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This article mainly explains "what are the reasons why Python built-in functions are not omnipotent". The explanation in this article is simple and clear, and it is easy to learn and understand. Please follow the editor's train of thought to study and learn "what are the reasons why Python built-in functions are not omnipotent?"

1. The search priority of the built-in function is the lowest.

The names of built-in functions do not belong to keywords; they can be reassigned.

For example, the following example:

# call the built-in function list (range (3)) # result: [0,1,2] # define any function and assign it to list def test (n): print ("Hello World!") List = test list (range (3)) # result: Hello World!

In this example, we assign a custom test to list, and the program does not report an error. This example can even be changed to directly define a new function with the same name, that is, "def list ():..." .

This shows that list is not a Python-qualified keyword / reserved word.

Looking at the official documents, you can find that Python3.9 has 35 keywords, the details are as follows:

If we assign the test in the above example to any keyword, such as "pass=test", we will get an error: SyntaxError: invalid syntax.

From this point of view, we can see that built-in functions are not omnipotent: their names are not as stable as keywords, although they are in the built-in scope of the system. but it can be easily intercepted by objects in the user's local scope!

Because the order in which the interpreter looks for names is "local scope-> global scope-> built-in scope", the built-in functions are actually at the lowest priority.

For beginners, this may happen unexpectedly (there are 69 built-in functions, which are difficult to remember).

So why doesn't Python set the names of all built-in functions to unrepeatable keywords?

On the one hand, it wants to control the number of keywords, on the other hand, it may want to give users more freedom. The built-in function is only the recommended implementation of the interpreter, and the developer can implement a function with the same name as the built-in function as needed.

However, such scenarios are rare, and developers generally define functions with different names. Take the Python standard library as an example, ast module has literal_eval () function (standard eval () built-in function), pprint module has pprint () function (standard print () built-in function), and itertools module has zip_longest () function (standard zip () built-in function).

2. Built-in functions may not be the fastest

Because the name of the built-in function is not a reserved keyword, and it is in the last order of name lookup, the built-in function may not be the fastest.

The last article showed the fact that [] is 2 times faster than list (). In fact, this can also be extended to built-in types such as str (), tuple (), set (), dict (), and so on, all of which are used literally slightly faster than built-in types.

For these built-in types, when we call xxx (), we can simply understand that we are instantiating the class. In an object-oriented language, it is perfectly normal for classes to be instantiated before they are used.

However, such a practice is sometimes cumbersome. For ease of use, Python provides literal notation for some commonly used built-in types, that is, "", [], (), {}, and so on, representing data types such as strings, lists, tuples, and dictionaries.

Document source: https://docs.python.org/3/reference/lexical_analysis.html#delimiters

In general, all programming languages must have some literal representation, but they are basically limited to numeric types, strings, Boolean types, and basic types such as null.

Python also adds literals to several types of data structures, so it's more convenient, which explains why built-in functions may not be the fastest.

Generally speaking, with the same complete functionality, the built-in function is always faster than our custom function, because the interpreter can do some low-level optimizations, such as the len () built-in function must be faster than the user-defined x.len () function.

Some people have formed the misconception that built-in functions are always faster.

Compared with the user-defined function, the interpreter built-in function is close to the back door, while the literal quantity representation is the faster backdoor compared to the built-in function.

In other words, when there is a literal representation, some built-in functions / built-in types are not the fastest!

Thank you for your reading, the above is the content of "saying that Python built-in functions are not omnipotent reasons". After the study of this article, I believe you have a deeper understanding of the reasons why Python built-in functions are not omnipotent, and the specific use needs to be verified in practice. Here is, the editor will push for you more related knowledge points of the article, welcome to follow!

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