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Distribute Candies to People in Python

Suppose we want to distribute some number of candies to a row of n people in the following way −

• We then give 1 candy to the first people, 2 candies to the second people, and so on until we give n candies to the last people.
• After that, we go back to the start of the row again, give n + 1 candies to the first people, n + 2 candies to the second people, and so on until we give 2 * n candies to the last people.

We will repeat this process until we run out of candies. The last people will get all of our remaining candies (not necessarily one more than the previous gift).

We have to return an array that represents the final distribution of candies. So suppose candies are 7, and n = 3, then the output will be [2, 2, 3]. So at first the first person will get 1. the array is [1, 0, 0], second one has got 2, then array is [1, 2, 0], third one has got 3, then array is [1, 2, 3], and finally first one again got 1, so array is [2, 2, 3]

To solve this, we will follow these steps −

• res is an array of n elements, and fill with 0
• res[index mod n] := res[index mod n] + min of candies and index + 1
• candies := candies – 1
• index := index + 1

Let us see the following implementation to get better understanding −

 Live Demo

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Category: Dynamic Programming

Difficulty: Medium

Given a list of students' ratings, determine the minimum number of candies needed to reward each student.

Input: an array \(a[n]\) of integers that represent the performance of each student. The students sit in a line in order, so student \(i\) sits next to students \(i - 1\) and \(i + 1\) , for example.

Output: the minimum total number of candies required to reward all of the students. Each student should earn at least one candy. For any two students sitting next to each other, if one has a higher rating, they must earn more candies than the other. Note that adjacent students with the same rating are not required to get the same number of candies.

It makes sense to try an iterative approach where you create an \(n\) -length array to track how many candies each student has. You start by giving each student one candy and make passes over \(a\) where you check which student needs to be given extra candies in each pair of adjacent students. If a student has a higher rating than one of their neighbors, they need at least 2 candies. If they have a higher rating than neighbors on both sides, they need at least 3 candies. They'll need more than that if those neighbors with lower ratings need multiple candies. The largest number of candies a student could ever need is \(n\) : imagine if the students are lined up in order by increasing rating. You could give the leftmost student one candy, so their neighbor needs 2 candies, and so on until the \(n^\text{th}\) student needs \(n\) candies. If each pass gives at least one candy to a student, you will eventually converge upon a good solution with some finite number of passes.

A different approach, which is still dynamic programming, can solve this problem with only two passes over \(a\) , one forwards and one backwards. Let \(a =\) [1, 5, 3, 2, 4] and consider the following two observations:

• You can split \(a\) into disjoint strictly increasing sections. These would be [1, 5] , [3] , and [2, 4] . For each of these sections, each student needs at least one more candy than the student on their left (if there is one).
• You can also split \(a\) into disjoint strictly decreasing sections. These would be [1] , [5, 3, 2] , and [4] . For each of these sections, each student needs at least one more candy than the student on their right (if there is one).

Starting with every student having one candy ( \(c =\) [1, 1, 1, 1, 1] ), we can first make a pass that satisfies the first condition: [1, 2, 1, 1, 2] . Then, we make a backward pass to increase each student's candy count until it satisfies the second condition: [1, 3, 2, 1, 2] . This ends up giving us an optimal candy assignment. On your forward pass, you let \(c[i] = c[i - 1] + 1\) wherever \(a[i] > a[i - 1]\) . On your backward pass, you let \(c[i] = c[i + 1] + 1\) wherever \(a[i] > a[i + 1]\) and \(c[i] \leq c[i + 1]\) . We started by only giving each student one candy, and we increased their candy counts a little as possible to satisfy the requirements. The answer is the sum of the \(n\) values in the array \(c\) .

Java 8: public static long candies ( int n , List < Integer > arr ) { long [] candies = new long [ n ] ; Arrays . fill ( candies , 1L ); for ( int i = 1 ; i < n ; i ++ ) { if ( arr . get ( i ) > arr . get ( i - 1 )) { candies [ i ] += candies [ i - 1 ] ; } } for ( int i = n - 2 ; i >= 0 ; i -- ) { if ( arr . get ( i ) > arr . get ( i + 1 )) { candies [ i ] = Math . max ( candies [ i ] , candies [ i + 1 ] + 1L ); } } long result = 0 L ; for ( int i = 0 ; i < n ; i ++ ) { result += candies [ i ] ; } return result ; }

C++: long candies ( int n , vector < int > arr ) { vector < long > candies ( n , 1L ); for ( int i = 1 ; i < n ; i ++ ) { candies [ i ] += candies [ i - 1 ] * ( arr [ i ] > arr [ i - 1 ]); } for ( int i = n - 2 ; i >= 0 ; i -- ) { if ( arr [ i ] > arr [ i + 1 ]) { candies [ i ] = max ( candies [ i ], candies [ i + 1 ] + 1L ); } } long result = 0L ; for ( int i = 0 ; i < n ; i ++ ) { result += candies [ i ]; } return result ; }

Python 3: def candies ( n , arr ): dp = [ 1 for _ in arr ] for i in range ( 1 , n ): dp [ i ] += dp [ i - 1 ] * ( arr [ i ] > arr [ i - 1 ]) for i in range ( n - 2 , - 1 , - 1 ): if arr [ i ] > arr [ i + 1 ]: dp [ i ] = max ( dp [ i ], dp [ i + 1 ] + 1 ) return sum ( dp )

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 | 51 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:  July 6, 2024 | 296 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 | 200 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 31, 2023 | 494 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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Posted on May 13, 2023

Distribute Candies

Alice has n candies, where the ith candy is of type candyType[i]. Alice noticed that she started to gain weight, so she visited a doctor.

The doctor advised Alice to only eat n / 2 of the candies she has (n is always even). Alice likes her candies very much, and she wants to eat the maximum number of different types of candies while still following the doctor's advice.

Given the integer array candyType of length n, return the maximum number of different types of candies she can eat if she only eats n / 2 of them.

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5 Effective Python Methods to Calculate Candy Distribution to Children

💡 Problem Formulation: The task is to write a Python program that simulates the distribution of candies to children while adhering to certain rules. For example, if there are 20 candies and each child must receive at least one candy before any child receives a second candy, then with a given number of children, how many will actually receive candies? Desired output is clear: the total number of children receiving candies.

Method 1: Iterative Distribution

This method involves using a loop to iteratively assign one candy to each child until all candies are distributed. It is an intuitive approach that mimics real-life distribution.

Here’s an example:

The function distribute_candies() accepts the total number of candies and the number of children. It returns the smaller of the two values, because each child can receive only one candy before any child can receive a second one. If the number of candies is less than the number of children, some children, unfortunately, will not get candies.

Method 2: Using Python’s Min Function

The Python built-in function min() can be directly employed to determine how many children can get candies by comparing the available candies with the number of children.

The code defines a function candies_to_children() that returns the result of the min() function. The printed output specifies that maximum 25 children will receive candies since that is the lower of the two values provided.

Method 3: Mathematical Calculation

This method involves a straightforward computation to find the minimum between the number of candies and children which can be accomplished without the need for a specific function or loop.

The code snippet performs a direct calculation with the min() function applying it directly to the variables and storing the result in children_with_candies , which is then printed.

Method 4: Conditional Expression

This method makes use of a conditional expression to decide how many children can get candies based on the available quantity.

The one-liner conditional expression (ternary operator) checks if there are fewer candies than children. The variable result will contain the number of candies or children depending on which one is lesser. The output states that 15 children can get candies because there are only 15 candies available.

Bonus One-Liner Method 5: Lambda Function

A one-liner approach using a lambda function can provide a quick solution for the problem at hand by encapsulating the candy distribution logic succinctly.

The amount_distributed lambda function applies the same logic as the previous methods encapsulated in a single line. When invoked, it determines the maximum number of children that can get candies.

Summary/Discussion

• Method 1: Iterative Distribution. Clearly understandable and easy to iterate upon. However, it may be overly simplistic for complex rules and larger datasets.
• Method 2: Using Python’s Min Function. Utilizes Python’s built-in functionality for a concise solution. Very efficient but doesn’t extend easily to more complex distribution rules.
• Method 3: Mathematical Calculation. Direct and straightforward without boilerplate code. It’s limited to the scenarios where the conditions are simple and predefined.
• Method 4: Conditional Expression. Offers an immediate, readable solution. Yet it may not be as explicit in intent for beginners or for more involved calculations.
• Bonus Method 5: Lambda Function. Compact and functional. Ideal for quick one-off calculations but could be less intuitive for those not familiar with lambda syntax.

Vending Machine Python Program With Code

Want to know how to create a vending machine python program, then you are at the right place. Today in this tutorial I will show you step by step on how to create a simple vending machine program using python.

We will create a similar vending machine program which will allow users to get the food item they want so let’s see how to do it.

Creating Vending Machine Python Program 

This program will be command line based and it will have no GUI. It will be a very simple and easy to use program.

Here’s how this vending machine program will work:

1. Download and Install Python

Skip this step if you have done it, First you need to download python from the official website and after downloading run the setup it will install python on your computer.

To check if python is installed you can open command prompt and type python, it should not give any errors.

2. Project Setup

Now you have python installed, create a new folder for this program, open it in a code editor if you don’t have a code editor you can download and use vs code.

3. Vending Machine Python Code

Above is the python vending machine code, copy it and paste it in your python file. I have not used any libraries, so you don’t have to install any.

Now to run the program, open a command prompt or terminal at the project folder location and paste the below command to run the program or else you can use this online python compiler.

This will start the program and you should see a welcome to vending machine message. It will show you a list of food items, you can add more if you want.

It will ask you to enter the code number of the food item which is given, then it will ask you to enter the price of the item, then you will get the food item. Below is an example output of this program. 

As you can see, we have successfully created a vending machine program and it works similar to a vending machine.

Python Vending Machine Program Explanation

First we have the items list which has nested python dictionaries. As you can see, each item has 3 keys: name, code and price. You can add more food items to this list if you want but remember to add the code value linearly.

Now this line of code asks the user to enter the code number of the food item they want, after that it finds the food item with the code number and set’s the food item into an item variable.

Inside else statement it prints the food details they want with price then it asks the user to enter the price amount to get the food and if it matches the price of the food they want they get the food, if not then they don’t get.

Lastly it asks the user if they want to continue or quit the program, so to continue they can enter anything except q because that is for quitting.

Here are more python programs you might find interesting:

Thanks for reading, have a nice day 🙂

Related content

Python's Assignment Operator: Write Robust Assignments

Table of Contents

The Assignment Statement Syntax

The assignment operator, assignments and variables, other assignment syntax, initializing and updating variables, making multiple variables refer to the same object, updating lists through indices and slices, adding and updating dictionary keys, doing parallel assignments, unpacking iterables, providing default argument values, augmented mathematical assignment operators, augmented assignments for concatenation and repetition, augmented bitwise assignment operators, annotated assignment statements, assignment expressions with the walrus operator, managed attribute assignments, define or call a function, work with classes, import modules and objects, use a decorator, access the control variable in a for loop or a comprehension, use the as keyword, access the _ special variable in an interactive session, built-in objects, named constants.

Python’s assignment operators allow you to define assignment statements . This type of statement lets you create, initialize, and update variables throughout your code. Variables are a fundamental cornerstone in every piece of code, and assignment statements give you complete control over variable creation and mutation.

Learning about the Python assignment operator and its use for writing assignment statements will arm you with powerful tools for writing better and more robust Python code.

In this tutorial, you’ll:

• Use Python’s assignment operator to write assignment statements
• Take advantage of augmented assignments in Python
• Explore assignment variants, like assignment expressions and managed attributes
• Become aware of illegal and dangerous assignments in Python

You’ll dive deep into Python’s assignment statements. To get the most out of this tutorial, you should be comfortable with several basic topics, including variables , built-in data types , comprehensions , functions , and Python keywords . Before diving into some of the later sections, you should also be familiar with intermediate topics, such as object-oriented programming , constants , imports , type hints , properties , descriptors , and decorators .

Free Source Code: Click here to download the free assignment operator source code that you’ll use to write assignment statements that allow you to create, initialize, and update variables in your code.

Assignment Statements and the Assignment Operator

One of the most powerful programming language features is the ability to create, access, and mutate variables . In Python, a variable is a name that refers to a concrete value or object, allowing you to reuse that value or object throughout your code.

To create a new variable or to update the value of an existing one in Python, you’ll use an assignment statement . This statement has the following three components:

• A left operand, which must be a variable
• The assignment operator ( = )
• A right operand, which can be a concrete value , an object , or an expression

Here’s how an assignment statement will generally look in Python:

Here, variable represents a generic Python variable, while expression represents any Python object that you can provide as a concrete value—also known as a literal —or an expression that evaluates to a value.

To execute an assignment statement like the above, Python runs the following steps:

• Evaluate the right-hand expression to produce a concrete value or object . This value will live at a specific memory address in your computer.
• Store the object’s memory address in the left-hand variable . This step creates a new variable if the current one doesn’t already exist or updates the value of an existing variable.

The second step shows that variables work differently in Python than in other programming languages. In Python, variables aren’t containers for objects. Python variables point to a value or object through its memory address. They store memory addresses rather than objects.

This behavior difference directly impacts how data moves around in Python, which is always by reference . In most cases, this difference is irrelevant in your day-to-day coding, but it’s still good to know.

The central component of an assignment statement is the assignment operator . This operator is represented by the = symbol, which separates two operands:

• A value or an expression that evaluates to a concrete value

Operators are special symbols that perform mathematical , logical , and bitwise operations in a programming language. The objects (or object) on which an operator operates are called operands .

Unary operators, like the not Boolean operator, operate on a single object or operand, while binary operators act on two. That means the assignment operator is a binary operator.

Note: Like C , Python uses == for equality comparisons and = for assignments. Unlike C, Python doesn’t allow you to accidentally use the assignment operator ( = ) in an equality comparison.

Equality is a symmetrical relationship, and assignment is not. For example, the expression a == 42 is equivalent to 42 == a . In contrast, the statement a = 42 is correct and legal, while 42 = a isn’t allowed. You’ll learn more about illegal assignments later on.

The right-hand operand in an assignment statement can be any Python object, such as a number , list , string , dictionary , or even a user-defined object. It can also be an expression. In the end, expressions always evaluate to concrete objects, which is their return value.

Here are a few examples of assignments in Python:

The first two sample assignments in this code snippet use concrete values, also known as literals , to create and initialize number and greeting . The third example assigns the result of a math expression to the total variable, while the last example uses a Boolean expression.

