Related
I'd like to parse tag/value descriptions using the delimiters :, and •
E.g. the Input would be:
Name:Test•Title: Test•Keywords: A,B,C
the expected result should be the name value dict
{
"name": "Test",
"title": "Title",
"keywords: "A,B,C"
}
potentially already splitting the keywords in "A,B,C" to a list. (This is a minor detail since the python built in split method of string will happily do this).
Also applying a mapping
keys={
"Name": "name",
"Title": "title",
"Keywords": "keywords",
}
as a mapping between names and dict keys would be helpful but could be a separate step.
I tried the code below https://trinket.io/python3/8dbbc783c7
# pyparsing named values
# Wolfgang Fahl
# 2023-01-28 for Stackoverflow question
import pyparsing as pp
notes_text="Name:Test•Title: Test•Keywords: A,B,C"
keys={
"Name": "name",
"Titel": "title",
"Keywords": "keywords",
}
keywords=list(keys.keys())
runDelim="•"
name_values_grammar=pp.delimited_list(
pp.oneOf(keywords,as_keyword=True).setResultsName("key",list_all_matches=True)
+":"+pp.Suppress(pp.Optional(pp.White()))
+pp.delimited_list(
pp.OneOrMore(pp.Word(pp.printables+" ", exclude_chars=",:"))
,delim=",")("value")
,delim=runDelim).setResultsName("tag", list_all_matches=True)
results=name_values_grammar.parseString(notes_text)
print(results.dump())
and variations of it but i am not even close to the expected result. Currently the dump shows:
['Name', ':', 'Test']
- key: 'Name'
- tag: [['Name', ':', 'Test']]
[0]:
['Name', ':', 'Test']
- value: ['Test']
Seems i don't know how to define the grammar and work on the parseresult in a way to get the needed dict result.
The main questions for me are:
Should i use parse actions?
How is the naming of part results done?
How is the navigation of the resulting tree done?
How is it possible to get the list back from delimitedList?
What does list_all_matches=True achieve - it's behavior seems strange
I searched for answers on the above questions here on stackoverflow and i couldn't find a consistent picture of what to do.
Pyparsing delimited list only returns first element
Finding lists of elements within a string using Pyparsing
PyParsing seems to be a great tool but i find it very unintuitive. There are fortunately lots of answers here so i hope to learn how to get this example working
Trying myself i took a stepwise approach:
First i checked the delimitedList behavior see https://trinket.io/python3/25e60884eb
# Try out pyparsing delimitedList
# WF 2023-01-28
from pyparsing import printables, OneOrMore, Word, delimitedList
notes_text="A,B,C"
comma_separated_values=delimitedList(Word(printables+" ", exclude_chars=",:"),delim=",")("clist")
grammar = comma_separated_values
result=grammar.parseString(notes_text)
print(f"result:{result}")
print(f"dump:{result.dump()}")
print(f"asDict:{result.asDict()}")
print(f"asList:{result.asList()}")
which returns
result:['A', 'B', 'C']
dump:['A', 'B', 'C']
- clist: ['A', 'B', 'C']
asDict:{'clist': ['A', 'B', 'C']}
asList:['A', 'B', 'C']
which looks promising and the key success factor seems to be to name this list with "clist" and the default behavior looks fine.
https://trinket.io/python3/bc2517e25a
shows in more detail where the problem is.
