Day 41: Python program to calculate simple interest

 

def calculate_simple_interest(principal, rate, time):

    simple_interest = (principal * rate * time) / 100

    return simple_interest

principal = float(input("Enter the principal amount: "))

rate = float(input("Enter the rate of interest: "))

time = float(input("Enter the time in years: "))

simple_interest = calculate_simple_interest(principal, rate, time)

print(f"The Simple Interest is: {simple_interest}")

#source code --> clcoding.com 

Code Explanation:

Function Definition

def calculate_simple_interest(principal, rate, time):

    simple_interest = (principal * rate * time) / 100

    return simple_interest

def: This keyword is used to define a function.

calculate_simple_interest: The name of the function. It describes its purpose—to calculate simple interest.

Parameters:

principal: The initial amount of money (loan or deposit).

rate: The rate of interest (as a percentage).

time: The time for which the money is borrowed or invested, in years.

Formula for Simple Interest:

Simple Interest = Principal × Rate × Time/100

​

Multiply the principal amount by the rate and time.

Divide the result by 100 to calculate the interest.

return simple_interest: Returns the calculated interest value back to where the function is called.

2. User Input

principal = float(input("Enter the principal amount: "))

rate = float(input("Enter the rate of interest: "))

time = float(input("Enter the time in years: "))

input(): Accepts user input as a string.

float(): Converts the input string into a floating-point number to perform arithmetic operations.

Prompts the user to input:

Principal: The starting loan or deposit amount.

Rate: The interest rate (percentage).

Time: The duration (in years) for which interest is calculated.

Example Input:

Enter the principal amount: 1000  

Enter the rate of interest: 5  

Enter the time in years: 2  

3. Function Call

simple_interest = calculate_simple_interest(principal, rate, time)

The calculate_simple_interest() function is called with the user-provided values for principal, rate, and time.

The calculated interest is stored in the variable simple_interest.

Example Calculation (using input values from above):

Simple Interest =1000×5×2/100 =100

4. Output

print(f"The Simple Interest is: {simple_interest}")

f-string: A formatted string used to print variables within a string.

{simple_interest}: Inserts the calculated interest value into the output message.

Example Output:

The Simple Interest is: 100.0

Read More

Day 40: Python Program to Convert Celsius to Fahrenheit

 def celsius_to_fahrenheit(celsius):

    fahrenheit = (celsius * 9/5) + 32

    return fahrenheit

celsius = float(input("Enter temperature in Celsius: "))

fahrenheit = celsius_to_fahrenheit(celsius)

print(f"{celsius}°C is equal to {fahrenheit}°F")

 #source code --> clcoding.com 

Code Explanation:

1. Function Definition

def celsius_to_fahrenheit(celsius):

    fahrenheit = (celsius * 9/5) + 32

    return fahrenheit

def keyword: Used to define a function in Python.

celsius_to_fahrenheit: The name of the function that describes its purpose (convert Celsius to Fahrenheit).

celsius: A parameter passed to the function, representing the temperature in Celsius.

Formula:

Fahrenheit =(Celsius×9/5)+32

Multiply the Celsius value by 9/5 to convert it to Fahrenheit scale.

Add 32 because 0°𝐶 equals 32°𝐹.

return fahrenheit: The function returns the calculated Fahrenheit value.

2. User Input

celsius = float(input("Enter temperature in Celsius: "))

input(): Takes input from the user as a string.

float(): Converts the input string to a floating-point number so calculations can be performed.

"Enter temperature in Celsius: ": A message displayed to the user prompting them to input a value.

Example Input:

Enter temperature in Celsius: 25

Here, 25 will be converted to 25.0 as a float.

3. Function Call

fahrenheit = celsius_to_fahrenheit(celsius)

The celsius_to_fahrenheit() function is called with the user-provided Celsius value.

The returned Fahrenheit value is stored in the variable fahrenheit.

Example Calculation:

If the user enters 25, the calculation will be:

Fahrenheit =(25×9/5)+32=77.0

4. Output

print(f"{celsius}°C is equal to {fahrenheit}°F")

f-string: A formatted string used to print the values of variables within a string.

{celsius}: Inserts the Celsius value entered by the user.

{fahrenheit}: Inserts the calculated Fahrenheit value.

Example Output:

25.0°C is equal to 77.0°F

Read More

Python Coding Challange - Question With Answer(02171224)

 

What will the following code output?

import pandas as pd  
data = {'Name': ['Alice', 'Bob'], 'Age': [25, 30]}  
df = pd.DataFrame(data)  
print(df.shape)

A) (2, 2)
B) (2, 1)
C) (1, 2)
D) [2, 2]

Step-by-Step Breakdown:

1. Importing Pandas:
   import pandas as pd imports the pandas library, which provides tools for working with structured data.

2. Creating a Dictionary:

   
   data = {'Name': ['Alice', 'Bob'], 'Age': [25, 30]}

   • The dictionary data has two keys: 'Name' and 'Age'.
   • 'Name' corresponds to a list of strings: ['Alice', 'Bob'].
   • 'Age' corresponds to a list of integers: [25, 30].

3. Creating a DataFrame:

   
   df = pd.DataFrame(data)

   • The pd.DataFrame() function converts the dictionary data into a DataFrame:

   markdown
       Name   Age
   0 Alice 25
   1 Bob 30

   • Each key becomes a column name.
   • Each list becomes the values of the column.
   • The DataFrame has 2 rows (index 0 and 1) and 2 columns (Name and Age).

4. Printing the Shape:

   
   print(df.shape)

   • df.shape returns a tuple (rows, columns).
   • Here:
     • Rows = 2 (Alice and Bob)
     • Columns = 2 (Name and Age)

Final Output:

(2, 2)

---

Why this happens:

• shape attribute provides a quick way to check the dimensions of the DataFrame.
• The first value 2 refers to the number of rows.
• The second value 2 refers to the number of columns.

Read More

Python Boolean Type Quiz

 

Read More

Data Collection and Processing with Python

Data Collection and Processing with Python

In the age of big data, the ability to gather, clean, and process information efficiently has become a critical skill for professionals across industries. The Coursera course "Data Collection and Processing with Python" provides a comprehensive foundation for mastering these essential techniques. Whether you’re a beginner eager to delve into data science or an experienced professional looking to enhance your Python skills, this course has something to offer. Let’s explore what makes this course a standout in the field of data science education.

Why Choose This Course?

The course, part of the University of Michigan’s Python for Everybody Specialization, focuses on the practical aspects of data collection and processing. Here are a few reasons why it’s worth your time:

Practical Learning Approach: The course emphasizes hands-on learning, equipping you with tools and techniques to solve real-world data challenges.

Comprehensive Coverage: From APIs to web scraping, it covers a wide range of data collection methods and processing techniques.

Flexible and Accessible: With a self-paced format, it’s suitable for learners at various skill levels.

Course Highlights

1. Introduction to Data Collection

The course begins by introducing key concepts and tools for gathering data.

 You’ll learn how to:

Work with APIs to extract structured data from web services.

Utilize libraries like requests to interact with web resources programmatically.

2. Web Scraping Fundamentals

Next, it dives into web scraping, teaching you how to:

Use Python libraries such as BeautifulSoup to extract information from HTML pages.

Handle challenges like navigating complex website structures and managing rate limits.

3. Data Cleaning and Processing

Once data is collected, the focus shifts to cleaning and organizing it for analysis. Key topics include:

Working with common Python libraries like Pandas and NumPy.

Understanding data formats (e.g., CSV, JSON) and handling missing or inconsistent data.

4. Automating Data Workflows

The course wraps up with lessons on automating repetitive tasks, providing insights into:

Writing reusable scripts for data processing.

Scheduling data collection and processing pipelines.

Skills You’ll Gain

By the end of the course, you will have acquired several valuable skills, including:

API Integration: Mastering the use of APIs to fetch and interact with external data sources.

Web Scraping Expertise: Extracting meaningful data from websites using Python.

Data Cleaning and Organization: Preparing raw data for analysis by handling inconsistencies and errors.

Automation: Streamlining workflows for greater efficiency.

Applications in the Real World

1. Business and Marketing

Data collection skills enable businesses to analyze customer behavior, monitor competitors, and refine marketing strategies.

2. Academic Research

Researchers can gather data from diverse online sources, enabling robust and scalable studies.

3. Data Science and Analytics

Professionals can leverage these skills to build powerful data pipelines, essential for machine learning and predictive modeling.

Who Should Enroll?

This course is ideal for:

Beginners who want a structured introduction to data collection and processing with Python.

Intermediate learners looking to solidify their knowledge and expand their skill set.

Professionals aiming to integrate Python into their data workflows.

Join Free: Data Collection and Processing with Python

Conclusion:

The Coursera course "Data Collection and Processing with Python" is more than just an introduction to Python’s data-handling capabilities. It’s a gateway to mastering the tools and techniques that define modern data science. By the time you complete this course, you’ll not only have a strong foundation in Python but also the confidence to tackle complex data challenges in any domain.

Read More

Web Scraping With GPT: Translate Foreign News Headlines

 

In a world brimming with diverse information, the ability to navigate, extract, and understand global content has become indispensable. The Coursera course “AI Web Scraping with GPT: Translating Foreign News Headlines,”  introduces learners to a groundbreaking approach that combines web scraping and AI-powered translation. This blog delves into the unique features and potential applications of this course.

Why This Course Stands Out

Designed for tech enthusiasts, beginners, and professionals alike, this course merges essential technical skills with practical applications. Rudi Hinds’ offering is particularly noteworthy for:

Focusing on Real-World Relevance: The course centers on scraping and translating foreign news headlines, a practical use case with applications in journalism, market research, and global communication.

Utilizing Advanced AI Tools: Learners are introduced to OpenAI’s GPT technology, renowned for its powerful natural language processing and translation capabilities.

Step-by-Step Learning: The course ensures accessibility by breaking down complex tasks into manageable steps, making it ideal for learners with basic Python skills.

Course Overview

1. Foundations of Web Scraping

Participants are guided through the fundamentals of web scraping using Python libraries like BeautifulSoup. This foundational skill allows users to extract structured data, such as foreign news headlines, from various websites.

2. Integrating GPT for Translation

A standout feature of the course is its integration of GPT for translating foreign headlines into the learner’s preferred language. Learners gain hands-on experience working with OpenAI’s API to:

• Generate accurate translations.
• Maintain contextual integrity across different languages.
• Experiment with parameters to fine-tune the output.

3. Storing and Analyzing Data

The course also covers data organization and storage, providing learners with the skills to compile, sort, and analyze translated headlines. This opens doors to insights into global trends and narratives.

4. Practical Applications

By the end of the course, participants can:

• Automate multilingual data collection.
• Analyze media trends across languages and regions.
• Apply these techniques to personal, academic, or professional projects.

What You Will Gain

The course equips learners with a versatile skill set that combines programming, AI, and global communication. Key takeaways include:

Technical Expertise: Hands-on experience with Python, BeautifulSoup, and OpenAI’s GPT.

Global Awareness: An ability to explore and understand foreign media content in your native language.

Scalable Insights: Skills that can be adapted to various domains, from business intelligence to policy research.

Real-World Applications

1. Journalism and Media

Journalists can use these skills to monitor and analyze international news stories, ensuring diverse coverage and perspectives.

2. Business Intelligence

Marketers and business strategists can uncover global trends, identify opportunities, and assess risks by translating and analyzing international headlines.

3. Education and Research

Academics and students can explore multilingual data sets, enabling cross-cultural studies and fostering global insights.

Why Learn AI-Powered Web Scraping and Translation?

With the proliferation of information online, the ability to automate data extraction and translate it effectively is a game-changer. Rudi Hinds’ course provides an accessible pathway to harnessing these technologies, empowering learners to:

Break language barriers.

Analyze data at scale.

Gain a competitive edge in an increasingly data-driven world.

Join Free: Web Scraping With GPT: Translate Foreign News Headlines

Conclusion:

 “AI Web Scraping with GPT: Translating Foreign News Headlines,” is a must-try for anyone looking to explore the intersection of AI and data. Whether you’re a tech enthusiast, researcher, or professional aiming to stay ahead of the curve, this course provides a robust foundation in one of the most impactful applications of AI today.

Read More

Python Coding Challange - Question With Answer(01171224)

 

Explanation:

1. Tuple t Creation:

• t is a tuple with three elements:
  • 1 → an integer
  • [2, 3] → a mutable list
  • 4 → another integer
• So, t looks like this:
  
  
• t = (1, [2, 3], 4)

2. Tuple Immutability:

   • In Python, tuples are immutable. You cannot change the tuple itself (e.g., reassign or delete elements directly).
   • However, tuples can hold mutable objects like lists. If a tuple contains a list, you can modify the list.

