start with geopandas

src/T18_MergingEx.py

@@ -41,8 +41,18 @@ print(positionen)
 
 
 # Aufgabe 1: Bestellungen mit Kundennamen
+#  - Nur bestellungen von existierenden Kunden
+order_customer = pd.merge(bestellungen, kunden, on="kunden_id", how="inner")
+print(order_customer)
+
 # Aufgabe 2: Vollständige Bestellübersicht (jede bestellung mit kunde und mit position mergen)
-# Aufgabe 3: Kunden ohne Bestellungen finden  [isna()]
+step1 = pd.merge(bestellungen, kunden, on="kunden_id", how="left")
+result = pd.merge(step1, positionen, on="bestell_id", how="left")
+print(result)
+# Aufgabe 3: Kunden ohne Bestellungen finden  [.isna()]
+kunden_ohne_best = pd.merge(kunden, bestellungen, on="kunden_id", how="left")
+print(kunden_ohne_best)
+print(kunden_ohne_best[kunden_ohne_best["bestell_id"].isna()][["kunden_id", "name"]])
 
 
 

src/T19_geopandas.py

@@ -0,0 +1,62 @@
+# pip/conda install geopandas
+
+import geopandas as gpd
+import matplotlib.pyplot as plt
+
+
+world = gpd.read_file("../data/110m_cultural.zip", layer="ne_110m_admin_0_countries")
+
+print(list(world.columns))
+
+print(world.iloc[0, -1])
+print(world.iloc[0]["NAME"])
+
+print()
+print(world.crs)
+
+# EPSG:4326  (latitude/longitude)
+
+# epsg:3587  Mercator
+
+w2 = world.to_crs("epsg:3587")
+print(w2.iloc[0, -1])
+
+print(world[["NAME", "CONTINENT", "POP_EST", "GDP_MD", "geometry"]].head())
+
+# wie groß ist jedes Land?
+world_proj_cylinder = world.to_crs("epsg:3587")  # zylinder
+world_proj_cylinder["area_sq_km"] = world_proj_cylinder.geometry.area / 1_000_000
+print(world_proj_cylinder[["NAME", "area_sq_km"]].head(10))
+
+
+cities = gpd.read_file("../data/110m_cultural.zip",
+                      layer="ne_110m_populated_places")
+
+print(cities[["NAME", "geometry"]].head())
+print(len(cities))
+
+fig, ax = plt.subplots()
+world.plot(column="CONTINENT", legend=True, ax=ax)
+cities.plot(ax=ax, color="red")
+plt.show()
+
+# Nachbar von Land X suchen
+def find_neighbouring_countries(df, country_name):
+    target = df[df["NAME"] == country_name]
+
+    if len(target) == 0:
+        print(f"{country_name} not found")
+        return []
+
+    # sjoin
+    neighbours = []
+    for idx, country in df.iterrows():
+        if country["NAME"] == country_name:
+            continue
+        if target.geometry.iloc[0].touches(country.geometry):
+            neighbours.append(country["NAME"])
+
+    return neighbours
+
+res = find_neighbouring_countries(world, "Spain")
+print(res)