From 030984d49edb84e249ed4c8977b11a294d392894 Mon Sep 17 00:00:00 2001
From: Valentin <vk18496@gmail.com>
Date: Sun, 13 Apr 2025 01:57:41 +0200
Subject: [PATCH 1/2] LOW: Inverse postal code search

---
 pgeocode.py | 26 ++++++++++++++++++++++++++
 1 file changed, 26 insertions(+)

diff --git a/pgeocode.py b/pgeocode.py
index 8695a46..4859e7c 100644
--- a/pgeocode.py
+++ b/pgeocode.py
@@ -387,6 +387,32 @@ def query_location(
 
         return pd.DataFrame(columns=self._data.columns)
 
+    def query_geocode(self, lat: float, lon: float):
+        """Get locations information from geo points
+
+        Args:
+            lat (float): latitude
+            lon (float): longitude
+
+        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,
+        )
+
+        # 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()
+        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)

From d3a1239002e9c6d2725c86750fbad3a60b0ef9a8 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Sat, 12 Apr 2025 23:58:47 +0000
Subject: [PATCH 2/2] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 pgeocode.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pgeocode.py b/pgeocode.py
index 4859e7c..5f8a510 100644
--- a/pgeocode.py
+++ b/pgeocode.py
@@ -395,7 +395,7 @@ def query_geocode(self, lat: float, lon: float):
             lon (float): longitude
 
         Returns:
-            pandas.DataFrame: a DataFrame with the relevant information 
+            pandas.DataFrame: a DataFrame with the relevant information
         """
         distances = self._data_frame.apply(
             lambda row: haversine_distance(
@@ -409,7 +409,7 @@ def query_geocode(self, lat: float, lon: float):
 
         # Return the nearest row
         nearest_row = self._data_frame.loc[nearest_index].copy()
-        nearest_row['calculated_distance'] = distances[nearest_index][0]
+        nearest_row["calculated_distance"] = distances[nearest_index][0]
 
         return nearest_row