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copy — Shallow and deep copy operations ¶

Source code: Lib/copy.py

Assignment statements in Python do not copy objects, they create bindings between a target and an object. For collections that are mutable or contain mutable items, a copy is sometimes needed so one can change one copy without changing the other. This module provides generic shallow and deep copy operations (explained below).

Interface summary:

Return a shallow copy of x .

Return a deep copy of x .

Raised for module specific errors.

The difference between shallow and deep copying is only relevant for compound objects (objects that contain other objects, like lists or class instances):

A shallow copy constructs a new compound object and then (to the extent possible) inserts references into it to the objects found in the original.

A deep copy constructs a new compound object and then, recursively, inserts copies into it of the objects found in the original.

Two problems often exist with deep copy operations that don’t exist with shallow copy operations:

Recursive objects (compound objects that, directly or indirectly, contain a reference to themselves) may cause a recursive loop.

Because deep copy copies everything it may copy too much, such as data which is intended to be shared between copies.

The deepcopy() function avoids these problems by:

keeping a memo dictionary of objects already copied during the current copying pass; and

letting user-defined classes override the copying operation or the set of components copied.

This module does not copy types like module, method, stack trace, stack frame, file, socket, window, or any similar types. It does “copy” functions and classes (shallow and deeply), by returning the original object unchanged; this is compatible with the way these are treated by the pickle module.

Shallow copies of dictionaries can be made using dict.copy() , and of lists by assigning a slice of the entire list, for example, copied_list = original_list[:] .

Classes can use the same interfaces to control copying that they use to control pickling. See the description of module pickle for information on these methods. In fact, the copy module uses the registered pickle functions from the copyreg module.

In order for a class to define its own copy implementation, it can define special methods __copy__() and __deepcopy__() . The former is called to implement the shallow copy operation; no additional arguments are passed. The latter is called to implement the deep copy operation; it is passed one argument, the memo dictionary. If the __deepcopy__() implementation needs to make a deep copy of a component, it should call the deepcopy() function with the component as first argument and the memo dictionary as second argument. The memo dictionary should be treated as an opaque object.

Discussion of the special methods used to support object state retrieval and restoration.

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Python Course #12: Pass by Assignment, Copy, Reference, and None

Now that the you know what mutable (e.g. lists , sets and dictionaries ) and immutable data types (e.g. tuples ) are it is important to talk about copies, references and None .

Python Pass by Assignment

When you call a pass data to a function such as print(...) there are two ways. The first option is to pass the data directly as a value:

The second option is to pass the data as a variable:

The difference between those two options is that the second option allows you to access the data you passed to the function later in your program by stating the variable. When the function that receives the data modifies it internally, you have to be aware of two critical things:

When a primitive data type ( bool , int , float , and str ) or immutable data type (e.g. tuple ) is passed to a function, its value is copied. Therefore, all changes made inside a function won’t affect the values stored in the variables passed to the function. This is called pass by value .

When a mutable data type (e.g. list , set , dict ) is passed to a function, the data isn’t copied. Because of this, changes that are made inside the function affect the values outside of the function. This is called pass by reference

This sounds pretty theoretical, so here comes an example:

Even though you haven’t learned about declaring functions in Python this example declares the function func(x, y) that takes two parameters. func(x, y) subtracts 1 from the first parameter, and calls pop() on the second one. That is everything you need to understand at the moment.

In the main program, the int i and the list l are declared and then passed to func(x, y) . When looking at i and l after func(x, y) has been executed, you can see that i is still 1 and not 0 because i ’s value was copied. However, l is missing its last element since it was passed as a reference.

This mix of pass by value and pass by reference in Python is called pass by assignment . It is essential to keep this concept always in mind when writing Python code.

In the previous parts of this Python course, you have seen that it is possible to get a copy of a mutable data type by calling the .copy() function:

By using .copy() , the data outside of the function isn’t changed.

In the following example the difference between a copy and reference is further amplified:

In the first line the list ['a', 'b', 'c'] is declared and than a new reference to this list is created with l = . In the second line another reference to the list ['a', 'b', 'c'] is created with: m = l . In the third line the list is copied and a reference to that copy is created with: n = l.copy() .

There are three references to two lists with the same content. l and m reference the first list and n reference the second list . Because l and m reference the same list every modification done using l or m will always be reflected in both. n won’t change as it references a different list .

