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Performance Tips in Python

Python is easy to use but can be slow for some tasks. Here are some tips to improve performance:

Use Built-in Functions

Built-in functions like map, filter, and comprehensions are faster than manual loops.

Avoid Unnecessary Computation

Cache results, use memoization, and avoid repeated work.

Use Efficient Data Structures

Choose the right data structure for the job (e.g., set for membership tests).

Profile Your Code

Use cProfile or timeit to find bottlenecks.

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import timeit
print(timeit.timeit('sum(range(100))'))

Use Libraries

  • numpy for numerical work
  • pandas for data analysis
  • multiprocessing for parallelism

Example: List Comprehension vs. Loop

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# Slower
result = []
for i in range(1000):
    result.append(i * 2)
# Faster
result = [i * 2 for i in range(1000)]

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