""" Process full Users dataset and export to Parquet. Processes all user survey files (users14.dat and questions.dat) and exports cleaned data. """ import sys import argparse from pathlib import Path from datetime import datetime sys.path.insert(0, str(Path(__file__).parent.parent)) from src.core.config import Config from src.core.output_lock import seal_output, unlock_for_write from src.processors.users_processor import UsersProcessor def main(): parser = argparse.ArgumentParser(description='Process Users dataset') parser.add_argument('--audit', action='store_true', help='Include source_file and source_row_number columns in output') parser.add_argument('--data-dir', help='Override raw data directory (default: data/)') args = parser.parse_args() print("="*70) print("Users Dataset Full Processing") print("="*70) print() start_time = datetime.now() # Load config config = Config('config/cleaning_config.yaml') # Override audit mode if --audit flag is provided if args.audit: config.set('processing.audit_mode', True) print("Audit mode: ENABLED (including source_file and source_row_number columns)") print() # Override data directory if --data-dir flag is provided if args.data_dir: config.set('directories.raw_data', args.data_dir) # Create output directory output_dir = config.output_dir / 'parquet' output_dir.mkdir(parents=True, exist_ok=True) output_file = output_dir / 'users.parquet' print(f"Output file: {output_file}") print() # Create processor processor = UsersProcessor(config) # Get file count try: all_files = processor.get_file_list() print(f"Total files to process: {len(all_files)}") for f in all_files: file_type = processor.detect_file_type(f) print(f" - {f.name} ({file_type})") print() except Exception as e: print(f"✗ Error getting file list: {e}") print() print("Expected directory: data/user_data/") print("Make sure the raw data is in the correct location.") print("Files expected: users14.dat and/or questions.dat") sys.exit(1) # Process all files print("Processing all user files...") print() try: df = processor.process() elapsed = datetime.now() - start_time print() print("="*70) print("✓ Processing Complete!") print("="*70) print() # Show stats stats = processor.get_stats() print("Processing Statistics:") print(f" Files processed: {stats['files_processed']:,}") print(f" Files failed: {stats['files_failed']:,}") print(f" Success rate: {stats['files_processed'] / len(all_files) * 100:.1f}%") print() print(f" users14.dat rows: {stats['users14_rows']:,}") print(f" questions.dat rows: {stats['questions_rows']:,}") print() print(f" Total rows (pre-dedup): {stats['rows_total']:,}") print(f" Duplicates removed: {stats['duplicates_removed']:,}") print(f" Final rows: {len(df):,}") print(f" NA usernames (kept): {stats['na_usernames_count']:,}") print() # Data quality metrics print("Data Quality:") completeness = df.notna().sum() / len(df) print(f" Overall completeness (avg): {completeness.mean():.1%}") print() # Column completeness print("Column Completeness:") key_cols = ['username', 'timestamp', 'email', 'state', 'coordinates', 'country', 'na_count_psi_and_hemi'] for col in key_cols: if col in df.columns: complete = df[col].notna().sum() pct = complete / len(df) * 100 print(f" {col:25s}: {complete:6,} / {len(df):6,} ({pct:5.1f}%)") print() # Psi columns completeness psi_cols = [f'psi_{i:02d}' for i in range(1, 16)] existing_psi = [col for col in psi_cols if col in df.columns] if existing_psi: psi_complete = df[existing_psi].notna().sum().sum() psi_total = len(df) * len(existing_psi) print(f"Psi columns (1-15) completeness: {psi_complete:,} / {psi_total:,} ({psi_complete/psi_total*100:.1f}%)") print() # Hemi columns completeness hemi_cols = [f'hemi_{i:02d}' for i in range(1, 11)] existing_hemi = [col for col in hemi_cols if col in df.columns] if existing_hemi: hemi_complete = df[existing_hemi].notna().sum().sum() hemi_total = len(df) * len(existing_hemi) print(f"Hemi columns (1-10) completeness: {hemi_complete:,} / {hemi_total:,} ({hemi_complete/hemi_total*100:.1f}%)") print() # Date range if 'timestamp' in df.columns: valid_ts = df['timestamp'].dropna() if not valid_ts.empty: print(f"Date range: {valid_ts.min()} to {valid_ts.max()}") print(f"Span: {(valid_ts.max() - valid_ts.min()).days} days") print() # File type distribution if 'file_type' in df.columns: print("File Type Distribution:") for file_type in df['file_type'].unique(): count = (df['file_type'] == file_type).sum() pct = count / len(df) * 100 print(f" {file_type}: {count:,} rows ({pct:.1f}%)") print() # Country distribution (top 10) if 'country' in df.columns: print("Top 10 Countries:") country_counts = df['country'].value_counts().head(10) for country, count in country_counts.items(): pct = count / len(df) * 100 print(f" {country}: {count:,} ({pct:.1f}%)") print() # NA count distribution if 'na_count_psi_and_hemi' in df.columns: print("NA Count (Psi + Hemi) Distribution:") na_stats = df['na_count_psi_and_hemi'].describe() print(f" Min: {na_stats['min']:.0f}") print(f" Max: {na_stats['max']:.0f}") print(f" Mean: {na_stats['mean']:.1f}") print(f" Median: {na_stats['50%']:.0f}") print() # Performance stats print(f"Processing time: {elapsed}") if elapsed.total_seconds() > 0: print(f"Speed: {len(df) / elapsed.total_seconds():.0f} rows/second") print() # Drop demographic columns scrubbed at the .dat level (see sanitize_raw.py) df = df.drop(columns=['city', 'how_find'], errors='ignore') # Export to Parquet print("Exporting to Parquet...") unlock_for_write(output_file) df.to_parquet( output_file, engine='pyarrow', compression='snappy', index=False ) seal_output(output_file) file_size_mb = output_file.stat().st_size / (1024 * 1024) print(f"✓ Exported to: {output_file}") print(f" File size: {file_size_mb:.1f} MB") print() # Compression ratio raw_size_estimate = len(df) * 400 # Rough estimate of ~400 bytes/row in CSV compression_ratio = raw_size_estimate / output_file.stat().st_size print(f" Compression ratio: {compression_ratio:.1f}x") print() # Show errata summary errata_summary = processor.errata_logger.get_summary() print("Error Summary:") print(f" Total errors: {errata_summary['total_errors']:,}") print(f" Files with errors: {errata_summary['files_with_errors']:,}") if errata_summary['error_types']: print(f" Error types:") for error_type, count in sorted(errata_summary['error_types'].items(), key=lambda x: x[1], reverse=True)[:10]: print(f" - {error_type}: {count:,}") print(f" Errata log: {errata_summary['log_file']}") print() print("="*70) print("✓ Users Dataset Processing Complete!") print("="*70) except KeyboardInterrupt: print("\n\n✗ Processing interrupted by user") sys.exit(1) except Exception as e: print(f"\n✗ Processing failed: {e}") import traceback traceback.print_exc() sys.exit(1) if __name__ == '__main__': main()