Parsing unstructured text in Python

Parsing unstructured text in Python - python

I wanted to parse a text file that contains unstructured text. I need to get the address, date of birth, name, sex, and ID.
. 55 MORILLO ZONE VIII,
BARANGAY ZONE VIII
(POB.), LUISIANA, LAGROS
F
01/16/1952
ALOMO, TERESITA CABALLES
3412-00000-A1652TCA2
12
. 22 FABRICANTE ST. ZONE
VIII LUISIANA LAGROS,
BARANGAY ZONE VIII
(POB.), LUISIANA, LAGROS
M
10/14/1967
AMURAO, CALIXTO MANALO13
In the example above, the first 3 lines is the address, the line with just an "F" is the sex, the DOB would be the line after "F", name after the DOB, the ID after the name, and the no. 12 under the ID is the index/record no.
However, the format is not consistent. In the second group, the address is 4 lines instead of 3 and the index/record no. is appended after the name (if the person doesn't have an ID field).
I wanted to rewrite the text into the following format:
name, ID, address, sex, DOB

Here is a first stab at a pyparsing solution (easy-to-copy code at the pyparsing pastebin). Walk through the separate parts, according to the interleaved comments.
data = """\
. 55 MORILLO ZONE VIII,
BARANGAY ZONE VIII
(POB.), LUISIANA, LAGROS
F
01/16/1952
ALOMO, TERESITA CABALLES
3412-00000-A1652TCA2
12
. 22 FABRICANTE ST. ZONE
VIII LUISIANA LAGROS,
BARANGAY ZONE VIII
(POB.), LUISIANA, LAGROS
M
10/14/1967
AMURAO, CALIXTO MANALO13
"""
from pyparsing import LineEnd, oneOf, Word, nums, Combine, restOfLine, \
alphanums, Suppress, empty, originalTextFor, OneOrMore, alphas, \
Group, ZeroOrMore
NL = LineEnd().suppress()
gender = oneOf("M F")
integer = Word(nums)
date = Combine(integer + '/' + integer + '/' + integer)
# define the simple line definitions
gender_line = gender("sex") + NL
dob_line = date("DOB") + NL
name_line = restOfLine("name") + NL
id_line = Word(alphanums+"-")("ID") + NL
recnum_line = integer("recnum") + NL
# define forms of address lines
first_addr_line = Suppress('.') + empty + restOfLine + NL
# a subsequent address line is any line that is not a gender definition
subsq_addr_line = ~(gender_line) + restOfLine + NL
# a line with a name and a recnum combined, if there is no ID
name_recnum_line = originalTextFor(OneOrMore(Word(alphas+',')))("name") + \
integer("recnum") + NL
# defining the form of an overall record, either with or without an ID
record = Group((first_addr_line + ZeroOrMore(subsq_addr_line))("address") +
gender_line +
dob_line +
((name_line +
id_line +
recnum_line) |
name_recnum_line))
# parse data
records = OneOrMore(record).parseString(data)
# output the desired results (note that address is actually a list of lines)
for rec in records:
if rec.ID:
print "%(name)s, %(ID)s, %(address)s, %(sex)s, %(DOB)s" % rec
else:
print "%(name)s, , %(address)s, %(sex)s, %(DOB)s" % rec
print
# how to access the individual fields of the parsed record
for rec in records:
print rec.dump()
print rec.name, 'is', rec.sex
print
Prints:
ALOMO, TERESITA CABALLES, 3412-00000-A1652TCA2, ['55 MORILLO ZONE VIII,', 'BARANGAY ZONE VIII', '(POB.), LUISIANA, LAGROS'], F, 01/16/1952
AMURAO, CALIXTO MANALO, , ['22 FABRICANTE ST. ZONE', 'VIII LUISIANA LAGROS,', 'BARANGAY ZONE VIII', '(POB.), LUISIANA, LAGROS'], M, 10/14/1967
['55 MORILLO ZONE VIII,', 'BARANGAY ZONE VIII', '(POB.), LUISIANA, LAGROS', 'F', '01/16/1952', 'ALOMO, TERESITA CABALLES', '3412-00000-A1652TCA2', '12']
- DOB: 01/16/1952
- ID: 3412-00000-A1652TCA2
- address: ['55 MORILLO ZONE VIII,', 'BARANGAY ZONE VIII', '(POB.), LUISIANA, LAGROS']
- name: ALOMO, TERESITA CABALLES
- recnum: 12
- sex: F
ALOMO, TERESITA CABALLES is F
['22 FABRICANTE ST. ZONE', 'VIII LUISIANA LAGROS,', 'BARANGAY ZONE VIII', '(POB.), LUISIANA, LAGROS', 'M', '10/14/1967', 'AMURAO, CALIXTO MANALO', '13']
- DOB: 10/14/1967
- address: ['22 FABRICANTE ST. ZONE', 'VIII LUISIANA LAGROS,', 'BARANGAY ZONE VIII', '(POB.), LUISIANA, LAGROS']
- name: AMURAO, CALIXTO MANALO
- recnum: 13
- sex: M
AMURAO, CALIXTO MANALO is M

