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Python Programming

Practice Python Exercises and Challenges with Solutions

Free Coding Exercises for Python Developers. Exercises cover Python Basics , Data structure , to Data analytics . As of now, this page contains 18 Exercises.

What included in these Python Exercises?

Each exercise contains specific Python topic questions you need to practice and solve. These free exercises are nothing but Python assignments for the practice where you need to solve different programs and challenges.

• All exercises are tested on Python 3.
• Each exercise has 10-20 Questions.
• The solution is provided for every question.
• Practice each Exercise in Online Code Editor

These Python programming exercises are suitable for all Python developers. If you are a beginner, you will have a better understanding of Python after solving these exercises. Below is the list of exercises.

Select the exercise you want to solve .

Basic Exercise for Beginners

Practice and Quickly learn Python’s necessary skills by solving simple questions and problems.

Topics : Variables, Operators, Loops, String, Numbers, List

Python Input and Output Exercise

Solve input and output operations in Python. Also, we practice file handling.

Topics : print() and input() , File I/O

Python Loop Exercise

This Python loop exercise aims to help developers to practice branching and Looping techniques in Python.

Topics : If-else statements, loop, and while loop.

Python Functions Exercise

Practice how to create a function, nested functions, and use the function arguments effectively in Python by solving different questions.

Topics : Functions arguments, built-in functions.

Python String Exercise

Solve Python String exercise to learn and practice String operations and manipulations.

Python Data Structure Exercise

Practice widely used Python types such as List, Set, Dictionary, and Tuple operations in Python

Python List Exercise

This Python list exercise aims to help Python developers to learn and practice list operations.

Python Dictionary Exercise

This Python dictionary exercise aims to help Python developers to learn and practice dictionary operations.

Python Set Exercise

This exercise aims to help Python developers to learn and practice set operations.

Python Tuple Exercise

This exercise aims to help Python developers to learn and practice tuple operations.

Python Date and Time Exercise

This exercise aims to help Python developers to learn and practice DateTime and timestamp questions and problems.

Topics : Date, time, DateTime, Calendar.

Python OOP Exercise

This Python Object-oriented programming (OOP) exercise aims to help Python developers to learn and practice OOP concepts.

Topics : Object, Classes, Inheritance

Python JSON Exercise

Practice and Learn JSON creation, manipulation, Encoding, Decoding, and parsing using Python

Python NumPy Exercise

Practice NumPy questions such as Array manipulations, numeric ranges, Slicing, indexing, Searching, Sorting, and splitting, and more.

Python Pandas Exercise

Practice Data Analysis using Python Pandas. Practice Data-frame, Data selection, group-by, Series, sorting, searching, and statistics.

Python Matplotlib Exercise

Practice Data visualization using Python Matplotlib. Line plot, Style properties, multi-line plot, scatter plot, bar chart, histogram, Pie chart, Subplot, stack plot.

Random Data Generation Exercise

Practice and Learn the various techniques to generate random data in Python.

Topics : random module, secrets module, UUID module

Python Database Exercise

Practice Python database programming skills by solving the questions step by step.

Use any of the MySQL, PostgreSQL, SQLite to solve the exercise

Exercises for Intermediate developers

The following practice questions are for intermediate Python developers.

If you have not solved the above exercises, please complete them to understand and practice each topic in detail. After that, you can solve the below questions quickly.

Exercise 1: Reverse each word of a string

Expected Output

• Use the split() method to split a string into a list of words.
• Reverse each word from a list
• finally, use the join() function to convert a list into a string

Steps to solve this question :

• Split the given string into a list of words using the split() method
• Use a list comprehension to create a new list by reversing each word from a list.
• Use the join() function to convert the new list into a string
• Display the resultant string

Exercise 2: Read text file into a variable and replace all newlines with space

Given : Assume you have a following text file (sample.txt).

Expected Output :

• First, read a text file.
• Next, use string replace() function to replace all newlines ( \n ) with space ( ' ' ).

Steps to solve this question : -

• First, open the file in a read mode
• Next, read all content from a file using the read() function and assign it to a variable.
• Display final string

Exercise 3: Remove items from a list while iterating

Description :

In this question, You need to remove items from a list while iterating but without creating a different copy of a list.

Remove numbers greater than 50

Expected Output : -

• Get the list's size
• Iterate list using while loop
• Check if the number is greater than 50
• If yes, delete the item using a del keyword
• Reduce the list size

Solution 1: Using while loop

Solution 2: Using for loop and range()

Exercise 4: Reverse Dictionary mapping

Exercise 5: display all duplicate items from a list.

• Use the counter() method of the collection module.
• Create a dictionary that will maintain the count of each item of a list. Next, Fetch all keys whose value is greater than 2

Solution 1 : - Using collections.Counter()

Solution 2 : -

Exercise 6: Filter dictionary to contain keys present in the given list

Exercise 7: print the following number pattern.

Refer to Print patterns in Python to solve this question.

• Use two for loops
• The outer loop is reverse for loop from 5 to 0
• Increment value of x by 1 in each iteration of an outer loop
• The inner loop will iterate from 0 to the value of i of the outer loop
• Print value of x in each iteration of an inner loop
• Print newline at the end of each outer loop

Exercise 8: Create an inner function

Question description : -

• Create an outer function that will accept two strings, x and y . ( x= 'Emma' and y = 'Kelly' .
• Create an inner function inside an outer function that will concatenate x and y.
• At last, an outer function will join the word 'developer' to it.

Exercise 9: Modify the element of a nested list inside the following list

Change the element 35 to 3500

Exercise 10: Access the nested key increment from the following dictionary

Under Exercises: -

Python Object-Oriented Programming (OOP) Exercise: Classes and Objects Exercises

Updated on:  December 8, 2021 | 52 Comments

Python Date and Time Exercise with Solutions

Updated on:  December 8, 2021 | 10 Comments

Python Dictionary Exercise with Solutions

Updated on:  May 6, 2023 | 56 Comments

Python Tuple Exercise with Solutions

Updated on:  December 8, 2021 | 96 Comments

Python Set Exercise with Solutions

Updated on:  October 20, 2022 | 27 Comments

Python if else, for loop, and range() Exercises with Solutions

Updated on:  August 29, 2024 | 298 Comments

Updated on:  August 2, 2022 | 155 Comments

Updated on:  September 6, 2021 | 109 Comments

Python List Exercise with Solutions

Updated on:  December 8, 2021 | 201 Comments

Updated on:  December 8, 2021 | 7 Comments

Python Data Structure Exercise for Beginners

Updated on:  December 8, 2021 | 116 Comments

Python String Exercise with Solutions

Updated on:  October 6, 2021 | 221 Comments

Updated on:  March 9, 2021 | 23 Comments

Updated on:  March 9, 2021 | 51 Comments

Updated on:  July 20, 2021 | 29 Comments

Python Basic Exercise for Beginners

Updated on:  August 29, 2024 | 498 Comments

Useful Python Tips and Tricks Every Programmer Should Know

Updated on:  May 17, 2021 | 23 Comments

Python random Data generation Exercise

Updated on:  December 8, 2021 | 13 Comments

Python Database Programming Exercise

Updated on:  March 9, 2021 | 17 Comments

• Online Python Code Editor

Updated on:  June 1, 2022 |

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Best Python Homework Help Websites (Reviewed by Experts)

Many teens are fond of technology in all its manifestation. As they grow up, their interest becomes rather professional, and many of them decide to become certified coders to create all those games, software, apps, websites, and so on. This is a very prospective area, which will always be in high demand. Yet, the path to the desired diploma is pretty complicated. Not all assignments can be handled properly. As a result, many learners look for professional help on the Internet.

Smart minds are aware of custom programming platforms that help with all kinds of programming languages. It’s hard to define which one is better than the others because all legal and highly reputed platforms offer pretty much the same guarantees and conveniences of high quality. This is when our comprehensive review will be helpful for students. We have checked the best Python homework help websites. We have opted for Python because of 2 reasons. Firstly, this is one of the most popular and widely used coding languages. You will surely have to master it. Secondly, only one language helps to narrow down your choice. So, read on to find out the possible options that really work.

6 Top Python Assignment Help Websites to Solve All Your Issues

It is good to have a rich choice of coding options. But, the abundance of choices can also be overwhelming. Here are some top recommendations to make the choice easier:

These are the top Python assignment help websites, according to the research of our quality control experts. Let’s look at each of them individually, focusing on a unique benefit offered by each site. This data may help you to define the type of aid you need – high quality, top speed, cheap prices, and so on.

Python Programming Homework Help – How to Choose the Right Site?

When it comes to choosing a custom coding site, you may be puzzled for a long time. There are many great options, and each seems to offer flawless Python programming assignment help. How to define the best platform in the niche? Well, the first step is to create a wish list. It should include the features you expect to get from a pro platform.

Secondly, use the comparison method. You need to shortlist all the options and check what exactly each of them offers. While one offers cheaper prices, another one is faster. So, the choice should be based on your priorities. We have already created a list of the most beneficial sites for you. The last task is to compare them after you read more detailed descriptions.

We’ve shortlisted the most beneficial sites for you. What you’ll be left with is to compare them and pick the one that suits your needs best.

1. CodingHomeworkHelp.org: A Top-Rated Solution for Python Homework

Our first option is called CodingHomeworkHelp , and it has the highest average rating, according to our experts. They’ve given it 9.8 out of 10 possible, which is surely a great achievement. The combination of conditions and guarantees makes it nearly ideal for students needing Python homework help. Let’s take a look at its main features:

• Outstanding quality. This custom coding agency is famous for the quality of its aid, which is always as high as the client desires. It employs only certified and skilled solvers who can meet the demands of the strictest educators and employers (if you already work).
• Timely aid . This platform has delivered almost 97% of all its orders on time. This indicator proves that you may not worry about your time limits. Its specialists are fast enough to meet the shortest timeframes.
• Unique projects . Its experts do all the orders from scratch. It means they never reuse even their own projects. They take into account the slightest details and make every project unique. Your code will surely differ from others, and it will be free of any signs of plagiarism. Don’t worry about this matter.
• Quite cheap prices . You will be pleasantly impressed by the price policy offered by this coding agency. It is quite cheap and fair. Ordinary students will be able to afford its professional aid. Moreover, they can count on great discounts and promos to save up even more of their funds.
• Effective customer support . This site offers a very welcoming and fast team of customer support, which consists of great consultants. They are always at work and provide clear answers to the questions related to the policies of this agency. The answers come in a couple of minutes or so.

2. DoMyAssignments.com: Affordable Python Assignment Assistance

The second site we’d like to recommend is DoMyAssignments , boasting a strong rating of 9.7 out of 10 stars. This impressive score reflects the site’s commitment to excellence, and you can be confident that it can satisfy all your coding needs to the fullest. With a team of real professionals, each selected through a special onboarding process that consists of several stages, only the most gifted specialists make the cut.

How can they assist with your Python assignment? They offer individualized solutions at the cheapest prices among our reviewed sites. You can even modify the order to fit your budget, considering factors like quality, type, size, and urgency.

Besides, the site ensures other vital benefits. Make allowances for them here below:

• Quick assistance : The experts at DoMyAssignments are known for their speed and diligence. An impressive 96% of all their orders were delivered without delays, and 79% were completed long before the deadline . These achievements demonstrate their commitment to timely delivery, even for the most urgent tasks.
• Full privacy : Security is a priority at DoMyAssignments. They ensure the confidentiality of your private data and never share it with third parties. With effective antivirus software and encrypted billing methods, you can trust that you’re 100% safe when using their platform.
• 24/7 support : Need help at any hour? DoMyAssignments runs 24/7, providing immediate access to competent technicians via live chat. Whether you have urgent questions about their policies or need clarification on specific details, you can expect fast and clear answers.
• Individual approach : Personalized service is a standout feature of DoMyAssignments. You can contact your helper at predetermined hours to discuss your project’s progress. This direct communication allows for real-time updates and changes, offering a convenient way to ensure that your project aligns perfectly with your requirements.

You should also know that it practices an individual approach. You are welcome to contact your helper during the predetermined hours. Just discuss with him or her when both of you can be online and check the progress of your project. It’s a fast and convenient way to offer changes on demand and without delays.

3. AssignCode.com: Fast and Reliable Python Homework Help

If speed is your priority, AssignCode is an excellent choice. How fast can you do my Python homework? Well, a lot depends on the demands you have. Nonetheless, most projects are completed there in 4–5 hours only!

Thus, you can place an order even later at night to get it done early in the morning. Just be sure you set manageable conditions. If it’s so, your order will be accepted and completed according to your demands. It will be delivered on time. As for other vital guarantees, you may count on:

• Great quality. This company has a professional staff, which consists of outstanding programmers. They all have confirmed their qualifications and were trained to match the top demands of every high school, college, or university. They can help even already working coders who face some issues at the moment.
• Fair prices. You will surely like the prices set by this coding company. They are quite cheap, and ordinary students will not face problems with ordering professional help on this site. There is a possibility to quickly regulate the prices online. You only need to change the quality, type, volume, or deadline of your assignment. A refund guarantee is given as well.
• All kinds of features . This platform is able to satisfy the slightest demands of the strictest customers. Everything will be done exactly as you want, and can contact your helper directly. He or she will offer all the necessary skills to complete your project perfectly. The platform’s specialists handle all assignment types. Python is only one of the possible areas of their competence.
• A responsive customer support team. In case you don’t understand some policies or limits of this company, turn to its team of support. It consists of polite and knowledgeable operators. They work day and night to provide detailed responses in about 2 minutes or so.

