Python 中淺拷貝和深拷貝的區別

有時您可能想在 Python 中復制列表。例如，如果您要保持原始列表不變。相反，您可能希望對複制的列表執行一些操作或其他一些操作。

下面的文章介紹了淺拷貝和深拷貝之間的區別，在 Python 中復制列表和嵌套列表的方法。

淺Vs。Python 中的深度複製

在確定淺拷貝和深拷貝之間的區別之前，我們想談談 Python 中的不變性和可變性。

顧名思義，不可變對像是無法更改的，因此，Python 會在修改此類對象的值時創建一個新對象。但是，可變對象可以更改。

不可變對象： int、string、tuple

可變對象：列表、集合、字典

Python中的不變性和可變性

# int objects in python are immutable
a = 10
b = a
a += 10
 
print(f"Values: a = {a}, b = {b}")
print(f"{id(a) == id(b)}")
 
# However, list objects in python are mutable
l1 = [[1],[2],[3]]
l2 = l1
l1[0][0] = 10
 
print(f"\nValues: l1 = {l1}, l2 = {l2}")
print(f"{id(l1) == id(l2)}")

不變性和可變性

Python 提供了兩種複制列表的方法；這些是：

• 淺拷貝
• 深拷貝

讓我們了解它們之間的區別。

淺拷貝

在淺拷貝中，創建了一個對象，然後使用原始列表中的項目的引用填充該對象。一層深拷貝發生在淺拷貝中。使用 copy 模塊的 copy 方法創建列表的淺拷貝。但是，還有其他方法。讓我們通過一個例子來理解這一點。

import copy
 
list1 = [[1,2,3],[4,5,6],[7,8,9]]
list2 = copy.copy(list1)
 
list1[0][0] = 101
print(f"list1:{list1}\nlist2:{list2}")

列表的淺拷貝

深拷貝

而在deep copy的情況下，它遞歸地將在原始列表中找到的項目複製到新對象。這樣，它會創建原始列表的克隆/副本。可以使用複制模塊的深拷貝方法創建列表的深拷貝。

import copy
 
list1 = [[1,2,3],[4,5,6],[7,8,9]]
list2 = copy.deepcopy(list1)
 
list1[0][0] = 101
print(f"list1:{list1}\nlist2:{list2}")

深拷貝列表

您可以從這裡閱讀更多關於淺拷貝和深拷貝操作的信息。

列出 Python 中的複制方法

使用列表方法

Python 的列表構造函數可以創建列表的副本。

list1 = [[2,4,6],[3,6,9],['erwin','levi','hange']]
list2 = list(list1)
 
print(f"list1:{list1}\nlist2:{list2}")

使用 list 方法複製列表

使用“=”運算符

賦值 = 運算符可以創建列表的副本。

list1 = [[21,24,66],[233,26,69],['kevin', 'ben']]
list2 = list1
 
print(f"list1:{list1}\nlist2:{list2}")

使用“=”運算符複製列表

使用列表切片

Python 的列表切片用於創建列表的副本。

list1 = [[1,2,3],[4,5,6],[7,8,9]]
list2 = list1[:]
 
print(f"list1:{list1}\nlist2:{list2}")

使用列表切片複製列表

複製列表，除了最後一個元素

為了獲得沒有最後一個元素的列表，您可以執行以下操作：

list1 = [[1,2,3],[4,5,6],[7,8,9]]
list2 = list1[:-1]

print(f”list1:{list1}\nlist2:{list2}”)

使用列表切片複製除最後一個元素之外的列表

使用列表推導

類似地，Python 的列表推導式用於創建列表的副本。

list1 = [[2,4,6],[3,6,9],[4,8,12]]
list2 = [item for item in list1]
 
print(f"list1:{list1}\nlist2:{list2}")

使用列表推導複製列表

使用擴展方法

Python 的 extend 方法可以創建列表的副本。

list1 = [[2,4,6],[3,6,9],[4,8,12]]
list2 = []
list2.extend(list1)
 
print(f"list1:{list1}\nlist2:{list2}")

使用擴展方法複製列表

使用附加方法

同樣，Python 的append方法也可以創建列表的副本。

list1 = [[12,14,6],[13,16,19],[14,81,112]]
list2 = []
list2.append(list1)
 
print(f"list1:{list1}\nlist2:{list2}")

使用 append 方法複製列表

多次復制列表

我們可以輕鬆地創建列表的多個副本。讓我們來看看不同的方法：

使用“*”運算符

此方法創建一個淺拷貝。

l1 = [[1,2,3,4,5]]
num_of_copies = int(input("Enter the number of copies:"))
l2 = l1 * num_of_copies
print(l2)

使用“*”運算符多次復制列表

使用列表推導

l1 = [1,2,3,4,5]
num_of_copies = int(input("Enter the number of copies:"))
l2 = [l1 for _ in range(num_of_copies)]
print(l2)

使用列表推導多次復制列表

使用 itertools 重複方法

from itertools import repeat
 
num_of_copies = int(input("Enter the number of copies:"))
l = [1,2,3,4]
l_copy = list(repeat(l,num_of_copies))
print(l_copy)