you have to exploit whatever regularity and structure the text does have.
I suggest you read one line at a time and match it to a regular expression to determine its type, fill in the appropriate field in a person object. writing out that object and starting a new one whenever you get a field that you already have filled in.

It may be overkill, but the leading edge machine learning algorithms for this type of problem are based on conditional random fields. For example, Accurate Information Extraction from Research Papers
using Conditional Random Fields.
There is software out there that makes training these models relatively easy. See Mallet or CRF++.

You can probably do this with regular expressions without too much difficulty. If you have never used them before, check out the python documentation, then fire up redemo.py (on my computer, it's in c:\python26\Tools\scripts).
The first task is to split the flat file into a list of entities (one chunk of text per record). From the snippet of text you gave, you could split the file with a pattern matching the beginning of a line, where the first character is a dot:
import re
re_entity_splitter = re.compile(r'^\.')
entities = re_entity_splitter.split(open(textfile).read())
Note that the dot must be escaped (it's a wildcard character by default). Note also the r before the pattern. The r denotes 'raw string' format, which excuses you from having to escape the escape characters, resulting in so-called 'backslash plague.'
Once you have the file split into individual people, picking out the gender and birthdate is a snap. Use these:
re_gender = re.compile(r'^[MF]')
re_birth_Date = re.compile(r'\d\d/\d\d/\d\d')
And away you go. You can paste the flat file into re demo GUI and experiment with creating patterns to match what you need. You'll have it parsed in no time. Once you get good at this, you can use symbolic group names (see docs) to pick out individual elements quickly and cleanly.

Here's a quick hack job.
f = open('data.txt')
def process(file):
address = ""
for line in file:
if line == '': raise StopIteration
line = line.rstrip() # to ignore \n
if line in ('M','F'):
sex = line
break
else:
address += line
DOB = file.readline().rstrip() # to ignore \n
name = file.readline().rstrip()
if name[-1].isdigit():
name = re.match(r'^([^\d]+)\d+', name).group(1)
ID = None
else:
ID = file.readline().rstrip()
file.readline() # ignore the record #
print (name, ID, address, sex, DOB)
while True:
process(f)

Related

How do I transform a non-CSV text file into a CSV using Python/Pandas?