Also read: The 10 Commandments of Coding: Study, Learn and Put into Practice

4. CWAssignments.com: Unique and Customized Python Assistance

Many students cannot create unique projects in coding, and that is why they may require the unique Python assignment help of CWAssignments . Its rating is 9.4 out of 10, which is a sign of a top-class coding site.

It does all the projects anew and never uses the projects of other coders. Its experts don’t reuse even their old assignments. The new conditions are taken into account and fulfilled uniquely. It also offers other vital benefits. These are as follows:

• Reasonable pricing . You will not spend too much if you request assistance there. The site sets relatively cheap prices and offers full customization of the orders. This puts you in full charge of the total cost. Fill out the compulsory fields and change them to see how you can impact the cost to stop when it suits your budget.
• Top quality. The agency hires only educated and talented coders. They surely understand how to handle any assignment in computer science, engineering, and math. They stick to the official requirements of all educational institutions and can satisfy even the most scrupulous educators. Thus, your chance to get an A+ grade sufficiently increases.
• A personified approach. You may get in touch with your solver whenever his or her aid may be required. Just set a reasonable schedule when both of you can be online to discuss the peculiarities of your order. The specialists will provide updated reports to let you know where your project stands.
• Total online confidentiality . This is a reliable and respectful coding platform that always protects the private data of its clients. It never shares any facts about them with anyone else. It utilizes effective software that protects its databases from all types of online dangers. Thanks to the billing methods offered by the site, you may not worry about your transactions within it. They are encrypted and hidden from other users.

5. HelpHomework.net: Personalized Approach to Python Coding

Many learners seek personalized attention for their coding projects. If you need a tailored approach to Python homework help, HelpHomework is the place to go. It offers flexible scheduling for real-time collaboration with your solver.

Simply coordinate a schedule to be online with your solver, using your preferred instant messenger for quick updates. Along with this personalized approach, the platform also provides other key guarantees, including:

• Outstanding Quality: This platform hires only certified coders who pass a rigorous selection process. They’re trained to handle any assignments in computer science, engineering, and math, ensuring precise completion to boost your success.
• Plagiarism-Free Projects: The platform ensures uniqueness in every project. Though coding may seem repetitive, the specialists craft each project from scratch, meeting educators’ expectations for originality.
• 24/7 Support and Supervision: Visit this site anytime, day or night. It operates around the clock, with kind operators ready to provide swift, detailed responses in live chat.
• Reasonable Prices: Offering affordable rates to fit students’ budgets, the company allows you to customize your order’s price by adjusting the project’s quality, size, type, and deadline.

6. CodingAssignments.com: A Wide Range of Python Experts at Your Service

A rich choice of specialists is significant to all. If you want to be sure that you will always find the kind of help with Python assignments or other programming languages, you should opt for CodingAssignments .

This highly reputed coding company boasts over 700 specialists. Thus, you will never be deprived of some privileges. You will find perfect solvers for whatever coding project you must do. The company likewise provides the next benefits:

• On-time deliveries. The experts of the company value the precious time of their customers. They polish all the necessary skills and master the most effective time management methods to meet really short deadlines. Just provide manageable terms. If the assignment is too large and urgent, place it as early as you can. The specialists will check the odds and will surely accept it if it can be completed within the stated period of time.
• Fair pricing . You can count on relatively cheap prices when you deal with this programming site. Thus, common students will be able to afford its aid. Besides, you can count on pleasant promo codes and discounts to save up even more of your funds. Thanks to the refund guarantee, all your investments are secured.
• Full online anonymity. Don’t worry about your online safety when you visit this site. It guards its databases and your private information with reliable antivirus software. The site never reveals any details about its customers to anybody else.
• Hourly supervision . This coding platform operates 24 hours round the clock to let its customers place urgent orders even late at night. Find the chat window and specify the problem you’re facing. There are always operators at work. They provide detailed answers in a couple of minutes or faster.

Also check: Python Course for Beginners Online FREE

FAQs About The Best Python Homework Help Websites

Let’s answer some of the commonly asked questions around our topic of discussion today.

Can I pay someone to do my Python assignment?

Using a legal online coding site requires payment, so choose wisely as different sites set different prices. While 2 sites offer the same level of quality, it would not be wise to choose the one with a more expensive price policy. You’d better study this case before you place the first order.

How can I pay someone to do my Python homework?

To pay for Python homework, register on the site and add a billing method such as PayPal, Visa, or Pioneer. The methods are very convenient and safe. Make sure your debit or credit card has enough money. When you place an order, you will pay the price automatically. The money will be in escrow until the job is done. Check its quality, and if it suits you, release the final payment to your solver.

How can I receive assistance with Python projects?

You can receive professional coding assistance by finding the right coding platforms and hiring the most suitable Python experts. Conduct thorough research to identify the most reliable and suitable sites.

One of the components of your research is surely reading reviews similar to ours. It helps to narrow down the list of potential helping platforms.

Once you’re on the site, check its top performers. Although their prices are higher, you will be safe about the success of your project. Yet, other experts with low ratings can suit you as well. Just check their detailed profiles and read reviews of other clients to be sure they can satisfy all your needs. Hire the required solver, explain what must be done, and pay to get it started.

Where can I get help with Python programming?

You can find Python programming homework help on the Internet. Open the browser and write an accurate keyword search combination.

It may be something like this – the swiftest or cheapest, or best coding site. Check the results, read customers’ reviews, check the reviews of rating agencies (like ours), compare the conditions, and select the most beneficial option for you.

What kind of guarantees can I expect from Python help services?

If you want to find help with Python projects and you will be treated fairly, you need to know the main guarantees every highly reputed programming site is supposed to ensure. These are as follows:

• High quality
• Availability of all skills and assignments
• An individual approach
• Full privacy of your data
• Timely deliveries
• 100% authentic projects
• 24/7 access and support
• Refunds and free revisions

These are the essential guarantees that every legitimate coding site must provide. If some of them lack, it may be better to switch to another option. These guarantees are compulsory, and you should enjoy them all automatically.

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Assignment Operators in Python

The Python Operators are used to perform operations on values and variables. These are the special symbols that carry out arithmetic, logical, and bitwise computations. The value the operator operates on is known as the Operand. Here, we will cover Different Assignment operators in Python .

Here are the Assignment Operators in Python with examples.

Assignment Operator

Assignment Operators are used to assign values to variables. This operator is used to assign the value of the right side of the expression to the left side operand.

Addition Assignment Operator

The Addition Assignment Operator is used to add the right-hand side operand with the left-hand side operand and then assigning the result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the addition assignment operator which will first perform the addition operation and then assign the result to the variable on the left-hand side.

S ubtraction Assignment Operator

The Subtraction Assignment Operator is used to subtract the right-hand side operand from the left-hand side operand and then assigning the result to the left-hand side operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the subtraction assignment operator which will first perform the subtraction operation and then assign the result to the variable on the left-hand side.

M ultiplication Assignment Operator

The Multiplication Assignment Operator is used to multiply the right-hand side operand with the left-hand side operand and then assigning the result to the left-hand side operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the multiplication assignment operator which will first perform the multiplication operation and then assign the result to the variable on the left-hand side.

D ivision Assignment Operator

The Division Assignment Operator is used to divide the left-hand side operand with the right-hand side operand and then assigning the result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the division assignment operator which will first perform the division operation and then assign the result to the variable on the left-hand side.

M odulus Assignment Operator

The Modulus Assignment Operator is used to take the modulus, that is, it first divides the operands and then takes the remainder and assigns it to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the modulus assignment operator which will first perform the modulus operation and then assign the result to the variable on the left-hand side.

F loor Division Assignment Operator

The Floor Division Assignment Operator is used to divide the left operand with the right operand and then assigs the result(floor value) to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the floor division assignment operator which will first perform the floor division operation and then assign the result to the variable on the left-hand side.

Exponentiation Assignment Operator

The Exponentiation Assignment Operator is used to calculate the exponent(raise power) value using operands and then assigning the result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the exponentiation assignment operator which will first perform exponent operation and then assign the result to the variable on the left-hand side.

Bitwise AND Assignment Operator

The Bitwise AND Assignment Operator is used to perform Bitwise AND operation on both operands and then assigning the result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the bitwise AND assignment operator which will first perform Bitwise AND operation and then assign the result to the variable on the left-hand side.

Bitwise OR Assignment Operator

The Bitwise OR Assignment Operator is used to perform Bitwise OR operation on the operands and then assigning result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the bitwise OR assignment operator which will first perform bitwise OR operation and then assign the result to the variable on the left-hand side.

Bitwise XOR Assignment Operator 

The Bitwise XOR Assignment Operator is used to perform Bitwise XOR operation on the operands and then assigning result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the bitwise XOR assignment operator which will first perform bitwise XOR operation and then assign the result to the variable on the left-hand side.

Bitwise Right Shift Assignment Operator

The Bitwise Right Shift Assignment Operator is used to perform Bitwise Right Shift Operation on the operands and then assign result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the bitwise right shift assignment operator which will first perform bitwise right shift operation and then assign the result to the variable on the left-hand side.

Bitwise Left Shift Assignment Operator

The Bitwise Left Shift Assignment Operator is used to perform Bitwise Left Shift Opertator on the operands and then assign result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the bitwise left shift assignment operator which will first perform bitwise left shift operation and then assign the result to the variable on the left-hand side.

Walrus Operator

The Walrus Operator in Python is a new assignment operator which is introduced in Python version 3.8 and higher. This operator is used to assign a value to a variable within an expression.

Example: In this code, we have a Python list of integers. We have used Python Walrus assignment operator within the Python while loop . The operator will solve the expression on the right-hand side and assign the value to the left-hand side operand ‘x’ and then execute the remaining code.

Assignment Operators in Python – FAQs

What are assignment operators in python.

Assignment operators in Python are used to assign values to variables. These operators can also perform additional operations during the assignment. The basic assignment operator is = , which simply assigns the value of the right-hand operand to the left-hand operand. Other common assignment operators include += , -= , *= , /= , %= , and more, which perform an operation on the variable and then assign the result back to the variable.

What is the := Operator in Python?

The := operator, introduced in Python 3.8, is known as the “walrus operator”. It is an assignment expression, which means that it assigns values to variables as part of a larger expression. Its main benefit is that it allows you to assign values to variables within expressions, including within conditions of loops and if statements, thereby reducing the need for additional lines of code. Here’s an example: # Example of using the walrus operator in a while loop while (n := int(input("Enter a number (0 to stop): "))) != 0: print(f"You entered: {n}") This loop continues to prompt the user for input and immediately uses that input in both the condition check and the loop body.

What is the Assignment Operator in Structure?

In programming languages that use structures (like C or C++), the assignment operator = is used to copy values from one structure variable to another. Each member of the structure is copied from the source structure to the destination structure. Python, however, does not have a built-in concept of ‘structures’ as in C or C++; instead, similar functionality is achieved through classes or dictionaries.

What is the Assignment Operator in Python Dictionary?

In Python dictionaries, the assignment operator = is used to assign a new key-value pair to the dictionary or update the value of an existing key. Here’s how you might use it: my_dict = {} # Create an empty dictionary my_dict['key1'] = 'value1' # Assign a new key-value pair my_dict['key1'] = 'updated value' # Update the value of an existing key print(my_dict) # Output: {'key1': 'updated value'}

What is += and -= in Python?

The += and -= operators in Python are compound assignment operators. += adds the right-hand operand to the left-hand operand and assigns the result to the left-hand operand. Conversely, -= subtracts the right-hand operand from the left-hand operand and assigns the result to the left-hand operand. Here are examples of both: # Example of using += a = 5 a += 3 # Equivalent to a = a + 3 print(a) # Output: 8 # Example of using -= b = 10 b -= 4 # Equivalent to b = b - 4 print(b) # Output: 6 These operators make code more concise and are commonly used in loops and iterative data processing.

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Python homework and assignment help.

Our expert Python programmers are here to help you with any aspect of your Python learning, from coding assignments and debugging to concept clarification and exam prep. Whatever Python challenge you're facing, we've got you covered. Chat with us now for personalized support!

Need Immediate Online Python Assignment Help? We’ve Got You Covered!

If you require immediate Python programming assistance, FavTutor can connect you with Python experts for online help right now. Python, as an object-oriented language, is highly sought after among students. However, with multiple classes, exams, and tight assignment deadlines, it can be challenging to manage everything. If you find yourself struggling with these demands, FavTutor offers a solution with our online Python homework help service, designed to relieve your stress.

