我正在寻找一些帮助在我的文本文件中循环遍历每个组并将三个变量与我的csv匹配，并且在成功匹配时它将向csv文件写入一些新变量：
在文本文件中，第1行与csv元素1匹配 在文本文件中，第2行与csv元素0匹配
每位学生将分为三个部分：
3 Tommy 144512/23332以及第1部分和第3部分将分别写入第12和第13部分。第2部分将用于第三场比赛，与csv elelment 8匹配，这是为了找出要写入的行。
“数据”将写入要素14（第15栏） “misc3”将写入要素15（第16栏） “bla3”将写入元素16（第17列）
评论文本文件：
Textfile Item 1 (Will loop/cycle/run 4 times, because there are 4 students)
|
v
MData (N/A) <-- Match Line 1 (matches to csv element 1)
DMATCH1 <-- Match Line 2 (matches to csv element 0)
3 Tommy 144512/23332 <-- Match Line 3 (matches to csv element 8) (Loop 1)
1 Jim 90000/222311 <-- Match Line 3 (matches to csv element 8) (Loop 2)
1 Elz M 90000/222311 <-- Match Line 3 (matches to csv element 8) (Loop 3)
1 Ben 90000/222311 <-- Match Line 3 (matches to csv element 8) (Loop 4)
Data $50.90 <-- If "Data" Exists then filewrite to csv element 14 (Loop 1)
misc2 $10.40 <-- If "misc2" Exists then filewrite to csv element 15 (Loop 1)
bla3 $20.20 <-- If "bla3" Exists then filewrite to csv element 16 (Loop 1)
Textfile Item 2 (Will loop/cycle/run 2 times, because there are 3 students)
|
v
MData (B/B) <-- Match Line 1 (matches to csv element 1)
DMATCH2 <-- Match Line 2 (matches to csv element 0)
4 James Smith 2333/114441 <-- Match Line 3 (matches to csv element 8) (Loop 1)
4 Mike 90000/222311 <-- Match Line 3 (matches to csv element 8) (Loop 2)
4 Jessica Long 2333/114441 <-- Match Line 3 (matches to csv element 8) (Loop 3)
Data $50.90 <-- If "Data" Exists then filewrite to csv element 14 (Loop 1)
bla3 $5.44 <-- If "bla3" Exists then filewrite to csv element 16 (Loop 1)
Textfile Item 3 (Will loop/cycle/run 2 times, because there are 2 students)
|
v
Mdata <-- Match Line 1 (matches to csv element 1)
DMATCH3 <-- Match Line 2 (matches to csv element 0)
5 Joe Reane 0/0 <-- Match Line 3 (matches to csv element 8) (Loop 1)
5 Peter Jones 90000/222311 <-- Match Line 3 (matches to csv element 8) (Loop 2)
misc2 $420.00 <-- If "misc2" Exists then filewrite to csv element 15 (Loop 1)
bla3 $210.00 <-- If "bla3" Exists then filewrite to csv element 16 (Loop 1)
未注释的Real Textfile：
MData (N/A)
DMATCH1
3 Tommy 144512/23332
1 Jim 90000/222311
1 Elz M 90000/222311
1 Ben 90000/222311
Data $50.90
misc2 $10.40
bla3 $20.20
MData (B/B)
DMATCH2
4 James Smith 2333/114441
4 Mike 90000/222311
4 Jessica Long 2333/114441
Data $50.90
bla3 $5.44
Mdata
DMATCH3
5 Joe Reane 0/0
5 Peter Jones 90000/222311
Data $10.91
misc2 $420.00
bla3 $210.00
CSV之前：
MATCH1,MATCH2,TITLE,TITLE,TITLE,TITLE,TITLE,TITLE,MATCH3,DATA,TITLE,TITLE
DMATCH1,MData (N/A),data,data,data,data,data,data,Tommy,55,data,data
DMATCH1,MData (N/A),data,data,data,data,data,data,Ben,54,data,data
DMATCH1,MData (N/A),data,data,data,data,data,data,Jim,52,data,data
DMATCH1,MData (N/A),data,data,data,data,data,data,Elz M,22,data,data
DMATCH2,MData (B/B),data,data,data,data,data,data,James Smith,15,data,data
DMATCH2,MData (B/B),data,data,data,data,data,data,Jessica Long,224,data,data
DMATCH2,MData (B/B),data,data,data,data,data,data,Mike,62,data,data
DMATCH3,Mdata,data,data,data,data,data,data,Joe Reane,66,data,data