I have a text file that looks like this:
Id Number: 12345678
Location: 1234561791234567090-8.9
Street: 999 Street AVE
Buyer: john doe
Id Number: 12345688
Location: 3582561791254567090-8.9
Street: 123 Street AVE
Buyer: Jane doe # buyer % LLC
Id Number: 12345689
Location: 8542561791254567090-8.9
Street: 854 Street AVE
Buyer: Jake and Bob: Owner%LLC: Inc
I'd like the file to look like this:
Id Number
Location
Street
Buyer
12345678
1234561791234567090-8.9
999 Street AVE
john doe
12345688
3582561791254567090-8.9
123 Street AVE
Jane doe # buyer % LLC
12345689
8542561791254567090-8.9
854 Street AVE
Jake and Bob: Owner%LLC: Inc
I have tried the following:
# 1 Read text file and ignore bad lines (lines with extra colons thus reading as extra fields).
tr = pd.read_csv('C:\\File Path\\test.txt', sep=':', header=None, error_bad_lines=False)
# 2 Convert into a dataframe/pivot table.
ndf = pd.DataFrame(tr.pivot(index=None, columns=0, values=1))
# 3 Clean up the pivot table to remove NaNs and reset the index (line by line).
nf2 = ndf.apply(lambda x: x.dropna().reset_index(drop=True))
Here is where got the last line (#3): https://stackoverflow.com/a/62481057/10448224
When I do the above and export to CSV the headers are arranged like the following:
(index)
Street
Buyer
Id Number
Location
The data is filled in nicely but at some point the Buyer field becomes inaccurate but the rest of the fields are accurate through the entire DF.
My guesses:
When I run #1 part of my script I get the following errors 507 times:
b'Skipping line 500: expected 2 fields, saw 3\nSkipping line 728: expected 2 fields, saw 3\
At the tail end of the new DF I am missing exactly 507 entries for the Byer field. So I think when I drop my bad lines, the field is pushing my data up.
Pain Points:
The Buyer field will sometimes have extra colons and other odd characters. So when I try to use a colon as a delimiter I run into problems.
I am new to Python and I am very new to using functions. I primarily use Pandas to manipulate data at a somewhat basic level. So in the words of the great Michael Scott: "Explain it to me like I'm five." Many many thanks to anyone willing to help.

Here's what I meant by reading in and using split. Very similar to other answers. Untested and I don't recall if inputline include eol, so I stripped it too.
with open('myfile.txt') as f:
data = [] # holds database
record = {} # holds built up record
for inputline in f:
key,value = inputline.strip().split(':',1)
if key == "Id Number": # new record starting
if len(record):
data.append(record) # write previous record
record = {}
record.update({key:value})
if len(record):
data.append(record) # out final record
df = pd.DataFrame(data)

This is a minimal example that demonstrates the basics:
cat split_test.txt
Id Number: 12345678
Location: 1234561791234567090-8.9
Street: 999 Street AVE
Buyer: john doe
Id Number: 12345688
Location: 3582561791254567090-8.9
Street: 123 Street AVE
Buyer: Jane doe # buyer % LLC
Id Number: 12345689
Location: 8542561791254567090-8.9
Street: 854 Street AVE
Buyer: Jake and Bob: Owner%LLC: Inc
import csv
with open("split_test.txt", "r") as f:
id_val = "Id Number"
list_var = []
for line in f:
split_line = line.strip().split(':')
print(split_line)
if split_line[0] == id_val:
d = {}
d[split_line[0]] = split_line[1]
list_var.append(d)
else:
d.update({split_line[0]: split_line[1]})
list_var
[{'Id Number': ' 12345689',
'Location': ' 8542561791254567090-8.9',
'Street': ' 854 Street AVE',
'Buyer': ' Jake and Bob'},
{'Id Number': ' 12345678',
'Location': ' 1234561791234567090-8.9',
'Street': ' 999 Street AVE',
'Buyer': ' john doe'},
{'Id Number': ' 12345688',
'Location': ' 3582561791254567090-8.9',
'Street': ' 123 Street AVE',
'Buyer': ' Jane doe # buyer % LLC'}]
with open("split_ex.csv", "w") as csv_file:
field_names = list_var[0].keys()
csv_writer = csv.DictWriter(csv_file, fieldnames=field_names)
csv_writer.writeheader()
for row in list_var:
csv_writer.writerow(row)

