如何遍历 2 个文件中的行，比较值并在满足条件时更新文件中的值？

Question

为了将值从 10 更改为 18、19 或 20，我正在拆分字符串，访问子字符串，然后尝试更改它。它的工作，但只是不改变价值观。这是我正在尝试实施的解决方案：

oldFileName = 'tryout.hmo'
newFileName = 'tryout_NEW.hmo'
topoFileName = 'Density.topo'

readme = open( oldFileName, "r" )
oldLines = readme.readlines()       

readme = open(topoFileName, "r")
Lines = readme.readlines()
readme.close()

newFile = open(newFileName,"w")


for row in oldLines:
    for line in Lines:
        tmp = line.split()
    list =  row.rstrip()
    tmp1 = list.split()
    newFile.write(row)
    if row.find("BEG_ELEM_DATA") > -1:
       if tmp[0] == tmp1[0]:
           if tmp[2] == 1 and tmp[3] == 0:
            # it is magnet, value 18
             newFile.write(tmp1.replace(tmp1[1], "18"))
         
           elif tmp[2] == 1 and tmp[3] == 1:               
              # it is iron, value 19
             newFile.write(tmp1.replace(tmp1[1], "19"))
         
           else:
            # it is air, value 20
             newFile.write(tmp1.replace(tmp1[1], "20")) 

newFile.close()      

如果你能在上面的脚本中解决这个问题，我将不胜感激，那么我想它应该可以工作。

Answer 1

最后，这是您正在寻找的解决方案。

import pandas as pd
df2 = pd.read_csv('Density.topo', header = 0, names = list('ABCD'), delimiter=r'\s+', skiprows=1)
df2[['C', 'D']]= df2[['C', 'D']].round()

new_file_content=''

with open('tryout.hmo', 'r') as f:
    for line in f:

        if line[11:13] == '10':

            if line[3].isspace():
                ID_to_search_for = line[4:8] # number with 4 digits
            else:
                ID_to_search_for = line[3:8] # number with 5 digits
            search_idx = df2[df2['A'] == ID_to_search_for].index[0]

            if df2['C'][search_idx] == 1 and df2['D'][search_idx] == 0:
                change = '18' #magnet
                new_line = line[:11] + change + line[13:]
            elif df2['C'][search_idx] == 1 and df2['D'][search_idx] == 1:
                change = '19' #iron
                new_line = line[:11] + change + line[13:]
            else:
                change = '20' #air
                new_line = line[:11] + change + line[13:]

            new_file_content += new_line

        else:
            new_file_content += line
    
with open('tryout_changed.hmo', 'w') as f:
    f.write(new_file_content)

如果你不想使用数据框，你可以这样做：

with open('density.topo') as f:
    lists_of_list = [line.rstrip().split() for line in f]

new_file_content=''
with open('tryout_test.hmo', 'r') as f:
    
    for line in f:
        
        if line[11:13] == '10':
            if line[3].isspace():
                ID_to_search_for = line[4:8] # number with 4 digits
            else:
                ID_to_search_for = line[3:8] # number with 5 digits
            for idx, sublist in enumerate(lists_of_list):
                if sublist[0] == ID_to_search_for:                    
                    if lists_of_list[idx][2] == 1 and lists_of_list[idx][3] == 0:
                        change = '18' #magnet
                        new_line = line[:11] + change + line[13:]
                    elif lists_of_list[idx][2] == 1 and lists_of_list[idx][3] == 1:
                        change = '19' #iron
                        new_line = line[:11] + change + line[13:]
                    else:
                        change = '20' #air
                        new_line = line[:11] + change + line[13:]

                    new_file_content += new_line

        else:
            new_file_content += line
    
with open('tryout_changed.hmo', 'w') as f:
    f.write(new_file_content)

好的，这是我的最终答案。它（再次）执行您正在搜索的所有内容。如果有问题，请在您的 IDE 中调试您的代码。您应该开始使用上下文管理器，而不是逐步打开和关闭文件。 我在问题中围绕您的代码编写了新代码，并向其中添加了一些 cmets。

oldFileName = 'tryout.hmo'
newFileName = 'tryout_NEW.hmo'
topoFileName = 'Density.topo'

readme = open( oldFileName, "r" )
oldLines = readme.readlines()

m = int(oldLines[3])
print(m)
new_m = m+3
m1 = str(m)
new_m1 = str(new_m)

Phrase = "END_COMP_DATA"
#n = "Phrase not found" #not used --> not needed

with open(oldFileName,"r") as oldFile: 
    for number, lin in enumerate(oldFile):
        if Phrase in lin:
          n = number
       
