Optimizing Python Code Performance: Enhancing Execution Speed

In the daily programming process, writing a fully functional program is just the first step; subsequent performance optimization is also an essential aspect of development that cannot be overlooked. Especially when using Python, due to its interpreted and dynamically typed nature, the execution efficiency may be relatively lower compared to some compiled languages (such as C++ and Java). This article will introduce some common methods for optimizing Python code performance and demonstrate them through examples.

1. Use Built-in Functions and Libraries

Python has many efficient built-in functions and libraries, which are often much faster than manually implementing the same functionality. For example, when processing lists, you can use <span>map()</span>, <span>filter()</span>, and other higher-order functions that are implemented in C, which are generally faster than pure Python loops.

Example:

# Original code
squares = []
for i in range(10):
    squares.append(i * i)
# Optimized code
squares = list(map(lambda x: x * x, range(10)))
print(squares)

In this example, by using the <span>map()</span> function instead of a traditional loop, the code becomes more concise and improves performance.

2. List Comprehensions

Using list comprehensions instead of traditional loops can reduce execution time and improve readability. It not only has a more concise syntax but also allows for direct application of expressions to generate new lists.

Example:

# Original code
result = []
for i in range(10000):
    if i % 2 == 0:
        result.append(i)
# Optimized code
result = [i for i in range(10000) if i % 2 == 0]
print(result)

This example demonstrates how to use list comprehensions for filtering, which not only speeds up execution but also significantly reduces the number of lines of code.

3. Reduce Global Variable Access

Global variables are slow to access, so it is advisable to avoid frequent access to them. Consider passing global variables into the methods or classes that need them to improve access speed.

Example:

# Global variable access is inefficient
global_var = "Hello, World!"
def print_global():
    print(global_var)
print_global()

def print_with_param(value):
    print(value)  # Reduce dependency on global variables for efficiency
print_with_param(global_var)

4. Use Appropriate Data Structures

Choosing the right data structure can significantly improve program efficiency. For example, for data that requires many lookup operations, consider using a set data structure, as it has an average time complexity of O(1), while a list has O(n).

Example:

# Using a list to check for element existence (slow)
my_list = [1, 2, 3, 4, 5]
if 3 in my_list:
    print("Found!")
# Using a set to check for element existence (fast)
my_set = {1, 2, 3, 4, 5}
if 3 in my_set:
    print("Found!")

5. Avoid Unnecessary Calculations

Improving performance by caching already computed data is known as the “memoization” technique. If a computation is time-consuming but the result can be reused, consider implementing a caching mechanism, such as <span>functools.lru_cache</span>.

Example:

from functools import lru_cache

@lru_cache(maxsize=None)
def fibonacci(n):
    if n < 2:
        return n
    return fibonacci(n-1) + fibonacci(n-2)
print(fibonacci(30))

Here, we define a recursive Fibonacci function, and by using <span>lru_cache</span>, we implement result caching, significantly reducing time complexity.

Conclusion

This article introduces some common yet effective methods to optimize the execution performance of Python programs, including using built-in functions, reducing global calls, selecting appropriate data structures, and utilizing caching techniques. These methods are very practical and can help novice users gradually improve their programming efficiency, making their programs perform excellently in scenarios involving large-scale data processing or requiring quick responses. In actual development, please choose the most suitable method based on specific situations, and your Python programs will become more efficient.