I would try reading the file line by line, splitting the key-value pairs into a list of dicts to look something like:
data = [
{
"Id Number": 12345678,
"Location": 1234561791234567090-8.9,
...
},
{
"Id Number": ...
}
]
# easy to create the dataframe from here
your_df = pd.DataFrame(data)

Search in List; Display names based on search input

I have sought different articles here about searching data from a list, but nothing seems to be working right or is appropriate in what I am supposed to implement.
I have this pre-created module with over 500 list (they are strings, yes, but is considered as list when called into function; see code below) of names, city, email, etc. The following are just a chunk of it.
empRecords="""Jovita,Oles,8 S Haven St,Daytona Beach,Volusia,FL,6/14/1965,32114,386-248-4118,386-208-6976,joles#gmail.com,http://www.paganophilipgesq.com,;
Alesia,Hixenbaugh,9 Front St,Washington,District of Columbia,DC,3/3/2000,20001,202-646-7516,202-276-6826,alesia_hixenbaugh#hixenbaugh.org,http://www.kwikprint.com,;
Lai,Harabedian,1933 Packer Ave #2,Novato,Marin,CA,1/5/2000,94945,415-423-3294,415-926-6089,lai#gmail.com,http://www.buergimaddenscale.com,;
Brittni,Gillaspie,67 Rv Cent,Boise,Ada,ID,11/28/1974,83709,208-709-1235,208-206-9848,bgillaspie#gillaspie.com,http://www.innerlabel.com,;
Raylene,Kampa,2 Sw Nyberg Rd,Elkhart,Elkhart,IN,12/19/2001,46514,574-499-1454,574-330-1884,rkampa#kampa.org,http://www.hermarinc.com,;
Flo,Bookamer,89992 E 15th St,Alliance,Box Butte,NE,12/19/1957,69301,308-726-2182,308-250-6987,flo.bookamer#cox.net,http://www.simontonhoweschneiderpc.com,;
Jani,Biddy,61556 W 20th Ave,Seattle,King,WA,8/7/1966,98104,206-711-6498,206-395-6284,jbiddy#yahoo.com,http://www.warehouseofficepaperprod.com,;
Chauncey,Motley,63 E Aurora Dr,Orlando,Orange,FL,3/1/2000,32804,407-413-4842,407-557-8857,chauncey_motley#aol.com,http://www.affiliatedwithtravelodge.com
"""
a = empRecords.strip().split(";")
And I have the following code for searching:
import empData as x
def seecity():
empCitylist = list()
for ct in x.a:
empCt = ct.strip().split(",")
empCitylist.append(empCt)
t = sorted(empCitylist, key=lambda x: x[3])
for c in t:
city = (c[3])
print(city)
live_city = input("Enter city: ")
for cy in city:
if live_city in cy:
print(c[1])
# print("Name: "+ c[1] + ",", c[0], "| Current City: " + c[3])
Forgive my idiotic approach as I am new to Python. However, what I am trying to do is user will input the city, then the results should display the employee's last name, first name who are living in that city (I dunno if I made sense lol)
By the way, the code I used above doesn't return any answers. It just loops to the input.
Thank you for helping. Lovelots. <3
PS: the format of the empData is: first name, last name, address, city, country, birthday, zip, phone, and email

You can use the csv module to read easily a file with comma separated values
import csv
with open('test.csv', newline='') as csvfile:
records = list(csv.reader(csvfile))
def search(data, elem, index):
out = list()
for row in data:
if row[index] == elem:
out.append(row)
return out
#test
print(search(records, 'Orlando', 3))

Based on your original code, you can do it like this:
# Make list of list records, sorted by city
t = sorted((ct.strip().split(",") for ct in x.a), key=lambda x: x[3])
# List cities
print("Cities in DB:")
for c in t:
city = (c[3])
print("-", city)
# Define search function
def seecity():
live_city = input("Enter city: ")
for c in t:
if live_city == c[3]:
print("Name: "+ c[1] + ",", c[0], "| Current City: " + c[3])
seecity()
Then, after you understand what's going on, do as #Hoxha Alban suggested, and use the csv module.