# Try out pyparsing delimitedList
# see https://stackoverflow.com/q/75266188/1497139
# WF 2023-01-28
from pyparsing import printables, oneOf, OneOrMore,Optional, ParseResults, Suppress,White, Word, delimitedList
def show_result(title:str,result:ParseResults):
"""
show pyparsing result details
Args:
result(ParseResults)
"""
print(f"result for {title}:")
print(f" result:{result}")
print(f" dump:{result.dump()}")
print(f" asDict:{result.asDict()}")
print(f" asList:{result.asList()}")
# asXML is deprecated and doesn't work any more
# print(f"asXML:{result.asXML()}")
notes_text="Name:Test•Title: Test•Keywords: A,B,C"
comma_text="A,B,C"
keys={
"Name": "name",
"Titel": "title",
"Keywords": "keywords",
}
keywords=list(keys.keys())
runDelim="•"
comma_separated_values=delimitedList(Word(printables+" ", exclude_chars=",:"),delim=",")("clist")
cresult=comma_separated_values.parseString(comma_text)
show_result("comma separated values",cresult)
grammar=delimitedList(
oneOf(keywords,as_keyword=True)
+Suppress(":"+Optional(White()))
+comma_separated_values
,delim=runDelim
)("namevalues")
nresult=grammar.parseString(notes_text)
show_result("name value list",nresult)
#ogrammar=OneOrMore(
# oneOf(keywords,as_keyword=True)
# +Suppress(":"+Optional(White()))
# +comma_separated_values
#)
#oresult=grammar.parseString(notes_text)
#show_result("name value list with OneOf",nresult)
output:
result for comma separated values:
result:['A', 'B', 'C']
dump:['A', 'B', 'C']
- clist: ['A', 'B', 'C']
asDict:{'clist': ['A', 'B', 'C']}
asList:['A', 'B', 'C']
result for name value list:
result:['Name', 'Test']
dump:['Name', 'Test']
- clist: ['Test']
- namevalues: ['Name', 'Test']
asDict:{'clist': ['Test'], 'namevalues': ['Name', 'Test']}
asList:['Name', 'Test']
while the first result makes sense for me the second is unintuitive. I'd expected a nested result - a dict with a dict of list.
What causes this unintuitive behavior and how can it be mitigated?
Issues with the grammar being that: you are encapsulating OneOrMore in delimited_list and you only want the outer one, and you aren't telling the parser how your data needs to be structured to give the names meaning.
You also don't need the whitespace suppression as it is automatic.
Adding parse_all to the parse_string function will help to see where not everything is being consumed.
name_values_grammar = pp.delimited_list(
pp.Group(
pp.oneOf(keywords,as_keyword=True).setResultsName("key",list_all_matches=True)
+ pp.Suppress(pp.Literal(':'))
+ pp.delimited_list(
pp.Word(pp.printables, exclude_chars=':,').setResultsName('value', list_all_matches=True)
, delim=',')
)
, delim='•'
).setResultsName('tag', list_all_matches=True)
Should i use parse actions? As you can see, you don't technically need to, but you've ended up with a data structure that might be less efficient for what you want. If the grammar gets more complicated, I think using some parse actions would make sense. Take a look below for some examples to map the key names (only if they are found), and cleaning up list parsing for a more complicated grammar.
How is the naming of part results done? By default in a ParseResults object, the last part that is labelled with a name will be returned when you ask for that name. Asking for all matches to be returned using list_all_matches will only work usefully for some simple structures, but it does work. See below for examples.
How is the navigation of the resulting tree done? By default, everything gets flattened. You can use pyparsing.Group to tell the parser not to flatten its contents into the parent list (and therefore retain useful structure and part names).
How is it possible to get the list back from delimitedList? If you don't wrap the delimited_list result in another list then the flattening that is done will remove the structure. Parse actions or Group on the internal structure again to the rescue.
What does list_all_matches=True achieve - its behavior seems strange It is a function of the grammar structure that it seems strange. Consider the different outputs in:
import pyparsing as pp
print(
pp.delimited_list(
pp.Word(pp.printables, exclude_chars=',').setResultsName('word', list_all_matches=True)
).parse_string('x,y,z').dump()
)
print(
pp.delimited_list(
pp.Word(pp.printables, exclude_chars=':,').setResultsName('key', list_all_matches=True)
+ pp.Suppress(pp.Literal(':'))
+ pp.Word(pp.printables, exclude_chars=':,').setResultsName('value', list_all_matches=True)
)
.parse_string('x:a,y:b,z:c').dump()
)
print(
pp.delimited_list(
pp.Group(
pp.Word(pp.printables, exclude_chars=':,').setResultsName('key', list_all_matches=True)
+ pp.Suppress(pp.Literal(':'))
+ pp.Word(pp.printables, exclude_chars=':,').setResultsName('value', list_all_matches=True)
)
).setResultsName('tag', list_all_matches=True)
.parse_string('x:a,y:b,z:c').dump()
)
The first one makes sense, giving you a list of all the tokens you would expect. The third one also makes sense, since you have a structure you can walk. But the second one you end up with two lists that are not necessarily (in a more complicated grammar) going to be easy to match up.