#insert 3 lines to tryout_new at the right position (--> row n)
magnet = f"      {m+1} "'"                     topo_magnet"'"\n" 
iron   = f"      {m+2} "'"                       topo_iron"'"\n" 
air    = f"      {m+3} "'"                        topo_air"'"\n"  
    
oldLines[n:n] = [magnet, iron, air]

newFile = open(newFileName,"w") 
flag = 0

with open('density.topo') as f:
    data_density = [line.rstrip().split() for line in f]

for idx, row in enumerate(oldLines):
    lst =  row.rstrip() #I think you shouldn't name a variable like a class in python (list). use 'lst' or something like that
    tmp_tryout = lst.split()
    if row.find("BEG_ELEM_DATA") > -1:
        flag = 1
    if flag == 1 and len(tmp_tryout)>1:
        # if the column has more than 2 columns (after split), check for the "10"
        if tmp_tryout[1] == '10':
            # density_idx_line searchs in density.topo for a match with tmp_tryout[0] (e.g. 3749) and stores the whole line
            density_idx_line = list(filter(lambda x: x[0] == tmp_tryout[0], data_density))
            if len(density_idx_line) >0:
                if density_idx_line[0][2] == '1.0' and density_idx_line[0][3] == '1e-05':
                    # the ' 10 ' is the 10 with a whitespace before and after it. Only like this only the 10 gets replaced (and not e.g. 3104 to 3184)    
                    newFile.write(row.replace(' 10 ', ' 18 '))       
                elif density_idx_line[0][2] == '1.0' and density_idx_line[0][3] == '1.0':               

                    newFile.write(row.replace(' 10 ', ' 19 ')) 
                else:

                    newFile.write(row.replace(' 10 ', ' 20 '))
        else: 
            newFile.write(row)
    else: 
        if idx == 3: 
            newFile.write(row.replace(m1, new_m1)) 
        else:
            newFile.write(row)       
                
newFile.close()
print ("script terminated successfully!")

Answer 2

好的，这是另一个解决方案。对于阅读本文的其他人：这仍然只是一个临时解决方案，但@Sagar 和我都不知道如何做得更好。

import pandas as pd
df = pd.read_csv('tryout.hmo', header = 0, names = list('ABCDEFGHIJKLM'), delimiter=r'\s+', skiprows=[i for i in range(52362)])
df2 = pd.read_csv('Density.topo', header = 0, names = list('ANOP'), delimiter=r'\s+', skiprows=1)
df2 = df2.iloc[:-3, :]

df3 = df.merge(df2, how='outer', on='A')
df3[['O','P']] = df3[['O','P']].fillna(-1).astype(int).replace(-1, np.nan)

df3['B']= df3.apply(lambda x: 18 if x['B']==10 and x['O']==1 and x['P']==0 else (
    19 if x['B']==10 and x['O']==1 and x['P']==1 else (
    20 if x['B']==10 and x['O']==0 and x['P']==0 else x['B'])), axis=1)

df3.to_csv('new_tryout.csv')

它在不到一秒的时间内完成了代码，因此它比 iterrows 或 itertuples 好得多。 新的 csv 文件包括试用文件和密度文件。它们由试用文件的第一列合并在一起（我猜是 ID）

我没有检查所有这些非常大的文件，但从我检查的几个随机点来看，似乎这种方式有效。

Answer 3

我还是 Python 的初学者，但我试图解决您的问题，这是我的解决方案：

我想有更好的方法可以做到这一点，因为您必须在比较之前将所有数据导入数据框。

另外我不知道您是否可以使用 pd.read_csv 将数据读取到数据框，因为我不知道 *.hmo 和 *.topo

import pandas as pd

df = pd.read_csv('tryout.csv', delimiter=';')
df2 = pd.read_csv('density.csv', delimiter=';')

for idx, row in df.iterrows():
    for idx2, row2 in df2.iterrows():
        if row[0] == row2[0]:
            if row2[2] == 1 and row2[3] == 0 :
                # it is magnet, value 18
                row[1] = 18
            elif row2[2] == 1 and row2[3] == 1 :
                # it is iron, value 19
                row[1] = 19
            else:
                # it is air, value 20
                row[1] = 20
df.to_csv('new_tryout.csv')

我的代码在这里做什么，它将两个文件都加载到数据帧中。然后遍历每一行以比较两个文件中的 ID 相同的位置（例如 3749）。 如果为真，则有 3 个 if 语句是否是磁铁/铁/空气，并将 df 中的值更改为正确的数字。

最后将新的df保存到一个新文件'new_tryout.csv'

我为它创建了 2 个测试文件，它按应有的方式工作。