3. Modifying the List:

   • t[1] refers to the list [2, 3] (the second element of the tuple).
   • t[1][0] = 100 changes the first element of the list [2, 3] to 100.
   • After this operation, the list becomes [100, 3].

4. Resulting Tuple:

   • The tuple t remains intact (as a container), but the list inside it has been modified.
   • The final tuple now looks like:

     t = (1, [100, 3], 4)

---

Output:

(1, [100, 3], 4)

---

Key Takeaways:

1. Tuples are immutable, but they can hold mutable objects like lists.
2. You can modify the contents of mutable objects inside a tuple.
3. Direct reassignment like t[1] = [100, 3] would raise an error because it tries to modify the tuple structure.

Read More

Python Coding Challange - Question With Answer (02161224)

 

What will the output of the following code be?

def puzzle():
    a, b, *c, d = (10, 20, 30, 40, 50)
    return a, b, c, d
print(puzzle())

A) (10, 20, [30], 40, 50)
B) (10, 20, [30, 40], 50)
C) (10, 20, [30, 40], 50)
D) (10, 20, [30, 40], 50)

Explanation:

The code involves tuple unpacking with the use of the * operator. Here's the step-by-step breakdown:

1. Unpacking the Tuple:
   The tuple (10, 20, 30, 40, 50) is unpacked using the variables a, b, *c, and d.

   • a gets the first value 10.
   • b gets the second value 20.
   • *c takes all the intermediate values as a list. In this case, *c will be [30, 40].
   • d gets the last value 50.

2. Return Statement:
   The function returns the unpacked values as a tuple: (a, b, c, d). This results in (10, 20, [30, 40], 50).

---

Correct Answer:

B) (10, 20, [30, 40], 50)

---

Key Concepts:

• Tuple Unpacking: The * operator collects multiple values into a list when unpacking.
• Order Matters: The first variable gets the first value, the last variable gets the last value, and * collects everything in between.

Read More

Python Coding challenge - Day 285| What is the output of the following Python Code?

Explanation:

1. Function Definition

def func(a, b, c): 

    print(a, b, c)

A function func is defined with three positional parameters: a, b, and c.

Inside the function, it simply prints the values of a, b, and c.

2. Creating a Tuple

args = (1, 2, 3)

A tuple named args is created with three elements: (1, 2, 3).

3. Calling the Function with Argument Unpacking

func(*args)

The *args syntax is called argument unpacking.

When *args is passed to a function, Python "unpacks" the elements of the tuple (or list) and passes them as separate arguments to the function.

In this case, func(*args) is equivalent to calling func(1, 2, 3).

The function receives:

a = 1

b = 2

c = 3

4. Execution of the Function

Inside the function, print(a, b, c) is executed, which prints the values of a, b, and c.

Output:

1 2 3

 

Read More

Web Scraping with Python

 

Exploring Python Web Scraping with Coursera’s Guided Project

In today’s digital era, data has become a crucial asset. From market trends to consumer preferences, accessing the right data can drive strategic decisions and innovative solutions. Python, with its simplicity and versatility, has emerged as one of the top tools for web scraping — the process of extracting information from websites. If you’re looking to dive into this domain, the Python Web Scraping guided project on Coursera offers an excellent starting point. Here, we’ll explore what this project entails, its benefits, and why it’s a great learning experience.

What is Python Web Scraping?

Web scraping is the technique of automatically extracting data from web pages. Using Python, developers can leverage powerful libraries such as Beautiful Soup, Requests, and Selenium to scrape, parse, and manipulate web content. Web scraping is widely used in applications like:

Gathering product prices from e-commerce sites.

Analyzing competitor data.

Extracting information for research purposes.

Automating tedious manual data collection tasks.

The Coursera project introduces you to these concepts in a structured, beginner-friendly manner.

Overview of the Coursera Guided Project

Coursera’s Python Web Scraping guided project is a hands-on, practical learning experience designed for beginners and intermediate learners. This project spans a few hours and teaches you the basics of web scraping with Python in a step-by-step format. Here are some key highlights:

Interactive Learning Environment

The project is hosted on Coursera’s interactive learning platform, which provides a virtual lab environment. This eliminates the need for complex setups, allowing you to focus on learning rather than installation hurdles.

Comprehensive Curriculum

You’ll explore fundamental tools and techniques, including:

Using the Requests library to fetch web page content.

Parsing HTML with Beautiful Soup.

Navigating and extracting specific elements like tables, images, and text from web pages.

Handling challenges like pagination and dynamic content.

Real-World Applications

The project emphasizes practical use cases, guiding you to scrape data from real websites. For instance, you might work on collecting data from job listing sites, news portals, or e-commerce platforms.

Guided Assistance

Every step of the project is accompanied by detailed explanations, ensuring that you understand the logic behind each line of code. Whether you’re a coding novice or a Python enthusiast, the instructions are clear and intuitive.

Flexible Pace

Coursera allows you to learn at your own pace. Pause, rewind, or revisit sections as needed to solidify your understanding.

Why Choose This Project?

Beginner-Friendly: The project assumes no prior web scraping experience, making it ideal for newcomers.

Practical Skills: By the end of the project, you’ll have a working web scraper and the confidence to build more complex tools.

Affordable Learning: Compared to traditional courses, guided projects are cost-effective, offering high value for a minimal investment.

Industry-Relevant Skills: Web scraping is a valuable skill in industries like data science, marketing, and finance. Learning it can boost your career prospects.

Prerequisites and Tools

Before starting the project, ensure you have a basic understanding of Python programming. Familiarity with concepts like loops, functions, and data structures will be helpful. The guided project uses the following tools:

Python: The primary programming language.

Requests Library: For fetching web page data.

Beautiful Soup: For parsing and navigating HTML.

Jupyter Notebook: For writing and testing your code interactively.

What you'll learn

• Parse complex HTML using Python 
• Apply powerful techniques for managing web scraping effectively 

Key Takeaways

• After completing this project, you’ll gain:
• A solid foundation in Python-based web scraping.
• Experience with essential libraries and their real-world applications.
• Insights into ethical scraping practices and handling website restrictions.
• Ethical Considerations

While web scraping is powerful, it’s essential to use it responsibly. Always respect website terms of service, avoid scraping private or sensitive data, and ensure your scripts do not overload servers. Ethical scraping builds trust and prevents legal complications.

How to Get Started

Visit the project page on Coursera: Python Web Scraping Guided Project.

Sign up and enroll in the project.

Follow the instructions to access the virtual lab environment.

Dive into the hands-on exercises and build your first web scraper.

