Python OOPS Challenge | