dell\\Desktop')

print('work_directory: ', os.getcwd())

loadfile_sheet = ['理事与会员名单.xlsx','理事与会员名单']

common_columns = ['回执参加','回执不参加']

concerned_columns = ['理事','会员']

disp_columns = ['参会','不参会','未回执']

savefile_sheet = ['理事和会员回执统计.xlsx','理事回执统计','会员回执统计']

def disp(ss, cap, num = True):

 #ss: 名单集合

 #cap: 开头描述

 print(cap,'({})'.format(len(ss)))

 for i in range(np.ceil(len(ss)/5).astype(int)):

 pre = i * 5

 nex = (i+1) * 5

 #调整显示格式

 dd = ''

 for each in list(ss)[pre:nex]:

  if len(each) == 2:

  dd = dd + ' ' + each

  elif len(each) == 3:

  dd = dd + ' ' + each

  else:

  dd = dd + '' + each

 print('{:3.0f} -{:3.0f} {}'.format(i*5+1,(i+1)*5,dd))

def trans_pd(df,ss,cap,i=1):

 df['_'*i]=pd.DataFrame([''])

 if len(ss) == 0:

 df['序号{}'.format(i)] = np.NaN

 df[cap] = np.NaN

 else:

 df['序号{}'.format(i)] = pd.DataFrame(np.arange(len(ss))+1)

 df[cap] = pd.DataFrame(list(ss)) 

 return df

def set2list(ss1,ss2):

 if len(ss1) > len(ss2):

 L = len(ss1)

 ss2_list = list(ss2)

 ss2_list.extend([''] * (L - len(ss2)))

 ss1_list = list(ss1)

 else:

 L = len(ss2)

 ss1_list = list(ss1)

 ss1_list.extend([''] * (L - len(ss1)))

 ss2_list = list(ss2)

 return ss1_list,ss2_list 

def get_df(loadfile_sheet,common_columns,concerned_column,disp_columns, display = True):

 #1. 载入excel

 data = pd.read_excel(loadfile_sheet[0],loadfile_sheet[1])

 common_set1 = set(data[common_columns[0]])

 common_set1.discard(np.NaN)

 common_set2 = set(data[common_columns[1]])

 common_set2.discard(np.NaN)

 concerned_set = set(data[concerned_column])

 concerned_set.discard(np.NaN)

 #2. 统计

 concerned_in_set_1 = set([])

 concerned_in_set_2 = set([])

 concerned_in_no_set = set([])

 for each in concerned_set:

 if each in common_set1:

  concerned_in_set_1.add(each)

 elif each in common_set2:

  concerned_in_set_2.add(each)

 else:

  concerned_in_no_set.add(each)

 #3. 显示

 if display:

 disp(concerned_in_set_1,'\n'+disp_columns[0]+concerned_column)

 disp(concerned_in_set_2,'\n'+disp_columns[1]+concerned_column)

 disp(concerned_in_no_set,'\n'+disp_columns[2]+concerned_column)

 #4. 返回DataFrame

 concerned_in_set_1_list, concerned_in_set_2_list = set2list(concerned_in_set_1, concerned_in_no_set) 

 df = pd.DataFrame(concerned_in_set_1_list,columns = [disp_columns[0]])

 df = trans_pd(df,concerned_in_set_2,disp_columns[1])

 df = trans_pd(df,concerned_in_no_set,disp_columns[2],2)

 df.index = df.index + 1

 return df

def save2excel(df, concerned_column, savefile_sheet):

 L = len(savefile_sheet) - 1

 idx = 0

 for i in np.arange(L)+1:

 if concerned_column in savefile_sheet[i]:

  idx = i

  break

 if idx != 0:  

 names = locals()

 for i in np.arange(L)+1:

  if i != idx:

  names['df%s' % i] = pd.read_excel(savefile_sheet[0], sheet_name=savefile_sheet[i])

 writer = pd.ExcelWriter(savefile_sheet[0])

 for i in np.arange(L)+1:

  if i != idx:

  names['df%s' % i].to_excel(writer, sheet_name=savefile_sheet[i])

  else:

  df.to_excel(writer, sheet_name=savefile_sheet[i])

 writer.save()

 else:  

 names = locals()

 for i in np.arange(L)+1:

  names['df%s' % i] = pd.read_excel(savefile_sheet[0], sheet_name=savefile_sheet[i])

 writer = pd.ExcelWriter(savefile_sheet[0])

 for i in np.arange(L)+1:

  names['df%s' % i].to_excel(writer, sheet_name=savefile_sheet[i])

 df.to_excel(writer, sheet_name=concerned_column)

 writer.save()

 print('writing success')

if __name__ == '__main__':

 for concerned_column in concerned_columns:

 df = get_df(loadfile_sheet,common_columns,

   concerned_column,disp_columns, display = True)

 save2excel(df, concerned_column, savefile_sheet)





version_final

相比较version_5，修复了一个bug，之前需要先验知识，现在更通用一点（prep函数取代了set2list函数）。





import os

import numpy as np

import pandas as pd

os.chdir('C:\\Users\\dell\\Desktop')

print('work_directory: ', os.getcwd())

loadfile_sheet = ['理事与会员名单.xlsx','理事与会员名单']

common_columns = ['回执参加','回执不参加']

concerned_columns = ['理事','会员']

disp_columns = ['参会','不参会','未回执']

savefile_sheet = ['理事和会员回执统计.xlsx','理事回执统计','会员回执统计']

def disp(ss, cap, num = True):

