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This article introduces the relevant knowledge of "what are the ways to speed up the take-off of Python code?" in the actual case operation process, many people will encounter such a dilemma, and then let the editor lead you to learn how to deal with these situations! I hope you can read it carefully and be able to achieve something!

1. Learn about your built-in functions

This technique helps save your time and time with your code. When I started learning Python, I never used built-in functions before, so to complete my absolute value code, I would run a for loop instead of using abs (). In order to convert a character to uppercase letters, I even convert it to ASCII characters equivalent to uppercase letters, because I refuse to learn string functions.

If you are serious about Python, it is worthwhile to learn all the Python built-in functions, because it not only makes your code cleaner and more reusable, but you can also avoid artificial inefficiencies in your code by simply using what Python provides to you.

2. A _

I'm sure you've used "temp" as a placeholder variable to exchange two elements before. What I can tell you is that this method is only used in class and should not be used in programming.

Instead, you can do a simple variable exchange by writing variables as a _ b = _. This will switch all variables in one line and prevent more than three interpreters (temp, aformab exchange methods).

This is a small fix that can save a fraction of a second-but that time will accumulate in the long run.

3. Import only within functions

As beginners, we all like to import a lot of everything we think we need at the top of the code. I remember one time I imported NumPy, Pandas, Scipy, Warnings, Math, Math, Os, and so on. When I finished my code, I only used three libraries. This will deplete your computer's memory.

Instead, import the required libraries in the appropriate functions (if multiple functions require the same library, you need to import multiple times). This means that the interpreter will only complete the import when you call the function, not at the beginning of the code. Now the Python library is cached, so when you call a different function, it doesn't take up extra time each time you import. However, it does take more time when you end up importing everything at the top without even using some of the functions in the code.

4. Use Numba

This is a niche technique that mainly helps people who use NumPy or scientific programming. Numba is a Python JIT compiler that applies decorators to functions, converting some functions to ultra-fast bytecode (almost as fast as C). Numba began to be known for its simplicity and huge functionality.

Numba currently supports other features, but I think it is currently mainly for scientific Python developers.

5. Check from the collection, not from the list

This is one of the oldest techniques in Python optimization books. If an element exists, do not search in the list. Instead, set the list to set (set (list)), and then check "element in set (list)". This small change will improve your runtime efficiency because Python uses a resizable hash table, increasing the average time complexity to O (1).

However, traversing a collection is no faster than traversing a list.

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