Master techniques for handling large Excel files efficiently.
📋 Learning Objectives
- Read large files with memory-efficient methods
- Use chunking for processing big datasets
- Stream large files without loading everything
- Optimize performance with appropriate libraries
- Handle files too large for memory
- Implement best practices for production environments
- Monitor and profile performance
📚 Topics Covered
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Memory-Efficient Reading - Using read_only mode in openpyxl - Chunking with pandas - Limiting rows and columns - Data type optimization
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Streaming Large Files - Iterator-based reading - Processing in batches - Generator functions - Lazy evaluation
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Performance Optimization - Choosing the right library - Data type optimization - Avoiding unnecessary operations - Using vectorized operations
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Handling Memory Constraints - Processing in chunks - Disk-based storage - Database integration - Distributed processing
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Writing Large Files - Write-only mode - Buffered writing - Progress tracking - Error recovery
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Monitoring and Profiling - Memory usage tracking - Time profiling - Bottleneck identification - Optimization strategies
🎯 Real-World Scenarios
- Processing million-row datasets
- Generating large reports
- Data warehouse exports
- ETL pipelines
📊 Performance Benchmarks
- Compare reading methods
- Memory usage analysis
- Speed optimization results
Next Session
Session 10: Automation and Batch Processing - Build robust automation workflows.