harph / zipcodes Goto Github PK

>>> from pprint import pprint
>>> import zipcodes

>>> # Simple zip-code matching.
>>> pprint(zipcodes.matching('77429'))
[{'zip_code': '77429',
  'zip_code_type': 'STANDARD',
  'city': 'CYPRESS',
  'state': 'TX',
  'lat': 29.96,
  'long': -95.69,
  'world_region': 'NA',
  'country': 'US',
  'active': True}]

>>> # Handles of Zip+4 zip-codes nicely. :)
>>> pprint(zipcodes.matching('77429-1145'))
[{'zip_code': '77429',
  'zip_code_type': 'STANDARD',
  'city': 'CYPRESS',
  'state': 'TX',
  'lat': 29.96,
  'long': -95.69,
  'world_region': 'NA',
  'country': 'US',
  'active': True}]

>>> # Will try to handle invalid zip-codes gracefully...
>>> print(zipcodes.matching('06463'))
[]

>>> # Until it cannot.
>>> zipcodes.matching('abc123')
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/sean/Development/Zipcodes/zipcodes/__init__.py", line 30, in <lambda>
    return lambda zipcode, *args, **kwargs: f(_validate(zipcode), *args, **kwargs)
  File "/home/sean/Development/Zipcodes/zipcodes/__init__.py", line 181, in _validate
    'state': 'SC',
TypeError: Zipcode may only contain digits and "-".

>>> # Whether the zip-code exists within the database.
>>> print(zipcodes.is_valid('06463'))
False

>>> # How handy!
>>> print(zipcodes.is_valid('06469'))
True

>>> # Search for zipcodes that begin with a pattern.
>>> pprint(zipcodes.similar_to('0643'))
[{'active': True,
  'city': 'GUILFORD',
  'country': 'US',
  'lat': 41.28,
  'long': -72.67,
  'state': 'CT',
  'world_region': 'NA',
  'zip_code': '06437',
  'zip_code_type': 'STANDARD'},
 {'active': True,
  'city': 'HADDAM',
  'country': 'US',
  'lat': 41.45,
  'long': -72.5,
  'state': 'CT',
  'world_region': 'NA',
  'zip_code': '06438',
  'zip_code_type': 'STANDARD'},
... # remaining results truncated for readability...
]

>>> # Use filter_by to filter a list of zip-codes by specific attribute->value pairs.
>>> pprint(zipcodes.filter_by(zipcodes.list_all(), active=True, city='WINDSOR'))
[{'active': True,
  'city': 'WINDSOR',
  'country': 'US',
  'lat': 44.31,
  'long': -69.58,
  'state': 'ME',
  'world_region': 'NA',
  'zip_code': '04363',
  'zip_code_type': 'STANDARD'},
 {'active': True,
  'city': 'WINDSOR',
  'country': 'US',
  'lat': 43.48,
  'long': -72.42,
  'state': 'VT',
  'world_region': 'NA',
  'zip_code': '05089',
  'zip_code_type': 'STANDARD'},
... # remaining results truncated for readability...
]

>>> # Arbitrary nesting of similar_to and filter_by calls, allowing for great precision while filtering.
>>> pprint(zipcodes.similar_to('2', zips=zipcodes.filter_by(zipcodes.list_all(), active=True, city='WINDSOR')))
[{'active': True,
  'city': 'WINDSOR',
  'country': 'US',
  'lat': 33.48,
  'long': -81.51,
  'state': 'SC',
  'world_region': 'NA',
  'zip_code': '29856',
  'zip_code_type': 'STANDARD'},
 {'active': True,
  'city': 'WINDSOR',
  'country': 'US',
  'lat': 36.8,
  'long': -76.73,
  'state': 'VA',
  'world_region': 'NA',
  'zip_code': '23487',
  'zip_code_type': 'STANDARD'},
 {'active': True,
  'city': 'WINDSOR',
  'country': 'US',
  'lat': 36.0,
  'long': -76.94,
  'state': 'NC',
  'world_region': 'NA',
  'zip_code': '27983',
  'zip_code_type': 'STANDARD'}]

>>> # Have any other ideas? Make a pull request and start contributing today!
>>> # Made with love by Sean Pianka