#!/usr/bin/env python3
"""Data Cleaning Plugin"""
import pandas as pd
from typing import Dict, Any

class DataCleaner:
    """Clean and standardize messy data for analytics."""
    def clean_dataframe(self, df: pd.DataFrame) -> pd.DataFrame:
        df = df.copy()
        df.columns = df.columns.astype(str).str.strip().str.lower().str.replace(' ', '_').str.replace(r'[^a-z0-9_]', '', regex=True)
        df = df.dropna(how='all', axis=0).dropna(how='all', axis=1)
        null_values = ['', 'null', 'NULL', 'None', 'N/A', 'n/a', '#N/A', '-', '?', 'unknown']
        for col in df.select_dtypes(include=['object', 'string']).columns:
            df[col] = df[col].astype(str).str.strip().replace(null_values, pd.NA)
        df = df.drop_duplicates()
        return df
    
    def enforce_schema(self, df: pd.DataFrame) -> pd.DataFrame:
        df = df.copy()
        for col in df.columns:
            try:
                if 'date' in col or 'time' in col:
                    df[col] = pd.to_datetime(df[col], errors='coerce')
                elif any(kw in col for kw in ['amount', 'price', 'cost', 'value', 'count']):
                    df[col] = pd.to_numeric(df[col], errors='coerce')
            except: pass
        return df