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A Python Book
def test():
args = sys.argv[1:]
if len(args) != 1:
print 'usage: python pyparsing_test3.py <datafile.txt>'
sys.exit(­1)
infilename = sys.argv[1]
infile = file(infilename, 'r')
for line in infile:
line = line.strip()
if line and line[0] != "#":
fields = record.parseString(line)
print fields
test()
And, here is some sample input:
Jabberer, Jerry 111­222­3333 Bakersfield, CA 95111
Kackler, Kerry 111­222­3334 Fresno, CA 95112
Louderdale, Larry 111­222­3335 Los Angeles, CA 94001
Here is output from parsing the above input:
[['Jabberer', 'Jerry'], '111­222­3333', [['Bakersfield'], 'CA', '95111']]
[['Kackler', 'Kerry'], '111­222­3334', [['Fresno'], 'CA', '95112']]
[['Louderdale', 'Larry'], '111­222­3335', [['Los', 'Angeles'], 'CA', '94001']]
Comments:
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We use the len=n argument to the Word constructor to restict the parser to accepting a specific number of characters, for example in the zip code and phone number. Word also accepts min=n'' and ``max=n to enable you to restrict the length of a word to within a range.
We use Group to group the parsed results into sub­lists, for example in the definition of city and name. Group enables us to organize the parse results into simple parse trees.
We use Combine to join parsed results back into a single string. For example, in the phone number, we can require dashes and yet join the results back into a single string.
We use Suppress to remove unneeded sub­elements from parsed results. For example, we do not need the comma between last and first name.
2.6.6.4 A more complex example
This example (thanks to Paul McGuire) parses a more complex structure and produces a dictionary.
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