def find_near_zips(zipc, city, state):
x = zipcodes.similar_to(zipc[0],
zips=zipcodes.filter_by(zipcodes.list_all(), active=True, city= city, state = state))
zipps = []
for zips in x:
zipps.append(zips['zip_code'])
return zipps
def test_matching(self):
self.assertEqual(
zipcodes.similar_to("10001"),
[{
"zip_code": "10001",
"zip_code_type": "STANDARD",
"city": "NEW YORK",
"state": "NY",
"lat": 40.71,
"long": -73.99,
"world_region": "NA",
"country": "US",
"active": True,
}],
)
def test_similar_to(self):
self.assertEqual(
zipcodes.similar_to("0643"),
[
{
"zip_code": "06437",
"zip_code_type": "STANDARD",
"city": "GUILFORD",
"state": "CT",
"lat": 41.28,
"long": -72.67,
"world_region": "NA",
"country": "US",
"active": True,
},
{
"zip_code": "06438",
"zip_code_type": "STANDARD",
"city": "HADDAM",
"state": "CT",
"lat": 41.45,
"long": -72.5,
"world_region": "NA",
"country": "US",
"active": True,
},
{
"zip_code": "06439",
"zip_code_type": "PO BOX",
"city": "HADLYME",
"state": "CT",
"lat": 41.4,
"long": -72.34,
"world_region": "NA",
"country": "US",
"active": True,
},
],
)
pd.get(url)
time.sleep(0.1)
name = fake.name()
first = name.split(' ')[0]
last = name.split(' ')[1]
textnows = []
index = str(randint(0, 500))
i = str(randint(0, 500))
p = fake.color_name() + index
u = (name.split(' ')[0] + name.split(' ')[1] + index).replace(".", "")
e = first + last + '+' + index + '@gmail.com'
z = str(randint(0, 9))
y = str(randint(0, 9))
length = len(zipcodes.similar_to(y + z))
random = (randint(0, length))
zipIn = zipcodes.similar_to(y + z)[random]['zip_code']
cityIn = zipcodes.similar_to(y + z)[random]['city']
stateIn = zipcodes.similar_to(y + z)[random]['state']
prof = " ".join(fake.words(nb=3, ext_word_list=None, unique=True))
para = fake.paragraph(nb_sentences=5,
variable_nb_sentences=True,
ext_word_list=None)
para2 = fake.paragraph(nb_sentences=5,
variable_nb_sentences=True,
ext_word_list=None)
para3 = fake.paragraph(nb_sentences=5,
variable_nb_sentences=True,
ext_word_list=None)
description2 = (para + '\n\n' + para2 + '\n\n' + para3)
def main():
# name of this stage, typically a name to reference the assertion
# assertion: lambda which returns unittest callable with self's (testcase's) context
# predicates: lambda or sequence of lambdas to call and pass to the assertion
unittests_schema = [
{
"name": "true",
"assertion": lambda self: self.assertTrue,
"predicates": [
lambda: zipcodes.is_real("06905"),
lambda: zipcodes._contains_nondigits("1234a"),
# bad length
lambda: callable_raise_exc(
lambda: zipcodes._clean("000000"), ValueError
),
# bad characters
lambda: callable_raise_exc(
lambda: zipcodes._clean("0000a"), ValueError
),
# ensure zips argument works
lambda: len(
zipcodes.similar_to(
"2", zips=zipcodes.filter_by(active=True, city="Windsor")
)
)
== 3,
],
},
{
"name": "false",
"assertion": lambda self: self.assertFalse,
"predicates": [
lambda: zipcodes.is_real("91239"),
# digits and "-" are acceptable
lambda: zipcodes._contains_nondigits("12345"),
lambda: zipcodes._contains_nondigits("1234-"),
],
},
{
"name": "equal",
"assertion": lambda self: self.assertEqual,
"predicates": [
# valid_zipcode_length parameter
(lambda: zipcodes._clean("0646", 4), lambda: "0646"),
# default behavior
(lambda: zipcodes._clean("06469"), lambda: "06469"),
(lambda: zipcodes.list_all(), lambda: zipcodes._zips),
(
lambda: zipcodes.filter_by(city="Old Saybrook"),
lambda: [
{
"zip_code": "06475",
"zip_code_type": "STANDARD",
"active": True,
"city": "Old Saybrook",
"acceptable_cities": [],
"unacceptable_cities": ["Fenwick"],
"state": "CT",
"county": "Middlesex County",
"timezone": "America/New_York",
"area_codes": ["860"],
"world_region": "NA",
"country": "US",
"lat": "41.3015",
"long": "-72.3879",
}
],
),
(
lambda: zipcodes.similar_to("1018"),
lambda: [
{
"acceptable_cities": [],
"active": False,
"area_codes": ["212"],
"city": "New York",
"country": "US",
"county": "New York County",
"lat": "40.71",
"long": "-74",
"state": "NY",
"timezone": "America/New_York",
"unacceptable_cities": ["J C Penney"],
"world_region": "NA",
"zip_code": "10184",
"zip_code_type": "UNIQUE",
},
{
"acceptable_cities": [],
"active": True,
"area_codes": ["212"],
"city": "New York",
"country": "US",
"county": "New York County",
"lat": "40.7143",
"long": "-74.0067",
"state": "NY",
"timezone": "America/New_York",
"unacceptable_cities": [],
"world_region": "NA",
"zip_code": "10185",
"zip_code_type": "PO BOX",
},
],
),
(
lambda: zipcodes.similar_to("1005"),
lambda: [
{
"zip_code": "10055",
"zip_code_type": "STANDARD",
"active": True,
"city": "New York",
"acceptable_cities": [],
"unacceptable_cities": ["Manhattan"],
"state": "NY",
"county": "New York County",
"timezone": "America/New_York",
"area_codes": ["212"],
"world_region": "NA",
"country": "US",
"lat": "40.7579",
"long": "-73.9743",
}
],
),
(
lambda: zipcodes.similar_to("10001"),
lambda: [
{
"zip_code": "10001",
"zip_code_type": "STANDARD",
"active": True,
"city": "New York",
"acceptable_cities": [],
"unacceptable_cities": [
"Empire State",
"G P O",
"Greeley Square",
"Macys Finance",
"Manhattan",
],
"state": "NY",
"county": "New York County",
"timezone": "America/New_York",
"area_codes": ["718", "917", "347", "646"],
"world_region": "NA",
"country": "US",
"lat": "40.7508",
"long": "-73.9961",
}
],
),
],
},
]
generate_unittests(unittests_schema)
logger.info("Zipcodes version: {}".format(zipcodes.__version__))
unittest.main()