def data_quality_pipeline(df):
    """Professional data inspection and validation workflow"""

    # Initial inspection
    print("=== DATA QUALITY REPORT ===")
    print(f"Shape: {df.shape}")
    print("\nFirst 5 rows:")
    print(df.head())

    print("\nLast 5 rows:")
    print(df.tail())

    # Type-specific validation
    numeric_cols = df.select_dtypes(include='number')
    print(f"\nNumeric data preview ({len(numeric_cols.columns)} columns):")
    print(numeric_cols.head(3))

    # Prepare for analysis
    print("\nConverting to NumPy for statistical analysis...")
    analysis_data = numeric_cols.to_numpy(dtype='float64')
    print(f"Analysis array shape: {analysis_data.shape}")

    return analysis_data

# Execute pipeline
analysis_data = data_quality_pipeline(df)