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Learn step-by-step solutions for string manipulation, file operations, data processing, and more with real-world examples.",0,"content:python-how-to:index.md","python-how-to/index.md","python-how-to/index",[519],{"_path":520,"_dir":6,"_draft":7,"_partial":7,"_locale":8,"title":521,"description":522,"slug":523,"parentId":473,"order":524,"body":602,"_type":15,"_id":525,"_source":17,"_file":526,"_stem":527,"_extension":20},{"type":603,"children":604,"toc":5283},"root",[605,614,620,1522,1529,1534,1632,1639,3061,3067,4971,4977,5099,5105,5214,5220,5225,5233,5261,5277],{"type":606,"tag":607,"props":608,"children":610},"element","h1",{"id":609},"processing-large-files",[611],{"type":612,"value":613},"text","📁 Processing Large Files",{"type":606,"tag":615,"props":616,"children":617},"p",{},[618],{"type":612,"value":619},"When working with large files that don't fit in memory, you need efficient techniques to process data without overwhelming your system. 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process_large_csv(filename, process_batch_size=1000):\n    \"\"\"Process large CSV file in batches\"\"\"\n    try:\n        batch = []\n        batch_count = 0\n        total_rows = 0\n        column_stats = defaultdict(int)\n        \n        with open(filename, 'r', encoding='utf-8') as file:\n            reader = csv.DictReader(file)\n            \n            for row in reader:\n                batch.append(row)\n                total_rows += 1\n                \n                # Count non-empty values per column\n                for column, value in row.items():\n                    if value and value.strip():\n                        column_stats[column] += 1\n                \n                # Process batch when it reaches target size\n                if len(batch) >= process_batch_size:\n                    batch_count += 1\n                    process_csv_batch(batch, batch_count)\n                    batch = []  # Clear batch\n                    \n                    if total_rows % 10000 == 0:\n                        print(f\"Processed {total_rows:,} rows...\")\n            \n            # Process remaining batch\n            if batch:\n                batch_count += 1\n                process_csv_batch(batch, batch_count)\n        \n        return {\n            'total_rows': total_rows,\n            'batches': batch_count,\n            'column_stats': dict(column_stats)\n        }\n    \n    except Exception as e:\n        print(f\"Error processing CSV: {e}\")\n        return None\n\ndef process_csv_batch(batch, batch_number):\n    \"\"\"Process a batch of CSV rows\"\"\"\n    # Example processing: calculate averages, find patterns, etc.\n    \n    # For demo, we'll just count rows with specific criteria\n    valid_rows = [row for row in batch if all(row.values())]\n    \n    print(f\"Batch {batch_number}: {len(batch)} rows, {len(valid_rows)} complete\")\n\ndef create_sample_csv(filename, num_rows=25000):\n    \"\"\"Create sample CSV for testing\"\"\"\n    import random\n    \n    with open(filename, 'w', newline='', encoding='utf-8') as file:\n        writer = csv.writer(file)\n        \n        # Write header\n        writer.writerow(['id', 'name', 'age', 'city', 'score'])\n        \n        cities = ['New York', 'Los Angeles', 'Chicago', 'Houston', 'Phoenix']\n        names = ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve', 'Frank', 'Grace']\n        \n        for i in range(num_rows):\n            writer.writerow([\n                i + 1,\n                random.choice(names) + str(random.randint(1, 100)),\n                random.randint(18, 80),\n                random.choice(cities),\n                round(random.uniform(0, 100), 2)\n            ])\n    \n    print(f\"Created sample CSV with {num_rows:,} rows\")\n\n# Create and process sample CSV\ncsv_file = 'large_sample.csv'\ncreate_sample_csv(csv_file, 25000)\n\ncsv_result = process_large_csv(csv_file)\nif csv_result:\n    print(f\"CSV processed: 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