added ws202425 courses

2024-11-14 13:11:04 +01:00
parent 800282c2e8
commit beb31897b1
461 changed files with 20368 additions and 0 deletions
@@ -0,0 +1,62 @@
+import pandas as pd
+
+def clean_bevoelkerung(file_path,cleaned_file_path):
+    df = pd.read_csv(file_path, delimiter=';', encoding='ISO-8859-1', skiprows=8)
+
+    df.columns = [
+        'region_code', 'region_name', 'total_population', 'male_population', 'female_population'
+    ]
+
+    df['total_population'] = pd.to_numeric(df['total_population'], errors='coerce').astype('Int64')
+    df['male_population'] = pd.to_numeric(df['male_population'], errors='coerce').astype('Int64')
+    df['female_population'] = pd.to_numeric(df['female_population'], errors='coerce').astype('Int64')
+
+    df = df.dropna(subset=['region_code', 'region_name', 'total_population', 'male_population', 'female_population'])
+
+    df['region_name'] = df['region_name'].str.replace(' ', '')
+
+    bundesland_mapping = {
+        '01': 'Schleswig-Holstein',
+        '02': 'Hamburg',
+        '03': 'Niedersachsen',
+        '04': 'Bremen',
+        '05': 'Nordrhein-Westfalen',
+        '06': 'Hessen',
+        '07': 'Rheinland-Pfalz',
+        '08': 'Baden-Württemberg',
+        '09': 'Bayern',
+        '10': 'Saarland',
+        '11': 'Berlin',
+        '12': 'Brandenburg',
+        '13': 'Mecklenburg-Vorpommern',
+        '14': 'Sachsen',
+        '15': 'Sachsen-Anhalt',
+        '16': 'Thüringen'
+    }
+
+    def get_region_info(region_code,region_name):
+        if 'kreis' in region_name.lower():
+            parts = region_name.split(',')
+            region_name_clean = parts[0]
+            region_type = 'kreis'
+        elif ',' in region_name:
+            parts = region_name.split(',')
+            region_name_clean = parts[0]
+            region_type = parts[1].lower()
+        else:
+            region_name_clean = region_name
+            region_type = 'bundesland'
+        
+        bundesland_code = region_code[:2]
+        bundesland_name = bundesland_mapping.get(bundesland_code, 'Unknown')
+        
+        return region_type, region_name_clean, bundesland_name
+
+    df[['region_type', 'region_name', 'bundesland']] = df.apply(lambda x: pd.Series(get_region_info(x['region_code'], x['region_name'])), axis=1)
+    
+    df.to_csv(cleaned_file_path, index=False)
+    return df
+
+bevoelkerung_file_path = 'data/source/bevoelkerung.csv'
+bevoelkerung_cleaned_file_path = 'data/preprocessed/cleaned_bevoelkerung.csv'
+cleaned_bevoelkerung_df = clean_bevoelkerung(bevoelkerung_file_path, bevoelkerung_cleaned_file_path)