Note: You can use the built-in id() function to inspect the memory address stored in a given variable.

Here’s a short example of how this function works:

The number in your output represents the memory address stored in number . Through this address, Python can access the content of number , which is the integer 42 in this example.

If you run this code on your computer, then you’ll get a different memory address because this value varies from execution to execution and computer to computer.

Unlike expressions, assignment statements don’t have a return value because their purpose is to make the association between the variable and its value. That’s why the Python interpreter doesn’t issue any output in the above examples.

Now that you know the basics of how to write an assignment statement, it’s time to tackle why you would want to use one.

The assignment statement is the explicit way for you to associate a name with an object in Python. You can use this statement for two main purposes:

• Creating and initializing new variables
• Updating the values of existing variables

When you use a variable name as the left operand in an assignment statement for the first time, you’re creating a new variable. At the same time, you’re initializing the variable to point to the value of the right operand.

On the other hand, when you use an existing variable in a new assignment, you’re updating or mutating the variable’s value. Strictly speaking, every new assignment will make the variable refer to a new value and stop referring to the old one. Python will garbage-collect all the values that are no longer referenced by any existing variable.

Assignment statements not only assign a value to a variable but also determine the data type of the variable at hand. This additional behavior is another important detail to consider in this kind of statement.

Because Python is a dynamically typed language, successive assignments to a given variable can change the variable’s data type. Changing the data type of a variable during a program’s execution is considered bad practice and highly discouraged. It can lead to subtle bugs that can be difficult to track down.

Unlike in math equations, in Python assignments, the left operand must be a variable rather than an expression or a value. For example, the following construct is illegal, and Python flags it as invalid syntax:

In this example, you have expressions on both sides of the = sign, and this isn’t allowed in Python code. The error message suggests that you may be confusing the equality operator with the assignment one, but that’s not the case. You’re really running an invalid assignment.

To correct this construct and convert it into a valid assignment, you’ll have to do something like the following:

In this code snippet, you first import the sqrt() function from the math module. Then you isolate the hypotenuse variable in the original equation by using the sqrt() function. Now your code works correctly.

Now you know what kind of syntax is invalid. But don’t get the idea that assignment statements are rigid and inflexible. In fact, they offer lots of room for customization, as you’ll learn next.

Python’s assignment statements are pretty flexible and versatile. You can write them in several ways, depending on your specific needs and preferences. Here’s a quick summary of the main ways to write assignments in Python:

Up to this point, you’ve mostly learned about the base assignment syntax in the above code snippet. In the following sections, you’ll learn about multiple, parallel, and augmented assignments. You’ll also learn about assignments with iterable unpacking.

Read on to see the assignment statements in action!

Assignment Statements in Action

You’ll find and use assignment statements everywhere in your Python code. They’re a fundamental part of the language, providing an explicit way to create, initialize, and mutate variables.

You can use assignment statements with plain names, like number or counter . You can also use assignments in more complicated scenarios, such as with:

• Qualified attribute names , like user.name
• Indices and slices of mutable sequences, like a_list[i] and a_list[i:j]
• Dictionary keys , like a_dict[key]

This list isn’t exhaustive. However, it gives you some idea of how flexible these statements are. You can even assign multiple values to an equal number of variables in a single line, commonly known as parallel assignment . Additionally, you can simultaneously assign the values in an iterable to a comma-separated group of variables in what’s known as an iterable unpacking operation.

In the following sections, you’ll dive deeper into all these topics and a few other exciting things that you can do with assignment statements in Python.

The most elementary use case of an assignment statement is to create a new variable and initialize it using a particular value or expression:

All these statements create new variables, assigning them initial values or expressions. For an initial value, you should always use the most sensible and least surprising value that you can think of. For example, initializing a counter to something different from 0 may be confusing and unexpected because counters almost always start having counted no objects.

Updating a variable’s current value or state is another common use case of assignment statements. In Python, assigning a new value to an existing variable doesn’t modify the variable’s current value. Instead, it causes the variable to refer to a different value. The previous value will be garbage-collected if no other variable refers to it.

Consider the following examples:

These examples run two consecutive assignments on the same variable. The first one assigns the string "Hello, World!" to a new variable named greeting .

The second assignment updates the value of greeting by reassigning it the "Hi, Pythonistas!" string. In this example, the original value of greeting —the "Hello, World!" string— is lost and garbage-collected. From this point on, you can’t access the old "Hello, World!" string.

Even though running multiple assignments on the same variable during a program’s execution is common practice, you should use this feature with caution. Changing the value of a variable can make your code difficult to read, understand, and debug. To comprehend the code fully, you’ll have to remember all the places where the variable was changed and the sequential order of those changes.

Because assignments also define the data type of their target variables, it’s also possible for your code to accidentally change the type of a given variable at runtime. A change like this can lead to breaking errors, like AttributeError exceptions. Remember that strings don’t have the same methods and attributes as lists or dictionaries, for example.

In Python, you can make several variables reference the same object in a multiple-assignment line. This can be useful when you want to initialize several similar variables using the same initial value:

In this example, you chain two assignment operators in a single line. This way, your two variables refer to the same initial value of 0 . Note how both variables hold the same memory address, so they point to the same instance of 0 .

When it comes to integer variables, Python exhibits a curious behavior. It provides a numeric interval where multiple assignments behave the same as independent assignments. Consider the following examples:

To create n and m , you use independent assignments. Therefore, they should point to different instances of the number 42 . However, both variables hold the same object, which you confirm by comparing their corresponding memory addresses.

Now check what happens when you use a greater initial value:

Now n and m hold different memory addresses, which means they point to different instances of the integer number 300 . In contrast, when you use multiple assignments, both variables refer to the same object. This tiny difference can save you small bits of memory if you frequently initialize integer variables in your code.

The implicit behavior of making independent assignments point to the same integer number is actually an optimization called interning . It consists of globally caching the most commonly used integer values in day-to-day programming.

Under the hood, Python defines a numeric interval in which interning takes place. That’s the interning interval for integer numbers. You can determine this interval using a small script like the following:

This script helps you determine the interning interval by comparing integer numbers from -10 to 500 . If you run the script from your command line, then you’ll get an output like the following:

This output means that if you use a single number between -5 and 256 to initialize several variables in independent statements, then all these variables will point to the same object, which will help you save small bits of memory in your code.

In contrast, if you use a number that falls outside of the interning interval, then your variables will point to different objects instead. Each of these objects will occupy a different memory spot.

You can use the assignment operator to mutate the value stored at a given index in a Python list. The operator also works with list slices . The syntax to write these types of assignment statements is the following:

In the first construct, expression can return any Python object, including another list. In the second construct, expression must return a series of values as a list, tuple, or any other sequence. You’ll get a TypeError if expression returns a single value.

Note: When creating slice objects, you can use up to three arguments. These arguments are start , stop , and step . They define the number that starts the slice, the number at which the slicing must stop retrieving values, and the step between values.

Here’s an example of updating an individual value in a list:

In this example, you update the value at index 2 using an assignment statement. The original number at that index was 7 , and after the assignment, the number is 3 .

Note: Using indices and the assignment operator to update a value in a tuple or a character in a string isn’t possible because tuples and strings are immutable data types in Python.

Their immutability means that you can’t change their items in place :

You can’t use the assignment operator to change individual items in tuples or strings. These data types are immutable and don’t support item assignments.