The beauty of python lies in list comprehension.
empRecords="""Jovita,Oles,8 S Haven St,Daytona Beach,Volusia,FL,6/14/1965,32114,386-248-4118,386-208-6976,joles#gmail.com,http://www.paganophilipgesq.com,;
Alesia,Hixenbaugh,9 Front St,Washington,District of Columbia,DC,3/3/2000,20001,202-646-7516,202-276-6826,alesia_hixenbaugh#hixenbaugh.org,http://www.kwikprint.com,;
Lai,Harabedian,1933 Packer Ave #2,Novato,Marin,CA,1/5/2000,94945,415-423-3294,415-926-6089,lai#gmail.com,http://www.buergimaddenscale.com,;
Brittni,Gillaspie,67 Rv Cent,Boise,Ada,ID,11/28/1974,83709,208-709-1235,208-206-9848,bgillaspie#gillaspie.com,http://www.innerlabel.com,;
Raylene,Kampa,2 Sw Nyberg Rd,Elkhart,Elkhart,IN,12/19/2001,46514,574-499-1454,574-330-1884,rkampa#kampa.org,http://www.hermarinc.com,;
Flo,Bookamer,89992 E 15th St,Alliance,Box Butte,NE,12/19/1957,69301,308-726-2182,308-250-6987,flo.bookamer#cox.net,http://www.simontonhoweschneiderpc.com,;
Jani,Biddy,61556 W 20th Ave,Seattle,King,WA,8/7/1966,98104,206-711-6498,206-395-6284,jbiddy#yahoo.com,http://www.warehouseofficepaperprod.com,;
Chauncey,Motley,63 E Aurora Dr,Orlando,Orange,FL,3/1/2000,32804,407-413-4842,407-557-8857,chauncey_motley#aol.com,http://www.affiliatedwithtravelodge.com
"""
rows = empRecords.strip().split(";")
data = [ r.strip().split(",") for r in rows ]
then you can use any condition to filter the list, like
print ( [ "Name: " + emp[1] + "," + emp[0] + "| Current City: " + emp[3] for emp in data if emp[3] == "Washington" ] )
['Name: Hixenbaugh,Alesia| Current City: Washington']

How to read a particular line of interest from a text file?