Python is vs ==

To check if two variables reference the same value you can use the is operator to compare the values use the == operator:

l is n evaluates to False because they reference differnt list s, however, those two list s contain the same values and therefore l == n evaluates to True . You can also check the id that a variable is referencing using id(...) :

You can see that the id of l and m is the same and the id of n is different (The numbers are different everytime you run this code). The is operator is actually implemented using == with id(...) == id(...) .

Python None

Now that you know the difference between a copy and a reference, there is one last thing to talk about: ` None . The keyword None in Python indicates no value at all. And there is only one None`:

None can be used to initialize a variable without assigning a value. This can be useful when the value of a variable is assigned under a condition:

None actually has it’s own type and is an immutable data type because it can’t be changed:

None concludes this article. Throughout this Python course, you will come across pass by assignment, copies, references, and None reasonably often. Make sure to get the free Python Cheat Sheets in my Gumroad shop . If you have any questions about this article, feel free to join our Discord community to ask them over there.

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Shallow and deep copy in Python: copy(), deepcopy()

In Python, you can make a shallow and deep copy using the copy() and deepcopy() functions from the copy module. A shallow copy can also be made with the copy() method of lists, dictionaries, and so on.

copy — Shallow and deep copy operations — Python 3.11.3 documentation

Shallow copy and deep copy in Python

Assignment to another variable, copy() method of list, dictionary, etc., list() , dict() , etc., copy.copy(), deep copy: copy.deepcopy().

The following is a summary of the differences between assignment to another variable, shallow copy, and deep copy.

The Python official documentation describes shallow copy and deep copy as follows:

The difference between shallow and deep copying is only relevant for compound objects (objects that contain other objects, like lists or class instances): A shallow copy constructs a new compound object and then (to the extent possible) inserts references into it to the objects found in the original. A deep copy constructs a new compound object and then, recursively, inserts copies into it of the objects found in the original. copy — Shallow and deep copy operations — Python 3.11.3 documentation

When objects are contained within mutable objects, like elements in a list or values in a dictionary, a shallow copy creates references to the original objects, while a deep copy creates new copies of the original objects. With references, the elements point to the same object, so modifying one of them also affects the other.

First, let's explore what happens when assigning an object to another variable.

When a mutable object, such as a list or a dictionary, is assigned to multiple variables, updating one variable (e.g., changing, adding, or deleting elements) will also affect the other variables.

As demonstrated in the above code example, the is operator shows that the two variables refer to the same object both before and after the value change.

Difference between the == and is operators in Python

To create a copy instead of a reference of the same object, use the copy() method or the copy.copy() and copy.deepcopy() functions described below.

For immutable objects like numbers ( int , float ) and strings ( str ), they cannot be updated. When these types of objects are assigned, the two variables refer to the same object initially. However, if one variable is updated to a new value, it becomes a separate object, and the other variable remains the same.

Shallow copy: copy() , copy.copy() , etc.

The copy() method is provided for lists, dictionaries, etc. The copy() method makes a shallow copy.

A shallow copy creates a new object that contains references to the elements found in the original object. For example, in the case of a list, the copied list is a new object, but its elements are references to the same objects in the original list.

Therefore, if the elements are mutable, updating one also affects the other. However, in the case of an immutable element, when its value is changed, it becomes a separate object. The corresponding element in the other list remains unchanged.

The same applies not only to a list of lists as in the example above, but also to a list of dictionaries, a nested dictionary (a dictionary containing other dictionaries as values), and so on.

Slices for mutable sequence types, such as lists, also make shallow copies.

How to slice a list, string, tuple in Python

For example, applying the slice [:] that specifies all elements makes a shallow copy of the whole list.

Before Python 3.3 introduced the copy() method for lists, the [:] notation was commonly used to create a shallow copy. For new code, it is generally recommended to use the copy() method to make your intentions clearer.

A slice of a part of the sequence also creates a shallow copy.

If you need to make a deep copy, you can apply the copy.deepcopy() function to the slice.

You can make a shallow copy of a list or dictionary by passing the original list or dictionary to list() or dict() .

You can also make a shallow copy with the copy() function from the copy module.

Use copy.copy() when you need to create a shallow copy of an object that does not provide a copy() method.

