jcadig1/uszipcode-project

USA zipcode programmable database, includes up-to-date census and geometry information.

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Welcome to uszipcode Documentation

If you are on www.pypi.org or www.github.com, this is not the complete document. Here is the Complete Document.

uszipcode is the most powerful and easy to use programmable zipcode database in Python. It comes with a rich feature and easy-to-use zipcode search engine. And it is easy to customize the search behavior as you wish.

Data Points

From version 0.2.0, uszipcode use a more up-to-date database, and having a crawler running every week to collection different data points from multiple data source. And API in 0.2.X NOT COMPATIBLE with 0.1.X, please read Document for more information.

Address, Postal

• zipcode
• zipcode_type
• major_city
• post_office_city
• common_city_list
• county
• state
• area_code_list

Geography

• lat
• lng
• timezone
• radius_in_miles
• land_area_in_sqmi
• water_area_in_sqmi
• bounds_west
• bounds_east
• bounds_north
• bounds_south
• border polygon

Stats and Demographics

• population
• population_density
• population_by_year
• population_by_age
• population_by_gender
• population_by_race
• head_of_household_by_age
• families_vs_singles
• households_with_kids
• children_by_age

Real Estate and Housing

• housing_units
• occupied_housing_units
• median_home_value
• median_household_income
• housing_type
• year_housing_was_built
• housing_occupancy
• vancancy_reason
• owner_occupied_home_values
• rental_properties_by_number_of_rooms
• monthly_rent_including_utilities_studio_apt
• monthly_rent_including_utilities_1_b
• monthly_rent_including_utilities_2_b
• monthly_rent_including_utilities_3plus_b

Employment, Income, Earnings, and Work

• employment_status
• average_household_income_over_time
• household_income
• annual_individual_earnings
• sources_of_household_income____percent_of_households_receiving_income
• sources_of_household_income____average_income_per_household_by_income_source
• household_investment_income____percent_of_households_receiving_investment_income
• household_investment_income____average_income_per_household_by_income_source
• household_retirement_income____percent_of_households_receiving_retirement_incom
• household_retirement_income____average_income_per_household_by_income_source
• source_of_earnings
• means_of_transportation_to_work_for_workers_16_and_over
• travel_time_to_work_in_minutes

Education

• educational_attainment_for_population_25_and_over
• school_enrollment_age_3_to_17

Example Usage

NOTE:

uszipcode has two backend database, SimpleZipcode and Zipcode. Zipcode has more info, but the database file is 450MB (takes more time to download). SimpleZipcode doesn't has all data points listed above, but the database file is smaller (9MB). By default uszipcode use SimpleZipcode. You can use this code to choose to use the rich info Zipcode:

>>> from uszipcode import SearchEngine
>>> search = SearchEngine(simple_zipcode=False)

Examples:

>>> from uszipcode import SearchEngine
>>> search = SearchEngine(simple_zipcode=True) # set simple_zipcode=False to use rich info database
>>> zipcode = search.by_zipcode("10001")
>>> zipcode
SimpleZipcode(zipcode=u'10001', zipcode_type=u'Standard', major_city=u'New York', post_office_city=u'New York, NY', common_city_list=[u'New York'], county=u'New York County', state=u'NY', lat=40.75, lng=-73.99, timezone=u'Eastern', radius_in_miles=0.9090909090909091, area_code_list=[u'718', u'917', u'347', u'646'], population=21102, population_density=33959.0, land_area_in_sqmi=0.62, water_area_in_sqmi=0.0, housing_units=12476, occupied_housing_units=11031, median_home_value=650200, median_household_income=81671, bounds_west=-74.008621, bounds_east=-73.984076, bounds_north=40.759731, bounds_south=40.743451)

>>> zipcode.values() # to list
[u'10001', u'Standard', u'New York', u'New York, NY', [u'New York'], u'New York County', u'NY', 40.75, -73.99, u'Eastern', 0.9090909090909091, [u'718', u'917', u'347', u'646'], 21102, 33959.0, 0.62, 0.0, 12476, 11031, 650200, 81671, -74.008621, -73.984076, 40.759731, 40.743451]

>>> zipcode.to_dict() # to dict
{'housing_units': 12476, 'post_office_city': u'New York, NY', 'bounds_east': -73.984076, 'county': u'New York County', 'population_density': 33959.0, 'radius_in_miles': 0.9090909090909091, 'timezone': u'Eastern', 'lng': -73.99, 'common_city_list': [u'New York'], 'zipcode_type': u'Standard', 'zipcode': u'10001', 'state': u'NY', 'major_city': u'New York', 'population': 21102, 'bounds_west': -74.008621, 'land_area_in_sqmi': 0.62, 'lat': 40.75, 'median_household_income': 81671, 'occupied_housing_units': 11031, 'bounds_north': 40.759731, 'bounds_south': 40.743451, 'area_code_list': [u'718', u'917', u'347', u'646'], 'median_home_value': 650200, 'water_area_in_sqmi': 0.0}

>>> zipcode.to_json() # to json
{
    "zipcode": "10001",
    "zipcode_type": "Standard",
    "major_city": "New York",
    "post_office_city": "New York, NY",
    "common_city_list": [
        "New York"
    ],
    "county": "New York County",
    "state": "NY",
    "lat": 40.75,
    "lng": -73.99,
    "timezone": "Eastern",
    "radius_in_miles": 0.9090909090909091,
    "area_code_list": [
        "718",
        "917",
        "347",
        "646"
    ],
    "population": 21102,
    "population_density": 33959.0,
    "land_area_in_sqmi": 0.62,
    "water_area_in_sqmi": 0.0,
    "housing_units": 12476,
    "occupied_housing_units": 11031,
    "median_home_value": 650200,
    "median_household_income": 81671,
    "bounds_west": -74.008621,
    "bounds_east": -73.984076,
    "bounds_north": 40.759731,
    "bounds_south": 40.743451
}

Rich search methods are provided for getting zipcode in the way you want.

>>> from uszipcode import Zipcode
# Search zipcode within 30 miles, ordered from closest to farthest
>>> result = search.by_coordinates(39.122229, -77.133578, radius=30, returns=5)
>>> len(res) # by default 5 results returned
5
>>> for zipcode in result:
...     # do whatever you want...

# Find top 10 population zipcode
>>> result = search.by_population(lower=0, upper=999999999,
... sort_by=Zipcode.population, ascending=False, returns=10)

# Find top 10 largest land area zipcode
>>> res = search.by_landarea(lower=0, upper=999999999,
... sort_by=Zipcode.land_area_in_sqmi, ascending=False, returns=10)

Fuzzy city name and state name search does not require developer to know the exact spelling of the city or state. And it is case, space insensitive, having high tolerance to typo. This is very helpful if you need to build a web app with it.

# Looking for Chicago and IL, but entered wrong spelling.
>>> res = search.by_city_and_state("cicago", "il")
>>> len(res) # 56 zipcodes in Chicago
56
>>> zipcode = res[0]
>>> zipcode.major_city
'Chicago'
>>> zipcode.state_abbr
'IL'

You can easily sort your results by any field, or distance from a coordinates if you query by location.

# Find top 10 population zipcode
>>> res = search.by_population(lower=0, upper=999999999,
... sort_by=Zipcode.population, ascending=False, returns=10)
>>> for zipcode in res:
...     # do whatever you want...

Install

uszipcode is released on PyPI, so all you need is:

$ pip install uszipcode

To upgrade to latest version:

$ pip install --upgrade uszipcode