geobatchpy 0.2.3

A CLI and Python Client for the Geoapify API.

A CLI and Python Client for the Geoapify API

We have been using the Geoapify API to geocode millions of location records for data validation and analytics. We built this package to make this process comfortable using Python and the command line.

Why Geoapify and this package may also be a good fit for you:

• You need to batch process large numbers of location records (geocode, reverse geocode, places & details).
• The license must support commercial use without restrictions.
• It needs to be cheap (or even for free if you don't need more than 6k addresses per day).

Sign up at geoapify.com and start with their free plan of 3k credits per day which translates to up to 6k address geocodings.

Install our package with pip

This package is available on the public PyPI:

pip install geobatchpy

Examples

See our documentation at geobatchpy.readthedocs.io for a growing number of comprehensive example use cases. Below we illustrate both, the Python API and the CLI, for a tiny batch geocoding example.

A simple batch geocoding example using the Python API

Below we geocode multiple addresses in a single batch. There are two ways how we can provide the location data as input. Either we use a list of strings, one string per address. These are then taken as free text searches. Or we provide structured input as a list of dictionaries, again one per address. See the Geoapify API documentation for a complete list of address attributes accepted by the geocoding services. Use the optional parameters dictionary if all your addresses have an attribute in common. E.g., below we request results in French.

from geobatchpy import Client

client = Client(api_key='<your-api-key>')

addresses = ['Hülser Markt 1, 47839 Krefeld',
             'DB Schenker, Essen, Germany',
             'JCI Beteiligungs GmbH, Am Schimmersfeld 5, Ratingen']

# see the geoapify.com API docs for more optional parameters
res = client.batch.geocode(locations=addresses, parameters={'lang': 'fr'}, simplify_output=True)

Alternatively you can provide a list of dictionaries, with every address in a structured form. And if you still need the free text search for some, you can do this with the 'text' attribute. Here is the same example, with the first two address translated to structured form:

addresses = [{'city': 'Krefeld', 'street': 'Hülser Markt', 'housenumber': 1, 'postcode': '47839'},
             {'name': 'DB Schenker', 'city': 'Essen', 'country': 'Germany'},
             {'text': 'JCI Beteiligungs GmbH, Am Schimmersfeld 5, Ratingen'}]

# Showing the first of three result sets: res[0]
{
    "query": {
        "text": "Hülser Markt 1, 47839 Krefeld",
        "parsed": {
            "housenumber": "1",
            "street": "hülser markt",
            "postcode": "47839",
            "city": "krefeld",
            "expected_type": "building",
        },
    },
    "datasource": {
        "sourcename": "openstreetmap",
        "attribution": "© OpenStreetMap contributors",
        "license": "Open Database License",
        "url": "https://www.openstreetmap.org/copyright",
    },
    "name": "Metzgerei Etteldorf",
    "housenumber": "1",
    "street": "Hülser Markt",
    "suburb": "Hüls",
    "city": "Krefeld",
    "state": "Rhénanie-du-Nord-Westphalie",
    "postcode": "47839",
    "country": "Allemagne",
    "country_code": "de",
    "lon": 6.510696417033254,
    "lat": 51.373026800000005,
    "formatted": "Metzgerei Etteldorf, Hülser Markt 1, 47839 Krefeld, Allemagne",
    "address_line1": "Metzgerei Etteldorf",
    "address_line2": "Hülser Markt 1, 47839 Krefeld, Allemagne",
    "category": "commercial.food_and_drink.butcher",
    "result_type": "amenity",
    "rank": {
        "importance": 0.31100000000000005,
        "popularity": 5.585340759145855,
        "confidence": 1,
        "confidence_city_level": 1,
        "confidence_street_level": 1,
        "match_type": "inner_part",
    },
    "place_id": "516b5e6500f40a1a40590a449957bfaf4940f00102f9010ecff70d00000000c002019203134d65747a676572656920457474656c646f7266",
}

The same batch geocoding example using the CLI

We built the geoapify command line interface to make batch processing large numbers of records more comfortable.

Steps:

1. Prepare a JSON file as input.
2. Use geoapify post-batch-jobs to submit one or more jobs to the Geoapify servers.
3. Use geoapify monitor-batch-jobs for monitoring progress and data retrieval.

# Step 1 - written in Python:
from geobatchpy.batch import parse_geocoding_inputs
from geobatchpy.utils import write_data_to_json_file

addresses = ['Hülser Markt 1, 47839 Krefeld',
             'DB Schenker, Essen, Germany',
             'JCI Beteiligungs GmbH, Am Schimmersfeld 5, Ratingen']

data = {
    'api': '/v1/geocode/search',  # see the Geoapify API docs for other APIs that work with batch processing
    'inputs': parse_geocoding_inputs(locations=addresses),
    'batch_len': 2,  # optional - will put first two addresses in batch 1, last address in batch 2
    'id': 'my-batch-geocoding-job'  # optional - a reference which will be reused in the output file
}

write_data_to_json_file(data=data, file_path='<path-data-in>')

The following command submits one or more jobs and stores job URLs to disk. Those URLs are required to monitor and retrieve results.

geobatch submit <path-data-in> <path-post-data-out> --api-key <your-key>

You can omit the --api-key option if you set the GEOAPIFY_KEY environment variable. Next we start monitoring progress:

geobatch receive <path-post-data-out> <path-results-data-out> --api-key <your-key>

We can abort the monitoring at any time and restart later - provided the jobs still are in the cache of Geoapify servers (24 hours).

References and further reading

• geoapify.com API documentation
• Towards Data Science - Deduplicate and clean-up millions of location records