Join Free: Web Scraping with Python

Conclusion:

The Python Web Scraping guided project on Coursera is an invaluable resource for anyone looking to harness the power of Python for data extraction. With its clear instructions, practical examples, and interactive platform, this project ensures a smooth learning curve. Whether you’re a student, researcher, or professional, mastering web scraping can open doors to countless opportunities. Start your journey today and unlock the potential of data-driven insights!

Read More

Python Coding challenge - Day 10| What is the output of the following Python Code?

 
Code Explanation:

Initialization of my_dict:

my_dict = {"a": 1, "b": 2, "c": 3}

Here, you're creating a dictionary called my_dict with three key-value pairs:

"a" is mapped to 1

"b" is mapped to 2

"c" is mapped to 3

So, initially, the dictionary looks like this:

{"a": 1, "b": 2, "c": 3}

Using the popitem() method:

result = my_dict.popitem()

The popitem() method removes and returns a randomly selected key-value pair from the dictionary.

It also removes that key-value pair from the dictionary.

After calling popitem(), my_dict is modified and loses one of its items.

For example, let's assume the last inserted item is ("c", 3) (which is usually the case in Python 3.7+). So after this call:

result will be the tuple ("c", 3)

The updated dictionary my_dict will look like this:

{"a": 1, "b": 2}

Printing result:

print(result)

This prints the key-value pair that was removed from my_dict by popitem(). In our example, it will 

print:

('c', 3)

Output:

('c', 3)

Read More

Python Coding challenge - Day 9| What is the output of the following Python Code?

 

Step-by-step Explanation:

Initialization of my_dict:

my_dict = {"a": 1, "b": 2, "c": 3}

Here, you're creating a dictionary named my_dict with three key-value pairs:

"a" is mapped to 1

"b" is mapped to 2

"c" is mapped to 3

The dictionary looks like this:

{"a": 1, "b": 2, "c": 3}

Using the clear() method:

my_dict.clear()

The clear() method is used to remove all items from the dictionary.

After calling clear(), my_dict becomes an empty dictionary because it deletes all the key-value pairs.

So after this line, my_dict is now:

{}

Printing my_dict:

print(my_dict)

This prints the current state of my_dict, which is now empty:

Output:

{}

Read More

Python Coding Challange - Question With Answer(01161224)

 

What will the following code output?

a = [1, 2, 3]
b = a[:]
a[1] = 5
print(a, b)

[1, 5, 3] [1, 5, 3]
[1, 2, 3] [1, 2, 3]
[1, 5, 3] [1, 2, 3]
Error

Step-by-Step Explanation:

1. a = [1, 2, 3]
   • A list a is created with elements [1, 2, 3].
2. b = a[:]
   • The slicing operation a[:] creates a shallow copy of the list a and assigns it to b.
   • At this point:
     • a and b are two separate objects in memory.
     • b contains the same elements as a, but any changes made to a will not affect b, and vice versa.
3. a[1] = 5
   • The element at index 1 of list a (which is 2) is replaced with 5.
   • After this modification:
     • a becomes [1, 5, 3].
     • b remains unchanged as [1, 2, 3].
print(a, b)
• The print() function outputs the current values of a and b:
• a is [1, 5, 3].
• b is [1, 2, 3].

Output: [1, 5, 3] [1, 2, 3]

---

Key Concept:

• Slicing ([:]) creates a shallow copy of the list.
  Changes made to the original list a do not affect the copied list b, because they are now stored in different memory locations.

Read More

Python Text Type Quiz

 

Read More

Python Coding challenge - Day 300| What is the output of the following Python Code?

 

Code Explanation:

def check_even_odd(n): 

Defines a function named check_even_odd that takes one parameter, n.

n is expected to be an integer.

The function will determine whether n is even or odd.

 

return "Even" if n % 2 == 0 else "Odd"

This single line uses a ternary conditional operator (a shorthand if-else statement).

Condition: n % 2 == 0 checks if the number n is divisible by 2.

If the remainder when n is divided by 2 is 0, the condition is True, and the function returns "Even".

If the condition is False, it returns "Odd".

print(check_even_odd(4))

Calls the check_even_odd function with the argument 4.

The function evaluates whether 4 is even or odd and returns the string "Even".

The print() function outputs the returned value ("Even") to the console.

Output:

When you run this corrected code, the output will be:

Even

Read More

Python Coding challenge - Day 299| What is the output of the following Python Code?

 

Code Explanation:

def sum_list(lst):

Defines a function named sum_list that takes one parameter, lst.

lst is expected to be a list of numbers.

The purpose of this function is to calculate the sum of all elements in the list.

return sum(lst)

Uses Python's built-in sum() function to calculate the sum of all the elements in the list lst.

sum(lst) iterates through the list and adds up all its elements.

The result is then returned to the caller.

print(sum_list([1, 2, 3, 4, 5]))

Calls the sum_list function with the list [1, 2, 3, 4, 5] as an argument.

The print() function then outputs the returned value (15) to the console.

Output:

When you run this code, the output will be:

15

Read More

Day 39: Python Program to Convert Gray to binary Code

 

def gray_to_binary(gray_str):

    """

    Function to convert a Gray Code to its equivalent Binary Code.

    :param gray_str: The input Gray Code string.

    :return: The converted Binary Code string.

    """

    binary_code = gray_str[0]

    for i in range(1, len(gray_str)):

        binary_bit = str(int(binary_code[i - 1]) ^ int(gray_str[i]))

        binary_code += binary_bit  

    return binary_code

if __name__ == "__main__":

    gray_input = input("Enter a Gray Code to convert into Binary Code: ")

     if all(bit in '01' for bit in gray_input):

        result = gray_to_binary(gray_input)

        

        print(f"Gray Code: {gray_input}")

        print(f"Corresponding Binary Code: {result}")

    else:

        print("Invalid input. Please enter a valid Gray Code consisting only of 0's and 1's.")

        #source code --> clcoding.com 

Code Explanation:

1. Function: gray_to_binary

Purpose

This function takes a Gray Code string as input and converts it into the corresponding Binary Code.

Parameters

gray_str: A string representing the Gray Code (e.g., "1011").

Logic

First Bit:

In Binary Code, the first bit is always the same as the first bit of the Gray Code:

binary_code = gray_str[0]

Subsequent Bits:

For each subsequent bit, the binary bit is calculated using the XOR operation between:

The last calculated binary bit (binary_code[i - 1]).

The current bit in the Gray Code (gray_str[i]).

