Data Preprocessing

def remove_duplicates(df, subset=None):
    """
    Remove duplicate rows from dataframe
    """
    n_before = len(df)
    df = df.drop_duplicates(subset=subset)
    n_after = len(df)
    
    print(f"Removed {n_before - n_after} duplicate rows")
    return df

def detect_outliers_zscore(data, threshold=3):
    z_scores = np.abs((data - data.mean()) / data.std())
    return z_scores > threshold

def detect_outliers_iqr(data):
    Q1 = data.quantile(0.25)
    Q3 = data.quantile(0.75)
    IQR = Q3 - Q1
    
    lower_bound = Q1 - 1.5 * IQR
    upper_bound = Q3 + 1.5 * IQR
    
    return (data < lower_bound) | (data > upper_bound)

from sklearn.ensemble import IsolationForest

def detect_outliers_iforest(X):
    iso_forest = IsolationForest(contamination=0.1)
    yhat = iso_forest.fit_predict(X)
    return yhat == -1  # True for outliers

from sklearn.neighbors import LocalOutlierFactor

def detect_outliers_lof(X):
    lof = LocalOutlierFactor()
    yhat = lof.fit_predict(X)
    return yhat == -1

def log_transform(data, epsilon=1e-10):
    """
    Apply log transformation with handling of zeros/negative values
    """
    return np.log(data + epsilon)

from scipy.stats import boxcox

def box_cox_transform(data):
    """
    Apply Box-Cox transformation
    """
    transformed_data, lambda_param = boxcox(data + 1)
    return transformed_data, lambda_param

def frequency_encode(data, column):
    """
    Replace categories with their frequencies
    """
    frequency = data[column].value_counts(normalize=True)
    return data[column].map(frequency)

def mean_encode_regularized(train, test, column, target, alpha=10):
    """
    Mean encoding with smoothing
    """
    global_mean = train[target].mean()
    agg = train.groupby(column)[target].agg(['count', 'mean'])
    
    # Apply smoothing
    counts = agg['count']
    means = agg['mean']
    smooth = (counts * means + alpha * global_mean) / (counts + alpha)
    
    # Map values
    train_encoded = train[column].map(smooth)
    test_encoded = test[column].map(smooth)
    
    return train_encoded, test_encoded

from sklearn.preprocessing import RobustScaler

def robust_scale(X_train, X_test):
    """
    Scale features using statistics that are robust to outliers
    """
    scaler = RobustScaler()
    X_train_scaled = scaler.fit_transform(X_train)
    X_test_scaled = scaler.transform(X_test)
    return X_train_scaled, X_test_scaled

from sklearn.preprocessing import QuantileTransformer

def quantile_transform(X_train, X_test, n_quantiles=1000):
    """
    Transform features to follow a normal distribution
    """
    transformer = QuantileTransformer(
        n_quantiles=n_quantiles,
        output_distribution='normal'
    )
    X_train_transformed = transformer.fit_transform(X_train)
    X_test_transformed = transformer.transform(X_test)
    return X_train_transformed, X_test_transformed

from sklearn.preprocessing import PolynomialFeatures

def create_polynomial_features(X, degree=2):
    """
    Create polynomial and interaction features
    """
    poly = PolynomialFeatures(degree=degree)
    return poly.fit_transform(X)

def create_datetime_features(df, date_column):
    """
    Extract useful features from datetime column
    """
    df = df.copy()
    df['hour'] = df[date_column].dt.hour
    df['day'] = df[date_column].dt.day
    df['month'] = df[date_column].dt.month
    df['year'] = df[date_column].dt.year
    df['day_of_week'] = df[date_column].dt.dayofweek
    df['is_weekend'] = df['day_of_week'].isin([5, 6]).astype(int)
    
    return df

from dataclasses import dataclass
from typing import List, Dict

@dataclass
class DataSchema:
    numerical_features: List[str]
    categorical_features: List[str]
    datetime_features: List[str]
    target: str
    
def validate_schema(df: pd.DataFrame, schema: DataSchema) -> bool:
    """
    Validate dataframe against schema
    """
    # Check all features exist
    all_features = (
        schema.numerical_features + 
        schema.categorical_features + 
        schema.datetime_features
    )
    
    if not all(feat in df.columns for feat in all_features):
        return False
    
    # Check data types
    for feat in schema.numerical_features:
        if not np.issubdtype(df[feat].dtype, np.number):
            return False
            
    return True

def check_data_quality(df: pd.DataFrame) -> Dict:
    """
    Perform various data quality checks
    """
    checks = {
        'missing_values': df.isnull().sum().to_dict(),
        'unique_counts': df.nunique().to_dict(),
        'negative_values': {
            col: (df[col] < 0).sum()
            for col in df.select_dtypes(include=[np.number]).columns
        },
        'zero_values': {
            col: (df[col] == 0).sum()
            for col in df.select_dtypes(include=[np.number]).columns
        }
    }
    return checks

from sklearn.base import BaseEstimator, TransformerMixin

class CustomPreprocessor(BaseEstimator, TransformerMixin):
    def __init__(self, numerical_features, categorical_features):
        self.numerical_features = numerical_features
        self.categorical_features = categorical_features
        
    def fit(self, X, y=None):
        return self
        
    def transform(self, X):
        X = X.copy()
        
        # Transform numerical features
        for feature in self.numerical_features:
            X[feature] = self._transform_numerical(X[feature])
            
        # Transform categorical features
        for feature in self.categorical_features:
            X[feature] = self._transform_categorical(X[feature])
            
        return X
        
    def _transform_numerical(self, series):
        # Add your numerical transformations
        return series
        
    def _transform_categorical(self, series):
        # Add your categorical transformations
        return series

def create_preprocessing_pipeline(numerical_features, 
                                categorical_features):
    """
    Create a complete preprocessing pipeline
    """
    numerical_transformer = Pipeline([
        ('imputer', SimpleImputer(strategy='median')),
        ('scaler', RobustScaler())
    ])

    categorical_transformer = Pipeline([
        ('imputer', SimpleImputer(strategy='constant', 
                                 fill_value='missing')),
        ('encoder', OneHotEncoder(handle_unknown='ignore'))
    ])

    preprocessor = ColumnTransformer(
        transformers=[
            ('num', numerical_transformer, numerical_features),
            ('cat', categorical_transformer, categorical_features)
        ])

    return preprocessor