Here I have a text file. I want to read Adress, Beneficiary, Beneficiary Bank, Acc Nbr, Total US$, Date which is at the top, RUT, BOX. I tried writing some code by myself but I am not able to correctly get the required information and moreover if the length of character changes I will not get correct output. How should I do this such that I will get every required information in a particular string.
The main problem will arise when my slicings will go wrong. For eg: I am using line[31:] for Acc Nbr. But if the address change then my slicing will also go wrong
My Text.txt
2014-11-09 BOX 1531 20140908123456 RUT 21 654321 0123
Girry S.A. CONTADO
G 5 Y Serie A
NO 098765
11 al Rayo 321 - Oqwerty 108 Monteaudio - Gruguay
Pharm Cosco, Inc - Britania PO Box 43215
Dirección Hot Springs AR 71903 - Estados Unidos
Oescripción Importe
US$
DO 7640183 - 50% of the Production Degree 246,123
Beneficiary Bank: Bankue Heritage (Gruguay) S.A Account Nbr: 1234563 Swift: MANIUYMM
Adress: Tencon 108 Monteaudio, Gruguay.
Beneficiary: Girry SA Acc Nbr: 1234567
Servicios prestados en el exterior, exentos de IVA o IRAE
Subtotal US$ 102,500
Iva US$ ---------------
Total US$ 102,500
I.V.A AL DIA Fecha de Vencimiento
IMPRENTA IRIS LTDA. - RUT 210161234015 - 0/40987 17/11/2015
CONSTANCIA N9 1234559842 -04/2013
CONTADO A 000.001/ A 000.050 x 2 VIAS
QWERTYAS ZXCVBIZADA
R. U.T. Bamprador Asdfumldor Final
Fecha 12/12/2014
1º ORIGINAL CLLLTE (Blanco) 2º CASIA AQWERVO (Rosasd)
My Code:
txt = 'Text.txt'
lines = [line.rstrip('\n') for line in open(txt)]
for line in lines:
if 'BOX' in line:
Date = line.split("BOX")[0]
BOX = line.split('BOX ', 1)[-1].split("RUT")[0]
RUT = line.split('RUT ',1)[-1]
print 'Date : ' + Date
print 'BOX : ' + BOX
print 'RUT : ' + RUT
if 'Adress' in line:
Adress = line[8:]
print 'Adress : ' + Adress
if 'NO ' in line:
Invoice_No = line.split('NO ',1)[-1]
print 'Invoice_No : ' + Invoice_No
if 'Swift:' in line:
Swift = line.split('Swift: ',1)[-1]
print 'Swift : ' + Swift
if 'Fecha' in line and '/' in line:
Invoice_Date = line.split('Fecha ',1)[-1]
print 'Invoice_Date : ' + Invoice_Date
if 'Beneficiary Bank' in line:
Beneficiary_Bank = line[18:]
Ben_Acc_Nbr = line.split('Nbr: ', 1)[-1]
print 'Beneficiary_Bank : ' + Beneficiary_Bank.split("Acc")[0]
print 'Ben_Acc_Nbr : ' + Ben_Acc_Nbr.split("Swift")[0]
if 'Beneficiary' in line and 'Beneficiary Bank' not in line:
Beneficiary = line[13:]
print 'Beneficiary : ' + Beneficiary.split("Acc")[0]
if 'Acc Nbr' in line:
Acc_Nbr = line.split('Nbr: ', 1)[-1]
print 'Acc_Nbr : ' + Acc_Nbr
if 'Total US$' in line:
Total_US = line.split('US$ ', 1)[-1]
print 'Total_US : ' + Total_US
Output:
Date : 2014-11-09
BOX : 1531 20140908123456
RUT : 21 654321 0123
Invoice_No : 098765
Swift : MANIUYMM
Beneficiary_Bank : Bankue Heritage (Gruguay) S.A
Ben_Acc_Nbr : 1234563
Adress : Tencon 108 Monteaudio, Gruguay.
Beneficiary : Girry SA
Acc_Nbr : 1234567
Total_US : 102,500
Invoice_Date : 12/12/2014
Some Code Changes
I have made some changes but still I am not convinced as I need to provide spaces also in split.

I would recommend you to use regular expressions to extract information you need. It helps to avoid the calculation of the numbers of offset characters.
import re
with open('C:\Quad.txt') as f:
for line in f:
match = re.search(r"Acc Nbr: (.*?)", line)
if match is not None:
Acc_Nbr = match.group(1)
print Acc_Nbr
# etc...

you can search to obtain index of it. for example:
if 'Acc Nbr' in line:
Acc_Nbr = line[line.find("Acc Nbr") + 10:]
print Acc_Nbr
note that find gives you index of first char of item you searched.