It’s important to note that you can’t add new values to a list by using indices that don’t exist in the target list:

In this example, you try to add a new value to the end of numbers by using an index that doesn’t exist. This assignment isn’t allowed because there’s no way to guarantee that new indices will be consecutive. If you ever want to add a single value to the end of a list, then use the .append() method.

If you want to update several consecutive values in a list, then you can use slicing and an assignment statement:

In the first example, you update the letters between indices 1 and 3 without including the letter at 3 . The second example updates the letters from index 3 until the end of the list. Note that this slicing appends a new value to the list because the target slice is shorter than the assigned values.

Also note that the new values were provided through a tuple, which means that this type of assignment allows you to use other types of sequences to update your target list.

The third example updates a single value using a slice where both indices are equal. In this example, the assignment inserts a new item into your target list.

In the final example, you use a step of 2 to replace alternating letters with their lowercase counterparts. This slicing starts at index 1 and runs through the whole list, stepping by two items each time.

Updating the value of an existing key or adding new key-value pairs to a dictionary is another common use case of assignment statements. To do these operations, you can use the following syntax:

The first construct helps you update the current value of an existing key, while the second construct allows you to add a new key-value pair to the dictionary.

For example, to update an existing key, you can do something like this:

In this example, you update the current inventory of oranges in your store using an assignment. The left operand is the existing dictionary key, and the right operand is the desired new value.

While you can’t add new values to a list by assignment, dictionaries do allow you to add new key-value pairs using the assignment operator. In the example below, you add a lemon key to inventory :

In this example, you successfully add a new key-value pair to your inventory with 100 units. This addition is possible because dictionaries don’t have consecutive indices but unique keys, which are safe to add by assignment.

The assignment statement does more than assign the result of a single expression to a single variable. It can also cope nicely with assigning multiple values to multiple variables simultaneously in what’s known as a parallel assignment .

Here’s the general syntax for parallel assignments in Python:

Note that the left side of the statement can be either a tuple or a list of variables. Remember that to create a tuple, you just need a series of comma-separated elements. In this case, these elements must be variables.

The right side of the statement must be a sequence or iterable of values or expressions. In any case, the number of elements in the right operand must match the number of variables on the left. Otherwise, you’ll get a ValueError exception.

In the following example, you compute the two solutions of a quadratic equation using a parallel assignment:

In this example, you first import sqrt() from the math module. Then you initialize the equation’s coefficients in a parallel assignment.

The equation’s solution is computed in another parallel assignment. The left operand contains a tuple of two variables, x1 and x2 . The right operand consists of a tuple of expressions that compute the solutions for the equation. Note how each result is assigned to each variable by position.

A classical use case of parallel assignment is to swap values between variables:

The highlighted line does the magic and swaps the values of previous_value and next_value at the same time. Note that in a programming language that doesn’t support this kind of assignment, you’d have to use a temporary variable to produce the same effect:

In this example, instead of using parallel assignment to swap values between variables, you use a new variable to temporarily store the value of previous_value to avoid losing its reference.

For a concrete example of when you’d need to swap values between variables, say you’re learning how to implement the bubble sort algorithm , and you come up with the following function:

In the highlighted line, you use a parallel assignment to swap values in place if the current value is less than the next value in the input list. To dive deeper into the bubble sort algorithm and into sorting algorithms in general, check out Sorting Algorithms in Python .

You can use assignment statements for iterable unpacking in Python. Unpacking an iterable means assigning its values to a series of variables one by one. The iterable must be the right operand in the assignment, while the variables must be the left operand.

Like in parallel assignments, the variables must come as a tuple or list. The number of variables must match the number of values in the iterable. Alternatively, you can use the unpacking operator ( * ) to grab several values in a variable if the number of variables doesn’t match the iterable length.

Here’s the general syntax for iterable unpacking in Python:

Iterable unpacking is a powerful feature that you can use all around your code. It can help you write more readable and concise code. For example, you may find yourself doing something like this:

Whenever you do something like this in your code, go ahead and replace it with a more readable iterable unpacking using a single and elegant assignment, like in the following code snippet:

The numbers list on the right side contains four values. The assignment operator unpacks these values into the four variables on the left side of the statement. The values in numbers get assigned to variables in the same order that they appear in the iterable. The assignment is done by position.

Note: Because Python sets are also iterables, you can use them in an iterable unpacking operation. However, it won’t be clear which value goes to which variable because sets are unordered data structures.

The above example shows the most common form of iterable unpacking in Python. The main condition for the example to work is that the number of variables matches the number of values in the iterable.

What if you don’t know the iterable length upfront? Will the unpacking work? It’ll work if you use the * operator to pack several values into one of your target variables.

For example, say that you want to unpack the first and second values in numbers into two different variables. Additionally, you would like to pack the rest of the values in a single variable conveniently called rest . In this case, you can use the unpacking operator like in the following code:

In this example, first and second hold the first and second values in numbers , respectively. These values are assigned by position. The * operator packs all the remaining values in the input iterable into rest .

The unpacking operator ( * ) can appear at any position in your series of target variables. However, you can only use one instance of the operator:

The iterable unpacking operator works in any position in your list of variables. Note that you can only use one unpacking operator per assignment. Using more than one unpacking operator isn’t allowed and raises a SyntaxError .

Dropping away unwanted values from the iterable is a common use case for the iterable unpacking operator. Consider the following example:

In Python, if you want to signal that a variable won’t be used, then you use an underscore ( _ ) as the variable’s name. In this example, useful holds the only value that you need to use from the input iterable. The _ variable is a placeholder that guarantees that the unpacking works correctly. You won’t use the values that end up in this disposable variable.

Note: In the example above, if your target iterable is a sequence data type, such as a list or tuple, then it’s best to access its last item directly.

To do this, you can use the -1 index:

Using -1 gives you access to the last item of any sequence data type. In contrast, if you’re dealing with iterators , then you won’t be able to use indices. That’s when the *_ syntax comes to your rescue.

The pattern used in the above example comes in handy when you have a function that returns multiple values, and you only need a few of these values in your code. The os.walk() function may provide a good example of this situation.

This function allows you to iterate over the content of a directory recursively. The function returns a generator object that yields three-item tuples. Each tuple contains the following items:

• The path to the current directory as a string
• The names of all the immediate subdirectories as a list of strings
• The names of all the files in the current directory as a list of strings

Now say that you want to iterate over your home directory and list only the files. You can do something like this:

This code will issue a long output depending on the current content of your home directory. Note that you need to provide a string with the path to your user folder for the example to work. The _ placeholder variable will hold the unwanted data.

In contrast, the filenames variable will hold the list of files in the current directory, which is the data that you need. The code will print the list of filenames. Go ahead and give it a try!

The assignment operator also comes in handy when you need to provide default argument values in your functions and methods. Default argument values allow you to define functions that take arguments with sensible defaults. These defaults allow you to call the function with specific values or to simply rely on the defaults.

As an example, consider the following function:

This function takes one argument, called name . This argument has a sensible default value that’ll be used when you call the function without arguments. To provide this sensible default value, you use an assignment.

Note: According to PEP 8 , the style guide for Python code, you shouldn’t use spaces around the assignment operator when providing default argument values in function definitions.

Here’s how the function works:

If you don’t provide a name during the call to greet() , then the function uses the default value provided in the definition. If you provide a name, then the function uses it instead of the default one.

