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import pandas as pd df = pd.read_csv("badulla_badu_numbers.csv", parse_dates=["Date"], dayfirst=True) # Schema required = ["ID","Location","Category","Count","Date","Source"] missing = [c for c in required if c not in df.columns] # Type and range checks df["Count_num"] = pd.to_numeric(df["Count"], errors="coerce") negatives = df[df["Count_num"] < 0] missing_counts = df["Count_num"].isna().sum() # Duplicates dups = df[df.duplicated(subset=["ID"], keep=False)] # Aggregation total = df["Count_num"].sum() outliers = df[(df["Count_num"] - df["Count_num"].mean()).abs() > 3*df["Count_num"].std()] print(missing, len(df), missing_counts, len(negatives), len(dups), total, len(outliers))