- GBlog, Python, TechTips

Tips to Improve the Performance of Python Application

Python…We all know the popularity of this language. Python is a powerful higher-order programming language. In today’s tech world, it’s almost everywhere. Whether you’re building a web application or working with machine learning, this language has become the first choice for developers. When it comes to optimizing developer productivity, Python comes first. You can create a program instantly and solve the business problems of your client.  Writing a solution in Python doesn’t give you a guarantee that it is optimized, and it improves Python performance. However, while writing your code in Python, you can follow some strategies to help you with boosting the performance of your application. These strategies can make your application faster. Some tips or strategies will have a big impact on execution and others will have smaller, more subtle effects. Let’s discuss those tips in detail…1. Use Built-In FunctionsBuilt-in function in any language is always useful because you don’t need to write your code from scratch. The same goes for Python. Python comes with many useful libraries and built-in functions. These libraries are helpful in writing the features at several places in your development project. You can write high-quality, efficient code, but it is difficult to beat the underlying libraries. Python libraries are optimized and tested rigorously (like your code). These built-in functions are easy to use in your project. You won’t have redundant code in your project and the code will be optimized very well.2. Write Your Own GeneratorIn Python use generator wherever it is possible. It allows you to return a single item at a time instead of returning the items all at once. Xrange() function is a generator in Python 2, similar to the range() function in Python 3.If you’re using lists, you should write your own generator. Generators give you lazy evaluation and memory will be used efficiently. Generators are very useful if you’re reading numerous large files. You can process a single chunk without worrying about the size of the files.Below is an example for your help…import requests
import re

def get_pages(link):
pages_to_visit = []
pattern = re.compile(‘https?’)
while pages_to_visit:
current_page = pages_to_visit.pop(0)
page = requests.get(current_page)
for url in re.findall(‘