Organize by Twitter unique identifier using python

I have a CSV file with each line containing information pertaining to a particular tweet (i.e. each line contains Lat, Long, User_ID, tweet and so on). I need to read the file and organize the tweets by the User_ID. I am trying to end up with a given User_ID attached to all of the tweets with that specific ID.
Here is what I want:
user_id: 'lat', 'long', 'tweet'
: 'lat', 'long', 'tweet'
user_id2: 'lat', 'long', 'tweet'
: 'lat', 'long', 'tweet'
: 'lat', 'long', 'tweet'
and so on...
This is a snip of my code that reads in the CSV file and creates a list:
UID = []
myID = []
ID = []
f = None
with open(csv_in,'rU') as f:
myreader = csv.reader(f, delimiter=',')
for row in myreader:
# Assign columns in csv to variables.
latitude = row[0]
longitude = row[1]
user_id = row[2]
user_name = row[3]
date = row[4]
time = row[5]
tweet = row[6]
flag = row[7]
compound = row[8]
Vote = row[9]
# Read variables into separate lists.
UID.append(user_id + ', ' + latitude + ', ' + longitude + ', ' + user_name + ', ' + date + ', ' + time + ', ' + tweet + ', ' + flag + ', ' + compound)
myID = ', '.join(UID)
ID = myID.split(', ')

I'd suggest you use pandas for this. It will allow you not only to list your tweets by user_id, as in your question, but also to do many other manipulations quite easily.
As an example, take a look at this python notebook from NLTK. At the end of it, you see an operation very closed to yours, reading a csv file containing tweets,
In [25]:
import pandas as pd

tweets = pd.read_csv('tweets.20150430-223406.tweet.csv', index_col=2, header=0, encoding="utf8")
You can also find a simple operation: looking for the tweets of a certain user,
In [26]:
tweets.loc[tweets['user.id'] == 557422508]['text']
Out[26]:
id
593891099548094465 VIDEO: Sturgeon on post-election deals http://...
593891101766918144 SNP leader faces audience questions http://t.c...
Name: text, dtype: object
For listing the tweets by user_id, you would simply do something like the following (this is not in the original notebook),
In [9]:
tweets.set_index('user.id')[0:4]
Out[9]:
created_at favorite_count in_reply_to_status_id in_reply_to_user_id retweet_count retweeted text truncated
user.id
107794703 Thu Apr 30 21:34:06 +0000 2015 0 NaN NaN 0 False RT #KirkKus: Indirect cost of the UK being in ... False
557422508 Thu Apr 30 21:34:06 +0000 2015 0 NaN NaN 0 False VIDEO: Sturgeon on post-election deals http://... False
3006692193 Thu Apr 30 21:34:06 +0000 2015 0 NaN NaN 0 False RT #LabourEoin: The economy was growing 3 time... False
455154030 Thu Apr 30 21:34:06 +0000 2015 0 NaN NaN 0 False RT #GregLauder: the UKIP east lothian candidat... False
Hope it helps.

How can I do a non-greedy (backtracking) match with OneOrMore etc. in pyparsing?

I am trying to parse a partially standardized street address into it's components using pyparsing. I want to non-greedy match a street name that may be N tokens long.
For example:
444 PARK GARDEN LN
Should be parsed into:
number: 444
street: PARK GARDEN
suffix: LN
How would I do this with PyParsing? Here's my initial code:
from pyparsing import *
def main():
street_number = Word(nums).setResultsName('street_number')
street_suffix = oneOf("ST RD DR LN AVE WAY").setResultsName('street_suffix')
street_name = OneOrMore(Word(alphas)).setResultsName('street_name')
address = street_number + street_name + street_suffix
result = address.parseString("444 PARK GARDEN LN")
print result.dump()
if __name__ == '__main__':
main()
but when I try parsing it, the street suffix gets gobbled up by the default greedy parsing behavior.

Use the negation, ~, to check to see if the upcoming street_name is actually a street_suffix.
from pyparsing import *
street_number = Word(nums)('street_number')
street_suffix = oneOf("ST RD DR LN AVE WAY")('street_suffix')
street_name = OneOrMore(~street_suffix + Word(alphas))('street_name')
address = street_number + street_name + street_suffix
result = address.parseString("444 PARK GARDEN LN")
print result.dump()
In addition, you don't have to use setResultsName, you can simply use the syntax above. IMHO it leads to a much cleaner grammar definition.

We Keep Coding

Python is a programming language that lets you work quickly and integrate systems more effectively.