10 Ways to Speed Up Your Python Code

Python is a high-level, interpreted programming language recognized for its simplicity and clarity. It supports a couple of programming paradigms, together with procedural, object-orientated, and purposeful programming. Python's dynamic typing and automated reminiscence management facilitate speedy improvement and prototyping. Its massive, trendy library and colorful environment of third-party packages make it flexible for a wide variety of packages, from net development and records evaluation to artificial intelligence and clinical computing. The language's layout philosophy emphasizes code readability and ease, encapsulated inside the guiding standards determined within the "Zen of Python". Python's network-pushed improvement ensures non-stop improvement and vast adoption across diverse industries.

10 Ways to Speed Up Your Python Code

Way 1: Use Built-in Functions and Libraries

Python's integrated features and libraries are implemented in C, making them plenty quicker than natural Python code. Leveraging these can offer substantial performance profits.

Example

 
# Using built-in sum() function
nums = list(range(1000000))
result = sum(nums)
print(result)   

Output:

499999500000

Explanation

The `sum()` function is implemented in C, making it faster than a guide loop.

Way 2: Optimize Algorithms and Data Structures

Choose the most appropriate algorithms and record structures for your problem. Efficient algorithms and record structures can dramatically reduce the complexity and execution time of your code.

Example

 
# Using a set for O(1) average time complexity lookups
nums = list(range(1000000))
num_set = set(nums)
print(999999 in num_set)   

Output:

 
True

Explanation

Sets offer average O(1) time complexity for lookups as compared to lists with O(n).

Way 3: Avoid Global Variables

Accessing global variables is slower than accessing neighborhood variables. Limiting the usage of worldwide variables and passing parameters to features can enhance performance.

Example

 
def compute_sum():
    nums = list(range(1000000))
    return sum(nums)
result = compute_sum()
print(result)   

Output:

499999500000

Explanation

Using nearby variables inside a feature is quicker than the usage of international variables.

Way 4: List Comprehensions and Generator Expressions

Use list comprehensions and generator expressions instead of conventional for-loops for concise and quicker code execution, particularly for big statistics units.

Example

 
# List comprehension
squares = [x**2 for x in range(10000)]
print(squares[:5])   

Output:

 
[0, 1, 4, 9, 16]

Explanation

List comprehensions are more concise and faster than conventional for-loops.

Way 5: Use Numpy for Numerical Operations

NumPy is an effective library for numerical operations. It provides optimized functions for array computations and can result in full-size velocity upgrades for mathematical operations.

Example

 
import numpy as np
arr = np.arange(1000000)
result = np.sum(arr)
print(result)   

Output:

499999500000

Explanation

NumPy operations are optimized and quicker than pure Python operations for large arrays.

Way 6: Limit the Use of Dot Notation

Accessing attributes using dot notation (e.g., `obj.Attr`) can be slower due to the overhead of searching for the attribute. Minimize its use in performance-essential sections of your code.

Example

 
class MyClass:
    def __init__(self, value):
        self.value = value
obj = MyClass(10)
value = obj.value
print(value)   

Output:

Explanation

Accessing `obj.value` once and storing it in a neighborhood variable `value` is quicker than repeated characteristic access.

Way 7: Employ Cython

Cython is a superset of Python that permits you to assemble Python code into C, presenting full-size performance enhancements, particularly for computation-heavy responsibilities.

Example

 
# Cython example (needs to be compiled)
def compute_sum(int[:] nums):
    cdef int total = 0
    for i in range(len(nums)):
        total += nums[i]
    return total   

Explanation

Cython code, while compiled, runs quicker than equal natural Python code.

Way 8: Use Parallel and Asynchronous Programming

Utilize libraries, including `multiprocessing` or `asyncio`, to carry out parallel or asynchronous execution, which could speed up I/O-bound and CPU-bound responsibilities.

Installing the nest_asyncio Library

Example:

 
import asyncio
import nest_asyncio
nest_asyncio.apply()
async def main():
    await asyncio.gather(
        asyncio.sleep(1),
        asyncio.sleep(1)
    )
    print("Both tasks are done!")
# Use asyncio.run() directly, which now works due to nest_asyncio
asyncio.run(main())   

Output:

 
Both tasks are done!

Explanation

Using `asyncio.gather` lets in responsibilities to run concurrently, dashing up I/O-certain operations.

Way 9: Optimize with Just-in-Time Compilation (JIT)

Libraries like PyPy enforce Just-in-Time (JIT) compilation, translating Python code into device code at runtime, which can greatly enhance execution speed.

Example

 
 from numba import jit
import numpy as np
@jit(nopython=True)
def compute_sum(arr):
    total = 0
    for i in range(len(arr)):
        total += arr[i]
    return total
arr = np.arange(1000000)
result = compute_sum(arr)
print(result)  

Output:

499999500000

Explanation

The `jit` decorator from Numba compiles the function into system code, speeding up execution.

Way 10: Profile and Benchmark Your Code

Use profiling equipment inclusive of `cProfile` to identify bottlenecks in your code. Once recognized, recognition optimization efforts on those vital sections to achieve nice overall performance improvements.

Example

 
import cProfile
def compute_sum():
    nums = list(range(1000000))
    return sum(nums)
cProfile.run('compute_sum()')   

Output:

 
ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        1    0.037    0.037    0.046    0.046 <ipython-input-11-9321582a1d3f>:3(compute_sum)
        1    0.012    0.012    0.058    0.058 <string>:1(<module>)
        1    0.000    0.000    0.058    0.058 {built-in method builtins.exec}
        1    0.008    0.008    0.008    0.008 {built-in method builtins.sum}
        1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}

Explanation

Using `cProfile` to profile the function helps pick out bottlenecks for optimization.

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