Python Best Practices and Tips - Python Performance Optimization

Question

Python has seen a huge increase in popularity in recent years. This is because of its simple syntax, a vast range of third-party libraries, and a great community. Python is a powerful language for trending technologies like Artificial intelligence, machine learning, and data science. It has many powerful features, but with that, some other factors need to be considered as a Python developer to bring out the maximum power in our applications.

Common Performance Bottleneck in Python

There are certain factors that cause performance bottlenecks while working on Python code. One of the factors is its dynamic nature, whereas the other is the use of the GIL (Global Interpreter Lock) mechanism. As we know, in Python code, type checking is performed at run time rather than at compile time, which can cause execution slowdown. Conversely, CPython is an interpreter in which the GIL mechanism is used to ensure the execution of a single thread at any time; however, it has inconsistencies while working on a multithreaded environment by limiting the performance of CPU-bound programs.
Therefore, in this article, I will cover the detailed strategies that encourage you to achieve strong performance in your code, optimization practices, and the selection of correct data and built-in libraries.

Profiling Python Code

Finding the cause and location of the problem is crucial before trying to implement a solution. If your Python program consists of many functions and is running too slowly and using too much RAM, then you will want to fix those parts of your code. You may directly try to fix it, but sometimes you often end up fixing the wrong part. Therefore, it is better to at least define the direction of the issue before making changes. Here, profiling comes into place to optimize our code.

1. Introduction to Profiling Tools

Software profiling is the concept of finding the bottlenecks or loopholes in the running program so that we can do the least amount of work to obtain the optimal performance gain. This software will help your code run fast enough based on your needs and requirements. It consists of the following profiling tools:

1. The cProfile module is a built-in profiling tool of the Python standard library. It hooks into the machine to measure the time taken to run each program function. Furthermore, it provides CPU information, along with additional details such as how long the program is executed and how many times each function is called within the program.
2. line_profiler is another tool for identifying the cause of CPU-bound problems in Python code. It works by profiling individual functions on a line-by-line basis. Therefore, the goal is to determine the slow parts that may need improvement.

Note: line_profile is the annotating version of the output as you modify your code, which means it has a record of changes that you can quickly check for loopholes.

2. Identifying Performance Bottlenecks using Profiling

Now, let's identify the bottlenecks using coding examples. Here, I define the same function with two different approaches to determine which is more efficient.

import cProfile
def addition(num1, num2):  #Naive approach for addition function
    result = 0
    result += num1
    result += num2
    return result

def addition_Opt(num1 ,num2): #optimized approach for addition function
    return num1 + num2 

if __name__ == "__main__":
    cProfile.run(addition(8, 0)) 
    cProfile.run(addition_Opt(9,9))

In the above code, two different approaches are used for the addition function. The first approach is the traditional method of adding two numbers, which takes a considerable amount of time to calculate the result of each variable. It starts from the result with a value of 0 by adding num1 first and then num2 and then returning the result. The second approach is more optimized and takes two numbers and returns the sum of these values. I am using the cProfile here to track bottlenecks and measure which functions take significantly more time than others.

3. Analyzing Profiling Results to Prioritize Optimization Efforts

Now, I will analyze the results obtained after performing addition using two different approaches. The output is as follows:

Output

The result shows that the compiler calls the two functions, and the first function takes 0.001 sec more than the other function. This is not a good example for demonstrating cProfile; therefore, we need to work on a real-time project that contains a large dataset.

Choosing Efficient Data Structures and Algorithms

One of the most important aspects of writing efficient programs is understanding the correct data structure you use. A large part of program performance depends on the data you have and on selecting the proper data structure for solving the problem.

1. Understanding the Performance Characteristics of Built-in Data Structures

List and dictionary are the classes of the array data structure. An array is simply a flat list of data with some intrinsic ordering. You can implement arrays in multiple ways, representing lists, dictionaries, and tuples differently. Therefore, the list and dictionary are dynamic arrays, whereas tuples are static arrays. Lists are mostly suitable for performing sequential access and indexing but are less efficient for insertion and deletion operations because of their arbitrary position. However, the dictionary is implemented using a hash table that is ideal for key-pair loops with constant time complexity. In addition, you can efficiently perform insertion and deletion operations because of the dynamic resizing property.

2. Choosing the Appropriate Data Structure for Specific Tasks

Choosing the correct data structure for a specific task can significantly impact the performance optimization of a Python program. Here, I discuss the use case of the list, dictionary, and set:

1. List: When you need to store the collection of elements that may change in size and where the order of the elements matters. Then, the list is the appropriate data structure.
2. Dictionary: When you need to map the unique keys to the corresponding values for fast lookup, then a dictionary is preferable because it implements using a hash table that provides the fastest lookups with an average time complexity of o(1).
3. Set: When you need to store the collection of unique values and perform the set operations such as union, intersection, and difference. Then, the set data structure is appropriate. It is also implemented using hash table like a dictionary but with unique values and no mapping.

Tip: Avoid unnecessary loops and use a single loop instead of a nested loop. Choose the right data structures such as dictionaries, sets, and lists to optimize memory usage and access times.