To make a deep copy, use the deepcopy() function from the copy module.

In a deep copy, actual copies of the objects are inserted instead of their references. As a result, changes to one object do not affect the other.

Here's an example of applying the deepcopy() function to a slice:

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Python Shallow Copy and Deep Copy

Copy an object in python.

In Python, we use = operator to create a copy of an object. You may think that this creates a new object; it doesn't. It only creates a new variable that shares the reference of the original object.

Let's take an example where we create a list named old_list and pass an object reference to new_list using = operator.

Example 1: Copy using = operator

When we run above program, the output will be:

As you can see from the output both variables old_list and new_list shares the same id i.e 140673303268168 .

So, if you want to modify any values in new_list or old_list , the change is visible in both.

Essentially, sometimes you may want to have the original values unchanged and only modify the new values or vice versa. In Python, there are two ways to create copies:

Shallow Copy

To make these copy work, we use the copy module.

Copy Module

We use the copy module of Python for shallow and deep copy operations. Suppose, you need to copy the compound list say x . For example:

Here, the copy() return a shallow copy of x . Similarly, deepcopy() return a deep copy of x .

A shallow copy creates a new object which stores the reference of the original elements.

So, a shallow copy doesn't create a copy of nested objects, instead it just copies the reference of nested objects. This means, a copy process does not recurse or create copies of nested objects itself.

Example 2: Create a copy using shallow copy

When we run the program , the output will be:

In above program, we created a nested list and then shallow copy it using copy() method.

This means it will create new and independent object with same content. To verify this, we print the both old_list and new_list .

To confirm that new_list is different from old_list , we try to add new nested object to original and check it.

Example 3: Adding [4, 4, 4] to old_list, using shallow copy

When we run the program, it will output:

In the above program, we created a shallow copy of old_list . The new_list contains references to original nested objects stored in old_list . Then we add the new list i.e [4, 4, 4] into old_list . This new sublist was not copied in new_list .

However, when you change any nested objects in old_list , the changes appear in new_list .

Example 4: Adding new nested object using Shallow copy

In the above program, we made changes to old_list i.e old_list[1][1] = 'AA' . Both sublists of old_list and new_list at index [1][1] were modified. This is because, both lists share the reference of same nested objects.

A deep copy creates a new object and recursively adds the copies of nested objects present in the original elements.

Let’s continue with example 2. However, we are going to create deep copy using deepcopy() function present in copy module. The deep copy creates independent copy of original object and all its nested objects.

Example 5: Copying a list using deepcopy()

In the above program, we use deepcopy() function to create copy which looks similar.

However, if you make changes to any nested objects in original object old_list , you’ll see no changes to the copy new_list .

Example 6: Adding a new nested object in the list using Deep copy

In the above program, when we assign a new value to old_list , we can see only the old_list is modified. This means, both the old_list and the new_list are independent. This is because the old_list was recursively copied, which is true for all its nested objects.

Copy an Object in Python

Sorry about that.

Python Copy Module

Copy Module is a set of functions that are related to copying different elements of a list, objects, arrays, etc. It can be used to create shallow copies as well as deep copies.

From the Python 3 documentation

Shallow copy operations

Shallow copy constructs a new compound object and then (to the extent possible) inserts references into it to the objects found in the original.

copy.copy(x) Return a shallow copy of x.

Without importing copy module you can’t use it

Deep copy operations.

A deep copy constructs a new compound object and then, recursively, inserts copies into it of the objects found in the original.

copy.deepcopy(x[, memo]) Return a deep copy of x.

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Python copy list - deepcopy() vs copy() with examples

In this article of Python programming, we will learn about copy module. We will use and compare Python deepcopy() , shallow copy() and normal assignment to copy or clone a list with multiple examples.

Python copy list using normal assignment

In python we use = operator to create a copy of an object.

Example-1: Use = operator to copy a list in Python

For example I have a list called myList with some elements. Next I copy the myList content into newList using = operator.

Python copy list - deepcopy() vs copy() with examples

So as you see the list elements were successfully copied to a new list.

Example-2: Append elements to old list after copying

In the same example I will append some element to the original list after copying the list .

In the output you can see that the same element is also automatically appended to the new list. So any such changes performed to the old list will also reflect in the newly copied list.

