Refactor code structure and remove redundant code blocks

check sociologie inleiding soc/script.py

@@ -0,0 +1,104 @@
+import os
+from pathlib import Path
+import pandas as pd
+
+
+def find_duplicates(base_file: Path, ps_files: list[Path], id_col_candidates=None, grade_col_candidates=None):
+    """Read the base registration file and several ps files, then find IDs that appear in both.
+
+    Returns a DataFrame with columns: ID, Cijfer, SourceFile
+    """
+    if id_col_candidates is None:
+        id_col_candidates = ["ID", "Id", "id", "inschrijving_id"]
+    if grade_col_candidates is None:
+        grade_col_candidates = ["Cijfer", "cijfer", "Grade", "grade"]
+
+    # Read base IDs
+    print(f"Reading base file: {base_file}")
+    base_df = pd.read_excel(base_file)
+
+    # find ID column in base
+    base_id_col = next((c for c in base_df.columns if c in id_col_candidates), None)
+    if base_id_col is None:
+        raise ValueError(f"Could not find an ID column in {base_file}. Tried: {id_col_candidates}")
+
+    base_ids = set(base_df[base_id_col].dropna().astype(str).str.strip())
+    print(f"Found {len(base_ids)} IDs in base file (column '{base_id_col}').")
+
+    duplicates = []
+
+    for pf in ps_files:
+        print(f"Processing ps file: {pf}")
+        try:
+            df = pd.read_excel(pf)
+        except Exception as e:
+            print(f"  Skipping {pf} - failed to read: {e}")
+            continue
+
+        # guess ID column
+        id_col = next((c for c in df.columns if c in id_col_candidates), None)
+        if id_col is None:
+            # try fuzzy: column name contains 'id'
+            id_col = next((c for c in df.columns if 'id' in str(c).lower()), None)
+        if id_col is None:
+            print(f"  No ID column found in {pf}; skipping.")
+            continue
+
+        grade_col = next((c for c in df.columns if c in grade_col_candidates), None)
+        if grade_col is None:
+            # try fuzzy: column name contains 'cij' or 'cijfer' or 'grade'
+            grade_col = next((c for c in df.columns if any(k in str(c).lower() for k in ['cij', 'grade'])), None)
+
+        # normalize IDs to string
+        df_ids = df[[id_col]].dropna()
+        df_ids[id_col] = df_ids[id_col].astype(str).str.strip()
+
+        # merge to find intersection
+        mask = df_ids[id_col].isin(base_ids)
+        matched = df.loc[mask]
+        if matched.empty:
+            print(f"  No duplicates found in {pf}.")
+            continue
+
+        # collect results
+        for _, row in matched.iterrows():
+            id_val = str(row[id_col]).strip()
+            grade_val = row[grade_col] if (grade_col in row and pd.notna(row[grade_col])) else None
+            duplicates.append({"ID": id_val, "Cijfer": grade_val, "SourceFile": pf.name})
+
+        print(f"  Found {len(matched)} duplicates in {pf}.")
+
+    dup_df = pd.DataFrame(duplicates)
+    return dup_df
+
+
+def main():
+    base = Path(__file__).parent / "inschrijvingslijst sociologie.xlsx"
+    # match files like: ps (82).xls.xlsx
+    ps_files = sorted(Path(__file__).parent.glob('ps *.xls.xlsx'))
+
+    if not base.exists():
+        print(f"Base file not found: {base}")
+        return
+
+    if not ps_files:
+        print("No ps files found matching pattern 'ps (*.xls).xlsx'")
+        return
+
+    dup_df = find_duplicates(base, ps_files)
+
+    if dup_df.empty:
+        print("No duplicates found across provided files.")
+    else:
+        # print duplicates
+        print("Duplicates found (ID - Cijfer - SourceFile):")
+        for _, r in dup_df.iterrows():
+            print(f"{r['ID']} - {r['Cijfer']} - {r['SourceFile']}")
+
+        out_csv = Path(__file__).parent / 'duplicates_summary.csv'
+        dup_df.to_csv(out_csv, index=False)
+        print(f"Wrote summary to {out_csv}")
+
+
+if __name__ == '__main__':
+    main()