Performance Optimization

Performance Optimization in Python

Performance Optimization in Python focuses on improving the speed, efficiency, and resource usage of Python programs. Writing optimized code is especially important for large applications, automation frameworks, and data-intensive tasks.

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Why Performance Optimization Matters

• Reduces execution time
• Improves scalability
• Efficient use of CPU and memory
• Better user experience

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Common Performance Bottlenecks

• Unnecessary loops
• Inefficient data structures
• Repeated computations
• Excessive file or network operations

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Choose the Right Data Structures

Selecting the right data structure can significantly improve performance.

# List vs Set lookup
items_list = [1, 2, 3, 4, 5]
items_set = {1, 2, 3, 4, 5}

print(3 in items_list)  # Slower
print(3 in items_set)   # Faster

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Use List Comprehensions

List comprehensions are faster and more readable than traditional loops.

# Without optimization
squares = []
for i in range(10):
    squares.append(i * i)

# Optimized
squares = [i * i for i in range(10)]

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Avoid Repeated Computations

Store results instead of recalculating them repeatedly.

# Inefficient
for i in range(5):
    print(len("performance optimization"))

# Optimized
text_length = len("performance optimization")
for i in range(5):
    print(text_length)

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Use Built-in Functions

Python’s built-in functions are implemented in C and are faster than custom logic.

numbers = [1, 2, 3, 4, 5]

# Slower
total = 0
for n in numbers:
    total += n

# Faster
total = sum(numbers)

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Efficient String Handling

Avoid repeated string concatenation inside loops.

# Inefficient
result = ""
for i in range(5):
    result += str(i)

# Optimized
result = "".join(str(i) for i in range(5))

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Use Generators for Large Data

Generators save memory by producing values one at a time.

# List consumes memory
numbers = [i for i in range(1000000)]

# Generator is memory efficient
numbers = (i for i in range(1000000))

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Measure Performance with time

Always measure performance before and after optimization.

import time

start = time.time()
sum(range(1000000))
end = time.time()

print("Execution Time:", end - start)

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Using cProfile for Analysis

Python provides cProfile to analyze performance bottlenecks.

import cProfile

def test_function():
    total = 0
    for i in range(10000):
        total += i
    return total

cProfile.run("test_function()")

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Performance Optimization in Automation

• Reuse browser sessions in Selenium
• Avoid hard waits (use explicit waits)
• Parallel test execution
• Efficient API calls in REST testing

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Best Practices

• Optimize only after measuring performance
• Focus on readability first
• Use appropriate data structures
• Avoid premature optimization

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Conclusion

Performance Optimization in Python is about writing efficient, maintainable code while ensuring speed and scalability. By following best practices and measuring performance, you can build faster and more reliable applications.