使用 itertools 重複方法多次復制列表

Python中副本列表的常見問題解答

Python 中的深拷貝是什麼？

Python 中的深層複製遞歸地將原始列表中的項目複製到新對象。這樣，它會創建原始列表的克隆/副本。

Python中的淺拷貝是什麼？

在淺拷貝中，創建一個對象，然後使用原始列表中的項目的引用填充該對象。一層深拷貝發生在淺拷貝中。

如何在 Python 中創建列表的深層副本？

通過使用 copy 模塊的 deepcopy 方法。例如：
from copy import deepcopy
list1 = [[1,2,3],[4,5,6],[7,8,9]]
list2 = deepcopy(list1)
print(f"list1:{list1}\nlist2:{list2}")
list1:[[1, 2, 3], [4, 5, 6], [7, 8, 9]]
list2:[[1, 2, 3], [4, 5, 6], [ 7、8、9]]
 

Python 將生成器複製到列表

我們可以使用 list 方法將生成器轉換為列表。例如：
l = [[1,2,3],[4,5,6],[7,8,9]]
list l converted to a generator
gen_l = (elem for elem in l)
to convert generator to a list
gen_l_to_list = list(gen_l)
print(gen_l_to_list)

Python將隊列複製到列表

我們可以使用 list 方法將隊列轉換為列表。例如：
from collections import deque
que = deque()
que.append('tangiro')
que.append('nezko')
print(f"que: {que}\nType: {type(que)}")
que_list = list(que)
print(f"\nque_list: {que}\nType: {type(que_list)}")

結論

了解淺拷貝和深拷貝的區別很重要。因此，兩者都有不同的用例。我們討論了在 Python 中復制列表的不同方法，例如，使用列表方法、= 運算符、列表推導等。我希望你今天學到了一些新東西。

來源：  https ://www.pythonpool.com

#python 

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Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map

Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas. 

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities. 

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly. 

5 Reasons to Utilize Python for Programming Web Apps 

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks 

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions. 

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events. 

Simple to read and compose 

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building. 

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties. 

Utilized by the best 

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player. 

Massive community support 

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions. 

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking. 

Progressive applications 

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.

Summary

Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential. 

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

#python development services #python development company #python app development #python development #python in web development #python software development

Python Tricks Every Developer Should Know

Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?

In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.

Let’s get started

Swapping value in Python

Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead

>>> FirstName = "kalebu"
>>> LastName = "Jordan"
>>> FirstName, LastName = LastName, FirstName 
>>> print(FirstName, LastName)
('Jordan', 'kalebu')

#python #python-programming #python3 #python-tutorials #learn-python #python-tips #python-skills #python-development

How to Remove all Duplicate Files on your Drive via Python

Today you’re going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates.

Intro

In many situations you may find yourself having duplicates files on your disk and but when it comes to tracking and checking them manually it can tedious.

Heres a solution

Instead of tracking throughout your disk to see if there is a duplicate, you can automate the process using coding, by writing a program to recursively track through the disk and remove all the found duplicates and that’s what this article is about.

But How do we do it?

If we were to read the whole file and then compare it to the rest of the files recursively through the given directory it will take a very long time, then how do we do it?

The answer is hashing, with hashing can generate a given string of letters and numbers which act as the identity of a given file and if we find any other file with the same identity we gonna delete it.

There’s a variety of hashing algorithms out there such as

• md5
• sha1
• sha224, sha256, sha384 and sha512

#python-programming #python-tutorials #learn-python #python-project #python3 #python #python-skills #python-tips

How To Compare Tesla and Ford Company By Using Magic Methods in Python

Magic Methods are the special methods which gives us the ability to access built in syntactical features such as ‘<’, ‘>’, ‘==’, ‘+’ etc…

You must have worked with such methods without knowing them to be as magic methods. Magic methods can be identified with their names which start with __ and ends with __ like init, call, str etc. These methods are also called Dunder Methods, because of their name starting and ending with Double Underscore (Dunder).

Now there are a number of such special methods, which you might have come across too, in Python. We will just be taking an example of a few of them to understand how they work and how we can use them.

1. init

class AnyClass:
    def __init__():
        print("Init called on its own")
obj = AnyClass()

The first example is _init, _and as the name suggests, it is used for initializing objects. Init method is called on its own, ie. whenever an object is created for the class, the init method is called on its own.

The output of the above code will be given below. Note how we did not call the init method and it got invoked as we created an object for class AnyClass.

Init called on its own

2. add

Let’s move to some other example, add gives us the ability to access the built in syntax feature of the character +. Let’s see how,

class AnyClass:
    def __init__(self, var):
        self.some_var = var
    def __add__(self, other_obj):
        print("Calling the add method")
        return self.some_var + other_obj.some_var
obj1 = AnyClass(5)
obj2 = AnyClass(6)
obj1 + obj2

#python3 #python #python-programming #python-web-development #python-tutorials #python-top-story #python-tips #learn-python