Our top-level experts are dedicated to conducting comprehensive research on your assignments and delivering effective solutions. With 24/7 Python online support available, students can confidently work towards completing their assignments and improving their grades. Don't let Python assignments overwhelm you—let FavTutor be your reliable partner in academic success.

About Python Programming Langugae

Python is a high-level, interpreted, object-oriented programming language with dynamic semantics. It's a language that's known for its friendliness and ease of use, making it a favorite among both beginners and seasoned developers.

What sets Python apart is its simplicity. You can write code that's clean and easy to understand, which comes in handy when you're working on projects with others or need to revisit your own code later on.

But Python isn't just easy to read; it's also incredibly powerful. It comes with a vast standard library that's like a toolkit filled with pre-built modules and functions for all sorts of tasks. Whether you're doing web development, data analysis, or even diving into machine learning, Python has you covered.

Speaking of web development, Python has some fantastic frameworks like Django and Flask that make building web applications a breeze. And when it comes to data science and artificial intelligence, Python shines with libraries like NumPy, pandas, and TensorFlow.

Perhaps the best thing about Python is its community. No matter where you are in your coding journey, you'll find a warm and welcoming community ready to help. There are tutorials, forums, and a wealth of resources to support you every step of the way. Plus, Python's open-source nature means it's constantly evolving and improving.

What Python Topics Are Covered by Our Experts?

Here are the topics that will be covered by our Python experts:

• Variables and Data Types: Python allows you to store and manipulate data using variables. Common data types include integers (whole numbers), floats (decimal numbers), strings (text), and boolean values (True or False).
• Conditional Statements: You can make decisions in your Python programs using conditional statements like "if," "else," and "elif." These help you execute different code blocks based on specific conditions.
• Loops: Loops allow you to repeat a set of instructions multiple times. Python offers "for" and "while" loops for different types of iterations.
• Functions: Functions are reusable blocks of code that perform specific tasks. You can define your own functions or use built-in ones from Python's standard library.
• Lists and Data Structures: Lists are collections of items that can hold different data types. Python also offers other data structures like dictionaries (key-value pairs) and tuples (immutable lists).
• File Handling: Python provides tools to work with files, including reading from and writing to them. This is essential for tasks like data manipulation and file processing.
• Exception Handling: Exceptions are errors that can occur during program execution. Python allows you to handle these exceptions gracefully, preventing your program from crashing.
• Object-Oriented Programming (OOP): Python supports OOP principles, allowing you to create and use classes and objects. This helps in organizing and structuring code for complex projects.
• Modules and Libraries: Python's extensive standard library and third-party libraries offer a wide range of pre-written code to extend Python's functionality. You can import and use these modules in your projects.
• List Comprehensions: List comprehensions are concise ways to create lists based on existing lists. They simplify operations like filtering and transforming data.
• Error Handling: Properly handling errors is crucial in programming. Python provides mechanisms to catch and manage errors, ensuring your programs run smoothly.
• Regular Expressions: Regular expressions are powerful tools for pattern matching and text manipulation. Python's "re" module allows you to work with regular expressions.
• Web Development with Flask or Django: Python is commonly used for web development, with frameworks like Flask and Django. These frameworks simplify the process of building web applications.
• Data Science with Pandas and NumPy: Python is widely used in data science. Libraries like Pandas and NumPy provide tools for data manipulation, analysis, and scientific computing.
• Machine Learning with TensorFlow or Scikit-Learn: Python is a popular choice for machine learning and artificial intelligence. Libraries like TensorFlow and Scikit-Learn offer machine learning algorithms and tools.

Advantages and Features of Python Programming

Below are some of the features and advantages of python programming:

• It's Free and Open Source: Python won't cost you a dime. You can download it from the official website without opening your wallet. That's a win-win!
• It's Easy to Learn: Python's syntax is simple and easy to understand. It's almost like writing in plain English. If you're new to coding, Python is a fantastic starting point.
• It's Super Versatile: Python can do it all. Whether you're building a website, analyzing data, or even diving into artificial intelligence, Python has your back.
• It's Fast and Flexible: Python might seem easygoing, but it's no slouch in terms of speed. Plus, Python code can run on pretty much any computer, making it super flexible.
• A Library Wonderland: Python's library collection is like a magical forest. There are libraries for just about anything you can think of: web development, data science, and more. It's like having a vast collection of pre-made tools at your disposal.
• Scientific Superpowers: Python isn't just for developers; it's a favorite among scientists too. It has specialized libraries for data analysis and data mining, making it a powerhouse for researchers.
• Code Interpreted in Real Time: Python doesn't wait around. It interprets and runs your code line by line. If it finds an issue, it stops and lets you know what went wrong, which can be a real lifesaver when debugging.
• Dynamic Typing: Python is smart. It figures out the data type of your variables as it goes along, so you don't have to declare them explicitly. It's like a built-in problem solver.
• Object-Oriented Magic: Python supports object-oriented programming. It lets you organize your code into neat, reusable objects, making complex problems more manageable.

Can You Help with my Python Homework or Assignment?

Yes, we provide 24x7 python assignment help online for students all around the globe. If you are struggling to manage your assignment commitments due to any reason, we can help you. Our Python experts are committed to delivering accurate assignments or homework help within the stipulated deadlines. The professional quality of our python assignment help can provide live assistance with your homework and assignments. They will provide plagiarism-free work at affordable rates so that students do not feel any pinch in their pocket. So, get the work delivered on time and carve the way to your dream grades. Chat now for python live help to get rid of all your python queries.

How Do We Help, Exactly?

At FavTutor, we believe that the best way to learn Python is through a combination of expert guidance and hands-on practice. That's why we offer a unique two-pronged approach to Python assignment help: 1) Detailed, Step-by-Step Solutions - Our experienced Python tutors will provide you with carefully crafted, easy-to-follow solutions to your assignments. These solutions not only give you the answers you need but also break down the thought process and logic behind each step, helping you understand the "why" behind the code.

2) Live 1:1 Tutoring Sessions - To cement your understanding, we pair our written solutions with live, one-on-one tutoring sessions. During these personalized sessions, our tutor will:

• Walk you through the solution, explaining each step in detail
• Answer any questions you have and clarify complex concepts
• Help you practice applying the concepts to new problems
• Offer tips, best practices, and insights from real-world Python experience

This powerful combination of detailed solutions and live tutoring ensures that you not only complete your Python assignments successfully but also gain a deep, practical understanding of the language that will serve you well in your future coding endeavors.

Challenges Faced By Students While Working on Python Assignments

Python is known for being beginner-friendly, but it can still be a bit challenging for newcomers. Let’s take a look at some of the hurdles you might face:

Setting Up Your Workspace Getting your development environment set up correctly can be confusing for beginners and might feel overwhelming at first.

Deciding What to Code Figuring out exactly what to code can be tough. Computers need clear instructions, and it can take time to get it right.

Dealing with Compiler Errors Running your code only to see errors can be discouraging, but it’s a normal part of the learning process.

Hunting Down Bugs Mistakes happen, especially when you’re new. Debugging is essential to learn how to fix errors and write better code.

If you’re facing these or any other challenges, don’t worry. Our team of Python experts is here to help. At FavTutor, we offer affordable, top-quality Python assignment assistance to help you overcome any hurdles in your coding journey. Reach out to us for support and conquer your Python assignments with confidence.

Frequently Asked Questions (FAQs)

A: We can assist with all types of Python assignments, including coding tasks, debugging, project development, data analysis, machine learning, and web development. Whether you're dealing with basic concepts or advanced topics, our experts are here to help.

A: We offer 24/7 support, so you can get immediate help with your Python assignments. The exact time required to complete your assignment will depend on its complexity, but we strive to provide timely assistance and meet your deadlines.

A: Yes, we offer budget-friendly pricing starting at just $35/hour. We understand the financial constraints of students and aim to provide top-quality help at affordable rates.

A: Absolutely! All our Python assignments and solutions are custom-made to meet your specific requirements and are guaranteed to be 100% plagiarism-free.

A: Yes, our experts are equipped to handle assignments with urgent deadlines. We focus on delivering high-quality solutions on time, even under tight schedules.

A: Yes, we offer live 1:1 Python tutoring sessions to help you understand the solutions provided and clarify any doubts. This ensures you gain a deeper understanding of the concepts involved in your assignment.

A: You can submit your assignment details through our student portal . Once you provide the necessary information, our experts will review it and get in touch with you to start the process.

A: We strive to ensure customer satisfaction. If you're not satisfied with the help provided, we offer revisions to meet your expectations. Your success is our priority. You can also check our refund policies for more details on how we handle such situations.

A: Yes, our Python assignment help service is available to students worldwide. No matter where you are, you can access our expert assistance anytime.

A: Once our experts upload the solutions on the FavTutor student portal, you will be able to access them through our portal, and you will also receive an email notification.

Reasons to choose FavTutor's Assignment Help Service

• Top rated experts- We pride in our programemrs who are experts in various subjects and provide excellent help to students for all their assignments, and help them secure better grades.
• Specialize in International education- We have programmers who work with students studying in the USA and Canada, and who understand the ins and outs of international education.
• Prompt delivery of assignments- With an extensive research, FavTutor aims to provide a timely delivery of your assignments. You will get adequate time to check your homework before submitting them.
• Student-friendly pricing- We follow an affordable pricing structure, so that students can easily afford it with their pocket money and get value for each penny they spend.
• Round the clock support- Our experts provide uninterrupted support to the students at any time of the day, and help them advance in their career.

3 Steps to Connect

Get help in your assignment within minutes with these three easy steps:

Click on the Signup button below & register your query or assignment.

You will be notified when we have assigned the best expert for your query.

Voila! You can start chatting with python expert and get started with your learning.

Python Tutorial

File handling, python modules, python numpy, python pandas, python matplotlib, python scipy, machine learning, python mysql, python mongodb, python reference, module reference, python how to, python examples, python assignment operators.

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Our team of skilled Python developers is committed to delivering exceptional Python homework help, ensuring you achieve the best grades in your Python coursework.

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“I was crippled with my Machine Learning Assignment and then I was recommended CodingZap by one of my friends. I was a bit skeptical in the beginning but after talking to my project manager, I had some relief. I was impressed with their communication since I did message back and forth. Finally got my product delivered in a couple of days and I submitted it to my college. I was spellbound guys when I saw my grades. When I had no hopes, these folks sailed me through. You guys are champions. “

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Just fill out the order form and upload your Python assignment details. Your dedicated programming manager will review your task details and provide a detailed analysis and price estimate.

Pay a secure first-half deposit to get the work started. We accept payment through secure payment gateways only.

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Pay the final price and receive your expertly completed Python coding assignment, along with explanations to solidify your understanding.

Why CodingZap for Python Programming Homework Help?

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CodingZap prioritize deadlines and always deliver Your Python Homewok before asked upon deadlines.

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Our tailor-made Python assignment solutions are of the best quality and fully coded from scratch by humans.

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Have you been having a hard time trying to finish Python assignments?

Are you browsing the internet with a request like, “Can someone do my Python homework?”  

Don’t stress over your Python assignments; we understand they can be quite challenging and demand expertise. But fret not, because our team of skilled professionals is here to help you out.

Gone are the days of working on your Python assignment tirelessly, hire our experts at CodingZap and forget about sleepless nights! From homework questions to complex coding projects, our programming experts provide the best programming solutions. 

So, if you find yourself stuck with any programming assignment, don’t hesitate to reach out to us for expert programming assistance . CodingZap is just an email away from you. 

What is the process of getting help with Python Homework by CodingZap?

To receive Python programming assignment help from our team, you don’t have to follow any complex procedure. Get accurate solutions to your Python assignment from our highly skilled Python experts in just 4 simple steps.

Step 1 – We analyse your requirements

Once you submit your Python assignment or project details, our dedicated programming manager will conduct a detailed analysis to calculate the necessary time and effort needed to complete the project and select the most suitable expert from our pool of professionals.

Step 2 – Code development starts here

Once the budget is finalized and approved by the client, we assign a dedicated Python expert to your project. After the requirements are set, we begin developing the project code. We utilize the latest trends and tools to ensure that your coding solution is of the highest quality and meets current standards.

Step 3 – Unit testing and feedback

Our experts conduct unit testing and the necessary functional testing to ensure that all requirements are met meticulously. Once this entire process is complete, we send a screenshot of the output to the client for confirmation that it meets their expectations. Should any changes be required, we are always ready to assist. 

Step 4 –  Project delivery & revisions

Once you approve the output, we send the final coding solution immediately. While we create the perfect solution for your assignment, you can relax.

After delivery, we will send an alert shortly. Don’t wait anymore. Get Python help today.

In short, at Codingzap, we guarantee three things for you:

• High-quality assignments
• Better outcome

The quicker you share your requirements, the sooner we can begin. We promise to complete your programming work in no time. So, don’t think twice and take advantage of programming assistance from CodingZap!

Let’s begin by understanding what is Python.

What is Python Programming & Python Homework Help?

To begin with, Python is an extremely interactive high-level programming language. It is an object-oriented programming language that is used across various domains like web development, machine learning, and data science.