Here's a different way of building the grammar so that it supports quoting strings with delimiters in them so they don't become lists, and keywords that aren't in your mapping. It's harder to do this without parse actions.
import pyparsing as pp
import json
test_string = "Name:Test•Title: Test•Extra: '1,2,3'•Keywords: A,B,C,'D,E',F"
keys={
"Name": "name",
"Title": "title",
"Keywords": "keywords",
}
g_key = pp.Word(pp.alphas)
g_item = pp.Word(pp.printables, excludeChars='•,\'') | pp.QuotedString(quote_char="'")
g_value = pp.delimited_list(g_item, delim=',')
l_key_value_sep = pp.Suppress(pp.Literal(':'))
g_key_value = g_key + l_key_value_sep + g_value
g_grammar = pp.delimited_list(g_key_value, delim='•')
g_key.add_parse_action(lambda x: keys[x[0]] if x[0] in keys else x)
g_value.add_parse_action(lambda x: [x] if len(x) > 1 else x)
g_key_value.add_parse_action(lambda x: (x[0], x[1].as_list()) if isinstance(x[1],pp.ParseResults) else (x[0], x[1]))
key_values = dict()
for k,v in g_grammar.parse_string(test_string, parse_all=True):
key_values[k] = v
print(json.dumps(key_values, indent=2))
Another approach using regular expressions would be:
def _extractByKeyword(keyword: str, string: str) -> typing.Union[str, None]:
"""
Extract the value for the given key from the given string.
designed for simple key value strings without further formatting
e.g.
Title: Hello World
Goal: extraction
For keyword="Goal" the string "extraction would be returned"
Args:
keyword: extract the value associated to this keyword
string: string to extract from
Returns:
str: value associated to given keyword
None: keyword not found in given string
"""
if string is None or keyword is None:
return None
# https://stackoverflow.com/a/2788151/1497139
# value is closure of not space not / colon
pattern = rf"({keyword}:(?P<value>[\s\w,_-]*))(\s+\w+:|\n|$)"
import re
match = re.search(pattern, string)
value = None
if match is not None:
value = match.group('value')
if isinstance(value, str):
value = value.strip()
return value
keys={
"Name": "name",
"Title": "title",
"Keywords": "keywords",
}
notes_text="Name:Test Title: Test Keywords: A,B,C"
lod = {v: _extractByKeyword(k, notes_text) for k,v in keys.items()}
The extraction function was tested with:
import typing
from dataclasses import dataclass
from unittest import TestCase
class TestExtraction(TestCase)
def test_extractByKeyword(self):
"""
tests the keyword extraction
"""
#dataclass
class TestParam:
expected: typing.Union[str, None]
keyword: typing.Union[str, None]
string: typing.Union[str, None]
testParams = [
TestParam("test", "Goal", "Title:Title\nGoal:test\nLabel:title"),
TestParam("test", "Goal", "Title:Title\nGoal:test Label:title"),
TestParam("test", "Goal", "Title:Title\nGoal:test"),
TestParam("test with spaces", "Goal", "Title:Title\nGoal:test with spaces\nLabel:title"),
TestParam("test with spaces", "Goal", "Title:Title\nGoal:test with spaces Label:title"),
TestParam("test with spaces", "Goal", "Title:Title\nGoal:test with spaces"),
TestParam("SQL-DML", "Goal", "Title:Title\nGoal:SQL-DML"),
TestParam("SQL_DML", "Goal", "Title:Title\nGoal:SQL_DML"),
TestParam(None, None, "Title:Title\nGoal:test"),
TestParam(None, "Label", None),
TestParam(None, None, None),
]
for testParam in testParams:
with self.subTest(testParam=testParam):
actual = _extractByKeyword(testParam.keyword, testParam.string)
self.assertEqual(testParam.expected, actual)
For the time being i am using a simple work-around see https://trinket.io/python3/7ccaa91f7e
# Try out parsing name value list
# WF 2023-01-28
import json
notes_text="Name:Test•Title: Test•Keywords: A,B,C"
keys={
"Name": "name",
"Title": "title",
"Keywords": "keywords",
}
result={}
key_values=notes_text.split("•")
for key_value in key_values:
key,value=key_value.split(":")
value=value.strip()
