Maverick Python Performance

Python Performance Skill

Performance optimization patterns and profiling techniques.

List Comprehensions vs Loops

# FAST - list comprehension
squares = [x**2 for x in range(1000)]

# SLOW - append in loop
squares = []
for x in range(1000):
    squares.append(x**2)

Generator Expressions (Lazy Evaluation)

# Memory efficient for large datasets
squares = (x**2 for x in range(1_000_000))

# Only compute when needed
for square in squares:
    if square > 1000:
        break

String Concatenation

# BAD - O(n²) due to string immutability
result = ""
for item in items:
    result += str(item) + ","

# GOOD - O(n)
result = ",".join(str(item) for item in items)

Dictionary Lookups

# Use dict.get() with default
value = d.get(key, default_value)

# Use defaultdict for accumulation
from collections import defaultdict
counts = defaultdict(int)
for item in items:
    counts[item] += 1

Profiling

import cProfile
import pstats

cProfile.run('my_function()', 'output.prof')
stats = pstats.Stats('output.prof')
stats.sort_stats('cumulative').print_stats(10)

Review Severity

• MAJOR: O(n²) string concatenation in loop
• MINOR: List when generator would suffice
• SUGGESTION: Could use comprehension instead of loop

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