Up to this point, you’ve learned a lot about the Python assignment operator and how to use it for writing different types of assignment statements. In the following sections, you’ll dive into a great feature of assignment statements in Python. You’ll learn about augmented assignments .

Augmented Assignment Operators in Python

Python supports what are known as augmented assignments . An augmented assignment combines the assignment operator with another operator to make the statement more concise. Most Python math and bitwise operators have an augmented assignment variation that looks something like this:

Note that $ isn’t a valid Python operator. In this example, it’s a placeholder for a generic operator. This statement works as follows:

• Evaluate expression to produce a value.
• Run the operation defined by the operator that prefixes the = sign, using the previous value of variable and the return value of expression as operands.
• Assign the resulting value back to variable .

In practice, an augmented assignment like the above is equivalent to the following statement:

As you can conclude, augmented assignments are syntactic sugar . They provide a shorthand notation for a specific and popular kind of assignment.

For example, say that you need to define a counter variable to count some stuff in your code. You can use the += operator to increment counter by 1 using the following code:

In this example, the += operator, known as augmented addition , adds 1 to the previous value in counter each time you run the statement counter += 1 .

It’s important to note that unlike regular assignments, augmented assignments don’t create new variables. They only allow you to update existing variables. If you use an augmented assignment with an undefined variable, then you get a NameError :

Python evaluates the right side of the statement before assigning the resulting value back to the target variable. In this specific example, when Python tries to compute x + 1 , it finds that x isn’t defined.

Great! You now know that an augmented assignment consists of combining the assignment operator with another operator, like a math or bitwise operator. To continue this discussion, you’ll learn which math operators have an augmented variation in Python.

An equation like x = x + b doesn’t make sense in math. But in programming, a statement like x = x + b is perfectly valid and can be extremely useful. It adds b to x and reassigns the result back to x .

As you already learned, Python provides an operator to shorten x = x + b . Yes, the += operator allows you to write x += b instead. Python also offers augmented assignment operators for most math operators. Here’s a summary:

The Example column provides generic examples of how to use the operators in actual code. Note that x must be previously defined for the operators to work correctly. On the other hand, y can be either a concrete value or an expression that returns a value.

Note: The matrix multiplication operator ( @ ) doesn’t support augmented assignments yet.

Consider the following example of matrix multiplication using NumPy arrays:

Note that the exception traceback indicates that the operation isn’t supported yet.

To illustrate how augmented assignment operators work, say that you need to create a function that takes an iterable of numeric values and returns their sum. You can write this function like in the code below:

In this function, you first initialize total to 0 . In each iteration, the loop adds a new number to total using the augmented addition operator ( += ). When the loop terminates, total holds the sum of all the input numbers. Variables like total are known as accumulators . The += operator is typically used to update accumulators.

Note: Computing the sum of a series of numeric values is a common operation in programming. Python provides the built-in sum() function for this specific computation.

Another interesting example of using an augmented assignment is when you need to implement a countdown while loop to reverse an iterable. In this case, you can use the -= operator:

In this example, custom_reversed() is a generator function because it uses yield . Calling the function creates an iterator that yields items from the input iterable in reverse order. To decrement the control variable, index , you use an augmented subtraction statement that subtracts 1 from the variable in every iteration.

Note: Similar to summing the values in an iterable, reversing an iterable is also a common requirement. Python provides the built-in reversed() function for this specific computation, so you don’t have to implement your own. The above example only intends to show the -= operator in action.

Finally, counters are a special type of accumulators that allow you to count objects. Here’s an example of a letter counter:

To create this counter, you use a Python dictionary. The keys store the letters. The values store the counts. Again, to increment the counter, you use an augmented addition.

Counters are so common in programming that Python provides a tool specially designed to facilitate the task of counting. Check out Python’s Counter: The Pythonic Way to Count Objects for a complete guide on how to use this tool.

The += and *= augmented assignment operators also work with sequences , such as lists, tuples, and strings. The += operator performs augmented concatenations , while the *= operator performs augmented repetition .

These operators behave differently with mutable and immutable data types:

Note that the augmented concatenation operator operates on two sequences, while the augmented repetition operator works on a sequence and an integer number.

Consider the following examples and pay attention to the result of calling the id() function:

Mutable sequences like lists support the += augmented assignment operator through the .__iadd__() method, which performs an in-place addition. This method mutates the underlying list, appending new values to its end.

Note: If the left operand is mutable, then x += y may not be completely equivalent to x = x + y . For example, if you do list_1 = list_1 + list_2 instead of list_1 += list_2 above, then you’ll create a new list instead of mutating the existing one. This may be important if other variables refer to the same list.

Immutable sequences, such as tuples and strings, don’t provide an .__iadd__() method. Therefore, augmented concatenations fall back to the .__add__() method, which doesn’t modify the sequence in place but returns a new sequence.

There’s another difference between mutable and immutable sequences when you use them in an augmented concatenation. Consider the following examples:

With mutable sequences, the data to be concatenated can come as a list, tuple, string, or any other iterable. In contrast, with immutable sequences, the data can only come as objects of the same type. You can concatenate tuples to tuples and strings to strings, for example.

Again, the augmented repetition operator works with a sequence on the left side of the operator and an integer on the right side. This integer value represents the number of repetitions to get in the resulting sequence:

When the *= operator operates on a mutable sequence, it falls back to the .__imul__() method, which performs the operation in place, modifying the underlying sequence. In contrast, if *= operates on an immutable sequence, then .__mul__() is called, returning a new sequence of the same type.

Note: Values of n less than 0 are treated as 0 , which returns an empty sequence of the same data type as the target sequence on the left side of the *= operand.

Note that a_list[0] is a_list[3] returns True . This is because the *= operator doesn’t make a copy of the repeated data. It only reflects the data. This behavior can be a source of issues when you use the operator with mutable values.

For example, say that you want to create a list of lists to represent a matrix, and you need to initialize the list with n empty lists, like in the following code:

In this example, you use the *= operator to populate matrix with three empty lists. Now check out what happens when you try to populate the first sublist in matrix :

The appended values are reflected in the three sublists. This happens because the *= operator doesn’t make copies of the data that you want to repeat. It only reflects the data. Therefore, every sublist in matrix points to the same object and memory address.

If you ever need to initialize a list with a bunch of empty sublists, then use a list comprehension :

This time, when you populate the first sublist of matrix , your changes aren’t propagated to the other sublists. This is because all the sublists are different objects that live in different memory addresses.

Bitwise operators also have their augmented versions. The logic behind them is similar to that of the math operators. The following table summarizes the augmented bitwise operators that Python provides:

The augmented bitwise assignment operators perform the intended operation by taking the current value of the left operand as a starting point for the computation. Consider the following example, which uses the & and &= operators:

Programmers who work with high-level languages like Python rarely use bitwise operations in day-to-day coding. However, these types of operations can be useful in some situations.

For example, say that you’re implementing a Unix-style permission system for your users to access a given resource. In this case, you can use the characters "r" for reading, "w" for writing, and "x" for execution permissions, respectively. However, using bit-based permissions could be more memory efficient:

You can assign permissions to your users with the OR bitwise operator or the augmented OR bitwise operator. Finally, you can use the bitwise AND operator to check if a user has a certain permission, as you did in the final two examples.