result[keys[key]]=value # could do another split here if need be
print(json.dumps(result,indent=2))
output:
{
"name": "Test",
"title": "Test",
"keywords": "A,B,C"
}
In Python, I'm looking for a way to extract regex groups given a string and a matching template pattern, for example:
file_path = "/101-001-015_fg01/4312x2156/101-001-015_fg01.0001.exr"
file_template = "/{CODE}_{ELEMENT}/{WIDTH}x{HEIGHT}/{CODE}_{ELEMENT}.{FRAME}.exr"
The output I'm looking for is the following:
{
"CODE": "101-001-015",
"ELEMENT": "fg01",
"WIDTH": "4312",
"HEIGHT: "2156",
"FRAME": "0001"
}
My initial approach was to format my template and find any and all matches, but it's not ideal:
import re
re_format = file_template.format(SHOT='(.*)', ELEMENT='(.*)', WIDTH='(.*)', HEIGHT='(.*)', FRAME='(.*)')
search = re.compile(re_format)
result = search.findall(file_path)
# result: [('101-001-015', 'fg01', '4312', '2156', '101-001-015', 'fg01.000', '')]
All template keys could be contain various characters and be of various lengths so I'm looking for a good matching algorithm. Any ideas if and how this could be done with Python re or any alternative libraries?
Thanks!
I would go for named capturing groups and extract the desired results with the groupdict() function:
import re
file_path = "/101-001-015_fg01/4312x2156/101-001-015_fg01.0001.exr"
rx = r"\/(?P<CODE>.+)_(?P<ELEMENT>.+)\/(?P<WIDTH>.+)x(?P<HEIGHT>.+)\/.+\.(?P<FRAME>\w+).exr"
m = re.match(rx, file_path)
result = m.groupdict()
# {'CODE': '101-001-015', 'ELEMENT': 'fg01', 'WIDTH': '4312', 'HEIGHT': '2156', 'FRAME': '0001'}
Kind of similar like Simon did, I'll also try with named captured group
import re
regex = r"(?P<CODE>[0-9-]+)_(?P<ELEMENT>[0-9a-z]+)\/(?P<WIDTH>[0-9]+)x(?P<HEIGHT>[0-9]+)\/\1_\2\.(?P<FRAME>[0-9]+)\.exr"
test_str = "101-001-015_fg01/4312x2156/101-001-015_fg01.0001.exr"
matches = re.match(regex, test_str)
print(matches.groupdict())
DEMO: https://rextester.com/BEZH21139
I am attempting to parse a log using the Parse library from Python. (https://pypi.python.org/pypi/parse) For my purposes I need to use the type specifiers in the format string, however, some of the data that I am parsing might be a combination of several of those types.
For example:
"4.56|test-1 Cool|dog"
I can parse the number of the front using the format specifier g (general number) and w (word) for "dog" at the end. However, the middle phrase "test-1 Cool" is a number, letters, whitespace, and a dash. Using any of the specifiers alone doesn't seem to work (have tried W,w,s, and S). I would like to extract that phrase as a string.
Without the problem phrase, I would just do this:
test = "|4.56|dog|"
result = parse('|{number:g}|{word:w}|', test)
EDIT: I have had some success using a custom type conversion shown below:
def SString(string):
return string
test = "|4.56|test-1 Cool|dog|"
result = parse('|{number:g}|{other:SString}|{word:w}|', test, dict(SString=SString))
You can do that with some code like this:
from parse import *
test = "4.56|test-1 Cool|dog"
result = parse('{number:g}|{other}|{word:w}', test)
print result
#<Result () {'other': 'test-1 Cool', 'word': 'dog', 'number': 4.56}>
Also, for type checking you can use re module (for example):
from parse import *
import re
def SString(string):
if re.match('\w+-\d+ \w+',string):
return string
else:
return None
test = "|4.56|test-1 Cool|dog|"
result = parse('|{number:g}|{other:SString}|{word:w}|', test, dict(SString=SString))
print(result)
#<Result () {'other': 'test-1 Cool', 'word': 'dog', 'number': 4.56}>
test = "|4.56|t3est Cool|dog|"
result = parse('|{number:g}|{other:SString}|{word:w}|', test, dict(SString=SString))
print(result)
#<Result () {'other': None, 'word': 'dog', 'number': 4.56}>
How about trying
test.split("|")
Okay, so I have the following piece of code.
