Dynamic Code Execution in Python: A Practical Guide (Python Beast Series: Mastering the Code Jungle Book 25) by Publishing Dargslan

Author:Publishing, Dargslan
Language: eng
Format: epub
Published: 2024-11-20T00:00:00+00:00

# Example usage

try:

result = safe_eval("2 + 2", {}, {})

print(f"Safe result: {result}")

result = safe_eval("__import__('os').system('ls')", {}, {})

print(f"Unsafe result: {result}")

except ValueError as e:

print(f"Error: {e}")

This example demonstrates a simple (though not comprehensive) safety check for dynamic code execution. Implementing such checks adds another layer of processing overhead to your dynamic execution pipeline.

Profiling Dynamic Code

Profiling dynamically executed code presents unique challenges compared to profiling static code. Standard profiling tools may not provide accurate insights into the performance of dynamically generated and executed code. However, there are several approaches you can take to gain valuable performance data.

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