You’ve learned a lot about augmented assignment operators and statements in this and the previous sections. These operators apply to math, concatenation, repetition, and bitwise operations. Now you’re ready to look at other assignment variants that you can use in your code or find in other developers’ code.

Other Assignment Variants

So far, you’ve learned that Python’s assignment statements and the assignment operator are present in many different scenarios and use cases. Those use cases include variable creation and initialization, parallel assignments, iterable unpacking, augmented assignments, and more.

In the following sections, you’ll learn about a few variants of assignment statements that can be useful in your future coding. You can also find these assignment variants in other developers’ code. So, you should be aware of them and know how they work in practice.

In short, you’ll learn about:

• Annotated assignment statements with type hints
• Assignment expressions with the walrus operator
• Managed attribute assignments with properties and descriptors
• Implicit assignments in Python

These topics will take you through several interesting and useful examples that showcase the power of Python’s assignment statements.

PEP 526 introduced a dedicated syntax for variable annotation back in Python 3.6 . The syntax consists of the variable name followed by a colon ( : ) and the variable type:

Even though these statements declare three variables with their corresponding data types, the variables aren’t actually created or initialized. So, for example, you can’t use any of these variables in an augmented assignment statement:

If you try to use one of the previously declared variables in an augmented assignment, then you get a NameError because the annotation syntax doesn’t define the variable. To actually define it, you need to use an assignment.

The good news is that you can use the variable annotation syntax in an assignment statement with the = operator:

The first statement in this example is what you can call an annotated assignment statement in Python. You may ask yourself why you should use type annotations in this type of assignment if everybody can see that counter holds an integer number. You’re right. In this example, the variable type is unambiguous.

However, imagine what would happen if you found a variable initialization like the following:

What would be the data type of each user in users ? If the initialization of users is far away from the definition of the User class, then there’s no quick way to answer this question. To clarify this ambiguity, you can provide the appropriate type hint for users :

Now you’re clearly communicating that users will hold a list of User instances. Using type hints in assignment statements that initialize variables to empty collection data types—such as lists, tuples, or dictionaries—allows you to provide more context about how your code works. This practice will make your code more explicit and less error-prone.

Up to this point, you’ve learned that regular assignment statements with the = operator don’t have a return value. They just create or update variables. Therefore, you can’t use a regular assignment to assign a value to a variable within the context of an expression.

Python 3.8 changed this by introducing a new type of assignment statement through PEP 572 . This new statement is known as an assignment expression or named expression .

Note: Expressions are a special type of statement in Python. Their distinguishing characteristic is that expressions always have a return value, which isn’t the case with all types of statements.

Unlike regular assignments, assignment expressions have a return value, which is why they’re called expressions in the first place. This return value is automatically assigned to a variable. To write an assignment expression, you must use the walrus operator ( := ), which was named for its resemblance to the eyes and tusks of a walrus lying on its side.

The general syntax of an assignment statement is as follows:

This expression looks like a regular assignment. However, instead of using the assignment operator ( = ), it uses the walrus operator ( := ). For the expression to work correctly, the enclosing parentheses are required in most use cases. However, there are certain situations in which these parentheses are superfluous. Either way, they won’t hurt you.

Assignment expressions come in handy when you want to reuse the result of an expression or part of an expression without using a dedicated assignment to grab this value beforehand.

Note: Assignment expressions with the walrus operator have several practical use cases. They also have a few restrictions. For example, they’re illegal in certain contexts, such as lambda functions, parallel assignments, and augmented assignments.

For a deep dive into this special type of assignment, check out The Walrus Operator: Python 3.8 Assignment Expressions .

A particularly handy use case for assignment expressions is when you need to grab the result of an expression used in the context of a conditional statement. For example, say that you need to write a function to compute the mean of a sample of numeric values. Without the walrus operator, you could do something like this:

In this example, the sample size ( n ) is a value that you need to reuse in two different computations. First, you need to check whether the sample has data points or not. Then you need to use the sample size to compute the mean. To be able to reuse n , you wrote a dedicated assignment statement at the beginning of your function to grab the sample size.

You can avoid this extra step by combining it with the first use of the target value, len(sample) , using an assignment expression like the following:

The assignment expression introduced in the conditional computes the sample size and assigns it to n . This way, you guarantee that you have a reference to the sample size to use in further computations.

Because the assignment expression returns the sample size anyway, the conditional can check whether that size equals 0 or not and then take a certain course of action depending on the result of this check. The return statement computes the sample’s mean and sends the result back to the function caller.

Python provides a few tools that allow you to fine-tune the operations behind the assignment of attributes. The attributes that run implicit operations on assignments are commonly referred to as managed attributes .

Properties are the most commonly used tool for providing managed attributes in your classes. However, you can also use descriptors and, in some cases, the .__setitem__() special method.

To understand what fine-tuning the operation behind an assignment means, say that you need a Point class that only allows numeric values for its coordinates, x and y . To write this class, you must set up a validation mechanism to reject non-numeric values. You can use properties to attach the validation functionality on top of x and y .

Here’s how you can write your class:

In Point , you use properties for the .x and .y coordinates. Each property has a getter and a setter method . The getter method returns the attribute at hand. The setter method runs the input validation using a try … except block and the built-in float() function. Then the method assigns the result to the actual attribute.

Here’s how your class works in practice:

When you use a property-based attribute as the left operand in an assignment statement, Python automatically calls the property’s setter method, running any computation from it.

Because both .x and .y are properties, the input validation runs whenever you assign a value to either attribute. In the first example, the input values are valid numbers and the validation passes. In the final example, "one" isn’t a valid numeric value, so the validation fails.

If you look at your Point class, you’ll note that it follows a repetitive pattern, with the getter and setter methods looking quite similar. To avoid this repetition, you can use a descriptor instead of a property.

A descriptor is a class that implements the descriptor protocol , which consists of four special methods :

• .__get__() runs when you access the attribute represented by the descriptor.
• .__set__() runs when you use the attribute in an assignment statement.
• .__delete__() runs when you use the attribute in a del statement.
• .__set_name__() sets the attribute’s name, creating a name-aware attribute.

Here’s how your code may look if you use a descriptor to represent the coordinates of your Point class:

You’ve removed repetitive code by defining Coordinate as a descriptor that manages the input validation in a single place. Go ahead and run the following code to try out the new implementation of Point :

Great! The class works as expected. Thanks to the Coordinate descriptor, you now have a more concise and non-repetitive version of your original code.

Another way to fine-tune the operations behind an assignment statement is to provide a custom implementation of .__setitem__() in your class. You’ll use this method in classes representing mutable data collections, such as custom list-like or dictionary-like classes.

As an example, say that you need to create a dictionary-like class that stores its keys in lowercase letters:

In this example, you create a dictionary-like class by subclassing UserDict from collections . Your class implements a .__setitem__() method, which takes key and value as arguments. The method uses str.lower() to convert key into lowercase letters before storing it in the underlying dictionary.

Python implicitly calls .__setitem__() every time you use a key as the left operand in an assignment statement. This behavior allows you to tweak how you process the assignment of keys in your custom dictionary.

Implicit Assignments in Python

Python implicitly runs assignments in many different contexts. In most cases, these implicit assignments are part of the language syntax. In other cases, they support specific behaviors.