Formula:

binary_bit = str(int(binary_code[i - 1]) ^ int(gray_str[i]))

int() converts the binary characters into integers.

^ performs the XOR operation.

str() converts the result back to a string for concatenation.

Concatenate the Binary Bit:

Append the computed binary bit to binary_code:

binary_code += binary_bit

Return Result:

After processing all bits, return the final Binary Code string.

Example Walkthrough:

For gray_str = "1011":

binary_code[0] = gray_str[0] = 1

Iteration 1 (i = 1):

binary_bit = binary_code[0] XOR gray_str[1] = 1 XOR 0 = 1

binary_code = "11"

Iteration 2 (i = 2):

binary_bit = binary_code[1] XOR gray_str[2] = 1 XOR 1 = 0

binary_code = "110"

Iteration 3 (i = 3):

binary_bit = binary_code[2] XOR gray_str[3] = 0 XOR 1 = 1

binary_code = "1101"

Final Binary Code: "1101"

2. Input Handling

The program prompts the user for a Gray Code:

gray_input = input("Enter a Gray Code to convert into Binary Code: ")

Validation

The program ensures the input contains only 0 and 1:

if all(bit in '01' for bit in gray_input):

all(bit in '01' for bit in gray_input):

Checks if every character in gray_input is either '0' or '1'.

If valid:

Calls the gray_to_binary function to compute the Binary Code.

Prints the input Gray Code and the corresponding Binary Code.

If invalid:

Displays an error message:

print("Invalid input. Please enter a valid Gray Code consisting only of 0's and 1's.")

3. Output

Valid Input:

For example:

Enter a Gray Code to convert into Binary Code: 1011

Gray Code: 1011

Corresponding Binary Code: 1101

Invalid Input:

If the user enters invalid characters:

Enter a Gray Code to convert into Binary Code: 12A

Invalid input. Please enter a valid Gray Code consisting only of 0's and 1's.

Read More

Snake Game in Python

CODE:

import pygame

import time

import random

pygame.init()

WIDTH, HEIGHT = 1200, 700

WHITE = (255, 255, 255)

BLACK = (0, 0, 0)

RED = (255, 0, 0)

GREEN = (0, 255, 0)

BLUE = (0, 0, 255)

screen = pygame.display.set_mode((WIDTH, HEIGHT))

pygame.display.set_caption("Snake Game")

clock = pygame.time.Clock()

snake_block = 10

snake_speed = 15

font_style = pygame.font.SysFont("bahnschrift", 25)

score_font = pygame.font.SysFont("comicsansms", 20)

def display_score(score):

    value = score_font.render(f"Your Score: {score}", True, RED)

    screen.blit(value, [10, 10])

def draw_snake(snake_block, snake_list):

    for block in snake_list:

        pygame.draw.rect(screen, GREEN, [block[0], block[1], snake_block, snake_block])

def message(msg, color):

    msg_surface = font_style.render(msg, True, color)

    screen.blit(msg_surface, [WIDTH / 6, HEIGHT / 3])

def game_loop():

    game_over = False

    game_close = False

    x, y = WIDTH // 2, HEIGHT // 2

    x_change, y_change = 0, 0

    snake_list = []

    snake_length = 1

    food_x = round(random.randrange(0, WIDTH - snake_block) / 10.0) * 10.0

    food_y = round(random.randrange(0, HEIGHT - snake_block) / 10.0) * 10.0

    while not game_over:

        while game_close:

            screen.fill(BLACK)

            message("Game Over! Press Q-Quit or C-Play Again", RED)

            display_score(snake_length - 1)

            pygame.display.update()

            for event in pygame.event.get():

                if event.type == pygame.KEYDOWN:

                    if event.key == pygame.K_q:

                        game_over = True

                        game_close = False

                    if event.key == pygame.K_c:

                        game_loop()

        for event in pygame.event.get():

            if event.type == pygame.QUIT:

                game_over = True

            if event.type == pygame.KEYDOWN:

                if event.key == pygame.K_LEFT:

                    x_change, y_change = -snake_block, 0

                elif event.key == pygame.K_RIGHT:

                    x_change, y_change = snake_block, 0

                elif event.key == pygame.K_UP:

                    x_change, y_change = 0, -snake_block

                elif event.key == pygame.K_DOWN:

                    x_change, y_change = 0, snake_block

        if x >= WIDTH or x < 0 or y >= HEIGHT or y < 0:

            game_close = True

        x += x_change

        y += y_change

        screen.fill(BLACK)

        pygame.draw.rect(screen, BLUE, [food_x, food_y, snake_block, snake_block])

        snake_head = [x, y]

        snake_list.append(snake_head)

        if len(snake_list) > snake_length:

            del snake_list[0]

        for block in snake_list[:-1]:

            if block == snake_head:

                game_close = True

        draw_snake(snake_block, snake_list)

        display_score(snake_length - 1)

        pygame.display.update()

        if x == food_x and y == food_y:

            food_x = round(random.randrange(0, WIDTH - snake_block) / 10.0) * 10.0

            food_y = round(random.randrange(0, HEIGHT - snake_block) / 10.0) * 10.0

            snake_length += 1

        clock.tick(snake_speed)

    pygame.quit()

    quit()

game_loop()

#source code --> clcoding.com

Code Explanation:

1. Imports and Initialization

import pygame

import time

import random

pygame.init()

pygame: A library used to create games.

time and random: Standard Python libraries for time delays and random number generation.

pygame.init(): Initializes all imported pygame modules.

2. Screen Setup

WIDTH, HEIGHT = 1200, 700

screen = pygame.display.set_mode((WIDTH, HEIGHT))

pygame.display.set_caption("Snake Game")

Defines the screen dimensions as 1200x700.

Creates the game window using pygame.display.set_mode().

Sets the title of the game window to "Snake Game".

3. Colors

WHITE = (255, 255, 255)

BLACK = (0, 0, 0)

RED = (255, 0, 0)

GREEN = (0, 255, 0)

BLUE = (0, 0, 255)

RGB tuples are used to define colors.

4. Game Variables

snake_block = 10

snake_speed = 15

snake_block: The size of each block of the snake.

snake_speed: Determines the snake's speed (frames per second).

5. Fonts

font_style = pygame.font.SysFont("bahnschrift", 25)

score_font = pygame.font.SysFont("comicsansms", 20)

Two fonts are created for rendering messages and scores.

6. Helper Functions

Displaying Score

def display_score(score):

    value = score_font.render(f"Your Score: {score}", True, RED)

    screen.blit(value, [10, 10])

Displays the player's score in red at the top-left corner.