out = out + re.sub('\{\{([A-z]+)\}\}', values[re.search('\{\{([A-z]+)\}\}',item).group().strip('{}')],item) + " "
Or, more broken down:
out = out + re.sub(
'\{\{([A-z]+)\}\}',
values[
re.search(
'\{\{([A-z]+)\}\}',
item
).group().strip('{}')
],
item
) + " "
So, basically, if you give it a string which contains {{reference}}, it will find instances of that, and replace them with the given reference. The issue with it in it's current form is that it can only work based on the first reference. For example, say my values dictionary was
values = {
'bob': 'steve',
'foo': 'bar'
}
and we passed it the string
item = 'this is a test string for {{bob}}, made using {{foo}}'
I want it to put into out
'this is a test string for steve, made using bar'
but what it currently outputs is
'this is a test string for steve, made using steve'
How can I change the code such that it takes into account the position in the loop.
It should be noted, that doing a word split would not work, as the code needs to work even if the input is {{foo}}{{steve}}
I got the output using the following code,
replace_dict = { 'bob': 'steve','foo': 'bar'}
item = 'this is a test string for {{foo}}, made using {{steve}}'
replace_lst = re.findall('\{\{([A-z]+)\}\}', item)
out = ''
for r in replace_lst:
if r in replace_dict:
item = item.replace('{{' + r + '}}', replace_dict[r])
print item
How's this?
import re
values = {
'bob': 'steve',
'foo': 'bar'
}
item = 'this is a test string for {{bob}}, made using {{foo}}'
pat = re.compile(r'\{\{(.*?)\}\}')
fields = pat.split(item)
fields[1] = values[fields[1]]
fields[3] = values[fields[3]]
print ''.join(fields)
If you could change the format of reference from {{reference}} to {reference}, you could achieve your needs just with format method (instead of using regex):
values = {
'bob': 'steve',
'foo': 'bar'
}
item = 'this is a test string for {bob}, made using {foo}'
print(item.format(**values))
# prints: this is a test string for steve, made using bar
In your code, re.search will start looking from the beginning of the string each time you call it, thus always returning the first match {{bob}}.
You can access the match object you are currently replacing by passing a function as replacement to re.sub:
values = { 'bob': 'steve','foo': 'bar'}
item = 'this is a test string for {{bob}}, made using {{foo}}'
pattern = r'{{([A-Za-z]+)}}'
# replacement function
def get_value(match):
return values[match.group(1)]
result = re.sub(pattern, get_value, item)
# print result => 'this is a test string for steve, made using bar'
What is the simplest way to convert a string of keyword=values to a dictionary, for example the following string:
name="John Smith", age=34, height=173.2, location="US", avatar=":,=)"
to the following python dictionary:
{'name':'John Smith', 'age':34, 'height':173.2, 'location':'US', 'avatar':':,=)'}
The 'avatar' key is just to show that the strings can contain = and , so a simple 'split' won't do. Any ideas? Thanks!
This works for me:
# get all the items
matches = re.findall(r'\w+=".+?"', s) + re.findall(r'\w+=[\d.]+',s)
# partition each match at '='
matches = [m.group().split('=', 1) for m in matches]
# use results to make a dict
d = dict(matches)
I would suggest a lazy way of doing this.
test_string = 'name="John Smith", age=34, height=173.2, location="US", avatar=":,=)"'
eval("dict({})".format(test_string))
{'age': 34, 'location': 'US', 'avatar': ':,=)', 'name': 'John Smith', 'height': 173.2}
Hope this helps someone !
