A simple Python wrapper for U.S. Census Geocoding Services API batch service
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README.md

python-censusbatchgeocoder

A simple Python wrapper for U.S. Census Geocoding Services API batch service.

Build Status PyPI version Coverage Status

Installation

$ pip install censusbatchgeocoder

Basic usage

Importing the library

>>> import censusbatchgeocoder

According to the official Census documentation, the input is expected to contain the following fields:

  • id: Your unique identifier for the record
  • address: Structure number and street name (required)
  • city: City name (required)
  • state: State (optional)
  • zipcode: ZIP Code (optional)

You can geocode a comma-delimited file from the filesystem. Results are returned as a list of dictionaries.

An example could look like this:

id,address,city,state,zipcode
1,1600 Pennsylvania Ave NW,Washington,DC,20006
2,202 W. 1st Street,Los Angeles,CA,90012

Which is then passed in like this:

>>> results = censusbatchgeocoder.geocode("./my_file.csv")

The results are returned with the following columns from the Census

  • id: The unique id provided with the record.
  • returned_address: The address that was submitted to the geocoder.
  • geocoded_address: The address of the match returned by the geocoder.
  • is_match: Whether or not the geocoder found a match.
  • is_exact: The precision of the match.
  • coordinates: The longitude and latitude of the match together in a string.
  • longitude: The longitude of the match as a float.
  • latitude: The latitude of the match as a float.
  • tiger_line: The Census TIGER line of the match.
  • side: The side of the Census TIGER line of the match.
  • state_fips: The FIPS state code identifying the state of the match.
  • county_fips: The FIPS county code identifying the county of the match.
  • tract: The Census tract of the match.
  • block: The Census block of the match.
>>> print results
[{u'address': u'1600 Pennsylvania Ave NW',
  u'block': u'1031',
  u'city': u'Washington',
  u'coordinates': u'-77.03535,38.898754',
  u'county_fips': u'001',
  u'geocoded_address': u'1600 Pennsylvania Ave NW, Washington, DC, 20006',
  u'id': u'1',
  u'is_exact': u'Non_Exact',
  u'is_match': u'Match',
  u'latitude': 38.898754,
  u'longitude': -77.03535,
  u'returned_address': u'1600 PENNSYLVANIA AVE NW, WASHINGTON, DC, 20502',
  u'side': u'L',
  u'state': u'DC',
  u'state_fips': u'11',
  u'tiger_line': u'76225813',
  u'tract': u'006202',
  u'zipcode': u'20006'},
 {u'address': u'202 W. 1st Street',
  u'block': u'1034',
  u'city': u'Los Angeles',
  u'coordinates': u'-118.24456,34.053005',
  u'county_fips': u'037',
  u'geocoded_address': u'202 W. 1st Street, Los Angeles, CA, 90012',
  u'id': u'2',
  u'is_exact': u'Exact',
  u'is_match': u'Match',
  u'latitude': 34.053005,
  u'longitude': -118.24456,
  u'returned_address': u'202 W 1ST ST, LOS ANGELES, CA, 90012',
  u'side': u'L',
  u'state': u'CA',
  u'state_fips': u'06',
  u'tiger_line': u'141618115',
  u'tract': u'207400',
  u'zipcode': u'90012'}]

Any extra metadata fields included in the file are still present in the returned data.

So the my_metadata column here...

id,address,city,state,zipcode,my_metadata
1,1600 Pennsylvania Ave NW,Washington,DC,20006,foo
2,202 W. 1st Street,Los Angeles,CA,90012,bar

.. is still there after you geocode.

>>> censusbatchgeocoder.geocode("./my_file.csv")
[{u'address': u'1600 Pennsylvania Ave NW',
  u'block': u'1031',
  u'city': u'Washington',
  u'coordinates': u'-77.03535,38.898754',
  u'county_fips': u'001',
  u'geocoded_address': u'1600 Pennsylvania Ave NW, Washington, DC, 20006',
  u'id': u'1',
  u'is_exact': u'Non_Exact',
  u'is_match': u'Match',
  u'latitude': 38.898754,
  u'longitude': -77.03535,
  u'returned_address': u'1600 PENNSYLVANIA AVE NW, WASHINGTON, DC, 20502',
  u'my_metadata': 'foo',
  u'side': u'L',
  u'state': u'DC',
  u'state_fips': u'11',
  u'tiger_line': u'76225813',
  u'tract': u'006202',
  u'zipcode': u'20006'},
 {u'address': u'202 W. 1st Street',
  u'block': u'1034',
  u'city': u'Los Angeles',
  u'coordinates': u'-118.24456,34.053005',
  u'county_fips': u'037',
  u'geocoded_address': u'202 W. 1st Street, Los Angeles, CA, 90012',
  u'id': u'2',
  u'is_exact': u'Exact',
  u'is_match': u'Match',
  u'latitude': 34.053005,
  u'longitude': -118.24456,
  u'returned_address': u'202 W 1ST ST, LOS ANGELES, CA, 90012',
  u'my_metadata': 'foo',
  u'side': u'L',
  u'state': u'CA',
  u'state_fips': u'06',
  u'tiger_line': u'141618115',
  u'tract': u'207400',
  u'zipcode': u'90012'}]

Custom column names

If you have column headers that do not exactly match those expected by the geocoder you should override them.

So a file like this:

foo,bar,baz,bada,boom
1,521 SWARTHMORE AVENUE,PACIFIC PALISADES,CA,90272-4350
2,2015 W TEMPLE STREET,LOS ANGELES,CA,90026-4913

Can be mapped like this:

>>> censusbatchgeocoder.geocode(
    self.weird_path,
    id="foo",
    address="bar",
    city="baz",
    state="bada",
    zipcode="boom"
)

Optional columns

The state and ZIP Code columns are optional. If your data doesn't have them, pass None as keyword arguments.

>>> censusbatchgeocoder.geocode("./my_file.csv", state=None, zipcode=None)

Lists of dictionaries

A list of dictionaries, like those created by the csv module's DictReader can also be mapped.

>>> my_list = [{'address': '521 SWARTHMORE AVENUE',
  'city': 'PACIFIC PALISADES',
  'id': '1',
  'state': 'CA',
  'zipcode': '90272-4350'},
 {'address': '2015 W TEMPLE STREET',
  'city': 'LOS ANGELES',
  'id': '2',
  'state': 'CA',
  'zipcode': '90026-4913'}]
>>> censusbatchgeocoder.geocode(my_list)

pandas DataFrames

You can geocode a pandas DataFrame by converting it into a list of dictionaries.

>>> result = censusbatchgeocoder.geocode(df.to_dict("records"))

Then convert it back into a DataFrame.

>>> result_df = pd.DataFrame(result)

That's it.

File objects

You can also geocode an in-memory file object of data in CSV format.

>>> my_data = """id,address,city,state,zipcode
1,1600 Pennsylvania Ave NW,Washington,DC,20006
2,202 W. 1st Street,Los Angeles,CA,90012"""
>>> censusbatchgeocoder.geocode(io.StringIO(my_data))

Different encodings

If you are using Python 2 and your CSV file has an unusual encoding that's causing problems, try explicitly passing in the encoding name.

>>> censusbatchgeocoder.geocode("./my_file.csv", encoding="utf-8-sig")