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VBA处理数据与Python Pandas处理数据案例比较分析

2020年06月23日  | 移动技术网IT编程  | 我要评论

需求：

现有一个 csv文件，包含'cnum'和'company'两列，数据里包含空行，且有内容重复的行数据。

要求：

1）去掉空行；
2）重复行数据只保留一行有效数据；
3）修改'company'列的名称为'company_new‘；
4）并在其后增加六列，分别为'c_col',‘d_col',‘e_col',‘f_col',‘g_col',‘h_col'。

在这里插入图片描述

一，使用 python pandas来处理：

import pandas as pd
import numpy as np
from pandas import dataframe,series

def deal_with_data(filepath,newpath):
  file_obj=open(filepath)
  df=pd.read_csv(file_obj)  # 读取csv文件，创建 dataframe
  df=df.reindex(columns=['cnum','company','c_col','d_col','e_col','f_col','g_col','h_col'],fill_value=none)  # 重新指定列索引
  df.rename(columns={'company':'company_new'}, inplace = true) # 修改列名
  df=df.dropna(axis=0,how='all')         # 去除 nan 即文件中的空行
  df['cnum'] = df['cnum'].astype('int32')    # 将 cnum 列的数据类型指定为 int32
  df = df.drop_duplicates(subset=['cnum', 'company_new'], keep='first') # 去除重复行
  df.to_csv(newpath,index=false,encoding='gbk')
  file_obj.close()
  
if __name__=='__main__':
  file_path=r'c:\users\12078\desktop\python\cnum_company.csv'
  file_save_path=r'c:\users\12078\desktop\python\cnum_company_output.csv'
  deal_with_data(file_path,file_save_path)

二，使用 vba来处理：

option base 1
option explicit

sub main()
 on error goto error_handling
 dim wb         as workbook
 dim wb_out       as workbook
 dim sht         as worksheet
 dim sht_out       as worksheet
 dim rng         as range
 dim usedrows      as byte
 dim usedrows_out    as byte
 dim dict_cnum_company  as object
 dim str_file_path    as string
    dim str_new_file_path  as string
    'assign values to variables:
    str_file_path = "c:\users\12078\desktop\python\cnum_company.csv"
    str_new_file_path = "c:\users\12078\desktop\python\cnum_company_output.csv"
 
 set wb = checkandattachworkbook(str_file_path)
 set sht = wb.worksheets("cnum_company")
 set wb_out = workbooks.add
 wb_out.saveas str_new_file_path, xlcsv 'create a csv file
 set sht_out = wb_out.worksheets("cnum_company_output")

 set dict_cnum_company = createobject("scripting.dictionary")
 usedrows = worksheetfunction.max(getlastvalidrow(sht, "a"), getlastvalidrow(sht, "b"))

 'rename the header 'company' to 'company_new',remove blank & duplicate lines/rows.
 dim cnum_company as string
 cnum_company = ""
 for each rng in sht.range("a1", "a" & usedrows)
   if vba.trim(rng.offset(0, 1).value) = "company" then
     rng.offset(0, 1).value = "company_new"
   end if
   cnum_company = rng.value & "-" & rng.offset(0, 1).value
   if vba.trim(cnum_company) <> "-" and not dict_cnum_company.exists(rng.value & "-" & rng.offset(0, 1).value) then
     dict_cnum_company.add rng.value & "-" & rng.offset(0, 1).value, ""
   end if
 next rng
 
 'loop the keys of dict split the keyes by '-' into cnum array and company array.
 dim index_dict as byte
 dim arr_cnum()
 dim arr_company()
 for index_dict = 0 to ubound(dict_cnum_company.keys)
   redim preserve arr_cnum(1 to ubound(dict_cnum_company.keys) + 1)
   redim preserve arr_company(1 to ubound(dict_cnum_company.keys) + 1)
   arr_cnum(index_dict + 1) = split(dict_cnum_company.keys()(index_dict), "-")(0)
   arr_company(index_dict + 1) = split(dict_cnum_company.keys()(index_dict), "-")(1)
   debug.print index_dict
 next

 'assigns the value of the arrays to the celles.
 sht_out.range("a1", "a" & ubound(arr_cnum)) = application.worksheetfunction.transpose(arr_cnum)
 sht_out.range("b1", "b" & ubound(arr_company)) = application.worksheetfunction.transpose(arr_company)

 'add 6 columns to output csv file:
 dim arr_columns() as variant
 arr_columns = array("c_col", "d_col", "e_col", "f_col", "g_col", "h_col")  '
 sht_out.range("c1:h1") = arr_columns
 call checkandcloseworkbook(str_file_path, false)
 call checkandcloseworkbook(str_new_file_path, true)

exit sub
error_handling:
  call checkandcloseworkbook(str_file_path, false)
  call checkandcloseworkbook(str_new_file_path, false)
end sub

' 辅助函数：
'get last row of column n in a worksheet
function getlastvalidrow(in_ws as worksheet, in_col as string)
  getlastvalidrow = in_ws.cells(in_ws.rows.count, in_col).end(xlup).row
end function

function checkandattachworkbook(in_wb_path as string) as workbook
  dim wb as workbook
  dim mywb as string
  mywb = in_wb_path
  
  for each wb in workbooks
    if lcase(wb.fullname) = lcase(mywb) then
      set checkandattachworkbook = wb
      exit function
    end if
  next
  
  set wb = workbooks.open(in_wb_path, updatelinks:=0)
  set checkandattachworkbook = wb

end function
 
function checkandcloseworkbook(in_wb_path as string, in_saved as boolean)
  dim wb as workbook
  dim mywb as string
  mywb = in_wb_path
  for each wb in workbooks
    if lcase(wb.fullname) = lcase(mywb) then
      wb.close savechanges:=in_saved
      exit function
    end if
  next
end function

三，输出结果：

在这里插入图片描述

两种方法输出结果相同：

四，比较总结：

python pandas 内置了大量处理数据的方法，我们不需要重复造轮子，用起来很方便，代码简洁的多。
excel vba 处理这个需求，使用了 数组，字典等数据结构（实际需求中，数据量往往很大，所以一些地方没有直接使用遍历单元格的方法），以及处理字符串，数组和字典的很多方法，对文件的操作也很复杂，一旦出错，调试起来比python也较困难，代码已经尽量优化，但还是远比 python要多。

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