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元组/列表..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
       }
}

...喜欢这么多的礼物我想基于单词“ALL Banks Report”传递元组/列表/数组，以便在列表[0]中进行以下操作

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
           }

并在列表[1]中，其余部分如下所示

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

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

Answer 1

IMO，这里使用pyparsing没有特别的优势。使用老式算法很容易处理这个文件。

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
       }
}

输出：

list

顺便说一下，我避免使用{{1}}作为标识符，因为它是Python关键字。

Answer 2

我是itertools.groupby的忠实粉丝，这是一种非传统的方式来使用它来查找您的银行组：

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：

--------------
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
       }
}

如何根据单词

2 个答案: