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This article mainly introduces "what are the ways to improve efficiency in Python". In daily operation, I believe that many people have doubts about the methods to improve efficiency in Python. The editor consulted all kinds of materials and sorted out simple and easy-to-use operation methods. I hope it will be helpful for you to answer the doubts about "what are the methods to improve efficiency in Python?" Next, please follow the editor to study!

1. Lambda function

The Lambda function is a relatively small anonymous function-- anonymous means that it doesn't actually have a function name.

The Python function is usually defined using the def a_function_name () style, but for the lambda function, we didn't name it at all. This is because the function of the lambda function is to perform some simple expression or operation without having to fully define the function.

The lambda function can take any number of arguments, but there can only be one expression.

X = lambda a, b: a * bprint (x (5,6)) # prints 30x = lambda a: ahem 3 + 3print (x (3)) # prints 12

Look how simple it is! We performed some simple mathematical operations without defining the entire function. This is one of the many features of Python that make it a clean, simple programming language.

2. Map function

Map () is a built-in Python function that applies functions to elements in various data structures, such as lists or dictionaries. This is a very clean and readable way to perform this kind of operation.

We can apply functions to single or multiple lists. In fact, you can use any Python function as input to the map function, as long as it is compatible with the sequence element you are working on.

3. Filter function

Filter built-in functions are very similar to map functions in that they also apply functions to sequence structures (lists, tuples, dictionaries). The key difference between the two is that filter () will return only the elements that the application function returns True.

We not only evaluated the True or False,filter () function of each list element, but also ensured that only elements matching True were returned. It is very easy to handle the two steps of checking expressions and building return lists.

4. Itertools module

Python's Itertools module is a collection of tools that deal with iterators. An iterator is a data type that can be used in for loop statements, including lists, tuples, and dictionaries.

Using the functions in the Itertools module allows you to perform many iterator operations, which usually require multi-line functions and complex list understanding.

5. Generator function

The Generator function is an iterator-like function, that is, it can also be used in for loop statements. This greatly simplifies your code and saves a lot of memory compared to a simple for loop.

For example, we want to add all the numbers from 1 to 1000, and the first part of the code block below shows you how to use the for loop to do this calculation.

If the list is small, such as 1000 rows, the memory required for calculation is OK. But if the list is very long, such as a billion floating points, this can be a problem. With this for loop, there will be a lot of lists in memory, but not everyone has unlimited RAM to store so many things. The range () function in Python does the same, building lists in memory.

The second part of the code shows the use of the Python generator function to sum a list of numbers. The generator function creates elements and stores them in memory only if necessary, one at a time. This means that if you want to create billions of floating point numbers, you can only store them in memory one at a time! The xrange () function in Python 2.x uses generator to build lists.

The above example shows that if you want to generate a list for a large range, you need to use the generator function. This method is especially important if you have limited memory, such as using a mobile device or edge computing.

That is, if you want to iterate over the list multiple times and it is small enough to fit in memory, it is best to use the for loop or the range function in Python 2.x. Because the generator and xrange functions will generate new list values every time you visit them, while the Python 2.x range function is a static list, and integers have been placed in memory for quick access.

At this point, the study of "what are the ways to improve efficiency in Python" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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