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Inverse postal code search #87

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Inverse postal code search #87

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LOW: Inverse postal code search
vk496 Apr 12, 2025
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pre-commit-ci[bot] Apr 12, 2025
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26 changes: 26 additions & 0 deletions pgeocode.py
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Original file line number Diff line number Diff line change
Expand Up @@ -387,6 +387,32 @@ def query_location(

return pd.DataFrame(columns=self._data.columns)

def query_geocode(self, lat: float, lon: float):
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@rth rth Jan 16, 2026

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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.

"""Get locations information from geo points

Args:
lat (float): latitude
lon (float): longitude
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@rth rth Jan 16, 2026

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Should use numpy style docstring.


Returns:
pandas.DataFrame: a DataFrame with the relevant information
"""
distances = self._data_frame.apply(
lambda row: haversine_distance(
np.array([[lat, lon]]), np.array([[row["latitude"], row["longitude"]]])
),
axis=1,
)
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@rth rth Jan 16, 2026

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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.


# Get the index of the row with the smallest distance
nearest_index = distances.idxmin()

# Return the nearest row
nearest_row = self._data_frame.loc[nearest_index].copy()
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@rth rth Jan 16, 2026

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Here you can argsort distances and return top_k

nearest_row["calculated_distance"] = distances[nearest_index][0]

return nearest_row

def _str_contains_search(self, text: str, col: str) -> pd.DataFrame:
match_mask = self._data[col].str.lower().str.contains(text.lower())
match_mask.fillna(False, inplace=True)
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