It may be that xiaobian has been paying attention to knowledge and news in cloud computing, cloud native and other fields for a long time. As soon as I open various we-media software, all the courses I push are related courses, among which Python is the most popular (it may be recommended as long as it is concerned about computer technology).
As a popular programming language, Python has many advantages: it is easy to learn, has a beautiful syntax, has a rich and powerful library, and has a wide range of applications.
However, Python is not without its drawbacks, one of the major ones being that Python is not fast enough to execute. To solve this problem, I’ve gathered a few common tricks used by the technical gurus on our team to help you run Python more efficiently.
Make Python run faster Tip 1: Use external packages for key code ****
Python simplifies many programming tasks, but it often fails to perform well for time-sensitive tasks. Using C/C++ or machine language external feature packs to handle time-sensitive tasks can effectively improve application efficiency.
These feature packs tend to be platform-specific, so you need to choose the right one for your platform. In short, this trick requires you to sacrifice the portability of your application for the efficiency that can only be achieved by direct programming to the underlying host.
Here are a few feature packs you can choose to use to improve your productivity:
Cython
Pylnlne
PyPy
Pyrex
These feature packs have different uses. For example, using C data types can make tasks involving memory operations more efficient or intuitive.
Pyrex helps Python extend this functionality. Pylnline enables you to use C code directly in Python applications. Inline code is compiled independently, but it keeps all compiled files in one place and takes advantage of the efficiency provided by C.
Make Python run faster Tip 2: Use keys when sorting
Python has a lot of ancient collation rules that can take up a lot of time when you create custom collation methods, which can slow down the actual speed of your program. The best way to sort is to use as many keys and the built-in sort() method as possible. For example, take the code below and say:
In each example, the list is sorted by the index you selected as the key parameter. This method works not only for numeric types, but also for string types.
Make Python run faster tip 3: Optimize for loops
Every programming language emphasizes optimal loop schemes. When using Python, you can use a wealth of tricks to make looping programs run faster. However, one technique that developers often forget is to try to avoid accessing variables’ properties in loops. For example, take the code below and say:
Each time str.upper is called, Python evaluates this expression. However, if this evaluation is assigned to a variable, the result of the evaluation is known in advance, and Python programs can run faster.
The key, therefore, is to minimize Python’s work in loops. Because of the nature of Python’s interpretation of execution, it slows it down considerably in the above example.
(Note: This is just one of many ways to optimize loops. For example, many programmers think list comprehensions are the best way to speed up loops. The point is, optimization loops are a great way to speed up your application.)
Python faster Tip 4: Use a newer Python version
Typically, each version of Python contains optimizations to make it run faster than the previous version. The limiting factor, however, is whether your favorite library has been updated synchronously to support the new Python version. Rather than arguing over whether the library should be updated, the key is whether the new Python version is efficient enough to support such an update.
You need to make sure your code works in the new version. You need to use the new library to play with the new Python version, and then you need to check your application before making any key changes. Only after you have made the necessary fixes will you be able to feel the difference in the new version.
However, if you just make sure your app works in the new version, you’re likely to miss out on the new features that the new version offers. Once you’ve decided to update, analyze how your app performs under the new version, check for areas that might be broken, and then prioritize those areas for the new version’s features. In this way, users will be able to see improvements in application performance at the beginning of the update.
Make Python run faster tip 5: Try multiple coding methods
Using the same coding method every time you create an application almost invariably leads to less efficient application performance. Some experimental approaches can be tried during program analysis. For example, when dealing with items in a dictionary, you can either use the safe method of making sure items already exist before updating them, or you can update items directly and treat items that don’t exist as special cases. Take a look at the first code below:
This code runs faster when myDict is empty at first. Often, however, myDict is so full of data, or at least most of it, that it’s more efficient to take another approach.
The output is the same in both methods. The difference is how the output is obtained. Thinking outside the box and creating new programming tricks can make your application more efficient.
Python runs faster Tip 6: Cross-compile your application
Developers sometimes forget that computers don’t really understand the programming languages used to create modern applications. Computers understand machine language. To run your application, you use an application to convert your human-readable code into machine-readable code. Sometimes, if you write an application in a language like Python and then run your application in a language like C++, it makes sense from an operational standpoint. What matters is what you want your application to accomplish and what resources your host system can provide.
These are six tips to make Python run faster, and I hope you can learn from them.
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