It is generally used by programmers to develop the backend of web applications and perform data analysis operations for your data science and machine learning project . From web scraping to modelling Artificial Intelligence systems, Python is one step ahead of other programming languages and it fulfils all your needs.

The syntax of Python is easy and communicative. This helps programmers to write shortcodes. Above all, Python is 100% portable and can run on operating systems like:

Not to mention, Python is also used as a Scripting language. It can also be used to read code on whitespaces. The best thing about Python is its vast libraries. These libraries let programmers run codes on different operating systems.

Let’s learn more about Python: Basics and Key Features

The Python programming language has several features. Some of the key features include:

• Object-oriented

Python, in the first place, is an object-oriented language. To design this language, data and functionality are used. There are four pillars of OOPs that we often encounter in computer science assignments: Abstraction, Encapsulation, Inheritance, and Polymorphism. 

• Easy to learn

Python is very easy to learn. This is because the syntax of this language is very simple. Additionally, the code of Python is also very easy to understand.

To understand the Python language, you can run the programs from the source code. To run the programs, you do not need to assemble and implement them.

• High-level programming language

It is important to realize that Python is a high-level language. Hence, you can write a program in Python freely. You do not have to worry about memory.

• Vast Library

Python has a huge library. This extensive library helps students to complete tasks like:

• Unit testing
• Writing expressions
• Web browsers

In most of our Python assignment help services requests, we work with libraries like NumPy, Pandas, Tkinter, Turtle, PyGame, and more.

We also solve questions that require web development frameworks like Flash, Django, TensorFlow, etc. Apart from that error handling and data manipulation-related Python problems may encounter while doing Python homework.

With our Python homework help, any student can excel in their academics. So, hire Python experts and get your Python assignment done.

Now let’s move to the Basics of Python Programming. Here are the top 5 reasons you must learn Python programming to boost your programming skills.

Let’s start with the basics.

To understand the in-depth concepts of Python, you need something. To understand Python in detail, you need to have strong basics.

Here are some of the basics of the Python programming language:

Presently, there are two different loops available. One is “while loop” while the next is “for loop”. Want to know more about loops in Python ? We use the while loop when it is not certain how many times we have to run the same instruction. Here’s an example.

print(“While loop example”)

while(i<5):

    print(i+1)

Here, we are printing numbers from 1 to 5 using the while loop. Here’s the output so you can understand better.

Let’s do the same with the for loop. Check the code snippet given below:

print(“For loop example”)

for i in range(0,5):

In the output given below, we can see that the loop was executed 5 times. Hence, the output shows numbers 1 to 5. 

• Control Statements

Python programming language has two types of statements. One is ‘if-else’ while the other is ‘switched’.

These are known as the control statements as they control the execution and flow of the program. 

Let’s see a simple example of the if-else statement to get an idea about control statements in Python.

print(“If-Else example”)

age = int(input(“Enter your age:”))

if (age <18):

    print(“You cannot vote as you are not eligible”)

    print(“You are eligible to vote”)

Here, we have a simple Python coding example that shows the working of control statements. If the age entered by the user is less than 18, the user is not eligible to vote. Check out its output below.

Usually, Python has two types of functions. One is static while the other is lambda. The static function helps to signal the object. The lambda, on the other hand, helps to describe a method.

Functions are the user-defined set of instructions that can be used repeatedly in a program. They provide code reusability and, hence, save memory. A simple addition() function is given in the example below.

In Python, we use the def keyword to define a function. Check the program given below.

def addition(num1, num2):

    return num1+num2

print(“Function example”)

num1 = int(input(“Enter first number:”))

num2 = int(input(“Enter second number:”))

print(“The sum of the numbers is: “, addition(num1, num2))

So, we are taking two integer values as input from the user and adding them using the function addition() that is called in the print statement. Look at the output to understand it in a better way.

This was just a brief overview of the Python Programming language. When we dive in-depth into the programming language, we may encounter many issues while solving Python assignments. 

Why do Students need Python Programming Help? Get Python expert help now.

Have you wondered ever what problems students face in programming assignments and why they look for assignment help online ?

Read on to know.

Being a computer science student is a tough job. You are constantly juggling between multiple classes, learning new programming languages, and completing your assignment work. Often, this affects your health and leads to stress as well.

Let’s see some more reasons why students look for Python assignment help online. Keep reading!

• Poor Coding skills

Usually, students face many problems while completing their assignments. One of the most common problems presently is writing the code in Python.

Often students face problems in identifying old source code characters. For this reason, they seek our Python coding help assistance.

• Content is plagiarised

In reality, Python takes a lot of time for students to understand. In the meantime, they write the wrong codes. Further, some students copy and paste content from other sites. Consequently, students get poor grades due to copied content.

• Practise is missing

In order to be perfect in Python, practice is important. When you practice regularly, you learn even the most difficult code.

Honestly speaking, students deal with many things and often fail to practice. Thus, they submit poor assignments. Under those circumstances, they seek professional Python homework help services.

• Difficult to understand

Often students lack the knowledge to understand Python codes. As a result, they fail to implement any program in this language.

That is why they seek Python homework assignment help services. It is free of bugs. So, what are you waiting for? We cover your ‘ do my coding homework ‘ requests as well as ‘do my Python homework’ requests to provide genuine help with Python homework.

Want to get help in “ deleting a variable in Python ‘?

What are the Topics Covered in Online Python Assignment Help?

Students often search online: ‘Can someone do my Python assignment’? They also inquiry about multiple Python topics to get help with such as:

• Python project ideas
• Syntax of Python
• Exception handling
• Collections
• Python fundamentals (Variables, Operators , Identifiers and keywords, data types, etc.)
• Data Science using Python
• Python capabilities
• Python usage and project structure
• Uses of Python libraries
• Data Structures
• Flow control
• Functions (Definitions, Arguments, Modules, Packages, Recursion, Anonymous Functions)
• Object-oriented programming (Inheritance, Class, Operator overloading, etc.)

Just connect with our team of experts. They will get your job done instantly. We make sure to deliver high-quality Python assignment codes and solutions so that you ace your classes! 

Top 5 reasons, CodingZap is the optimal choice to get help with Python homework

Here are the 5 reasons to choose Codingzap for Python Homework Help

• Confidential and Low-priced

The first thing to remember about Codingzap is its privacy policy. We maintain strict privacy standards. Furthermore, we keep personal information safe.

For this purpose, we use secure network options. In addition, our price quotes are reasonable and depend on your project.

• Fast Delivery and Accuracy

Meeting the deadline is our primary objective. We stick to the due dates particularly. For this reason, students get time to check the assignment for them.

In case, the student has any problem, we take care of it. We make the change without any hesitation.

• Customized Projects

At Codingzap, we prepare all the assignments from the very beginning. Also, we follow the guidelines given by students very sincerely.

Finally, we create an assignment that meets all your requirements. In short, we make sure you get the best with zero plagiarism.

• Highly Flexible

Our highly customized Python homework help services are very flexible. Furthermore, our assignments can adapt to your requirements easily.

• 24×7 Customer Support

We understand the importance of having a support team for any time queries. For this reason, we have a customer support team.

Moreover, this team is available 24x7x365 days a year. Our experts not only provide the best solutions but also treat students equally.

To summarize, CodingZap can fix all your Python assignment problems easily.

That’s all.

Our other Programming and coding help services:

• Affordable Java HW Help Services
• C++ Programming Homework Help
• Computer Science Homework Help
• C Programming Homework Help 
• PHP Programming Assignment Help

At Codingzap, we know how difficult it is to score. In case you miss your submission date, getting good grades is impossible. However, with Codingzap, we make sure that never happens.

Get rid of last-minute submission stress instantly. Just hire our Python homework help services, today.

/* Write a Program where you input a list of employee and their salaries and store them in parallel arrays. The Salaries are floating point numbers. Find the average of all the salaries of the employees. */

Coding Example of a Sample Python Assignment :

How do we write your Python Code? Example Explained

At CodingZap, we follow a well-structured approach to writing your Python code. Here’s an overview of our process:

Analysis and Design:

Our team thoroughly analyzes your assignment details and the instructions provided. We develop a plan and design for the Python code based on the analysis of your homework rubric. This involves planning the overall structure, and systematic approach, and choosing appropriate data structures and libraries.

Well-Commented Code

We practice a standard coding methodology that fits your requirements. Our Python experts are well-trained to write a properly commented coding solution. It helps you understand the code properly and help you get a better grade in your Python course.

Sticking to the Assignment Rubric

Every day CodingZap caters its services to a large number of students across the globe. We totally understand our student’s concern about sticking to the exact assignment details. So, our developers are well notified to not use any libraries or anything that is not being taught in your class. We strictly abide by your assignment details so that you don’t get into any trouble.

Unit Testing of Coding Solution

We thoroughly test the code before we deliver it to you. Our coding solutions are passed through multiple unit and functional testing before t hey get into your hand.

Lets get started 🙂

Here are some best practices we follow while writing your Python Programming Assignment:

• Consistent Indentation

 Python doesn’t use braces for representing blocks of code. Instead, it uses indentation for the same. Improper indentation in Python leads to indentation error that results in traceback calls.

Thus, we make sure to maintain proper and consistent indentation in your codes and follow four spaces for each indentation level.

• Meaningful Naming Conventions

While it may be tempting to write short names for variables and functions, they often confuse when you visit them again or for other programmers review the code. Therefore, we use descriptive names and follow proper naming conventions to enhance the readability of your code.

• Using Blank lines

Making the use of one or two blank lines between each block of code also enhances the readability of your Python code. It makes the code more organized.

• Testing The Code

Testing the code is essential. When we submit your Python assignment code, we make sure that it is properly implemented and tested so that it is free of any errors and exceptions that may cause it to terminate.

Still Confused? Check Our Sample Python Solution

Download our sample Python project solution crafted by our programming experts. We strictly follow your instructions and coding standards to meet your academic goals.

Python Assignment Sample

Check Stock Analyser Project developed using Python Programming Language..

FAQs(Frequently Asked Questions by You)

CodingZap is a registered company Under Private Limited companies and it has a registered office in USA. We are helping students since 2015 and this journey still continues.

Apart from that, we believe in providing genuine help to students at an affordable cost. If you are not satisfied with our services and your assignment is not being delivered on time you can always ask us for a refund.

Python Homework Help services are offered by CodingZap to all students at pocket-friendly prices. We totally understand the student’s concern on budget and we never ever compromise the quality and academic integrity of our services despite being affordable.

The cost of Python Homework Help from CodingZap depends on the 2 main factors, Complexity and Urgency of your Python assignment. We offer competitive and economical pricing options for students. You can reach out to us for a price quote and discuss the details of your assignment.

CodingZap offers a free and multiple time revisios to ensure your maximum satisfaction. If you require any modifications or clarifications in the delivered Python homework solution, you can simply reach out to our experts and we would be happy to assist you.

Once you avail of our Python Homework Help service, you can use multiple communication channels like Whatsapp, Email, and Telegram like an app to talk with the assigned expert. We can have a seamless communication experience through which you can ask questions, provide additional instructions, and receive updates on the progress of your assignment.

CodingZap understands the importance of meeting deadlines. We have never failed to deliver Python homework within the agreed-upon deadlines. However, we recommend you to provide us with sufficient time to ensure thorough understanding of your assignment.

Well, CodingZap has a team of skilled Python developers who are highly proficient and experienced in Python programming language. They have successfully completed numerous Python assignments and have a proven track record of delivering quality coding solutions.

Absolutely! CodingZap’s Python Homework Help covers a broad range of topics and levels, including beginners, intermediate, and advanced levels. Our experts have the knowledge and expertise to tackle easy as well as complex projects in Python.

Hire the best Python experts to boost your grades now.

Backed by top-rated Industry experts, you can hire the best Python programmers at affordable rates. 

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CodingZap is founded back in 2015 with a mindset to provide genuine programming help to students across the globe. We cater to a broad range of programming homework help services to students and techies who are struggling with their code.

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Assignment in Python

00:00 Since Python’s argument passing mechanism relies so much on how Python deals with assignment, the next couple of lessons will go into a bit more depth about how assignment works in Python.

00:12 Recall some things I’ve already mentioned: Assignment is the process of binding a name to an object. Parameter names are also bound to objects on function entry in Python. And again, this is how Python’s argument passing mechanism gets its name.

00:29 But how exactly does that work?

00:33 Let’s take a closer look.

00:36 In other languages like C++, assignment is quite simple, especially for basic types. In the first statement, a space of memory which has been designated for the variable x has the value 5 stored in it.

00:51 Then, in that second statement, that same piece of memory is overwritten with the value of 10 , and the value 5 is lost completely.

01:01 Python works much differently. In the first statement, an object representing the number 5 is created, if one doesn’t already exist, and then x is made to refer— or we sometimes say “point”—to that object, basically by storing the memory address to where the object 5 is stored.

01:21 In the second statement, x is reassigned to refer to an object representing the value 10 . Again, if one didn’t already exist, one is created.

01:31 In other words, x is rebound to a new object. The object representing 5 may still exist because there could be other variables or names referring to that object.