Edit: since the csv module doesn't deal as desired with quotes inside fields, it takes a bit more work to implement this functionality:
import re
quoted = re.compile(r'"[^"]*"')
class QuoteSaver(object):
def __init__(self):
self.saver = dict()
self.reverser = dict()
def preserve(self, mo):
s = mo.group()
if s not in self.saver:
self.saver[s] = '"%d"' % len(self.saver)
self.reverser[self.saver[s]] = s
return self.saver[s]
def expand(self, mo):
return self.reverser[mo.group()]
x = 'name="John Smith", age=34, height=173.2, location="US", avatar=":,=)"'
qs = QuoteSaver()
y = quoted.sub(qs.preserve, x)
kvs_strings = y.split(',')
kvs_pairs = [kv.split('=') for kv in kvs_strings]
kvs_restored = [(k, quoted.sub(qs.expand, v)) for k, v in kvs_pairs]
def converter(v):
if v.startswith('"'): return v.strip('"')
try: return int(v)
except ValueError: return float(v)
thedict = dict((k.strip(), converter(v)) for k, v in kvs_restored)
for k in thedict:
print "%-8s %s" % (k, thedict[k])
print thedict
I'm emitting thedict twice to show exactly how and why it differs from the required result; the output is:
age 34
location US
name John Smith
avatar :,=)
height 173.2
{'age': 34, 'location': 'US', 'name': 'John Smith', 'avatar': ':,=)',
'height': 173.19999999999999}
As you see, the output for the floating point value is as requested when directly emitted with print, but it isn't and cannot be (since there IS no floating point value that would display 173.2 in such a case!-) when the print is applied to the whole dict (because that inevitably uses repr on the keys and values -- and the repr of 173.2 has that form, given the usual issues about how floating point values are stored in binary, not in decimal, etc, etc). You might define a dict subclass which overrides __str__ to specialcase floating-point values, I guess, if that's indeed a requirement.
But, I hope this distraction doesn't interfere with the core idea -- as long as the doublequotes are properly balanced (and there are no doublequotes-inside-doublequotes), this code does perform the required task of preserving "special characters" (commas and equal signs, in this case) from being taken in their normal sense when they're inside double quotes, even if the double quotes start inside a "field" rather than at the beginning of the field (csv only deals with the latter condition). Insert a few intermediate prints if the way the code works is not obvious -- first it changes all "double quoted fields" into a specially simple form ("0", "1" and so on), while separately recording what the actual contents corresponding to those simple forms are; at the end, the simple forms are changed back into the original contents. Double-quote stripping (for strings) and transformation of the unquoted strings into integers or floats is finally handled by the simple converter function.
Here is a more verbose approach to the problem using pyparsing. Note the parse actions
which do the automatic conversion of types from strings to ints or floats. Also, the
QuotedString class implicitly strips the quotation marks from the quoted value. Finally,
the Dict class takes each 'key = val' group in the comma-delimited list, and assigns
results names using the key and value tokens.
from pyparsing import *
key = Word(alphas)
EQ = Suppress('=')
real = Regex(r'[+-]?\d+\.\d+').setParseAction(lambda t:float(t[0]))
integer = Regex(r'[+-]?\d+').setParseAction(lambda t:int(t[0]))
qs = QuotedString('"')
value = real | integer | qs
dictstring = Dict(delimitedList(Group(key + EQ + value)))
Now to parse your original text string, storing the results in dd. Pyparsing returns an
object of type ParseResults, but this class has many dict-like features (support for keys(),
items(), in, etc.), or can emit a true Python dict by calling asDict(). Calling dump()
shows all of the tokens in the original parsed list, plus all of the named items. The last
two examples show how to access named items within a ParseResults as if they were attributes of
a Python object.
text = 'name="John Smith", age=34, height=173.2, location="US", avatar=":,=)"'
dd = dictstring.parseString(text)
print dd.keys()
print dd.items()
print dd.dump()
print dd.asDict()
print dd.name
print dd.avatar
Prints:
['age', 'location', 'name', 'avatar', 'height']
[('age', 34), ('location', 'US'), ('name', 'John Smith'), ('avatar', ':,=)'), ('height', 173.19999999999999)]
[['name', 'John Smith'], ['age', 34], ['height', 173.19999999999999], ['location', 'US'], ['avatar', ':,=)']]
- age: 34
- avatar: :,=)
- height: 173.2
- location: US
- name: John Smith
{'age': 34, 'height': 173.19999999999999, 'location': 'US', 'avatar': ':,=)', 'name': 'John Smith'}
John Smith
:,=)
The following code produces the correct behavior, but is just a bit long! I've added a space in the avatar to show that it deals well with commas and spaces and equal signs inside the string. Any suggestions to shorten it?
