import great_expectations as gx

context = gx.get_context()

undefined

def validate_data_quality(df):
    issues = []
    
    # Check for nulls
    null_counts = df.isnull().sum()
    if null_counts.any():
        issues.append(f"Null values found: {null_counts[null_counts > 0]}")
    
    # Check for duplicates
    duplicates = df.duplicated().sum()
    if duplicates > 0:
        issues.append(f"Found {duplicates} duplicate rows")
    
    # Check data freshness
    max_date = df['created_at'].max()
    if (datetime.now() - max_date).days > 1:
        issues.append("Data is stale")
    
    return issues

def calculate_quality_metrics(df):
    return {
        'completeness': 1 - (df.isnull().sum().sum() / df.size),
        'uniqueness': df.drop_duplicates().shape[0] / df.shape[0],
        'validity': (df['email'].str.contains('@').sum() / len(df)),
        'timeliness': (datetime.now() - df['created_at'].max()).days
    }