Example-3: Modify elements of new list after copying

Similar to example-2, we will now modify the content of newList (in the previous example we modified original list so here we are doing vice versa) which is a copy of old list created using normal assignment.

So this shows that using an = operator we don't create a new object, instead it just creates a new variable which will share the reference of the original object.

Python copy module

The python copy module provides functions for making shallow and deep copies of compound objects, including lists, tuples, dictionaries, and class instances.

The copy module provides two functions

shallow copy()

The copy functions don’t work with modules, class objects, functions, methods, tracebacks, stack frames, files, sockets, and other similar types. When an object can’t be copied, the copy.error exception is raised.

Python shallow copy() function

When you create a shallow copy, you create a new instance of the current object and copy values of members of the original to the new one but do not create copies of children (referenced) objects.

The copy() function only creates a shallow copy of the original list. This means it copies the top level of the list only . If in that list you have more complex data structures having nested elements, then those will not be cloned . So the new list only hold half the reference of the original list.

Example-4: Use copy() function to create a shallow copy of a list

In this example we will use shallow copy() function to create a clone of a list.

Here you can see that even though we have an exact copy of the original list. Although the id of both the lists are different, this is because copy() created a new object here unlike the = operator we used earlier. Output from this script:

Example-5: Append and modify top level elements in a list with shallow copy()

In this example we will append and modify some existing elements of our list. Tis is to verify if modifying an element in original list is also reflected in new list and vice versa. Let us verify this theory with some practical examples:

Example-6: Modify nested object with python shallow copy() function

In this example we will create a dictionary inside the original list and clone it to a new list variable using shallow copy() function.

In the script we are modifying the dictionary value from ' maruti ' to ' honda ' in the old list and the same is also reflecting in the new list because shallow copy doesn't store the nested data in the memory. Hence both lists share the reference of same nested objects.

Python deepcopy() function

Python deepcopy() function is more subtle as it copies even the nested objects recursively. Although copy.deepcopy() is slightly slower than copy.copy() , it’s safer to use if you don’t know whether the list being copied contains other lists (or other mutable objects like dictionaries or sets).

Example-7: Use python deepcopy() function to create a deep copy of a list

We will use the same code from Example-6, just replace copy.copy() with copy.deepcopy() function.

The output will have two different objects with same content. Since deepcopy() creates a new object, the ids are expected to be different of both the variables.

Example-8: Modify nested object in the list using python deepcopy() function

Since we know that appending/modifying element of top level lists are not cloned to the new list or vice versa. So I will not repeat example-5 use case with python deepcopy() .

But let us validate Example-6 use case wherein we modify the dictionary content within the list as shallow copy was not able to retain the changes there.

As expected, python deepcopy() function has successfully copied the content of original list into a new one recursively. Hence any change to the old list is not visible in the new list and they both are completely independent.

Normal assignment vs shallow copy() vs deepcopy()

Here is a short table which briefs the difference of behaviour between normal assignment operator, shallow copy() and deepcopy() function available in Python :

In this tutorial we learned about list cloning using different available methods. We learned that we can create an identical new object from an existing one using = operator and copy module. The copy module contains shallow copy() function as well as deepcopy() function. The major difference between shallow copy() and deepcopy() function is that the deepcopy() function copies the data of an object " recursively ". This means that any complex data structures and nested elements are also copied from the original list unlike shallow copy which ignored the nested data. This is the reason why any change made to the nested data in the original list is also cloned in the new list when using shallow copy() function.

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The list Copy() method makes a new shallow copy of a list.

What is List Copy() Method?

The list Copy() function in Python is used to create a copy of a list. There are two main ways to create a copy of the list Shallow copy and Deep copy. We will discuss the list copy() method in detail below.

The list copy() function is used to create a copy of a list, which can be used to work and it will not affect the values in the original list. This gives freedom to manipulate data without worrying about data loss.

List copy() Method Syntax

list.copy()

The copy() method doesn’t take any parameters.

Returns: Returns a shallow copy of a list.

Note : A shallow copy means any modification in the new list won’t be reflected in the original list.

How to Create a Simple Copy of a List in Python

Copying and creating a new list can be done using copy() function in Python.

More Examples on List copy() Method

Let us see a few examples of the list copy() method.

Example 1: Simple List Copy

In this example, we are creating a List of Python strings and we are using copy() method to copy the list to another variable.

