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This article mainly explains why there is no function overloading in Python. Interested friends may wish to have a look at it. The method introduced in this paper is simple, fast and practical. Now let the editor take you to learn why there is no function overloading in Python.

Topic: why is there no function overloading in Python?

Comments: C++, Java, C# and many other programming languages support function overloading. The so-called function overloading means that there are multiple functions with the same name in the same scope, which have different parameter lists (different number of parameters or different parameter types or both) and can be distinguished from each other. Overloading is also a kind of polymorphism, because it is usually determined by the number and type of parameters at compile time to determine which overloaded function is called, so it is also called compile-time polymorphism or pre-binding. The subtext of this question is to ask the interviewee whether they have experience in other programming languages, whether they understand that Python is a dynamically typed language, and whether they know the concepts of variable parameters and keyword parameters of functions in Python.

First of all, Python is an interpretive language, and the phenomenon of function overloading usually occurs in compiled languages. Secondly, Python is a dynamically typed language, and the parameters of the function have no type constraints, so it is impossible to distinguish overloading according to the parameter type. In addition, the parameters of the function in Python can have default values, variable parameters and keyword parameters can be used, so even if there is no function overload, you can make a function behave differently according to the parameters passed by the caller.

Topic 012: use Python code to implement Python built-in function max.

Comments: this topic seems simple, but in fact it is still a comparative study of the skills of the interviewer. Because Python's built-in max function can not only pass in iterable objects to find the maximum, but also pass in two or more parameters to find the maximum; the most important thing is that you can also specify a function for element comparison by naming the keyword parameter key, and you can also specify the default value returned when the iterable object is empty by naming the keyword parameter default.

The following code is for reference only:

Def my_max (* args, key=None, default=None):

"

Gets the largest element in an iterable object or the largest element in two or more arguments

: param args: an iterable object or multiple elements

: param key: a function to extract eigenvalues for element comparison. Default is None.

: param default: returns the default value if the iterable object is empty, and throws a ValueError exception if no default value is given

: return: returns the largest element of an iterable object or multiple elements

"

If len (args) = = 1 and len (args [0]) = = 0:

If default:

Return default

Else:

Raise ValueError ('max () arg is an empty sequence')

Items = args [0] if len (args) = = 1 else args

Max_elem, max_value = items [0], items [0]

If key:

Max_value = key (max_value)

For item in items:

Value = item

If key:

Value = key (item)

If value > max_value:

Max_elem, max_value = item, value

Return max_elem

Topic 013: write a function to count the number of occurrences of each number in the incoming list and return the corresponding dictionary.

Comments: the title of sending a person's head is not explained.

Def count_letters (items):

Result = {}

For item in items:

If isinstance (item, (int, float)):

Result [item] = result.get (item, 0) + 1

Return result

You can also solve this problem directly by using the Counter class of the collections module in the Python standard library. Counter, a subclass of dict, constructs a dictionary with each element in the passed sequence as the key and the number of times the element appears as the value.

From collections import Counterdef count_letters (items):

Counter = Counter (items)

Return {key: value for key, value in counter.items ()\

If isinstance (key, (int, float))}

Topic 014: use Python code to achieve the operation of traversing a folder.

The walk function of the Python standard library os module provides the ability to traverse a folder, which returns a generator. You can use this generator to get all the files and folders under the folder.

Import os

G = os.walk ('/ Users/Hao/Downloads/')

For path, dir_list, file_list in g:

For dir_name in dir_list:

Print (os.path.join (path, dir_name))

For file_name in file_list:

Print (os.path.join (path, file_name))

Description: the os.path module provides a lot of tool functions for path operation, and it is often used in project development. If the title clearly requires that the os.walk function cannot be used, then you can use the os.listdir function to get the files and folders under the specified directory, and then loop through the os.isdir function to determine which folders are folders, and for folders can be traversed by recursive calls, so you can also achieve the operation of traversing a folder.

Topic 015: there are three denominations of 2 yuan, 3 yuan and 5 yuan. If you need change for 99 yuan, how many ways are there for change?

Comments: there is also a very similar topic: "if a child takes the stairs, he can walk one step, two steps or three steps at a time. How many ways are there after walking 10 steps?" The idea of these two topics is the same, which is very simple if you write it with a recursive function.

From functools import lru_cache@lru_cache ()

Def change_money (total):

If total = = 0:

Return 1

If total < 0:

Return 0

Return change_money (total-2) + change_money (total-3) + change_money (total-5)

Description: in the above code, we use lru_cache decorator to decorate the recursive function change_money, if we do not do this optimization, the asymptotic time complexity of the above code will be, and if the value of the parameter total is 99, the amount of computation is very huge. The lru_cache decorator caches the execution results of the function, thus reducing the overhead caused by repeated operations, which is a space-for-time strategy and the programming idea of dynamic planning.

At this point, I believe you have a deeper understanding of "why there is no function overloading in Python". You might as well do it in practice. Here is the website, more related content can enter the relevant channels to inquire, follow us, continue to learn!

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