This repository demonstrates how Python Generators can efficiently process large amounts of data without loading everything into memory.
Generators are commonly used in data engineering, backend development, machine learning pipelines, and log processing systems.
Process server log files line by line using Python Generators.
- Read logs efficiently
- Count log levels
- Filter ERROR logs
- Search keywords
- Memory-efficient processing
- Generate summary report
Topics Covered
- Generator Functions
- yield
- File Handling
- Dictionary
- Exception Handling
Simulate processing a very large dataset using Python Generators.
- Generate records on demand
- Filter high-value records
- Calculate statistics
- Stream data
- Memory-efficient iteration
Topics Covered
- Generator Functions
- Generator Expressions
- Lazy Evaluation
- Data Streaming
- Large Dataset Processing
- Generators
- yield
- Generator Expressions
- Iterators
- File Handling
- Dictionaries
- Loops
- Exception Handling
- Memory Optimization
- Python 3
- Google Colab
After completing these projects you will understand:
- Why generators are memory efficient
- How to process huge files
- How data pipelines work
- How Python streams data
- Real-world generator applications