pygeocodio 2.0.1

Geocoding

Geocoding an individual address:

>>> geocoded_location = client.geocode("42370 Bob Hope Drive, Rancho Mirage CA")
>>> geocoded_location.coords
(33.738987255507, -116.40833849559)

Geocode a set of address components:

>>> geocoded_location = client.geocode(components_data={
  "postal_code": "02210",
  "country": "US"
})
>>> geocoded_location.coords
(42.347547, -71.040645)

Batch geocoding

You can also geocode a list of addresses:

>>> geocoded_addresses = client.geocode([
        '2 15th St NW, Washington, DC 20024',
        '3101 Patterson Ave, Richmond, VA, 23221'
    ])

Return a list of just the coordinates for the resultant geocoded addresses:

>>> geocoded_addresses.coords
[(38.890083, -76.983822), (37.560446, -77.476008)]
>>> geocoded_addresses[0].coords
(38.890083, -76.983822)

Lookup an address by the queried address:

>>> geocoded_addresses.get('2 15th St NW, Washington, DC 20024').coords
(38.879138, -76.981879))

You can also geocode a list of address component dictionaries:

>>> geocoded_addresses = client.geocode(components_data=[{
        'street': '1109 N Highland St',
        'city': 'Arlington',
        'state': 'VA'
    }, {
        'city': 'Toronto',
        'country': 'CA'
    }])

And geocode a keyed mapping of address components:

>>> gecoded_addresses = client.geocode(components_data={
        "1": {
            "street": "1109 N Highland St",
            "city": "Arlington",
            "state": "VA"
        },
        "2": {
            "city": "Toronto",
            "country": "CA"
        }})

And geocode even a keyed mapping of addresses:

>>> geocoded_addresses = client.geocode({
        "1": "3101 patterson ave, richmond, va",
        "2": "1657 W Broad St, Richmond, VA"
    })

Return a list of just the coordinates for the resultant geocoded addresses:

>>> geocoded_addresses.coords
{'1': (37.560454, -77.47601), '2': (37.555176, -77.458273)}

Lookup an address by its key:

>>> geocoded_addresses.get("1").coords
(37.560454, -77.47601)

Address parsing

And if you just want to parse an individual address into its components:

>>> client.parse('1600 Pennsylvania Ave, Washington DC')
  {
      "address_components": {
          "number": "1600",
          "street": "Pennsylvania",
          "suffix": "Ave",
          "city": "Washington",
          "state": "DC"
      },
      "formatted_address": "1600 Pennsylvania Ave, Washington DC"
  }

Reverse geocoding

Reverse geocode a point to find a matching address:

>>> location = client.reverse((33.738987, -116.4083))
>>> location.formatted_address
"42370 Bob Hope Dr, Rancho Mirage CA, 92270"

Batch reverse geocoding

And multiple points at a time:

>>> locations = client.reverse([
        (33.738987, -116.4083),
        (33.738987, -116.4083),
        (38.890083, -76.983822)
    ])

Return the list of formatted addresses:

>>> locations.formatted_addresses
["42370 Bob Hope Dr, Rancho Mirage CA, 92270",  "42370 Bob Hope Dr, Rancho Mirage CA, 92270", "2 15th St NW, Washington, DC 20024"]

Access a specific address by the queried point tuple:

>>> locations.get("38.890083,-76.983822").formatted_address
"2 15th St NW, Washington, DC 20024"

Or by the more natural key of the queried point tuple:

>>> locations.get((38.890083, -76.983822)).formatted_address
"2 15th St NW, Washington, DC 20024"