start geopandas exercise

src/T20_Geopandas_joining.py

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+import geopandas as gpd
+import matplotlib.pyplot as plt
+
+
+world = gpd.read_file("../data/110m_cultural.zip", layer="ne_110m_admin_0_countries")
+cities = gpd.read_file("../data/110m_cultural.zip",
+                      layer="ne_110m_populated_places")
+
+
+def find_neighbouring_countries(df, country_name):
+    target = df[df["NAME"] == country_name]
+
+    if len(target) == 0:
+        print(f"{country_name} not found")
+        return gpd.GeoDataFrame()
+
+    # sjoin  spatial-join
+    neighbours = gpd.sjoin(target, df, how="inner", predicate="touches")
+    return neighbours[["NAME_right", "CONTINENT_right", "geometry"]]
+
+def get_close_cities(df, city_name):
+    target = df[df["NAMEASCII"] == city_name]
+
+    if len(target) == 0:
+        print(f"{city_name} not found")
+        return gpd.GeoDataFrame()
+
+    # sjoin  spatial-join
+    neighbours = gpd.sjoin_nearest(target, df, max_distance=10e12)
+    return neighbours
+
+
+#intersects
+#contains
+#within
+#touches
+#crosses
+#overlaps
+german_neighbours = find_neighbouring_countries(world, "Germany")
+print(german_neighbours[["NAME_right", "CONTINENT_right"]])
+print(len(german_neighbours))
+print("-"*100)
+w2 = world.to_crs("EPSG:3857")
+c2 = cities.to_crs("EPSG:3857")
+print(list(c2.columns))
+german_neighbours = get_close_cities(c2, "Paris")
+print(len(german_neighbours))
+print(german_neighbours)
+
+w2.plot(column="CONTINENT", legend=True)
+plt.show()
+
+
+# 1) get_cities_in_country    (sjoin)
+# 2) get_cities_in_countries  (concat)