3. Optimizing Algorithms for Time and Space Complexity

Let's understand the optimization algorithm for time and space complexity using a coding example of a list. Suppose we have a list of elements and want to retrieve an element similar to our target value. The problem is that the elements are not in the correct order for linear search and the target is not in the list. Now, We move to the coding part:

import time 
my_list = [9,18,28,88,29,61,56,42,19,95, 100, 46, 27, 28, 10, 38, 58, 38, 58 ,79, 27 ,69, 17, 48, 69, 26, 3,85, 64] 
def linear_Search(elem, array): #take element and array 
    for i, item in enumerate(array): #pick the value at index i 
        if (item == elem): #matchs the elem with the value 
            return i #return if found 
    return -1 #return if not found 

linearSearch_Time = time.time() #to measure the time taken for search 
result = linear_Search(2,my_list) #pass the target value and list
linearSearch_Time = time.time() - linearSearch_Time  #minus the staring time with the current time taken by linearsearch function 
print(f"Time Taken by linear Search {linearSearch_Time:.4f} seconds") 
def binary_Search(array, elem, low = 0, high = None): 
    if high is None:  
        high  = len(array) - 1 
    while low <= high: 
        mid = (low + high) // 2  #measure the mid point  
        if mid >= 0 and mid < len(array) and array[mid] == elem: #check for target within the array #here check the number at mid-left 
            return mid 
        elif elem < array[mid]: #check value at mid-right 
            return binary_Search(array, elem, low, mid-1) 
        else: 
            return binary_Search(array, elem, mid+1, high) 
        return -1 

#sorted list 
my_list2 = [9,18,28,38,49,51,66,72,89,95] 
binarySearch_Time = time.time() 
result2 = binary_Search(my_list2, 42) 
binarySearch_Time = time.time() - binarySearch_Time #minus the starting time with the function start time 
print(f"Time Taken by binary Search {binarySearch_Time:.4f} seconds") 

In the above code, the time complexity for the linear_Search function is o(n), which is best for small datasets. However, if the list is an unsorted large dataset, linear search is not an efficient approach because as the size increases the complexity also increases. Therefore, binary search is a better option with a time complexity of o(log(n)) that does not affect performance even if the list size increases.

Note: Select algorithms with better time complexity to significantly improve performance.

Utilizing Built-in Functions and Libraries

1. Leveraging Built-in Functions for Performance-Critical Operations

Python is a versatile programming language with many built-in functions and libraries. Utilization of these built-in functions will optimize the Python code. Whenever possible, opt for using these libraries instead of manually writing the code from scratch. Doing so will not only save you time but also optimize the performance of your program.

2. Exploring Optimized Libraries for Numerical Computations

Python has introduced many optimized libraries for performance-critical operations. These libraries are specifically designed to provide numerical computations and scientific calculation tasks. Let's explore them with the following well-structured table:

Adhering to these built-in libraries and their function will help you to write efficient and performant code to cover a wide range of tasks in Python including data analysis, math calculations, and some numerical computations.

3. Using Standard Libraries for I/O Operations

Just like numerical computation libraries, some other libraries are defined for I/O operations. They are the key libraries in Python where each library serves a specific purpose related to file handling, management, directory operations, and some system-specific interactions. Leveraging these libraries can help you manage the input-output operation in your program efficiently. These are:

Memory Management and Optimization Techniques

1. Understanding Python's Memory Management Model

Memory management is a prominent topic in all programming languages for writing efficient programs. We should manage memory efficiently. Therefore, understanding the deep knowledge of the memory management model of Python is necessary:

Memory Allocation and Deallocation

Python manages memory dynamically using the private heap space during runtime instead of the traditional method. It consists of two parts: memory allocation and deallocation. Memory allocation occurs during program execution, in which a certain amount of memory is assigned to an object by the memory allocator. After execution, the built-in garbage collector deallocates the memory by scanning the heap to reclaim the memory from objects that are not used.

Garbage Collection

Python removes objects that have no reference. When the object is left idle and not in use, the garbage collector removes the object to free up space. Garbage collection also deals with the reference cycle, in which objects are referenced to each other in a circular motion. In addition, it scans the memory for objects that are not in use and removes them to free the space associated with them.

Reference Counting

One of the garbage collection algorithms is reference counting. It is a primary memory management strategy in which each object has a reference count. When the reference count of an object drops to zero, the garbage collector removes that object to deallocate the memory associated with that object. This is how optimal memory management is achieved in Python.

2. Managing Memory Leaks and Circular References

To prevent excessive memory consumption and ensure optimal performance managing memory leaks and circular reference is crucial.

1. Utilization of the profiling concept will help identify the memory leakage problems in your code. As discussed above, profiling will give you the loopholes in your code with detailed information on memory usage by each function or object. It will help you track memory over time and identify objects that are sitting idle.
2. After resource utilization, it is vital to release the resources to avoid the halt conditions. Most specifically, while working with resources like files, databases, or APIs. The incorporation of a context manager will explicitly ensure that the resources are released.
3. While using circular references between the object references, be attentive because sometimes they will prevent the object from being considered unused even if they are not in use which will waste memory. Use weak references so that it is easy for garbage collectors to break the references when necessary.

Warning: Be cautious while optimizing the performance to ensure that the code remains readable and maintainable for future development and debugging purposes.

FAQs

Q: Why performance optimization is necessary?
A: Performance optimization is necessary because it makes your program execution faster with less memory usage which improves the overall efficiency of your code.

Q: Name of common performance optimization strategies?
A: Following are the common performance optimization strategies such as choosing the right data structure, avoiding nested loops, minimizing function calls, and utilizing built-in modules.

Wrapping Up

Python has provided us with many strategies to ensure performance optimization in our program. Profiling is the first choice of every developer because it helps you identify the loopholes that minimize your code performance. We also learned some built-in data structures. If incorporated into the program correctly, optimization is guaranteed. It is better to use these built-in modules instead of writing from scratch. It is also important to test how fast your program is working and try different ways to make it faster. Do not forget the readability and maintainability of the program, and keep learning to speed up your code. Thank you for reading this guide. Happy coding!

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Reference

cProfile Library
timeit Library
High Performance Book: Python Performance Optimization Tips

Comments

There is an issue on your sample profiling code.

The correct one is:

cProfile.run("addition(8, 0)")

Pass the argument as a string.

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Thanks for pointing out Ferdy...