Whenever you complete an action in the following list, Python runs an implicit assignment for you:

• Define or call a function
• Define or instantiate a class
• Use the current instance , self
• Import modules and objects
• Use a decorator
• Use the control variable in a for loop or a comprehension
• Use the as qualifier in with statements , imports, and try … except blocks
• Access the _ special variable in an interactive session

Behind the scenes, Python performs an assignment in every one of the above situations. In the following subsections, you’ll take a tour of all these situations.

When you define a function, the def keyword implicitly assigns a function object to your function’s name. Here’s an example:

From this point on, the name greet refers to a function object that lives at a given memory address in your computer. You can call the function using its name and a pair of parentheses with appropriate arguments. This way, you can reuse greet() wherever you need it.

If you call your greet() function with fellow as an argument, then Python implicitly assigns the input argument value to the name parameter on the function’s definition. The parameter will hold a reference to the input arguments.

When you define a class with the class keyword, you’re assigning a specific name to a class object . You can later use this name to create instances of that class. Consider the following example:

In this example, the name User holds a reference to a class object, which was defined in __main__.User . Like with a function, when you call the class’s constructor with the appropriate arguments to create an instance, Python assigns the arguments to the parameters defined in the class initializer .

Another example of implicit assignments is the current instance of a class, which in Python is called self by convention. This name implicitly gets a reference to the current object whenever you instantiate a class. Thanks to this implicit assignment, you can access .name and .job from within the class without getting a NameError in your code.

Import statements are another variant of implicit assignments in Python. Through an import statement, you assign a name to a module object, class, function, or any other imported object. This name is then created in your current namespace so that you can access it later in your code:

In this example, you import the sys module object from the standard library and assign it to the sys name, which is now available in your namespace, as you can conclude from the second call to the built-in dir() function.

You also run an implicit assignment when you use a decorator in your code. The decorator syntax is just a shortcut for a formal assignment like the following:

Here, you call decorator() with a function object as an argument. This call will typically add functionality on top of the existing function, func() , and return a function object, which is then reassigned to the func name.

The decorator syntax is syntactic sugar for replacing the previous assignment, which you can now write as follows:

Even though this new code looks pretty different from the above assignment, the code implicitly runs the same steps.

Another situation in which Python automatically runs an implicit assignment is when you use a for loop or a comprehension. In both cases, you can have one or more control variables that you then use in the loop or comprehension body:

The memory address of control_variable changes on each iteration of the loop. This is because Python internally reassigns a new value from the loop iterable to the loop control variable on each cycle.

The same behavior appears in comprehensions:

In the end, comprehensions work like for loops but use a more concise syntax. This comprehension creates a new list of strings that mimic the output from the previous example.

The as keyword in with statements, except clauses, and import statements is another example of an implicit assignment in Python. This time, the assignment isn’t completely implicit because the as keyword provides an explicit way to define the target variable.

In a with statement, the target variable that follows the as keyword will hold a reference to the context manager that you’re working with. As an example, say that you have a hello.txt file with the following content:

You want to open this file and print each of its lines on your screen. In this case, you can use the with statement to open the file using the built-in open() function.

In the example below, you accomplish this. You also add some calls to print() that display information about the target variable defined by the as keyword:

This with statement uses the open() function to open hello.txt . The open() function is a context manager that returns a text file object represented by an io.TextIOWrapper instance.

Since you’ve defined a hello target variable with the as keyword, now that variable holds a reference to the file object itself. You confirm this by printing the object and its memory address. Finally, the for loop iterates over the lines and prints this content to the screen.

When it comes to using the as keyword in the context of an except clause, the target variable will contain an exception object if any exception occurs:

In this example, you run a division that raises a ZeroDivisionError . The as keyword assigns the raised exception to error . Note that when you print the exception object, you get only the message because exceptions have a custom .__str__() method that supports this behavior.

There’s a final detail to remember when using the as specifier in a try … except block like the one in the above example. Once you leave the except block, the target variable goes out of scope , and you can’t use it anymore.

Finally, Python’s import statements also support the as keyword. In this context, you can use as to import objects with a different name:

In these examples, you use the as keyword to import the numpy package with the np name and pandas with the name pd . If you call dir() , then you’ll realize that np and pd are now in your namespace. However, the numpy and pandas names are not.

Using the as keyword in your imports comes in handy when you want to use shorter names for your objects or when you need to use different objects that originally had the same name in your code. It’s also useful when you want to make your imported names non-public using a leading underscore, like in import sys as _sys .

The final implicit assignment that you’ll learn about in this tutorial only occurs when you’re using Python in an interactive session. Every time you run a statement that returns a value, the interpreter stores the result in a special variable denoted by a single underscore character ( _ ).

You can access this special variable as you’d access any other variable:

These examples cover several situations in which Python internally uses the _ variable. The first two examples evaluate expressions. Expressions always have a return value, which is automatically assigned to the _ variable every time.

When it comes to function calls, note that if your function returns a fruitful value, then _ will hold it. In contrast, if your function returns None , then the _ variable will remain untouched.

The next example consists of a regular assignment statement. As you already know, regular assignments don’t return any value, so the _ variable isn’t updated after these statements run. Finally, note that accessing a variable in an interactive session returns the value stored in the target variable. This value is then assigned to the _ variable.

Note that since _ is a regular variable, you can use it in other expressions:

In this example, you first create a list of values. Then you call len() to get the number of values in the list. Python automatically stores this value in the _ variable. Finally, you use _ to compute the mean of your list of values.

Now that you’ve learned about some of the implicit assignments that Python runs under the hood, it’s time to dig into a final assignment-related topic. In the following few sections, you’ll learn about some illegal and dangerous assignments that you should be aware of and avoid in your code.

Illegal and Dangerous Assignments in Python

In Python, you’ll find a few situations in which using assignments is either forbidden or dangerous. You must be aware of these special situations and try to avoid them in your code.

In the following sections, you’ll learn when using assignment statements isn’t allowed in Python. You’ll also learn about some situations in which using assignments should be avoided if you want to keep your code consistent and robust.

You can’t use Python keywords as variable names in assignment statements. This kind of assignment is explicitly forbidden. If you try to use a keyword as a variable name in an assignment, then you get a SyntaxError :

Whenever you try to use a keyword as the left operand in an assignment statement, you get a SyntaxError . Keywords are an intrinsic part of the language and can’t be overridden.

If you ever feel the need to name one of your variables using a Python keyword, then you can append an underscore to the name of your variable:

In this example, you’re using the desired name for your variables. Because you added a final underscore to the names, Python doesn’t recognize them as keywords, so it doesn’t raise an error.

Note: Even though adding an underscore at the end of a name is an officially recommended practice , it can be confusing sometimes. Therefore, try to find an alternative name or use a synonym whenever you find yourself using this convention.

For example, you can write something like this:

In this example, using the name booking_class for your variable is way clearer and more descriptive than using class_ .

You’ll also find that you can use only a few keywords as part of the right operand in an assignment statement. Those keywords will generally define simple statements that return a value or object. These include lambda , and , or , not , True , False , None , in , and is . You can also use the for keyword when it’s part of a comprehension and the if keyword when it’s used as part of a ternary operator .

In an assignment, you can never use a compound statement as the right operand. Compound statements are those that require an indented block, such as for and while loops, conditionals, with statements, try … except blocks, and class or function definitions.

Sometimes, you need to name variables, but the desired or ideal name is already taken and used as a built-in name. If this is your case, think harder and find another name. Don’t shadow the built-in.