Drawing the Snake

def draw_snake(snake_block, snake_list):

    for block in snake_list:

        pygame.draw.rect(screen, GREEN, [block[0], block[1], snake_block, snake_block])

Draws the snake as a series of green blocks using the snake_list, which tracks the coordinates of each block.

Displaying Messages

def message(msg, color):

    msg_surface = font_style.render(msg, True, color)

    screen.blit(msg_surface, [WIDTH / 6, HEIGHT / 3])

Displays messages on the screen, such as "Game Over".

7. Main Game Loop

Variables

def game_loop():

    game_over = False

    game_close = False

    x, y = WIDTH // 2, HEIGHT // 2

    x_change, y_change = 0, 0

    snake_list = []

    snake_length = 1

    food_x = round(random.randrange(0, WIDTH - snake_block) / 10.0) * 10.0

    food_y = round(random.randrange(0, HEIGHT - snake_block) / 10.0) * 10.0

game_over and game_close: Flags for the game's state.

x and y: Initial coordinates of the snake's head.

x_change and y_change: Tracks the snake's movement direction.

snake_list and snake_length: Represents the snake and its current length.

food_x and food_y: Randomly generated coordinates for the food.

Game Over Logic

while game_close:

    screen.fill(BLACK)

    message("Game Over! Press Q-Quit or C-Play Again", RED)

    display_score(snake_length - 1)

    pygame.display.update()

Displays a "Game Over" screen, along with options to quit (Q) or restart (C).

Event Handling

for event in pygame.event.get():

    if event.type == pygame.QUIT:

        game_over = True

    if event.type == pygame.KEYDOWN:

        if event.key == pygame.K_LEFT:

            x_change, y_change = -snake_block, 0

        elif event.key == pygame.K_RIGHT:

            x_change, y_change = snake_block, 0

        elif event.key == pygame.K_UP:

            x_change, y_change = 0, -snake_block

        elif event.key == pygame.K_DOWN:

            x_change, y_change = 0, snake_block

Handles quitting the game and arrow key input to change the snake's direction.

Boundary Collision

if x >= WIDTH or x < 0 or y >= HEIGHT or y < 0:

    game_close = True

Checks if the snake has hit the boundaries of the screen.

Snake Movement

x += x_change

y += y_change

Updates the snake's position.

Snake Growth and Collision Detection

snake_head = [x, y]

snake_list.append(snake_head)

if len(snake_list) > snake_length:

    del snake_list[0]

for block in snake_list[:-1]:

    if block == snake_head:

        game_close = True

Adds the new position of the snake's head to the snake_list.

Removes the last block if the snake hasn’t eaten food.

Checks if the snake collides with itself.

Food Collision

if x == food_x and y == food_y:

    food_x = round(random.randrange(0, WIDTH - snake_block) / 10.0) * 10.0

    food_y = round(random.randrange(0, HEIGHT - snake_block) / 10.0) * 10.0

    snake_length += 1

If the snake's head overlaps with the food, it generates new food and increases the snake's length.

8. Rendering

screen.fill(BLACK)

pygame.draw.rect(screen, BLUE, [food_x, food_y, snake_block, snake_block])

draw_snake(snake_block, snake_list)

display_score(snake_length - 1)

pygame.display.update()

Clears the screen, redraws the food and snake, updates the score, and refreshes the display.

9. Frame Rate

clock.tick(snake_speed)

Controls the frame rate based on snake_speed.

10. Quit

pygame.quit()

quit()

Exits the game when the loop ends.

Read More

Python Numeric Types Quiz

 

Read More

The Fundamentals of RDMA Programming

The Fundamentals of RDMA Programming

The "Fundamentals of RDMA Programming" course offered by NVIDIA on Coursera focuses on teaching Remote Direct Memory Access (RDMA), a crucial technology for high-speed server-to-server communication. RDMA enables direct memory access between systems without involving the CPU, making it ideal for applications requiring high throughput and low latency, such as HPC (high-performance computing) and data centers.

The course covers RDMA basics, core components, operations, code examples, and connection management. Learners will gain skills in writing RDMA applications over InfiniBand and Ethernet, understanding data-path flows, and managing memory efficiently. By the end, participants will be proficient in developing RDMA-based applications.

The Fundamentals of RDMA Programming course on Coursera provides comprehensive training in Remote Direct Memory Access (RDMA), a technology that allows servers to access each other's memory directly, bypassing the CPU. 

RDMA Basics: Understanding the core concepts, such as memory regions, queues, and buffers.

InfiniBand and Ethernet: Learning how RDMA operates over different networking fabrics.

Programming with RDMA: Hands-on exercises on building RDMA-based applications.

Data Path Flows: Understanding how data is transferred with minimal CPU involvement.

The Fundamentals of RDMA Programming course on Coursera offers the following key highlights:

Key Highlights of the course:

Introduction to RDMA: Learn the core concepts of Remote Direct Memory Access, including its benefits for high-performance applications.

Networking Protocols: Understand RDMA protocols such as InfiniBand and RoCE (RDMA over Ethernet).

Memory Management: Master memory region registration, allocation, and how they affect performance.

Efficient Data Communication: Explore how RDMA enables direct memory access between systems, bypassing the CPU for faster data transfer.

Hands-on Exercises: Gain practical experience writing RDMA-based applications, managing connections, and optimizing network performance.

This course is designed to equip learners with the skills to develop efficient, low-latency applications in high-performance computing environments.

What you'll learn

• RDMA Basics: Bypassing the OS, Memory zero copy and transport offload
• Core RDMA:  RDMA verbs and objects, data-path flow and memory management
• RDMA Operations:  Send and receive operations, RDMA write and RDMA read operations, atomic operations
• Become familiar with InfiniBand architecture layer, RDMA basic flow, create a completion queue (CQ) and a queue pair (QP), and execute RDMA operation
•  Establish connection using RDMA_CM API

Future enhancements in RDMA programming may include:

Support for New Protocols: Expanding support for upcoming RDMA protocols like PCIe-based RDMA and advanced Ethernet technologies.

Integration with AI/ML Workloads: Optimizing RDMA for AI and machine learning environments, which require low latency and high-throughput communication.

Scalability Improvements: Increasing the scalability of RDMA in large-scale distributed systems.

Security Enhancements: Implementing better security features, like encryption, in RDMA communication to prevent data breaches in critical environments.