01:42 Let’s look at this process in more detail. There’s a particular structure used to implement objects in Python. A reference counter keeps track of how many references a specific object has. So again, in the statement x = 5 , an object representing the value 5 is found or created, the reference counter for that object is incremented, and an entry is made binding that variable name to the object.

02:14 This is basically done in a dictionary, and depending on the namespace of the variable, it can be found using either the locals() or globals() function.

02:26 Then, when x is reassigned to the value 10 , the reference count for the object representing 5 is reduced by one, the reference counter for the object representing 10 is increased by one, and finally, the appropriate dictionary is updated to indicate that x is now bound to that new object 10 . To see this happen, you can write a small program which uses the sys.getrefcount() function.

02:54 This function takes an object as an argument and returns the number of references to it. Here is an example. It will track the number of references to two objects, cleverly named "value_1" and "value_2" . First, it displays the number of references to each object before either are assigned to a variable.

03:20 Then it assigns x to be bound to the "value_1" . It repeats calls to getrefcount() to see how the counts have been changed as a result of that assignment statement.

03:33 Then it reassigns x from "value_1" to "value_2" and displays the ref counts again. Let’s try this out.

03:48 Well, that’s interesting! Before either object is bound to x , they each already have three references. Why is that? Well, one is an internal reference to the object when it was created.

04:01 Python has to know where it is so it can be bound to something else as the program runs. Second, it’s being used as an argument to this function call, so there’s a reference. And it’s used in the parameter variable inside getrefcount() .

04:17 So you likely always get a count of at least 3 for any object you try. The point to notice is that once x was assigned to be "value_1" , the ref count for "value_1" was incremented.

04:32 Then when x was reassigned to "value_2" , the ref count for "value_1" went back down to 3 and the ref count for "value_2" increased to 4 .

04:44 So, you can see the ref counts are going up and down as objects are bound and unbound to the variable x .

04:54 Next, you’ll see how this notion of assignments and bindings works with function arguments.

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How to Approach Solving Programming Assignments in Python

Programming assignments, especially those involving mathematical computations and data manipulations, often appear daunting at first glance. The complexity of these tasks can lead to uncertainty and hesitation, particularly when students encounter new concepts or methods. However, with a structured approach and systematic breakdown of the problem, tackling such Python assignments becomes not only manageable but also rewarding. This blog aims to provide you with a clear framework for approaching programming assignments in Python, focusing on a practical example. Specifically, we will explore the process of reading a list of numbers, performing calculations to find their sum, mean, and standard deviation, and finally presenting these results effectively. This guide will offer the necessary steps and insights to successfully complete your task.

In the realm of programming, breaking down tasks into smaller, more manageable components is a fundamental strategy for success. Each component serves as a building block that contributes to the overall solution. By understanding the problem statement and identifying the key operations required—such as input handling, computation, and output formatting—you can systematically address the assignment's requirements. This approach not only simplifies the task but also enhances your understanding of how different programming concepts integrate to solve real-world problems.

The example assignment provided involves fundamental statistical computations: sum, mean, and standard deviation. These operations are foundational in both programming and data analysis contexts. Learning to execute these calculations programmatically not only sharpens your coding skills but also equips you with valuable tools for future assignments and projects. Python, with its clear syntax and extensive libraries like math for mathematical operations, proves to be an excellent choice for such tasks, enabling concise and efficient implementation of complex algorithms.

Throughout this guide, we will delve into each step of the solution, emphasizing clarity and correctness in coding practices. Understanding how to handle user input, compute mathematical formulas, and present results accurately are essential skills for any programmer. By mastering these techniques, you not only enhance your proficiency in Python programming but also build a strong foundation for tackling a wide range of programming challenges.

By the end of this blog, you will have gained practical insights into solving programming assignments, laying a solid groundwork for future learning and application. Whether you're a beginner seeking to understand the basics or an experienced programmer aiming to refine your skills, this guide aims to empower you with the tools and strategies needed to excel in solving similar programming tasks effectively. Let's embark on this journey to demystify programming assignments and empower your problem-solving capabilities in Python.

Understanding the Problem Statement

The first step in tackling any programming assignment is to thoroughly understand the problem statement. Read the instructions carefully and identify the key tasks required. For instance, in the given example, the assignment requires you to:

• Read a list of numbers from the user.
• Compute the sum of these numbers.
• Calculate the mean (average) of the numbers.
• Determine the standard deviation of the numbers.
• Display the computed sum, mean, and standard deviation.

Understanding these requirements helps in planning the solution effectively.

Breaking Down the Task

Once you understand the problem, the next step is to break it down into smaller, manageable tasks. This modular approach not only simplifies the problem but also makes your code more organized and easier to debug. Here, we can divide the assignment into the following functions:

• read_data(): This function will handle user input and store the numbers in a list.
• compute_sum(list_of_numbers): This function will compute the sum of the numbers in the list.
• compute_mean(list_of_numbers): This function will calculate the mean of the numbers.
• compute_sd(list_of_numbers): This function will compute the standard deviation of the numbers.
• display_result(sum, mean, sd): This function will print the sum, mean, and standard deviation.

Writing the Functions

Let’s dive into writing each function step by step.

Reading Data

The read_data() function is responsible for reading numbers from the user until an empty string is entered. These numbers are then stored in a list.

In this function, we use a while loop to continuously prompt the user for input. If the user enters an empty string, the loop breaks, indicating the end of input. We also include error handling to ensure that only valid integers are added to the list.

Computing the Sum

Next, the compute_sum(list_of_numbers) function calculates the sum of the numbers in the list.

Python's built-in sum() function makes this task straightforward and efficient.

Computing the Mean

The compute_mean(list_of_numbers) function calculates the mean by dividing the sum of the numbers by the length of the list.

Here, we reuse the compute_sum() function to get the sum of the numbers, ensuring that our code is modular and reusable.

Computing the Standard Deviation

The compute_sd(list_of_numbers) function calculates the standard deviation, which is a measure of the amount of variation or dispersion in a set of values.

This function calculates the variance by summing the squared differences between each number and the mean, then dividing by the number of observations minus one. The standard deviation is the square root of the variance.

Displaying the Results

Finally, the display_result(sum, mean, sd) function prints the computed sum, mean, and standard deviation in a formatted manner.

Using formatted strings (f-strings) ensures that the results are displayed with two decimal places, making them easy to read.

Integrating the Functions

With all the functions defined, the final step is to integrate them into a main program. The main() function coordinates the execution of all the individual functions.

In this main function, we call read_data() to get the list of numbers, then sequentially compute the sum, mean, and standard deviation, and finally display the results.

Debugging and Testing

A crucial part of solving programming assignments is debugging and testing your code. Here are some tips to help you debug and test effectively:

• Test Incrementally: Test each function individually before integrating them. This helps isolate any errors and makes debugging easier.
• Use Print Statements: Add print statements to check the values of variables at different stages of your program. This can help you understand the flow of data and identify where things might be going wrong.
• Edge Cases: Consider edge cases, such as an empty list, a list with a single number, or very large numbers. Ensure your program handles these cases gracefully.
• Code Review: If possible, have someone else review your code. A fresh set of eyes can often spot mistakes that you might have overlooked.

Error Handling and User Input Validation

Robust programs include error handling to manage unexpected inputs or situations. In our read_data() function, we included a try-except block to handle invalid inputs. Here’s a more detailed example:

This approach ensures that the user is prompted to enter valid numbers, enhancing the robustness of the program.

Documentation and Code Comments

Good documentation and code comments are essential for making your code understandable to others and to your future self. Use docstrings to describe the purpose and parameters of each function. Inline comments can explain complex or non-obvious parts of the code.

For example:

Enhancing and Extending the Program

Once you have a working solution, consider ways to enhance or extend the program. For example, you might add functionality to:

• Save Results to a File: Allow users to save the results to a text file for future reference.
• Plot Data: Use libraries like Matplotlib to create visualizations of the data and computed statistics.
• Handle More Statistical Measures: Extend the program to compute other statistical measures, such as the median or mode.
• Graphical User Interface (GUI): Create a GUI using Tkinter or another framework to make the program more user-friendly.

Practice and Continuous Learning

Programming is a skill that improves with practice. Continuously challenge yourself with new assignments and projects to build your problem-solving abilities and coding skills. Explore online resources, coding communities, and tutorials to learn new techniques and best practices.

In conclusion, successfully navigating Python programming assignments requires a methodical approach to problem-solving. Breaking down tasks into manageable components, writing clear and modular code, and diligently testing and debugging are essential steps in crafting robust solutions. By adhering to these principles, you can confidently tackle assignments that involve tasks such as data input handling, mathematical computations like sum and mean calculations, and the calculation of standard deviation. It's crucial to prioritize thorough documentation of your code to ensure clarity for both you and others who may review or use your work. Additionally, embracing a mind-set of continuous learning and improvement is key to advancing your skills as a programmer. Regular practice and exposure to diverse programming challenges will sharpen your abilities and pave the way towards becoming a proficient programmer.

In summary, approaching Python programming assignments effectively hinges on a structured methodology and attention to detail. By following the steps outlined in this blog, you will be well-equipped to tackle a variety of programming tasks with confidence and precision. Remember, each assignment is an opportunity to refine your coding skills and deepen your understanding of Python's capabilities. Embrace challenges as learning opportunities, and always strive for clarity, correctness, and efficiency in your code. With persistence and dedication, you'll build a strong foundation for success in your programming journey.

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8 best Python homework help services | Top Python assignment help websites

Python is a high-level programming language that’s popular among data scientists and software engineers. It has become a preferred study area among students in the U.S. and other developed countries. Still, mastering Python programming is no easy feat. Fortunately, there are many online Python homework help websites that offer reliable homework help to Python students from around the world. The websites work with experienced and knowledgeable Python experts who help students struggling with assignments.

Unfortunately, with so many options on the web, identifying the best Python homework help websites to handle your Python assignments can be consuming and time-consuming. That’s why we’ve compiled this guide to safeguard your hard-earned money from scam websites and ensure you get top-notch assignment help. Read on to learn the top-ranked Python homework helpers you can trust.

List of 8 top Python assignment help websites

We have scoured the internet to identify the top Python assignment help services with the best reviews from programming students and high-quality solutions for Python problems. Take a look at the 8 best sites and their unique advantages:

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• CWAssignments.com — Best for personalized help with ‘do my Python homework” requests
• AssignCode.com — Best customer support with “do my Python assignment” 24/7
• Reddit.com — Ideal place to find Python expert fast
• Quora.com — Best for free help with your Python homework

Detailed overview of the best Python programming homework help services

The top Python programming websites offer the best value for money and are chosen based on student reviews, quality of service, affordability, and online reputation. Let’s review the features that make the sites on the list the best Python programming assignment help companies on the web.

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DoMyAssignments is a popular academic writing company that offers a variety of academic support services. One of their most popular services is online help with Python homework assignments for computer science students and professionals. The website has hundreds of vetted Python programming experts who can handle all “do my Python assignment” requests.

Outstanding features

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Student reviews

Students often praise the site’s professionalism. For example, Edwin K. says,” I was stressed about my Python project, but my worry went away when I heard about DoMyAssignments from a friend. The customer agent I talked to was helpful and matched my task with a real expert. I was impressed by the final deliverable. I have never felt such immense relief.”

DoMyAssignments has reliable, skilled, and speedy experts. Their unparalleled knowledge gives the most value for money on Python programming tasks.

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As the name suggests, CodingHomeworkHelp specializes in helping programming students with homework issues, no matter how big or small they are. The site works with top-rated coding gurus that support Python students with programming tasks, irrespective of the deadline. The site is recognized for its punctual and top-quality programming assistance.

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When you need comprehensive and well-crafted Python homework assistance, CodingHomeworkHelp can meet your exact needs and deliver the project before the deadline.

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There are no limits to what DoMyCoding can help with. Its unmatched variety of programming services and affordable price make it worth it.

4.    ProgrammingDoer.com — Best site for complex help with Python assignment orders

If you’re exploring the complex world of Python programming as a college student or self-taught learner, ProgrammingDoer is a reliable source of help with Python homework. They have experienced Python experts who have worked with thousands of students, so they understand the kind of support that computer science students need.

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• The website is easy to navigate, so you can order a Python project easily and quickly.
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Students endorse its exceptional value proposition. Mark1999 says, “Since I discovered ProgrammingDoer, Python is no longer challenging. Their experts have simplified the learning process. Also, their services are reasonably priced.”

When you order Python assignment help from ProgrammingDoer, you get more than a solution; you receive a top-notch project. They promise to deliver the task on time with attention to detail and accuracy.

5.    CWAssignments.com — Best for personalized help with ‘do my Python homework” requests

Are you looking to succeed in a Python programming course without anxiety or spending too much money? CWAssignments specializes in helping STEM students with all types of assignments. The site is most famous for excelling in Python programming tasks for college and university levels. With CWAssignments, you get the best balance between cost and quality.

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Students love the customized projects by CWAssignments. Benedict O. wrote, “I used CWAssignments for my last three assignments and it has transformed my academic performance. I feel extremely reassured while submitting my Python homework.