Example 2: Demonstrating the working of List copy()

Here we will create a Python list and then create a shallow copy using the copy() function. Then we will append a value to the copied list to check if copying a list using copy() method affects the original list.

Note: A shallow copy means if we modify any of the nested list elements, changes are reflected in both lists as they point to the same reference.

Shallow Copy and Deep Copy

A deep copy is a copy of a list, where we add an element in any of the lists, only that list is modified.

In list copy() method, changes made to the copied list are not reflected in the original list. The changes made to one list are not reflected on other lists except for in nested elements (like a list within a list).

We can use the copy.deepcopy() from the copy module to avoid this problem.

Using copy.deepcopy()
Using copy.copy()
Using list.copy()
Using slicing

To gain a deeper understanding, Refer to this article on Deep Copy vs Shallow Copy .

Demonstrating Techniques of Shallow and Deep copy

Here we will create a list and then create a shallow copy using the assignment operator , list copy() method, and copy.copy() method of the Python copy module.

We also create a deep copy using deepcopy() in Python. Then we will make changes to the original list and see if the other lists are affected or not.

Copy List Using Slicing

Here we are copying the list using the list slicing method [:] and we are appending the ‘a’ to the new_list. After printing we can see the newly appended character ‘a’ is not appended to the old list.

We discussed the definition, syntax and examples of list copy() method. copy() function is very useful when working with sensitive data and you can’t risk mistakes.

We also briefly talked about shallow vs deep copy. Hope this helped you in understanding copy() function in Python.

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Jarvis like Voice Assistant
Automate Website Login
Gender Recognition by voice
File Explorer in Python
Notepad like Text Editor using Python
Fake News Detection
Story Generator
Rock Paper Scissor Game
Draw Various Shapes in Python
Generate QR-Code for a website
Minecraft in Python
Happy Birthday in Python

Python projects for Turtle Graphics

The purpose of creating a library like turtle is very clear, learning programming and logic by drawing simple drawings using Python Turtle graphics. If you are a beginner in the programming world, then you should start with the turtle. You can even create famous games using the python turtle like snake game.

Here is the list of top 30 turtle projects with source code and links:

Radha Krishna using Python Turtle
Drawing letter A using Python Turtle
Wishing Happy New Year 2023 in Python Turtle
Snake and Ladder Game in Python
Draw Goku in Python Turtle
Draw Mickey Mouse in Python Turtle
Happy Diwali in Python Turtle
Draw Halloween in Python Turtle
Write Happy Halloween in Python Turtle
Draw Happy Diwali in Python Turtle
Extract Audio from Video using Python
Drawing Application in Python Tkinter
Draw Flag of USA using Python Turtle
Draw Iron Man Face with Python Turtle: Tony Stark Face
Draw TikTok Logo with Python Turtle
Draw Instagram Logo using Python Turtle
I Love You Text in ASCII Art
Python Turtle Shapes- Square, Rectangle, Circle
Python Turtle Commands and All Methods
Happy Birthday Python Program In Turtle
I Love You Program In Python Turtle
Draw Python Logo in Python Turtle
Space Invaders game using Python
Draw Google Drive Logo Using Python
Draw Instagram Reel Logo Using Python
Draw The Spotify Logo in Python Turtle
Draw The CRED Logo Using Python Turtle
Draw Javascript Logo using Python Turtle
Draw Dell Logo using Python Turtle
Draw Spider web using Python Turtle

Automation Python Projects

Here is the list of automation Python projects:

Instagram Automation
ChatBot Automation
Algorithmic Trading using Python
Automate email marketing campaigns
Auto Update Excel Sheets
Automatic Online Form Filling
Twitter bot automation
Tic-Tac-Toe Automation
Automate Bills Payment
Automating Your Digital Morning Routine with Python
Automate WhatsApp

Python Tkinter GUI Projects

Python Tkinter is the best Python library to create desktop applications using Python. We are going to present you with the best Python Tkinter projects with source code and links. These projects will be of different types, some will contain databases and some will be very simple. Click here to get the top 10 Tkinter project ideas for beginners.