import hashlib
string = 'name="John Smith", age=34, height=173.2, location="US", avatar=":, =)"'
strings = {}
def simplify(value):
try:
return int(value)
except:
return float(value)
while True:
try:
p1 = string.index('"')
p2 = string.index('"',p1+1)
substring = string[p1+1:p2]
key = hashlib.md5(substring).hexdigest()
strings[key] = substring
string = string[:p1] + key + string[p2+1:]
except:
break
d = {}
for pair in string.split(', '):
key, value = pair.split('=')
if value in strings:
d[key] = strings[value]
else:
d[key] = simplify(value)
print d
Here is a approach with eval, I considered it is as unreliable though, but its works for your example.
>>> import re
>>>
>>> s='name="John Smith", age=34, height=173.2, location="US", avatar=":,=)"'
>>>
>>> eval("{"+re.sub('(\w+)=("[^"]+"|[\d.]+)','"\\1":\\2',s)+"}")
{'age': 34, 'location': 'US', 'name': 'John Smith', 'avatar': ':,=)', 'height': 173.19999999999999}
>>>
Update:
Better use the one pointed by Chris Lutz in the comment, I believe Its more reliable, because even there is (single/double) quotes in dict values, it might works.
Here's a somewhat more robust version of the regexp solution:
import re
keyval_re = re.compile(r'''
\s* # Leading whitespace is ok.
(?P<key>\w+)\s*=\s*( # Search for a key followed by..
(?P<str>"[^"]*"|\'[^\']*\')| # a quoted string; or
(?P<float>\d+\.\d+)| # a float; or
(?P<int>\d+) # an int.
)\s*,?\s* # Handle comma & trailing whitespace.
|(?P<garbage>.+) # Complain if we get anything else!
''', re.VERBOSE)
def handle_keyval(match):
if match.group('garbage'):
raise ValueError("Parse error: unable to parse: %r" %
match.group('garbage'))
key = match.group('key')
if match.group('str') is not None:
return (key, match.group('str')[1:-1]) # strip quotes
elif match.group('float') is not None:
return (key, float(match.group('float')))
elif match.group('int') is not None:
return (key, int(match.group('int')))
It automatically converts floats & ints to the right type; handles single and double quotes; handles extraneous whitespace in various locations; and complains if a badly formatted string is supplied
>>> s='name="John Smith", age=34, height=173.2, location="US", avatar=":,=)"'
>>> print dict(handle_keyval(m) for m in keyval_re.finditer(s))
{'age': 34, 'location': 'US', 'name': 'John Smith', 'avatar': ':,=)', 'height': 173.19999999999999}
do it step by step
d={}
mystring='name="John Smith", age=34, height=173.2, location="US", avatar=":,=)"';
s = mystring.split(", ")
for item in s:
i=item.split("=",1)
d[i[0]]=i[-1]
print d
I think you just need to set maxsplit=1, for instance the following should work.
string = 'name="John Smith", age=34, height=173.2, location="US", avatar=":, =)"'
newDict = dict(map( lambda(z): z.split("=",1), string.split(", ") ))
Edit (see comment):
I didn't notice that ", " was a value under avatar, the best approach would be to escape ", " wherever you are generating data. Even better would be something like JSON ;). However, as an alternative to regexp, you could try using shlex, which I think produces cleaner looking code.
import shlex
string = 'name="John Smith", age=34, height=173.2, location="US", avatar=":, =)"'
lex = shlex.shlex ( string )
lex.whitespace += "," # Default whitespace doesn't include commas
lex.wordchars += "." # Word char should include . to catch decimal
words = [ x for x in iter( lex.get_token, '' ) ]
newDict = dict ( zip( words[0::3], words[2::3]) )
Always comma separated? Use the CSV module to split the line into parts (not checked):
import csv
import cStringIO
parts=csv.reader(cStringIO.StringIO(<string to parse>)).next()