CWAssignments is renowned as one of the leading websites for Python homework help in the computer science world. It’s known for providing carefully crafted assignments and is a top pick for scholars in need of academic support.

6.    AssignCode.com — Best customer support with “do my Python assignment” 24/7

AssignCode has solidified its position among the best Python homework help services by exceeding user expectations. They understand the importance of deadlines and guarantee prompt delivery without sacrificing quality. Whether your ‘do my Python homework” request is for an intricate algorithm or simple grammar, AssignCode is your go-to source for trustworthy expert assistance.

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Most student reviews of the site showcase the exceptional work of AssignCode’s support team. For instance, Remy F. says, “The customer service is on another level. They responded to my chat message immediately and addressed all my concerns.”

AssignCode stands out in the field of Python homework help by offering customized solutions and excellent customer support that allay any fear a new user may have. They can help you improve your academic performance.

7.    Reddit.com — Ideal place to find Python expert fast

If you’re looking for Python assignment help, you can get it quickly on Reddit . The social website hosts many Python experts who give advice and answers to difficult Programming tasks. You can also use the site to search for reviews of the best Python homework help services in your country.

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The hallmarks of Reddit seem to be accessibility and effectiveness. For example, Zane J. says, “I procrastinated in my assignment until 1 day before the deadline, but an expert from Reddit came to my rescue. I would like to give a shootout to Leonard for completing it for me on time!”

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8.    Quora.com — Best for free help with your Python homework

Quora allows students to post questions to get answers from experts on the site. You can also get assistance by reading answers to related questions posted by other students. Thus, it’s a reliable platform for getting helpful information to help you complete a problematic assignment quickly and easily.

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Most students who ask for help with your Python assignment on Quora mention high-quality and quick solutions. Kabana R. says, “I encountered many challenges with my Python code, but a Quora user quickly resolved it.”

Quora combines innovation and advanced methods to provide free Python assignment help to students at all levels of learning. It’s a valuable asset for solving STEM-related problems, especially Python.

Can I pay someone to do my Python assignment?

Yes, you can pay a professional coding homework helper to handle your Python assignment. All you need is to identify a reliable site with skilled experts. All the sites reviewed in this article are vetted as the best in the industry, so you can pay someone to do my Python homework on these platforms with total peace of mind.

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How can I receive assistance with Python projects?

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Where can I get help with Python programming?

You can get instant Python programming homework help from DoMyAssignmnet.com, a leading company in the academic help industry. It offers help from top Python experts to help you build projects, review codes, or debug. They have experts who can cover Python tasks for all levels of learning, no matter how difficult it may seem.

Other trustworthy Python programming homework help options include CodingHomeworkHelp, DoMyCoding, CWAssignments, and AssignCode. All these sites have excellent customer reviews from customers, flexible prices, and high-quality assistance. Their experts can provide the support you’re looking for.

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Python Enhancement Proposals

• Python »
• PEP Index »

PEP 572 – Assignment Expressions

The importance of real code, exceptional cases, scope of the target, relative precedence of :=, change to evaluation order, differences between assignment expressions and assignment statements, specification changes during implementation, _pydecimal.py, datetime.py, sysconfig.py, simplifying list comprehensions, capturing condition values, changing the scope rules for comprehensions, alternative spellings, special-casing conditional statements, special-casing comprehensions, lowering operator precedence, allowing commas to the right, always requiring parentheses, why not just turn existing assignment into an expression, with assignment expressions, why bother with assignment statements, why not use a sublocal scope and prevent namespace pollution, style guide recommendations, acknowledgements, a numeric example, appendix b: rough code translations for comprehensions, appendix c: no changes to scope semantics.

This is a proposal for creating a way to assign to variables within an expression using the notation NAME := expr .

As part of this change, there is also an update to dictionary comprehension evaluation order to ensure key expressions are executed before value expressions (allowing the key to be bound to a name and then re-used as part of calculating the corresponding value).

During discussion of this PEP, the operator became informally known as “the walrus operator”. The construct’s formal name is “Assignment Expressions” (as per the PEP title), but they may also be referred to as “Named Expressions” (e.g. the CPython reference implementation uses that name internally).

Naming the result of an expression is an important part of programming, allowing a descriptive name to be used in place of a longer expression, and permitting reuse. Currently, this feature is available only in statement form, making it unavailable in list comprehensions and other expression contexts.

Additionally, naming sub-parts of a large expression can assist an interactive debugger, providing useful display hooks and partial results. Without a way to capture sub-expressions inline, this would require refactoring of the original code; with assignment expressions, this merely requires the insertion of a few name := markers. Removing the need to refactor reduces the likelihood that the code be inadvertently changed as part of debugging (a common cause of Heisenbugs), and is easier to dictate to another programmer.

During the development of this PEP many people (supporters and critics both) have had a tendency to focus on toy examples on the one hand, and on overly complex examples on the other.

The danger of toy examples is twofold: they are often too abstract to make anyone go “ooh, that’s compelling”, and they are easily refuted with “I would never write it that way anyway”.

The danger of overly complex examples is that they provide a convenient strawman for critics of the proposal to shoot down (“that’s obfuscated”).

Yet there is some use for both extremely simple and extremely complex examples: they are helpful to clarify the intended semantics. Therefore, there will be some of each below.

However, in order to be compelling , examples should be rooted in real code, i.e. code that was written without any thought of this PEP, as part of a useful application, however large or small. Tim Peters has been extremely helpful by going over his own personal code repository and picking examples of code he had written that (in his view) would have been clearer if rewritten with (sparing) use of assignment expressions. His conclusion: the current proposal would have allowed a modest but clear improvement in quite a few bits of code.

Another use of real code is to observe indirectly how much value programmers place on compactness. Guido van Rossum searched through a Dropbox code base and discovered some evidence that programmers value writing fewer lines over shorter lines.

Case in point: Guido found several examples where a programmer repeated a subexpression, slowing down the program, in order to save one line of code, e.g. instead of writing:

they would write:

Another example illustrates that programmers sometimes do more work to save an extra level of indentation:

This code tries to match pattern2 even if pattern1 has a match (in which case the match on pattern2 is never used). The more efficient rewrite would have been:

Syntax and semantics

In most contexts where arbitrary Python expressions can be used, a named expression can appear. This is of the form NAME := expr where expr is any valid Python expression other than an unparenthesized tuple, and NAME is an identifier.

The value of such a named expression is the same as the incorporated expression, with the additional side-effect that the target is assigned that value:

There are a few places where assignment expressions are not allowed, in order to avoid ambiguities or user confusion:

This rule is included to simplify the choice for the user between an assignment statement and an assignment expression – there is no syntactic position where both are valid.

Again, this rule is included to avoid two visually similar ways of saying the same thing.

This rule is included to disallow excessively confusing code, and because parsing keyword arguments is complex enough already.

This rule is included to discourage side effects in a position whose exact semantics are already confusing to many users (cf. the common style recommendation against mutable default values), and also to echo the similar prohibition in calls (the previous bullet).

The reasoning here is similar to the two previous cases; this ungrouped assortment of symbols and operators composed of : and = is hard to read correctly.

This allows lambda to always bind less tightly than := ; having a name binding at the top level inside a lambda function is unlikely to be of value, as there is no way to make use of it. In cases where the name will be used more than once, the expression is likely to need parenthesizing anyway, so this prohibition will rarely affect code.

This shows that what looks like an assignment operator in an f-string is not always an assignment operator. The f-string parser uses : to indicate formatting options. To preserve backwards compatibility, assignment operator usage inside of f-strings must be parenthesized. As noted above, this usage of the assignment operator is not recommended.

An assignment expression does not introduce a new scope. In most cases the scope in which the target will be bound is self-explanatory: it is the current scope. If this scope contains a nonlocal or global declaration for the target, the assignment expression honors that. A lambda (being an explicit, if anonymous, function definition) counts as a scope for this purpose.

There is one special case: an assignment expression occurring in a list, set or dict comprehension or in a generator expression (below collectively referred to as “comprehensions”) binds the target in the containing scope, honoring a nonlocal or global declaration for the target in that scope, if one exists. For the purpose of this rule the containing scope of a nested comprehension is the scope that contains the outermost comprehension. A lambda counts as a containing scope.

The motivation for this special case is twofold. First, it allows us to conveniently capture a “witness” for an any() expression, or a counterexample for all() , for example:

Second, it allows a compact way of updating mutable state from a comprehension, for example:

However, an assignment expression target name cannot be the same as a for -target name appearing in any comprehension containing the assignment expression. The latter names are local to the comprehension in which they appear, so it would be contradictory for a contained use of the same name to refer to the scope containing the outermost comprehension instead.

For example, [i := i+1 for i in range(5)] is invalid: the for i part establishes that i is local to the comprehension, but the i := part insists that i is not local to the comprehension. The same reason makes these examples invalid too:

While it’s technically possible to assign consistent semantics to these cases, it’s difficult to determine whether those semantics actually make sense in the absence of real use cases. Accordingly, the reference implementation [1] will ensure that such cases raise SyntaxError , rather than executing with implementation defined behaviour.

This restriction applies even if the assignment expression is never executed:

For the comprehension body (the part before the first “for” keyword) and the filter expression (the part after “if” and before any nested “for”), this restriction applies solely to target names that are also used as iteration variables in the comprehension. Lambda expressions appearing in these positions introduce a new explicit function scope, and hence may use assignment expressions with no additional restrictions.

Due to design constraints in the reference implementation (the symbol table analyser cannot easily detect when names are re-used between the leftmost comprehension iterable expression and the rest of the comprehension), named expressions are disallowed entirely as part of comprehension iterable expressions (the part after each “in”, and before any subsequent “if” or “for” keyword):

A further exception applies when an assignment expression occurs in a comprehension whose containing scope is a class scope. If the rules above were to result in the target being assigned in that class’s scope, the assignment expression is expressly invalid. This case also raises SyntaxError :

(The reason for the latter exception is the implicit function scope created for comprehensions – there is currently no runtime mechanism for a function to refer to a variable in the containing class scope, and we do not want to add such a mechanism. If this issue ever gets resolved this special case may be removed from the specification of assignment expressions. Note that the problem already exists for using a variable defined in the class scope from a comprehension.)

See Appendix B for some examples of how the rules for targets in comprehensions translate to equivalent code.

The := operator groups more tightly than a comma in all syntactic positions where it is legal, but less tightly than all other operators, including or , and , not , and conditional expressions ( A if C else B ). As follows from section “Exceptional cases” above, it is never allowed at the same level as = . In case a different grouping is desired, parentheses should be used.

The := operator may be used directly in a positional function call argument; however it is invalid directly in a keyword argument.

Some examples to clarify what’s technically valid or invalid:

Most of the “valid” examples above are not recommended, since human readers of Python source code who are quickly glancing at some code may miss the distinction. But simple cases are not objectionable:

This PEP recommends always putting spaces around := , similar to PEP 8 ’s recommendation for = when used for assignment, whereas the latter disallows spaces around = used for keyword arguments.)

In order to have precisely defined semantics, the proposal requires evaluation order to be well-defined. This is technically not a new requirement, as function calls may already have side effects. Python already has a rule that subexpressions are generally evaluated from left to right. However, assignment expressions make these side effects more visible, and we propose a single change to the current evaluation order:

• In a dict comprehension {X: Y for ...} , Y is currently evaluated before X . We propose to change this so that X is evaluated before Y . (In a dict display like {X: Y} this is already the case, and also in dict((X, Y) for ...) which should clearly be equivalent to the dict comprehension.)

Most importantly, since := is an expression, it can be used in contexts where statements are illegal, including lambda functions and comprehensions.

Conversely, assignment expressions don’t support the advanced features found in assignment statements:

• Multiple targets are not directly supported: x = y = z = 0 # Equivalent: (z := (y := (x := 0)))
• Single assignment targets other than a single NAME are not supported: # No equivalent a [ i ] = x self . rest = []
• Priority around commas is different: x = 1 , 2 # Sets x to (1, 2) ( x := 1 , 2 ) # Sets x to 1
• Iterable packing and unpacking (both regular or extended forms) are not supported: # Equivalent needs extra parentheses loc = x , y # Use (loc := (x, y)) info = name , phone , * rest # Use (info := (name, phone, *rest)) # No equivalent px , py , pz = position name , phone , email , * other_info = contact
• Inline type annotations are not supported: # Closest equivalent is "p: Optional[int]" as a separate declaration p : Optional [ int ] = None
• Augmented assignment is not supported: total += tax # Equivalent: (total := total + tax)

The following changes have been made based on implementation experience and additional review after the PEP was first accepted and before Python 3.8 was released:

• for consistency with other similar exceptions, and to avoid locking in an exception name that is not necessarily going to improve clarity for end users, the originally proposed TargetScopeError subclass of SyntaxError was dropped in favour of just raising SyntaxError directly. [3]
• due to a limitation in CPython’s symbol table analysis process, the reference implementation raises SyntaxError for all uses of named expressions inside comprehension iterable expressions, rather than only raising them when the named expression target conflicts with one of the iteration variables in the comprehension. This could be revisited given sufficiently compelling examples, but the extra complexity needed to implement the more selective restriction doesn’t seem worthwhile for purely hypothetical use cases.