Here is the list of top 30 Tkinter projects in Python:

Create your own ChatGPT with Python
SQLite | CRUD Operations in Python
Event Management System Project in Python
Ticket Booking and Management in Python
Hostel Management System Project in Python
Sales Management System Project in Python
Bank Management System Project in C++
Python Download File from URL | 4 Methods
Python Programming Examples | Fundamental Programs in Python
Spell Checker in Python
Portfolio Management System in Python
Stickman Game in Python
Contact Book project in Python
Loan Management System Project in Python
Cab Booking System in Python
Brick Breaker Game in Python
Tank game in Python
GUI Piano in Python
Ludo Game in Python
Rock Paper Scissors Game in Python
Puzzle Game in Python
Medical Store Management System Project in Python
Creating Dino Game in Python
Tic Tac Toe Game in Python
Test Typing Speed using Python App
Scientific Calculator in Python
GUI To-Do List App in Python Tkinter
Scientific Calculator in Python using Tkinter
GUI Chat Application in Python Tkinter

Python Projects for Machine Learning and Data Science

Here is the list of the best 30 python projects for machine learning and data science with source code:

Flower classification using CNN
Music Recommendation System in Machine Learning
Top 15 Machine Learning Projects in Python with source code
Gender Recognition by Voice using Python
Top 15 Python Libraries For Data Science in 2022
Setup and Run Machine Learning in Visual Studio Code
Diabetes prediction using Machine Learning
Customer Behaviour Analysis – Machine Learning and Python
Data Science Projects for Final Year
Multiclass Classification in Machine Learning
Movie Recommendation System: with Streamlit and Python-ML
Machine Learning Projects for Final Year
Reinforcement learning in Python
Face recognition Python
Hate speech detection with Python
MNIST Handwritten Digit Classification using Deep Learning
Stock Price Prediction using Machine Learning
Control Mouse with hand gestures detection python
Traffic Signal Violation Detection System using Computer Vision
Deepfake Detection Project Using Deep-Learning
Employment Trends Of Private & Government Sector in UAE | Data Analysis
Pokemon Analysis Project in ML and Data Science using Python
Garment Factory Analysis Project using Python
Titanic Survival Prediction – Machine Learning Project (Part-2)
Titanic Survival Prediction – Machine Learning Project (Part-1)

Game development projects in Python

Here is the list of best 30 python projects for games:

Tetris game in Python Code
Python Games Code | Copy and Paste
How to make KBC Quiz game in python?
Creating a Pong Game using Python Turtle
Hangman Game using Python
Balloon Shooter Game using Python PyGame
Complete PyGame Tutorial and Projects
Flappy Bird In Python Pygame with source code
Tic Tac Toe in Python
Make A Telegram Bot Using Python
How to Draw the Superman Logo Using Python Turtle
3D animation in Python: vpython
Complete Racing Game In Python Using PyGame
Complete Game In PyGame and Python
Snake Game with Python Turtle Graphics
Star Wars Game with Python Turtle
Turtle Race Python Project: Complete Guide
Car Race Game in PyGame Python: From Scratch

Python projects for final year students of Computer Science

We already have a good article on python projects for final-year students. Click here to check the best Python projects for final-year students.

Top 10 Python Projects for Final year Students

You can check more final year projects below, here is the list:

Download 1000+ Projects, All B.Tech & Programming Notes, Job, Resume & Interview Guide, and More – Get Your Ultimate Programming Bundle!
100+ Java Projects for Beginners 2023
Courier Tracking System in HTML CSS and JS
Top 15 Java Projects For Resume
Top 10 Java Projects with source code
Top 10 Final Year Projects for Computer Science Students
Library Management System Project in Java
Bank Management System Project in Java
CS Class 12th Python Projects
Top 10 Python Projects for Final year Students
Python OOP Projects | Source code and example
Inventory Management System Project in python
Courier Management System project in Python
Contact Management System Project in Python
Python SQLite Tutorial
Student Management System Project in Python
20 Python Projects for Resume
Restaurant management system project in Python
Employee Management System Project in Python
Bank Management System Project in Python

Final thoughts

Don’t forget to bookmark this page and share it with your friend, you may lose it. We have really worked hard and researched too much for most of the projects in this blog so that our users like you can save their time by directly selecting their favorite python project from the best projects available on the Internet. We selected only the best projects so that you can learn and try from the top and best projects only. That’s all for this tutorial, we hope you will find your dream Python project.

Thank you for reading till here.

Keep learning

Author: Harry

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