from python_lib_util import csvtools as c
from operator import itemgetter
import collections
# STEP ONE: Read in needed data as a list of dicts
data = c.read_as_dict('raw-csvs/2015-combined.csv')
issuers_by_county = list()
for row in data:
att = dict()
att ['i'] = row['Issuer Name']
att['s'] = row['State ']
att['c'] = row['County']
# and
if att['c'] == 'King And Queen':
att['c'] = 'King and Queen'
if att['c'] == 'Lake And Peninsula':
att['c'] = 'Lake and Peninsula'
if att['c'] == 'Lewis And Clark':
att['c'] = 'Lewis and Clark'
# census areas
if att['c'] == 'Aleutians West':
att['c'] = 'Aleutians West Census'
if att['c'] == 'Dillingham':
att['c'] = 'Dillingham Census'
if att['c'] == 'Wade Hampton':
att['c'] = 'Wade Hampton Census'
if att['c'] == 'Bethel':
from python_lib_util import csvtools as c
from operator import itemgetter
import collections
# STEP ONE: Read in needed data as a list of dicts
data = c.read_as_dict('raw-csvs/2014-combined.csv')
issuers_by_county = list()
for row in data:
att = dict()
att['i'] = row['Issuer Name']
att['s'] = row['State']
att['c'] = row['County'].title()
# and
if att['c'] == 'King And Queen':
att['c'] = 'King and Queen'
if att['c'] == 'Lake And Peninsula':
att['c'] = 'Lake and Peninsula'
if att['c'] == 'Lewis And Clark':
att['c'] = 'Lewis and Clark'
# census areas
if att['c'] == 'Aleutians West':
att['c'] = 'Aleutians West Census'
if att['c'] == 'Dillingham':
att['c'] = 'Dillingham Census'
if att['c'] == 'Wade Hampton':
att['c'] = 'Wade Hampton Census'
if att['c'] == 'Bethel':
att['c'] = 'Bethel Census'
from python_lib_util import csvtools as c
import collections
from itertools import izip_longest, ifilter
# STEP ONE: Read in needed data as a list of dicts
data = c.read_as_dict('fips-join/2017-joined.csv')
# list of dicts, group by fips id
data_grp = collections.defaultdict(list)
for row in data:
data_grp[row['f']].append(row['i'])
# get count of issuers by fips code
issuers_by_county = list()
for k, v in data_grp.items():
att = dict()
# all issuers in a county
att['ict'] = len(v)
counter = collections.Counter(v)
# convert counter object to dict
counter_dict = dict(counter.items())
# split dict by key into a list of dicts
chunks = [counter_dict.iteritems()]
split = (dict(ifilter(None, v)) for v in izip_longest(*chunks))
split_list = list(split)
# sort our list of dicts
sorted_split = sorted(split_list)
# split up our dicts into one dict per county
for row in sorted_split:
from python_lib_util import csvtools as c
# STEP ONE: Generate a list of fips codes, by combining stfips and ctyfips.
# Also, include full county name + name w/o county + state in new lists
data = c.read_as_dict('fips-join/fips.csv')
aca_2014 = c.read_as_dict('filtered-csvs/2014-issuers-filtered.csv')
aca_2015 = c.read_as_dict('filtered-csvs/2015-issuers-filtered.csv')
aca_2016 = c.read_as_dict('filtered-csvs/2016-issuers-filtered.csv')
aca_2017 = c.read_as_dict('filtered-csvs/2017-issuers-filtered.csv')
fips_codes = list()
# what about OBrien
for row in data:
att = dict()
att['f'] = row['stfips'] + row['ctyfips']
att['c'] = row['name']
att['s'] = row['state']
c0 = row['name'].split(' ')
# range is from 2 - 5
if 'city' == c0[1]:
att['c0'] = " ".join(c0[:2])
if 'city' != c0[1] and len(c0) == 2:
att['c0'] = c0[0]
if len(c0) == 3 and 'city' == c0[2]:
att['c0'] = " ".join(c0)
if len(c0) == 3 and 'city' != c0[2]:
att['c0'] = " ".join(c0[:2])
if len(c0) == 4:
att['c0'] = " ".join(c0[:3])
# print att['c0']
if len(c0) == 5:
from python_lib_util import csvtools as c
from operator import itemgetter
import collections
# STEP ONE: Read in needed data as a list of dicts
data = c.read_as_dict('raw-csvs/2016.csv')
issuers_by_county = list()
for row in data:
att = dict()
att ['i'] = row['Issuer Name']
att['s'] = row['State Code']
att['c'] = row['County Name']
# and
if att['c'] == 'King And Queen':
att['c'] = 'King and Queen'
if att['c'] == 'Lake And Peninsula':
att['c'] = 'Lake and Peninsula'
if att['c'] == 'Lewis And Clark':
att['c'] = 'Lewis and Clark'
# census areas
if att['c'] == 'Aleutians West':
att['c'] = 'Aleutians West Census'
if att['c'] == 'Dillingham':
att['c'] = 'Dillingham Census'
if att['c'] == 'Wade Hampton':
att['c'] = 'Wade Hampton Census'
if att['c'] == 'Bethel':
from python_lib_util import csvtools as c
from operator import itemgetter
import collections
# STEP ONE: Read in needed data as a list of dicts
data = c.read_as_dict('filtered-csvs/2016-issuers-filtered.csv')
issuers_2017 = list()
# STEP TWO: Remove issuers reported as leaving in 2017
for row in data:
# AL changes
if row['s'] == 'AL' and row['i'] == 'Humana Insurance Company':
continue
if row['s'] == 'AL' and row['i'] == 'UnitedHealthcare of Alabama, Inc.':
continue
# AK changes
if row['s'] == 'AK' and row['i'] == 'Moda Health Plan, Inc.':
continue
# AZ changes
if row['s'] == 'AZ' and row['i'] == 'All Savers Insurance Company':
continue
if row['s'] == 'AZ' and row['i'] == 'Health Net of Arizona, Inc.':
continue
if row['s'] == 'AZ' and row['c'] == 'Maricopa' and row['i'] == 'Blue Cross and Blue Shield of Arizona, Inc.':
continue
if row['s'] == 'AZ' and row['c'] == 'Pinal' and row['i'] == 'Blue Cross and Blue Shield of Arizona, Inc.':
continue
# AR changes
if row['s'] == 'AR' and row['i'] == 'UnitedHealthcare of Arkansas, Inc.':
continue
from python_lib_util import csvtools as c
from operator import itemgetter
import collections
# STEP ONE: Read in needed data as a list of dicts
data = c.read_as_dict('raw-csvs/2016.csv')
issuers_by_county = list()
for row in data:
att = dict()
att['i'] = row['Issuer Name']
att['s'] = row['State Code']
att['c'] = row['County Name']
# and
if att['c'] == 'King And Queen':
att['c'] = 'King and Queen'
if att['c'] == 'Lake And Peninsula':
att['c'] = 'Lake and Peninsula'
if att['c'] == 'Lewis And Clark':
att['c'] = 'Lewis and Clark'
# census areas
if att['c'] == 'Aleutians West':
att['c'] = 'Aleutians West Census'
if att['c'] == 'Dillingham':
att['c'] = 'Dillingham Census'
if att['c'] == 'Wade Hampton':
att['c'] = 'Wade Hampton Census'
if att['c'] == 'Bethel':
att['c'] = 'Bethel Census'