Examples from the Python standard library

env_base is only used on these lines, putting its assignment on the if moves it as the “header” of the block.

• Current: env_base = os . environ . get ( "PYTHONUSERBASE" , None ) if env_base : return env_base
• Improved: if env_base := os . environ . get ( "PYTHONUSERBASE" , None ): return env_base

Avoid nested if and remove one indentation level.

• Current: if self . _is_special : ans = self . _check_nans ( context = context ) if ans : return ans
• Improved: if self . _is_special and ( ans := self . _check_nans ( context = context )): return ans

Code looks more regular and avoid multiple nested if. (See Appendix A for the origin of this example.)

• Current: reductor = dispatch_table . get ( cls ) if reductor : rv = reductor ( x ) else : reductor = getattr ( x , "__reduce_ex__" , None ) if reductor : rv = reductor ( 4 ) else : reductor = getattr ( x , "__reduce__" , None ) if reductor : rv = reductor () else : raise Error ( "un(deep)copyable object of type %s " % cls )
• Improved: if reductor := dispatch_table . get ( cls ): rv = reductor ( x ) elif reductor := getattr ( x , "__reduce_ex__" , None ): rv = reductor ( 4 ) elif reductor := getattr ( x , "__reduce__" , None ): rv = reductor () else : raise Error ( "un(deep)copyable object of type %s " % cls )

tz is only used for s += tz , moving its assignment inside the if helps to show its scope.

• Current: s = _format_time ( self . _hour , self . _minute , self . _second , self . _microsecond , timespec ) tz = self . _tzstr () if tz : s += tz return s
• Improved: s = _format_time ( self . _hour , self . _minute , self . _second , self . _microsecond , timespec ) if tz := self . _tzstr (): s += tz return s

Calling fp.readline() in the while condition and calling .match() on the if lines make the code more compact without making it harder to understand.

• Current: while True : line = fp . readline () if not line : break m = define_rx . match ( line ) if m : n , v = m . group ( 1 , 2 ) try : v = int ( v ) except ValueError : pass vars [ n ] = v else : m = undef_rx . match ( line ) if m : vars [ m . group ( 1 )] = 0
• Improved: while line := fp . readline (): if m := define_rx . match ( line ): n , v = m . group ( 1 , 2 ) try : v = int ( v ) except ValueError : pass vars [ n ] = v elif m := undef_rx . match ( line ): vars [ m . group ( 1 )] = 0

A list comprehension can map and filter efficiently by capturing the condition:

Similarly, a subexpression can be reused within the main expression, by giving it a name on first use:

Note that in both cases the variable y is bound in the containing scope (i.e. at the same level as results or stuff ).

Assignment expressions can be used to good effect in the header of an if or while statement:

Particularly with the while loop, this can remove the need to have an infinite loop, an assignment, and a condition. It also creates a smooth parallel between a loop which simply uses a function call as its condition, and one which uses that as its condition but also uses the actual value.

An example from the low-level UNIX world:

Rejected alternative proposals

Proposals broadly similar to this one have come up frequently on python-ideas. Below are a number of alternative syntaxes, some of them specific to comprehensions, which have been rejected in favour of the one given above.

A previous version of this PEP proposed subtle changes to the scope rules for comprehensions, to make them more usable in class scope and to unify the scope of the “outermost iterable” and the rest of the comprehension. However, this part of the proposal would have caused backwards incompatibilities, and has been withdrawn so the PEP can focus on assignment expressions.

Broadly the same semantics as the current proposal, but spelled differently.

Since EXPR as NAME already has meaning in import , except and with statements (with different semantics), this would create unnecessary confusion or require special-casing (e.g. to forbid assignment within the headers of these statements).

(Note that with EXPR as VAR does not simply assign the value of EXPR to VAR – it calls EXPR.__enter__() and assigns the result of that to VAR .)

Additional reasons to prefer := over this spelling include:

• In if f(x) as y the assignment target doesn’t jump out at you – it just reads like if f x blah blah and it is too similar visually to if f(x) and y .
• import foo as bar
• except Exc as var
• with ctxmgr() as var

To the contrary, the assignment expression does not belong to the if or while that starts the line, and we intentionally allow assignment expressions in other contexts as well.

• NAME = EXPR
• if NAME := EXPR

reinforces the visual recognition of assignment expressions.

This syntax is inspired by languages such as R and Haskell, and some programmable calculators. (Note that a left-facing arrow y <- f(x) is not possible in Python, as it would be interpreted as less-than and unary minus.) This syntax has a slight advantage over ‘as’ in that it does not conflict with with , except and import , but otherwise is equivalent. But it is entirely unrelated to Python’s other use of -> (function return type annotations), and compared to := (which dates back to Algol-58) it has a much weaker tradition.

This has the advantage that leaked usage can be readily detected, removing some forms of syntactic ambiguity. However, this would be the only place in Python where a variable’s scope is encoded into its name, making refactoring harder.

Execution order is inverted (the indented body is performed first, followed by the “header”). This requires a new keyword, unless an existing keyword is repurposed (most likely with: ). See PEP 3150 for prior discussion on this subject (with the proposed keyword being given: ).

This syntax has fewer conflicts than as does (conflicting only with the raise Exc from Exc notation), but is otherwise comparable to it. Instead of paralleling with expr as target: (which can be useful but can also be confusing), this has no parallels, but is evocative.

One of the most popular use-cases is if and while statements. Instead of a more general solution, this proposal enhances the syntax of these two statements to add a means of capturing the compared value:

This works beautifully if and ONLY if the desired condition is based on the truthiness of the captured value. It is thus effective for specific use-cases (regex matches, socket reads that return '' when done), and completely useless in more complicated cases (e.g. where the condition is f(x) < 0 and you want to capture the value of f(x) ). It also has no benefit to list comprehensions.

Advantages: No syntactic ambiguities. Disadvantages: Answers only a fraction of possible use-cases, even in if / while statements.

Another common use-case is comprehensions (list/set/dict, and genexps). As above, proposals have been made for comprehension-specific solutions.

This brings the subexpression to a location in between the ‘for’ loop and the expression. It introduces an additional language keyword, which creates conflicts. Of the three, where reads the most cleanly, but also has the greatest potential for conflict (e.g. SQLAlchemy and numpy have where methods, as does tkinter.dnd.Icon in the standard library).

As above, but reusing the with keyword. Doesn’t read too badly, and needs no additional language keyword. Is restricted to comprehensions, though, and cannot as easily be transformed into “longhand” for-loop syntax. Has the C problem that an equals sign in an expression can now create a name binding, rather than performing a comparison. Would raise the question of why “with NAME = EXPR:” cannot be used as a statement on its own.

As per option 2, but using as rather than an equals sign. Aligns syntactically with other uses of as for name binding, but a simple transformation to for-loop longhand would create drastically different semantics; the meaning of with inside a comprehension would be completely different from the meaning as a stand-alone statement, while retaining identical syntax.

Regardless of the spelling chosen, this introduces a stark difference between comprehensions and the equivalent unrolled long-hand form of the loop. It is no longer possible to unwrap the loop into statement form without reworking any name bindings. The only keyword that can be repurposed to this task is with , thus giving it sneakily different semantics in a comprehension than in a statement; alternatively, a new keyword is needed, with all the costs therein.

There are two logical precedences for the := operator. Either it should bind as loosely as possible, as does statement-assignment; or it should bind more tightly than comparison operators. Placing its precedence between the comparison and arithmetic operators (to be precise: just lower than bitwise OR) allows most uses inside while and if conditions to be spelled without parentheses, as it is most likely that you wish to capture the value of something, then perform a comparison on it:

Once find() returns -1, the loop terminates. If := binds as loosely as = does, this would capture the result of the comparison (generally either True or False ), which is less useful.

While this behaviour would be convenient in many situations, it is also harder to explain than “the := operator behaves just like the assignment statement”, and as such, the precedence for := has been made as close as possible to that of = (with the exception that it binds tighter than comma).

Some critics have claimed that the assignment expressions should allow unparenthesized tuples on the right, so that these two would be equivalent:

(With the current version of the proposal, the latter would be equivalent to ((point := x), y) .)

However, adopting this stance would logically lead to the conclusion that when used in a function call, assignment expressions also bind less tight than comma, so we’d have the following confusing equivalence:

The less confusing option is to make := bind more tightly than comma.

It’s been proposed to just always require parentheses around an assignment expression. This would resolve many ambiguities, and indeed parentheses will frequently be needed to extract the desired subexpression. But in the following cases the extra parentheses feel redundant:

Frequently Raised Objections

C and its derivatives define the = operator as an expression, rather than a statement as is Python’s way. This allows assignments in more contexts, including contexts where comparisons are more common. The syntactic similarity between if (x == y) and if (x = y) belies their drastically different semantics. Thus this proposal uses := to clarify the distinction.

The two forms have different flexibilities. The := operator can be used inside a larger expression; the = statement can be augmented to += and its friends, can be chained, and can assign to attributes and subscripts.

Previous revisions of this proposal involved sublocal scope (restricted to a single statement), preventing name leakage and namespace pollution. While a definite advantage in a number of situations, this increases complexity in many others, and the costs are not justified by the benefits. In the interests of language simplicity, the name bindings created here are exactly equivalent to any other name bindings, including that usage at class or module scope will create externally-visible names. This is no different from for loops or other constructs, and can be solved the same way: del the name once it is no longer needed, or prefix it with an underscore.

(The author wishes to thank Guido van Rossum and Christoph Groth for their suggestions to move the proposal in this direction. [2] )

As expression assignments can sometimes be used equivalently to statement assignments, the question of which should be preferred will arise. For the benefit of style guides such as PEP 8 , two recommendations are suggested.

• If either assignment statements or assignment expressions can be used, prefer statements; they are a clear declaration of intent.
• If using assignment expressions would lead to ambiguity about execution order, restructure it to use statements instead.

The authors wish to thank Alyssa Coghlan and Steven D’Aprano for their considerable contributions to this proposal, and members of the core-mentorship mailing list for assistance with implementation.

Appendix A: Tim Peters’s findings

Here’s a brief essay Tim Peters wrote on the topic.

I dislike “busy” lines of code, and also dislike putting conceptually unrelated logic on a single line. So, for example, instead of:

instead. So I suspected I’d find few places I’d want to use assignment expressions. I didn’t even consider them for lines already stretching halfway across the screen. In other cases, “unrelated” ruled:

is a vast improvement over the briefer:

The original two statements are doing entirely different conceptual things, and slamming them together is conceptually insane.

In other cases, combining related logic made it harder to understand, such as rewriting:

as the briefer:

The while test there is too subtle, crucially relying on strict left-to-right evaluation in a non-short-circuiting or method-chaining context. My brain isn’t wired that way.

But cases like that were rare. Name binding is very frequent, and “sparse is better than dense” does not mean “almost empty is better than sparse”. For example, I have many functions that return None or 0 to communicate “I have nothing useful to return in this case, but since that’s expected often I’m not going to annoy you with an exception”. This is essentially the same as regular expression search functions returning None when there is no match. So there was lots of code of the form:

I find that clearer, and certainly a bit less typing and pattern-matching reading, as:

It’s also nice to trade away a small amount of horizontal whitespace to get another _line_ of surrounding code on screen. I didn’t give much weight to this at first, but it was so very frequent it added up, and I soon enough became annoyed that I couldn’t actually run the briefer code. That surprised me!

There are other cases where assignment expressions really shine. Rather than pick another from my code, Kirill Balunov gave a lovely example from the standard library’s copy() function in copy.py :

The ever-increasing indentation is semantically misleading: the logic is conceptually flat, “the first test that succeeds wins”:

Using easy assignment expressions allows the visual structure of the code to emphasize the conceptual flatness of the logic; ever-increasing indentation obscured it.

A smaller example from my code delighted me, both allowing to put inherently related logic in a single line, and allowing to remove an annoying “artificial” indentation level:

That if is about as long as I want my lines to get, but remains easy to follow.

So, in all, in most lines binding a name, I wouldn’t use assignment expressions, but because that construct is so very frequent, that leaves many places I would. In most of the latter, I found a small win that adds up due to how often it occurs, and in the rest I found a moderate to major win. I’d certainly use it more often than ternary if , but significantly less often than augmented assignment.

I have another example that quite impressed me at the time.

Where all variables are positive integers, and a is at least as large as the n’th root of x, this algorithm returns the floor of the n’th root of x (and roughly doubling the number of accurate bits per iteration):

It’s not obvious why that works, but is no more obvious in the “loop and a half” form. It’s hard to prove correctness without building on the right insight (the “arithmetic mean - geometric mean inequality”), and knowing some non-trivial things about how nested floor functions behave. That is, the challenges are in the math, not really in the coding.

If you do know all that, then the assignment-expression form is easily read as “while the current guess is too large, get a smaller guess”, where the “too large?” test and the new guess share an expensive sub-expression.

