from collections import defaultdict

import GNAF
import re
import itertools
import operator

import pandas as pd

unlabelled = pd.read_csv("C:/Users/farrelp1/Documents/GNAFAPI/parserator/dataunlabeled_2017-04-21DHHS_CTAddress.csv", header=None)

unlabelled = unlabelled[0].apply(lambda x: re.sub(r', Australia$', "", x))

#unlabelled[unlabelled.apply(lambda x: x.endswith("Australia"))]

def accumulate(l):
    it = itertools.groupby(l, operator.itemgetter(1))
    for key, subiter in it:
       yield key, ' '.join(item[0] for item in subiter)

labelled = unlabelled.apply(lambda x: GNAF.parse(x))

labelled = labelled.apply(lambda x: list(accumulate(x)))

cols = ['RAW_STRING', 'BUILDING_NAME', 'LEVEL_TYPE', 'LEVEL_NUMBER', 'FLAT_TYPE', 'FLAT_NUMBER_PREFIX', 'FLAT_NUMBER',
        'NUMBER_FIRST', 'NUMBER_FIRST_SUFFIX',  'NUMBER_LAST', 'STREET_NAME', 'STREET_TYPE_CODE', 'LOCALITY_NAME',
        'POSTCODE', 'STATE_ABBREVIATION']

df = pd.DataFrame(columns=cols)

df['RAW_STRING'] = unlabelled[0]

for i, row in enumerate(labelled):
    for item in row:
        df[item[0]][i] = item[1]

df.to_csv('labels_2017-05-08DHHS_CTAddress2.csv')

58f71acaecf3d0e0108b64e347be1c79469f8660
<mask token>


def accumulate(l):
    it = itertools.groupby(l, operator.itemgetter(1))
    for key, subiter in it:
        yield key, ' '.join(item[0] for item in subiter)


<mask token>

<mask token>


def accumulate(l):
    it = itertools.groupby(l, operator.itemgetter(1))
    for key, subiter in it:
        yield key, ' '.join(item[0] for item in subiter)


<mask token>
for i, row in enumerate(labelled):
    for item in row:
        df[item[0]][i] = item[1]
df.to_csv('labels_2017-05-08DHHS_CTAddress2.csv')

<mask token>
unlabelled = pd.read_csv(
    'C:/Users/farrelp1/Documents/GNAFAPI/parserator/dataunlabeled_2017-04-21DHHS_CTAddress.csv'
    , header=None)
unlabelled = unlabelled[0].apply(lambda x: re.sub(', Australia$', '', x))


def accumulate(l):
    it = itertools.groupby(l, operator.itemgetter(1))
    for key, subiter in it:
        yield key, ' '.join(item[0] for item in subiter)


labelled = unlabelled.apply(lambda x: GNAF.parse(x))
labelled = labelled.apply(lambda x: list(accumulate(x)))
cols = ['RAW_STRING', 'BUILDING_NAME', 'LEVEL_TYPE', 'LEVEL_NUMBER',
    'FLAT_TYPE', 'FLAT_NUMBER_PREFIX', 'FLAT_NUMBER', 'NUMBER_FIRST',
    'NUMBER_FIRST_SUFFIX', 'NUMBER_LAST', 'STREET_NAME', 'STREET_TYPE_CODE',
    'LOCALITY_NAME', 'POSTCODE', 'STATE_ABBREVIATION']
df = pd.DataFrame(columns=cols)
df['RAW_STRING'] = unlabelled[0]
for i, row in enumerate(labelled):
    for item in row:
        df[item[0]][i] = item[1]
df.to_csv('labels_2017-05-08DHHS_CTAddress2.csv')

from collections import defaultdict
import GNAF
import re
import itertools
import operator
import pandas as pd
unlabelled = pd.read_csv(
    'C:/Users/farrelp1/Documents/GNAFAPI/parserator/dataunlabeled_2017-04-21DHHS_CTAddress.csv'
    , header=None)
unlabelled = unlabelled[0].apply(lambda x: re.sub(', Australia$', '', x))


def accumulate(l):
    it = itertools.groupby(l, operator.itemgetter(1))
    for key, subiter in it:
        yield key, ' '.join(item[0] for item in subiter)


labelled = unlabelled.apply(lambda x: GNAF.parse(x))
labelled = labelled.apply(lambda x: list(accumulate(x)))
cols = ['RAW_STRING', 'BUILDING_NAME', 'LEVEL_TYPE', 'LEVEL_NUMBER',
    'FLAT_TYPE', 'FLAT_NUMBER_PREFIX', 'FLAT_NUMBER', 'NUMBER_FIRST',
    'NUMBER_FIRST_SUFFIX', 'NUMBER_LAST', 'STREET_NAME', 'STREET_TYPE_CODE',
    'LOCALITY_NAME', 'POSTCODE', 'STATE_ABBREVIATION']
df = pd.DataFrame(columns=cols)
df['RAW_STRING'] = unlabelled[0]
for i, row in enumerate(labelled):
    for item in row:
        df[item[0]][i] = item[1]
df.to_csv('labels_2017-05-08DHHS_CTAddress2.csv')

from collections import defaultdict

import GNAF
import re
import itertools
import operator

import pandas as pd

unlabelled = pd.read_csv("C:/Users/farrelp1/Documents/GNAFAPI/parserator/dataunlabeled_2017-04-21DHHS_CTAddress.csv", header=None)

unlabelled = unlabelled[0].apply(lambda x: re.sub(r', Australia$', "", x))

#unlabelled[unlabelled.apply(lambda x: x.endswith("Australia"))]

def accumulate(l):
    it = itertools.groupby(l, operator.itemgetter(1))
    for key, subiter in it:
       yield key, ' '.join(item[0] for item in subiter)

labelled = unlabelled.apply(lambda x: GNAF.parse(x))

labelled = labelled.apply(lambda x: list(accumulate(x)))

cols = ['RAW_STRING', 'BUILDING_NAME', 'LEVEL_TYPE', 'LEVEL_NUMBER', 'FLAT_TYPE', 'FLAT_NUMBER_PREFIX', 'FLAT_NUMBER',
        'NUMBER_FIRST', 'NUMBER_FIRST_SUFFIX',  'NUMBER_LAST', 'STREET_NAME', 'STREET_TYPE_CODE', 'LOCALITY_NAME',
        'POSTCODE', 'STATE_ABBREVIATION']

df = pd.DataFrame(columns=cols)

df['RAW_STRING'] = unlabelled[0]

for i, row in enumerate(labelled):
    for item in row:
        df[item[0]][i] = item[1]

df.to_csv('labels_2017-05-08DHHS_CTAddress2.csv')