DMATCH3,Mdata,data,data,data,data,data,data,Peter Jones,256,data,data
DMATCH3,Mdata,data,data,data,data,data,data,Lesley Lope,5226,data,data
CSV After：
MATCH1,MATCH2,TITLE,TITLE,TITLE,TITLE,TITLE,TITLE,MATCH3,DATA,TITLE,TITLE,,,,,
DMATCH1,MData (N/A),data,data,data,data,data,data,Tommy,55,data,data,3,144512/23332,Data $50.90,misc2 $10.40,bla3 $20.20
DMATCH1,MData (N/A),data,data,data,data,data,data,Ben,54,data,data,1,90000/222311,,,
DMATCH1,MData (N/A),data,data,data,data,data,data,Jim,52,data,data,1,90000/222311,,,
DMATCH1,MData (N/A),data,data,data,data,data,data,Elz M,22,data,data,1,90000/222311,,,
DMATCH2,MData (B/B),data,data,data,data,data,data,James Smith,15,data,data,4,2333/114441,Data $50.90,,bla3 $5.44
DMATCH2,MData (B/B),data,data,data,data,data,data,Jessica Long,224,data,data,4,2333/114441,,,
DMATCH2,MData (B/B),data,data,data,data,data,data,Mike,62,data,data,4,90000/222311,,,
DMATCH3,Mdata,data,data,data,data,data,data,Joe Reane,66,data,data,5,0/0,,misc2 $420.00,bla3 $210.00
DMATCH3,Mdata,data,data,data,data,data,data,Peter Jones,256,data,data,5,90000/222311,,,
DMATCH3,Mdata,data,data,data,data,data,data,Lesley Lope,5226,data,data,,,,,
任何人都知道如何实现这个目标吗？
非常感谢任何帮助！
答案 0 :(得分：5)
这个问题实际上有几个子问题。首先，我们必须阅读有趣格式的文本文件：
# each block in the text file will be one element of this list
matchers = [[]]
i = 0
with open('test.txt') as infile:
for line in infile:
line = line.strip()
# Blocks are seperated by blank lines
if len(line) == 0:
i += 1
matchers.append([])
# assume there are always two blank lines between items
# and just skip to the lext line
infile.next()
continue
matchers[i].append(line)
此时我们有一个列表列表，每个块一个元素，每行包含一个元素。然后我们必须转换为更像桌子的东西
import re
# This regular expression matches the variable number of students in each block
studentlike = re.compile('(\d+) (.+) (\d+/\d+)')
# We will build a table containing a list of elements for each student
table = []
for matcher in matchers:
# We use an iterator over the block lines to make indexing simpler
it = iter(matcher)
# The first two elements are match values
m1, m2 = it.next(), it.next()
# then there are a number of students
students = []
for possiblestudent in it:
m = studentlike.match(possiblestudent)
if m:
students.append(list(m.groups()))
else:
break
# After the students come the data elements, which we read into a dictionary
# We also add in the last possible student line as that didn't match the student re
dataitems = dict(item.split() for item in [possiblestudent] + list(it))
datanames = dataitems.keys()
# Finally we construct the table
for student in students:
# We use the dictionary .get() method to return blanks for the missing fields
table.append([m1, m2] + student + [dataitems.get(d, '') for d in datanames])
print table
现在，我们可以加入数据。我在这里使用过Pandas，因为它对于这种加入非常有用：
import pandas
csvdata = pandas.read_csv('test.csv')
textdata = pandas.DataFrame(table, columns=['MATCH2', 'MATCH1', 'TITLE01', 'MATCH3', 'TITLE02', 'Data', 'misc2', 'bla3'])
mergeddata = pandas.merge(csvdata, textdata, how='left', on=['MATCH1', 'MATCH2', 'MATCH3'], sort=False)
mergeddata.to_csv('output.csv', index=False)