To my eyes, the original form is harder to understand:

This appendix attempts to clarify (though not specify) the rules when a target occurs in a comprehension or in a generator expression. For a number of illustrative examples we show the original code, containing a comprehension, and the translation, where the comprehension has been replaced by an equivalent generator function plus some scaffolding.

Since [x for ...] is equivalent to list(x for ...) these examples all use list comprehensions without loss of generality. And since these examples are meant to clarify edge cases of the rules, they aren’t trying to look like real code.

Note: comprehensions are already implemented via synthesizing nested generator functions like those in this appendix. The new part is adding appropriate declarations to establish the intended scope of assignment expression targets (the same scope they resolve to as if the assignment were performed in the block containing the outermost comprehension). For type inference purposes, these illustrative expansions do not imply that assignment expression targets are always Optional (but they do indicate the target binding scope).

Let’s start with a reminder of what code is generated for a generator expression without assignment expression.

• Original code (EXPR usually references VAR): def f (): a = [ EXPR for VAR in ITERABLE ]
• Translation (let’s not worry about name conflicts): def f (): def genexpr ( iterator ): for VAR in iterator : yield EXPR a = list ( genexpr ( iter ( ITERABLE )))

Let’s add a simple assignment expression.

• Original code: def f (): a = [ TARGET := EXPR for VAR in ITERABLE ]
• Translation: def f (): if False : TARGET = None # Dead code to ensure TARGET is a local variable def genexpr ( iterator ): nonlocal TARGET for VAR in iterator : TARGET = EXPR yield TARGET a = list ( genexpr ( iter ( ITERABLE )))

Let’s add a global TARGET declaration in f() .

• Original code: def f (): global TARGET a = [ TARGET := EXPR for VAR in ITERABLE ]
• Translation: def f (): global TARGET def genexpr ( iterator ): global TARGET for VAR in iterator : TARGET = EXPR yield TARGET a = list ( genexpr ( iter ( ITERABLE )))

Or instead let’s add a nonlocal TARGET declaration in f() .

• Original code: def g (): TARGET = ... def f (): nonlocal TARGET a = [ TARGET := EXPR for VAR in ITERABLE ]
• Translation: def g (): TARGET = ... def f (): nonlocal TARGET def genexpr ( iterator ): nonlocal TARGET for VAR in iterator : TARGET = EXPR yield TARGET a = list ( genexpr ( iter ( ITERABLE )))

Finally, let’s nest two comprehensions.

• Original code: def f (): a = [[ TARGET := i for i in range ( 3 )] for j in range ( 2 )] # I.e., a = [[0, 1, 2], [0, 1, 2]] print ( TARGET ) # prints 2
• Translation: def f (): if False : TARGET = None def outer_genexpr ( outer_iterator ): nonlocal TARGET def inner_generator ( inner_iterator ): nonlocal TARGET for i in inner_iterator : TARGET = i yield i for j in outer_iterator : yield list ( inner_generator ( range ( 3 ))) a = list ( outer_genexpr ( range ( 2 ))) print ( TARGET )

Because it has been a point of confusion, note that nothing about Python’s scoping semantics is changed. Function-local scopes continue to be resolved at compile time, and to have indefinite temporal extent at run time (“full closures”). Example:

This document has been placed in the public domain.

Source: https://github.com/python/peps/blob/main/peps/pep-0572.rst

Last modified: 2023-10-11 12:05:51 GMT

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Answer to Question #165127 in Python for teja

Prefix Suffix

Write a program to check the overlapping of one string's suffix with the prefix of another string.Input

The first line of the input will contain a string A.

The second line of the input will contain a string B.Output

The output should contain overlapping word if present else print "No overlapping".Explanation

For example, if the given two strings, A and B, are "ramisgood" "goodforall"

The output should be "good" as good overlaps as a suffix of the first string and prefix of next.

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• 7. 25.0C 77.0F 298.0Kthis is expecetd output and when i enterded 37.5F iam getting value error ValueErr
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Concatenating two lists - difference between '+=' and extend()

I've seen there are actually two (maybe more) ways to concatenate lists in Python:

One way is to use the extend() method:

the other to use the plus (+) operator:

Now I wonder: which of those two options is the 'pythonic' way to do list concatenation and is there a difference between the two? (I've looked up the official Python tutorial but couldn't find anything anything about this topic).

• 1 Maybe the difference has more implications when it comes to ducktyping and if your maybe-not-really-a-list-but-like-a-list supports .__iadd__() / .__add__() / .__radd__() versus .extend() –  Nick T Commented Dec 15, 2014 at 22:22

12 Answers 12

The only difference on a bytecode level is that the .extend way involves a function call, which is slightly more expensive in Python than the INPLACE_ADD .

It's really nothing you should be worrying about, unless you're performing this operation billions of times. It is likely, however, that the bottleneck would lie some place else.

• 23 Maybe the difference has more implications when it comes to ducktyping and if your maybe-not-really-a-list-but-like-a-list supports .__iadd__() / .__add__() / .__radd__() versus .extend() –  Nick T Commented Dec 15, 2014 at 22:21
• 17 This answer fails to mention the important scoping differences. –  wim Commented Jan 27, 2017 at 16:11
• 12 Well actually, extends is faster than the INPLACE_ADD() i.e. the list concatenation. gist.github.com/mekarpeles/3408081 –  Archit Commented Jul 18, 2018 at 7:03
• 4 For me, this answer didn't really help me decide which one I should use as a general principle. I think consistency is important, and knowing things like how it can't be used with non-locals, and can't be chained (from the other answers) provides a more practical, functional reason to use extend() over the operator, even when there's a choice. "Billions of operations" use case is a valid point, but not one I run into more than a handful of times in my career. –  void.pointer Commented Feb 17, 2021 at 17:18
• 9 .extend is faster than + . There is nothing to do with extend having an extra function call. + is an operator and it also causes a function call. The reason .extend is faster is because it does much less work. + will (1) create a list, copy all elements (references) from that list, then it will get the second list and add the references. .extend will not create a new list nor copy references elements from that list. extend is equivalent to a[len(a):] = iterable . extend will operate over the list you are doing the operation and should be used instead of L = L + iterable –  fsan Commented Feb 1, 2023 at 16:58

You can't use += for non-local variable (variable which is not local for function and also not global)

It's because for extend case compiler will load the variable l using LOAD_DEREF instruction, but for += it will use LOAD_FAST - and you get *UnboundLocalError: local variable 'l' referenced before assignment*

• 7 I having difficulties with your explanation "variable which is not local for function and also not global " could you give example of such a variable ? –  Stephane Rolland Commented Aug 7, 2014 at 7:44
• 13 Variable 'l' in my example is exactly of that kind. It's not local for 'foo' and 'boo' functions (outside of their scopes), but it's not global (defined inside 'main' func, not on module level) –  monitorius Commented Aug 7, 2014 at 9:34
• 3 I can confirm that this error still occurs with python 3.4.2 (you'll need to add parentheses to print but everything else can stay the same). –  trichoplax is on Codidact now Commented Jul 1, 2015 at 15:53
• 8 That's right. But at least you can use nonlocal l statement in boo in Python3. –  monitorius Commented Jul 2, 2015 at 9:58
• 1 compiler -> interpreter? –  joel Commented Mar 17, 2020 at 16:21

+= only works if the statement would also work with an = sign, i.e. the left-hand side can be assigned to. This is because a += b actually becomes a = a.__iadd__(b) under the hood. So if a is something that can't be assigned to (either by syntax or semantics), such as a function call or an element of an immutable container, the += version will fail.

Example 1: Function calls

You can't assign to a function call (the syntax of Python forbids it), so you also can't += a function call's result directly:

Example 2: Element of immutable container

A perhaps weirder case is when the list is an element of an immutable container, e.g. a tuple:

Since you can't do my_tuple[0] = something , you also can't do += .

To sum up, you can use += if you can use = .

Actually, there are differences among the three options: ADD , INPLACE_ADD and extend . The former is always slower, while the other two are roughly the same.

With this information, I would rather use extend , which is faster than ADD , and seems to me more explicit of what you are doing than INPLACE_ADD .

Try the following code a few times (for Python 3):

• 3 You can't compare ADD with INPLACE_ADD and extend() . ADD produces a new list and copies the elements of the two original lists to it. For sure it will be slower than inplace operation of INPLACE_ADD and extend() . –  wombatonfire Commented Mar 22, 2019 at 23:05
• 5 I know that. The point of this example is comparing different ways of having a list with all elements together. Sure it takes longer because it does different things, but still it is good to know in case you are interested in preserving the original objects unaltered. –  dalonsoa Commented Apr 10, 2019 at 13:40

I would say that there is some difference when it comes with numpy (I just saw that the question ask about concatenating two lists, not numpy array, but since it might be a issue for beginner, such as me, I hope this can help someone who seek the solution to this post), for ex.

it will return with error

ValueError: operands could not be broadcast together with shapes (0,) (4,4,4)

b.extend(a) works perfectly

ary += ext creates a new List object, then copies data from lists "ary" and "ext" into it.

ary.extend(ext) merely adds reference to "ext" list to the end of the "ary" list, resulting in less memory transactions.

As a result, .extend works orders of magnitude faster and doesn't use any additional memory outside of the list being extended and the list it's being extended with.

The first script also uses over 200MB of memory, while the second one doesn't use any more memory than a 'naked' python3 process.

Having said that, the in-place addition does seem to do the same thing as .extend.

• 2 Could you add the contents of /list_plus.py and /list_extend.py here? –  Niko Fohr Commented Feb 3, 2023 at 17:07

The .extend() method on lists works with any iterable*, += works with some but can get funky.

Python 3.6 *pretty sure .extend() works with any iterable but please comment if I am incorrect

Edit: "extend()" changed to "The .extend() method on lists" Note: David M. Helmuth's comment below is nice and clear.

• Tuple is definitely an iterable, but it has no extend() method. extend() method has nothing to do with iteration. –  wombatonfire Commented Mar 22, 2019 at 23:00
• .extend is a method of the list class. From the Python documentation: list.extend(iterable) Extend the list by appending all the items from the iterable. Equivalent to a[len(a):] = iterable. Guess I answered my own asterisk. –  grofte Commented Mar 23, 2019 at 11:57
• Oh, you meant that you can pass any iterable to extend(). I read it as "extend() is available for any iterable" :) My bad, but it sounds a little ambiguous. –  wombatonfire Commented Mar 23, 2019 at 12:03
• 1 All in all, this is not a good example, at least not in the context of this question. When you use a += operator with objects of different types (contrary to two lists, as in the question), you can't expect that you will get a concatenation of the objects. And you can't expect that there will be a list type returned. Have a look at your code, you get an numpy.ndarray instead of list . –  wombatonfire Commented Mar 23, 2019 at 13:18
• 1 @grofte provided the correct answer; however, the answer requires some clarification, so here is my suggested clarification: When using the extend() method to concatenate your list with the values held in another iterable, you get consistent behaviour regardless of whether the other iterable is a list, tuple or even a NumPy array. That consistent behaviour isn’t the case when using the += operator to concatenate a second iterable to a list. (See examples given below) - That's how they differ –  David M. Helmuth Commented Jul 5, 2022 at 21:23

From the CPython 3.5.2 source code : No big difference.

I've looked up the official Python tutorial but couldn't find anything anything about this topic

This information happens to be buried in the Programming FAQ :

... for lists, __iadd__ [i.e. += ] is equivalent to calling extend on the list and returning the list. That's why we say that for lists, += is a "shorthand" for list.extend

You can also see this for yourself in the CPython source code: https://github.com/python/cpython/blob/v3.8.2/Objects/listobject.c#L1000-L1011

The += operator is negligibly if at all faster than list.extend() which is confirmed by dalonsoa's answer. You're literally exchanging a method call for two other operations.

Note, that this does not apply to numpy arrays, since numpy arrays are not at all Python lists and should not be treated as such (Lance Ruo Zhang's answer).

The += will not work for list in tuples most likely because of the STORE_SUBSCR operation (Jann Poppinga's answer). Note, however, that in this case list.__iadd__() (being a method call) works perfectly fine.

The += does not create a new list (ding's answer).

I apologise for posting all this as an answer, I do not have enough rep to comment.

Only .extend() can be used when the list is in a tuple

This will work

while this won't

The reason is that += generates a new object. If you look at the long version:

you can see how that would change which object is in the tuple, which is not possible. Using .extend() modifies an object in the tuple, which is allowed.

According to the Python for Data Analysis.

“Note that list concatenation by addition is a comparatively expensive operation since a new list must be created and the objects copied over. Using extend to append elements to an existing list, especially if you are building up a large list, is usually preferable. ” Thus,

is faster than the concatenative alternative:

• 5 everything = everything + temp is not necessarily implemented in the same way as everything += temp . –  David Harrison Commented Aug 1, 2018 at 22:12
• 1 You are right. Thank you for your reminder. But my point is about the difference of efficiency. : ) –  littlebear333 Commented Aug 2, 2018 at 5:35
• 8 @littlebear333 everything += temp is implemented in a way such that everything does not need to be copied. This pretty much makes your answer a moot point. –  nog642 Commented Aug 19, 2018 at 20:27

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