These developments could expand RDMA’s applications, making it even more powerful for modern high-performance computing systems.

Join Free: The Fundamentals of RDMA Programming

Conclusion:

The Fundamentals of RDMA Programming course provides essential knowledge and hands-on experience in building efficient, high-performance applications using RDMA. Through understanding RDMA protocols, memory management, and network communication, learners gain the skills to optimize data transfer processes. The course also equips professionals to work with technologies like InfiniBand and RoCE, making it a valuable resource for those seeking to enhance their expertise in high-performance computing and networking. As technology evolves, RDMA's role in low-latency, high-throughput systems will continue to grow.

Read More

Python Coding challenge - Day 298| What is the output of the following Python Code?

 

Code Explanation:

Function Definition (sort_list(lst)):

The function sort_list(lst) is defined to take one parameter, lst, which is expected to be a list.

Inside the function, the goal is to return a sorted version of the list lst.

Sorting the List (return sorted(lst)):

Inside the function, sorted(lst) is used to sort the list lst.

The sorted() function is a built-in Python function that returns a new list with the elements of the input list sorted in ascending order by default.

The sorted() function does not modify the original list, but instead, it returns a new list that is sorted.

For example, if the input list lst = [5, 2, 9, 1, 5, 6], the sorted() function will return [1, 2, 5, 5, 6, 9].

Calling the Function (print(sort_list([5, 2, 9, 1, 5, 6]))):

The sort_list() function is called with the list [5, 2, 9, 1, 5, 6] as the argument.

Inside the function, sorted([5, 2, 9, 1, 5, 6]) is called, which returns [1, 2, 5, 5, 6, 9].

The result [1, 2, 5, 5, 6, 9] is then returned by the function.

Printing the Result (print(sort_list([5, 2, 9, 1, 5, 6]))):

The print() function is used to display the result returned by the sort_list() function.

Since the sorted list [1, 2, 5, 5, 6, 9] is returned, it will be printed to the console.

Summary:

The code defines a function sort_list(lst) that takes a list as an argument and returns a sorted version of that list using the sorted() function.

The list [5, 2, 9, 1, 5, 6] is passed to the function, and the sorted version of the list [1, 2, 5, 5, 6, 9] is returned and printed.

Output:

[1, 2, 5, 5, 6, 9]

Read More

Python Coding challenge - Day 297| What is the output of the following Python Code?

 

Code Explanation:

List Definition (nums = [3, 1, 4, 1, 5, 9]):

The first line defines a list called nums, which contains the following integers: [3, 1, 4, 1, 5, 9].

This list represents a collection of numbers from which we will find the largest value.

Using the max() function (largest = max(nums)):

The max() function is a built-in Python function that takes an iterable (like a list, tuple, or string) as an argument and returns the largest element in the iterable.

In this case, max(nums) finds the largest number in the nums list.

The max() function compares each element in the list to find the highest value.

Here, the list is [3, 1, 4, 1, 5, 9], and the largest number is 9. So, the max() function returns 9.

Storing the Result (largest = max(nums)):

The result of the max(nums) call (which is 9) is stored in the variable largest.

After this line, largest now holds the value 9.

Printing the Result (print(largest)):

The print(largest) statement outputs the value stored in the variable largest.

Since largest was assigned the value 9 from the max(nums) function call, the output will be:

9

Summary:

The code defines a list of numbers and uses the max() function to find and store the largest number in the list.

The largest number, 9, is then printed.

Final Output:

9

Read More

Python Coding challenge - Day 296| What is the output of the following Python Code?

Code Explanation:

Function Definition (factorial(n)):

The function factorial(n) is designed to calculate the factorial of a number n. The factorial of a number is the product of all positive integers less than or equal to that number. For example, 5! = 5 * 4 * 3 * 2 * 1 = 120.

Base Case (if n == 1:):

The line if n == 1: checks whether the number n is equal to 1.

If n is 1, the function returns 1. This is because the factorial of 1 is defined as 1. This is called the base case in recursion, which prevents the recursion from continuing indefinitely.

Recursive Case (return n * factorial(n-1)):

If n is not 1, the function proceeds to the line return n * factorial(n-1).

This is the recursive step, where the function calls itself with the value n-1 and multiplies the result by n.

The idea is that the factorial of n can be calculated as n * factorial(n-1). For example, to calculate 5!, the function will first calculate 4!, then multiply it by 5, and so on until it reaches the base case (factorial(1)).

First Call (factorial(5)):

When factorial(5) is called, n is 5, which is not equal to 1, so it proceeds to the recursive case and calls factorial(4).

The call stack becomes:

factorial(5) -> 5 * factorial(4)

factorial(4) -> 4 * factorial(3)

factorial(3) -> 3 * factorial(2)

factorial(2) -> 2 * factorial(1)

factorial(1) -> 1

Unwinding the Recursion:

Once factorial(1) returns 1, the recursion "unwinds" and each function call returns its result:

factorial(2) returns 2 * 1 = 2

factorial(3) returns 3 * 2 = 6

factorial(4) returns 4 * 6 = 24

factorial(5) returns 5 * 24 = 120

Final Output:

The final result of factorial(5) is 120, which is printed by the print(factorial(5)) statement.

Visualizing the Process:

factorial(5) calls factorial(4)

factorial(4) calls factorial(3)

factorial(3) calls factorial(2)

factorial(2) calls factorial(1)

factorial(1) returns 1

factorial(2) returns 2 * 1 = 2

factorial(3) returns 3 * 2 = 6

factorial(4) returns 4 * 6 = 24

factorial(5) returns 5 * 24 = 120

Thus, the output is 120.

Read More

AI Infrastructure and Operations Fundamentals

 

AI Infrastructure and Operations Fundamentals

The "AI Infrastructure and Operations Fundamentals" course by NVIDIA on Coursera is designed for IT professionals and those new to AI. It covers AI technologies, machine learning, deep learning, and the essential role of GPUs in AI workloads. The course provides insights into deploying AI infrastructure across various environments such as on-premises, cloud, and hybrid setups. With modules on AI operations and infrastructure management, it equips learners with the tools to support and manage AI workflows effectively. Completing this course also prepares you for the NVIDIA Certified Associate certification.

The AI Infrastructure and Operations Fundamentals course by NVIDIA on Coursera is a comprehensive learning experience aimed at IT professionals, engineers, and anyone looking to understand the foundational aspects of AI infrastructure. This course dives deep into how AI workloads are managed, optimized, and deployed across different environments, including on-premises, cloud, and hybrid setups.