 #功能：显示名单

 #ss : 名单集合

 #cap ：开头描述

 print(cap,'({})'.format(len(ss)))

 for i in range(np.ceil(len(ss)/5).astype(int)):

 pre = i * 5

 nex = (i+1) * 5

 #调整显示格式

 dd = ''

 for each in list(ss)[pre:nex]:

  if len(each) == 2:

  dd = dd + ' ' + each

  elif len(each) == 3:

  dd = dd + ' ' + each

  else:

  dd = dd + '' + each

 print('{:3.0f} -{:3.0f} {}'.format(i*5+1,(i+1)*5,dd))

def trans_pd(df,ll,cap,i=1):

 #功能：生成三列--空列、序号列、数据列

 #df : DataFrame结构

 #ll : 列表

 #cap : 显示的列名

 #i : 控制空列的名字

 df['_'*i]=pd.DataFrame([''])

 if len(set(ll)) == 1:

 df['序号{}'.format(i)] = np.NaN

 df[cap] = np.NaN

 else:

 df['序号{}'.format(i)] = pd.DataFrame(np.arange(len(set(ll))-1)+1)

 df[cap] = pd.DataFrame(ll) 

 return df

def prep(ss, N):

 #功能：预处理，生成列表，并补齐到长度N

 #ss : 集体

 #N ：长度

 ll = list(ss)

 L = len(ll)

 ll.extend([np.NaN] * (N-L))

 return ll

def get_df(loadfile_sheet,common_columns,concerned_column,disp_columns, display = True):

 #1. 载入excel

 data = pd.read_excel(loadfile_sheet[0],loadfile_sheet[1]) 

 common_set1 = set(data[common_columns[0]])

 common_set2 = set(data[common_columns[1]]) 

 concerned_set = set(data[concerned_column])

 common_set1.discard(np.NaN)

 common_set2.discard(np.NaN)

 concerned_set.discard(np.NaN)

 #2. 统计

 concerned_in_set_1 = set([])

 concerned_in_set_2 = set([])

 concerned_in_no_set = set([])

 for each in concerned_set:

 if each in common_set1:

  concerned_in_set_1.add(each)

 elif each in common_set2:

  concerned_in_set_2.add(each)

 else:

  concerned_in_no_set.add(each)

 #3. 显示

 if display:

 disp(concerned_in_set_1,'\n'+disp_columns[0]+concerned_column)

 disp(concerned_in_set_2,'\n'+disp_columns[1]+concerned_column)

 disp(concerned_in_no_set,'\n'+disp_columns[2]+concerned_column)

 #4. 返回DataFrame

 N = np.max([len(concerned_in_set_1),len(concerned_in_set_2),len(concerned_in_no_set)])

 concerned_in_set_1_list = prep(concerned_in_set_1,N)

 concerned_in_set_2_list = prep(concerned_in_set_2,N)

 concerned_in_no_list = prep(concerned_in_no_set,N)

 df = pd.DataFrame(concerned_in_set_1_list,columns = [disp_columns[0]])

 df = trans_pd(df,concerned_in_set_2_list,disp_columns[1])

 df = trans_pd(df,concerned_in_no_list,disp_columns[2],2)

 df.index = df.index + 1

 return df

def save2excel(df, concerned_column, savefile_sheet):

 L = len(savefile_sheet) - 1

 idx = 0

 for i in np.arange(L)+1:

 if concerned_column in savefile_sheet[i]:

  idx = i

  break

 if idx != 0: #如果有对应sheet  

 names = locals()

 for i in np.arange(L)+1:

  if i != idx:

  names['df%s' % i] = pd.read_excel(savefile_sheet[0], sheet_name=savefile_sheet[i])

 writer = pd.ExcelWriter(savefile_sheet[0])

 for i in np.arange(L)+1:

  if i != idx:

  names['df%s' % i].to_excel(writer, sheet_name=savefile_sheet[i])

  else:

  df.to_excel(writer, sheet_name=savefile_sheet[i])

 writer.save()

 else: #如果没有对应sheet，创建一个新sheet  

 names = locals()

 for i in np.arange(L)+1:

  names['df%s' % i] = pd.read_excel(savefile_sheet[0], sheet_name=savefile_sheet[i])

 writer = pd.ExcelWriter(savefile_sheet[0])

 for i in np.arange(L)+1:

  names['df%s' % i].to_excel(writer, sheet_name=savefile_sheet[i])

 df.to_excel(writer, sheet_name=concerned_column)

 writer.save()

 print('writing success')

if __name__ == '__main__':

 for concerned_column in concerned_columns:

 df = get_df(loadfile_sheet,common_columns,

   concerned_column,disp_columns, display = True)

 save2excel(df, concerned_column, savefile_sheet)



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