https://github.com/MarcoGorelli/polars-reverse-geocode

Fast offline reverse geocoder
https://github.com/MarcoGorelli/polars-reverse-geocode

Last synced: 30 days ago
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Fast offline reverse geocoder

• Host: GitHub
• URL: https://github.com/MarcoGorelli/polars-reverse-geocode
• Owner: MarcoGorelli
• License: mit
• Created: 2023-12-20T12:25:02.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-08-15T14:24:24.000Z (5 months ago)
• Last Synced: 2024-08-16T15:24:55.299Z (5 months ago)
• Language: Python
• Homepage:
• Size: 193 KB
• Stars: 5
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

• awesome-polars - polars-reverse-geocode - This plugin is an offline reverse geocoder for finding the closest city to a given (latitude, longitude) pair by [@MarcoGorelli](https://github.com/MarcoGorelli). (Libraries/Packages/Scripts / Polars plugins)

README

        
# Polars-Reverse-Geocode

Polars plugin based on https://github.com/gx0r/rrgeo.

> rrgeo takes a latitude and longitude as input and returns the closest city, country, latitude, and longitude, using a k-d tree to efficiently find the nearest neighbour based on a known list of locations. This can be useful if you need to reverse geocode a large number of coordinates quickly, or just need the rough location of coordinates but don't want the expense or complication of an online reverse geocoder.

## Installation

```

pip install polars-reverse-geocode

```

## Usage example

```python

import polars as pl

from polars_reverse_geocode import find_closest_city, find_closest_state, find_closest_country

df = pl.DataFrame({

    'lat': [37.7749, 51.01, 52.5],

    'lon': [-122.4194, -3.9, -.91]

})

print(

    df.with_columns(

        city = find_closest_city('lat', 'lon'),

        state = find_closest_state('lat', 'lon'),

        country_code = find_closest_country('lat', 'lon')

    )

)

```

```

shape: (3, 5)

┌─────────┬───────────┬───────────────────┬────────────┬──────────────┐

│ lat     ┆ lon       ┆ city              ┆ state      ┆ country_code │

│ ---     ┆ ---       ┆ ---               ┆ ---        ┆ ---          │

│ f64     ┆ f64       ┆ str               ┆ str        ┆ str          │

╞═════════╪═══════════╪═══════════════════╪════════════╪══════════════╡

│ 37.7749 ┆ -122.4194 ┆ San Francisco     ┆ California ┆ US           │

│ 51.01   ┆ -3.9      ┆ South Molton      ┆ England    ┆ GB           │

│ 52.5    ┆ -0.91     ┆ Market Harborough ┆ England    ┆ GB           │

└─────────┴───────────┴───────────────────┴────────────┴──────────────┘

```