Inverse postal code search #87
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rth
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Jan 16, 2026
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rth
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Thanks! a few comments below and this also needs some unit tests.
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| Args: | ||
| lat (float): latitude | ||
| lon (float): longitude |
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| return pd.DataFrame(columns=self._data.columns) | ||
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| def query_geocode(self, lat: float, lon: float): |
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let's call it query_coordinates maybe.
Also I think it would be better to also add a top_k: int=1 param and returns a dataframe not a series, similar to how query_location works. Add the return type annotations.
| np.array([[lat, lon]]), np.array([[row["latitude"], row["longitude"]]]) | ||
| ), | ||
| axis=1, | ||
| ) |
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You can build the array of coordinates _data_frame and pass it to haversine_distance directly (it should work with arrays of shape N, that would be faster.
| nearest_index = distances.idxmin() | ||
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| # Return the nearest row | ||
| nearest_row = self._data_frame.loc[nearest_index].copy() |
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Here you can argsort distances and return top_k
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#7
Feel free to edit as you wish