Key highlights include:

Understanding AI infrastructure: Learn how GPUs power AI applications, and explore the deployment of AI systems.

Machine Learning and Deep Learning: Discover the crucial roles these technologies play in AI.

Practical skills: Learn best practices for infrastructure management and how to build efficient AI pipelines.

Certification: Completing this course can lead to the NVIDIA Certified Associate certification, enhancing your professional credibility in the AI space.

Ideal for those starting in AI or those working in operations and looking to expand their skill set, the course is designed to equip learners with the skills necessary to support AI workflows, optimize performance, and ensure smooth operations across AI infrastructures. Whether you're a beginner or already have some experience with AI, this course offers valuable insights into managing AI infrastructure efficiently.

What you'll learn

• Explore diverse applications of AI across various industries.
• Understand concepts like Machine Learning, Deep Leaning, training and inference.
• Trace the evolution of AI Technologies. From its inception to the revolutionary advances brought by Generative AI, and the role of GPUs.
• You will become familiar with deep learning frameworks and AI software stack.
• Learn about considerations when deploying AI workloads on a data center on prem, in the cloud, on a hybrid model, or on a multi-cloud environment.

The AI Infrastructure and Operations Fundamentals course by NVIDIA on Coursera is a valuable resource for those seeking to understand AI infrastructure management. It covers:

GPU-Powered AI: Learn about GPUs and their significance in accelerating AI and ML workloads.

Deployment Environments: Explore on-premise, cloud, and hybrid deployment strategies for AI systems.

AI Workflow Optimization: Gain practical skills in managing and optimizing AI pipelines for improved performance.

Scalability: Learn how to scale AI systems and ensure long-term efficiency.

Join Free: AI Infrastructure and Operations Fundamentals

Conclusion:

 This course is ideal for anyone looking to build or manage AI systems, with the added benefit of preparing learners for the NVIDIA Certified Associate certification, making it a great step forward in a tech career. The AI Infrastructure and Operations Fundamentals course by NVIDIA provides learners with essential skills to manage and deploy AI systems across various environments. By focusing on GPUs, AI workflows, and infrastructure optimization, it prepares professionals for AI infrastructure management and supports certification for the NVIDIA Certified Associate role. This course is perfect for those wanting to gain practical insights into AI operations and scale AI systems effectively, making it a strong foundation for a career in AI infrastructure

Read More

Python Coding challenge - Day 8| What is the output of the following Python Code?

 

Code Explanation:

my_list = [1, 2, 3, 4, 5]

This line creates a list called my_list with the values [1, 2, 3, 4, 5].

result = my_list[1:4:2]

This line uses list slicing with a start, stop, and step.

Start index (1): This tells Python where to start slicing. It starts from index 1, which is the second element in the list (2).

Stop index (4): This tells Python where to stop slicing. It stops just before index 4. So, it will slice up to index 3 (which is the value 4), but does not include index 4 (the element 5).

Step size (2): This tells Python to take every second element between the start and stop indices. In this case, starting at index 1, it will skip every other element. So, it will take:

The element at index 1 (which is 2),

Then skip index 2 (which is 3), and

Take the element at index 3 (which is 4).

Therefore, the sliced list will contain [2, 4].

print(result)

This line prints the resulting sublist, which is [2, 4].

Summary:

You started with the list [1, 2, 3, 4, 5].

Using slicing with a start index of 1, stop index of 4, and a step size of 2, you selected every second element between index 1 and index 3 (inclusive).

The result is the list [2, 4].

Output:

[2, 4]

Read More

Python Coding challenge - Day 7| What is the output of the following Python Code?

 

Code Explanation:

my_list = [1, 2, 3, 4, 5]

This line creates a list called my_list, which contains the numbers [1, 2, 3, 4, 5].

In Python, a list is an ordered collection of items, and each item in the list has an index that represents its position.

result = my_list[-3:-1]

This line uses list slicing to create a sublist from my_list.

Negative indices are used here:

-3: Refers to the third element from the end of the list. Since the list is [1, 2, 3, 4, 5], the third-to-last element is 3.

-1: Refers to the last element of the list. So, my_list[-1] is 5, but in slicing, the stop index (-1 here) is excluded. That means the slice will stop right before 5.

The slice starts at the element at index -3 (which is 3), and it goes up to but does not include the element at index -1 (which is 5). So, it includes the elements at indices -3 (3) and -2 (4), which results in the sublist [3, 4].

print(result)

This line prints the sublist result, which contains [3, 4].

Summary:

You started with the list [1, 2, 3, 4, 5].

Using negative indices, you sliced the list starting from the third-to-last element (3) and stopping just before the last element (5), giving you [3, 4].

The output is:

[3, 4]

Read More

Day 40: Python Program to Convert Celsius to Fahrenheit

def celsius_to_fahrenheit(celsius):

    fahrenheit = (celsius * 9/5) + 32

    return fahrenheit

celsius = float(input("Enter temperature in Celsius: "))

fahrenheit = celsius_to_fahrenheit(celsius)

print(f"{celsius}°C is equal to {fahrenheit}°F")

 #source code --> clcoding.com 

Code Explanation:

1. Function: celsius_to_fahrenheit

def celsius_to_fahrenheit(celsius):

    fahrenheit = (celsius * 9/5) + 32

    return fahrenheit

Purpose:

Converts a given temperature in Celsius to Fahrenheit.

Parameter:

celsius: A float or integer representing the temperature in Celsius.

Formula:

The conversion formula from Celsius to Fahrenheit is:

F=(C×9/5​)+32

Where:

𝐹

F is the temperature in Fahrenheit.

𝐶

C is the temperature in Celsius.

Steps:

Multiply the Celsius temperature by 9/5.

Add 32 to the result.

Return the calculated Fahrenheit temperature.

2. User Input

celsius = float(input("Enter temperature in Celsius: "))

input():

Prompts the user to enter a temperature value in Celsius.

float():

Converts the input string to a floating-point number, allowing decimal values.

celsius:

Stores the user-provided temperature.

3. Function Call

fahrenheit = celsius_to_fahrenheit(celsius)

The program calls the celsius_to_fahrenheit function, passing the celsius value entered by the user as an argument.

The function returns the equivalent temperature in Fahrenheit, which is stored in the variable fahrenheit.

4. Output

print(f"{celsius}°C is equal to {fahrenheit}°F")

Formatted String (f-string):

Embeds the values of celsius and fahrenheit into the output string.

Displays the conversion result in a user-friendly format.

 

Read More