如何在python中基于word将部分文本传递给tuple/list

Question

我有以下示例文本，需要根据单词“all Banks Report”将所有文本行传递给tuple/list。。原始文本如下

%Bank PARSED MESSAGE FILE
%VERSION   : PIL 98.7
%nex MODULE   : SIL 98

2018 Jan 31  16:44:53.050 ALL Banks Report SBI
name id ID = 0,  ID = 58
    Freq = 180

    conserved NEXT:
      message c1 : ABC1 : 
          {
            XYZ2
           }
2018 Jan 31  16:44:43.050 ALL Banks Report HDFC
conserved LATE:

World ::= 
{
  Asia c1 : EastAsia : 
      {
        India
       }
}

…就像很多人一样 我想根据单词“ALL Banks Report”传递tuple/List/array，以便在List[0]中执行以下操作

2018 Jan 31  16:44:53.050 ALL Banks Report SBI
name id ID = 0,  ID = 58
    Freq = 180

    conserved NEXT:
      message c1 : ABC1 : 
          {
            XYZ2
           }

在清单[1]中，其余的如下所示

2018 Jan 31  16:44:43.050 ALL Banks Report HDFC
conserved LATE:

World ::= 
{
  Asia c1 : EastAsia : 
      {
        India
       }
}

Answer 1

我是itertools.groupby的超级粉丝，这里有一种非传统的方法来使用它来查找您的银行额度组：

from itertools import groupby

is_header = lambda s: "ALL Banks Report" in s

lines = sample.splitlines()

# call groupby to group lines by whether or not the line is a header or not
group_iter = groupby(lines, key=is_header)

# skip over leading group of non-header lines if the first line is not a header
if not is_header(lines[0]):
    next(group_iter)

groups = []
while True:
    head_lines = next(group_iter, None)

    # no more lines? we're done
    if head_lines is None:
        break

    # extract header lines, which is required before trying to advance the groupby iter
    head_lines = list(head_lines[1])

    # if there were multiple header lines in a row, with no bodies, create group items for them
    while len(head_lines) > 1:
        groups.append([head_lines.pop(0)])

    # get next set of lines which are NOT header lines
    body_lines = next(group_iter, (None, []))

    # extract body lines, which is required before trying to advance the groupby iter
    body_lines = list(body_lines[1])

    # we've found a head line and a body, save it as a single list
    groups.append(head_lines + body_lines)

# what did we get?
for group in groups:
    print('       ')
    print('\n'.join(group))
    print('')

使用您的数据集可以：

       
2018 Jan 31  16:44:53.050 ALL Banks Report SBI
name id ID = 0,  ID = 58
    Freq = 180

    conserved NEXT:
      message c1 : ABC1 : 
          {
            XYZ2
           }

       
2018 Jan 31  16:44:43.050 ALL Banks Report HDFC
conserved LATE:

World ::= 
{
  Asia c1 : EastAsia : 
      {
        India
       }
}

Answer 2

在我看来，这里使用pyparsing没有什么特别的优势。使用老式的算法很容易处理这个文件。你知道吗

output_list = []
items = []
with open('spark.txt') as spark:
    for line in spark:
        line = line.rstrip()
        if line and not line.startswith('%'):
            if 'ALL Banks Report' in line:
                if items:
                    output_list.extend(items)
                items = [line]
            else:
                items.append(line)
if items:
    output_list.extend(items)

for item in output_list:
    print (item)

输出：

2018 Jan 31  16:44:53.050 ALL Banks Report SBI
name id ID = 0,  ID = 58
    Freq = 180
    conserved NEXT:
      message c1 : ABC1 :
          {
            XYZ2
           }
2018 Jan 31  16:44:43.050 ALL Banks Report HDFC
conserved LATE:
World ::=
{
  Asia c1 : EastAsia :
      {
        India
       }
}

顺便说一句，我避免使用list作为标识符，因为它是Python关键字。你知道吗