Boost Python Performance: 7 Proven Tips to Speed Up Your Code

Mastering a few techniques can significantly improve Python program performance and avoid unnecessary resource waste.

1. Use Local Variables

Prefer local variables over globals; they are faster to look up and reduce memory usage. For example, assign ls = os.linesep instead of repeatedly accessing os.linesep.

2. Reduce Function Calls

When checking object types, isinstance() is the most efficient, followed by identity comparison with id(), and finally comparing type(). Example:

# Check variable type
type(num) == type(0)   # three calls
type(num) is type(0)    # identity comparison
isinstance(num, int)    # single call

Avoid recomputing loop conditions such as len(a) inside the loop; compute it once before the loop.

3. Use Direct Imports

Import specific functions or objects directly (e.g., from X import Y) instead of importing the module and accessing X.Y, saving a lookup.

4. Replace Conditional Chains with Mappings

Dictionary lookups are faster than multiple if/elif statements. Example:

d = {1: 10, 2: 20, ...}
b = d[a]

5. Iterate Over Sequence Elements Directly

Iterating over items directly is faster than iterating over indices.

for item in a:
    print(item)

6. Prefer Generator Expressions Over List Comprehensions

Generator expressions produce items on demand and use less memory than list comprehensions.

# Using generator expression
l = sum(len(word) for line in f for word in line.split())
# Equivalent list comprehension (creates full list)
l = sum([len(word) for line in f for word in line.split()])

7. Compile Before Execution

When using eval() or exec(), compile the code string first with compile() and then execute the code object. The same applies to regular expressions: compile the pattern with re.compile() before matching.

8. Module Programming Practices

Place executable code inside functions and guard test code with if __name__ == "__main__": to avoid running it on import.