Shadowing built-in names can cause hard-to-identify problems in your code. A common example of this issue is using list or dict to name user-defined variables. In this case, you override the corresponding built-in names, which won’t work as expected if you use them later in your code.

Consider the following example:

The exception in this example may sound surprising. How come you can’t use list() to build a list from a call to map() that returns a generator of square numbers?

By using the name list to identify your list of numbers, you shadowed the built-in list name. Now that name points to a list object rather than the built-in class. List objects aren’t callable, so your code no longer works.

In Python, you’ll have nothing that warns against using built-in, standard-library, or even relevant third-party names to identify your own variables. Therefore, you should keep an eye out for this practice. It can be a source of hard-to-debug errors.

In programming, a constant refers to a name associated with a value that never changes during a program’s execution. Unlike other programming languages, Python doesn’t have a dedicated syntax for defining constants. This fact implies that Python doesn’t have constants in the strict sense of the word.

Python only has variables. If you need a constant in Python, then you’ll have to define a variable and guarantee that it won’t change during your code’s execution. To do that, you must avoid using that variable as the left operand in an assignment statement.

To tell other Python programmers that a given variable should be treated as a constant, you must write your variable’s name in capital letters with underscores separating the words. This naming convention has been adopted by the Python community and is a recommendation that you’ll find in the Constants section of PEP 8 .

In the following examples, you define some constants in Python:

The problem with these constants is that they’re actually variables. Nothing prevents you from changing their value during your code’s execution. So, at any time, you can do something like the following:

These assignments modify the value of two of your original constants. Python doesn’t complain about these changes, which can cause issues later in your code. As a Python developer, you must guarantee that named constants in your code remain constant.

The only way to do that is never to use named constants in an assignment statement other than the constant definition.

You’ve learned a lot about Python’s assignment operators and how to use them for writing assignment statements . With this type of statement, you can create, initialize, and update variables according to your needs. Now you have the required skills to fully manage the creation and mutation of variables in your Python code.

In this tutorial, you’ve learned how to:

• Write assignment statements using Python’s assignment operators
• Work with augmented assignments in Python
• Explore assignment variants, like assignment expression and managed attributes
• Identify illegal and dangerous assignments in Python

Learning about the Python assignment operator and how to use it in assignment statements is a fundamental skill in Python. It empowers you to write reliable and effective Python code.

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Vending Machine Project - Guidance on OOP code [Python]

Quite new to programming and have got an issue with some of my python code. I feel like there would be an easier way to write it/ simplify it. I am still working through it (working on the first item before and have got my 'tea' in the virtual vending machine. However I am not sure using that many if elif statements is the cleanest way to complete the code? Also, I want the code to constantly have an input available to the user so they can order more that one drink (if they have the money). So I would like it to loop and start again without losing the coin, how would I go about that?

I know it isn't much, but its my first assignment and I am happy that I have got it this far!

2 Answers 2

This is probably better suited for the code review board , but anyways... Really great for a first project! That being said, you've already identified the major shortcoming of your code - you shouldn't have to use all those if / elif s. I also don't think you can justify wrapping most of your code into classes, like you've done. These are my suggestions:

• Define two classes, VendingMachine and Item (an instance of this class represents a single available item in the vending machine).
• Follow good naming conventions. Notice the class names start with an uppercase letter, and are camel-case.
• Define an explicit entry point for your program, such as main .
• Use a loop to iterate over your vending machine items when displaying them. You can do something similar to cull all those if-statements.

• What do you mean by: Use a loop to iterate over your vending machine items when displaying them. You can do something similar to cull all those if-statements? –  wiggles Commented Apr 4, 2020 at 3:07
• Thank you very much! I have been working on a different angle. But I will post over on code review board, as you suggested. Thanks again for looking at it. –  wiggles Commented Apr 5, 2020 at 2:36

Good work. I've got one word to say to you: loops.

Where you've got 4 lines printing the vending machine items (at the start), you could replace it with a loop, like so:

Hopefully that gives you enough information to work out how to shrink the rest of your code too. If it needs to be elif (as in, you only want to check one of them, not all of them), use a break in the loop (see below for an example of a break).

As for your second question, you could have a loop around the insert_coin function call (in the user_input function) which keeps adding coins until the user inputs 'done'.

Here's a pretty basic example. There could be better ways to do this, but it should work.

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Code coach python Halloween candy

I am quite a beginner, and I am doing the Halloween candy challenge. And I have a problem, the goal is to calculate the chance that you take a dollar bill out of your pocket. For the whole thing search it up for yourself.) The code that I've written is: houses = int(input()) x = 2 / houses * 100 print(round(x)) and when I run the code it says 2 out of five right. But those two are the only ones I can see. So I don't know what to improve. Please help me by explaining it.

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Python Assignment Help: Expert Solutions and Guidance

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About Python

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.

Key Topics in Python

Let us understand some of the key topics of Python programming language below:

• 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:

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• 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

While Python is often touted as a beginner-friendly programming language, newcomers can run into a few hurdles that might make it seem a tad tricky. Let's explore some of these challenges:

1) Setting Up Your Workspace

Before you even start coding, you need to set up your development environment just right. Now, for beginners, this can be a bit of a puzzle. Figuring out all the necessary configurations can sometimes feel like a maze, and it might even leave you a bit demotivated at the beginning of your coding journey.

2) Deciding What to Code

Computers are like really obedient but somewhat clueless pets. You have to spell out every single thing for them. So, here's the thing: deciding what to tell your computer in your code can be a head-scratcher. Every line you type has a purpose, and that can get a bit overwhelming. It's like giving really detailed instructions to your pet, but in this case, your pet is a computer.

3) Dealing with Compiler Errors

Now, imagine this: You've written your code, hit that magic "run" button, and... oops! Compiler errors pop up on your screen. For beginners, this can be a heart-sinking moment. But hey, don't worry, it happens to the best of us.

4) Hunting Down Bugs

Making mistakes is perfectly normal, especially when you're just starting out. Syntax errors, in particular, can be a real pain. However, the good news is that with practice and time, these errors become less frequent. Debugging, or finding and fixing these issues, is a crucial part of learning to code. It helps you understand what can go wrong and how to write better code in the future.

If you find yourself grappling with these challenges or any others while working on your Python homework, don't sweat it. Our team of Python programmers is here to lend a helping hand. At Favtutor, we offer top-notch Python assignment help. Our experts, hailing from all around the globe, can provide efficient solutions to address your questions and challenges, all at prices that won't break the bank. So, don't hesitate to reach out for assistance and conquer your Python assignment obstacles.

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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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Answer to Question #346018 in Python for Nir

Cipher with Key

A Keyword Cipher is a monoalpha

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In a substitution cipher each letter of

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Using these letter matchings, every letter of the esse

substituted to get the encrypted messag

In a keyword cipher, the key determines the let he ciphertext alphabet is created by keeping the lette the beginning (after removing the repeated letters then the rest of remaining letters are used in aph

For example, if the key is zebras

Plaintext Alphabet: albicidjeifigibili Ciphertext Alphabet : zjelb/r/als/cidition

Notice that with this key, we are able to match each lecer of engl alphabet with a different letter. Using these matchings we can encrypt the word discovered as rpbluacerda

replaced with f,i is replaced with and soon

Given a key K and the message M, encrypt the message Keyword Cipher, using the given key

The first tine contains two

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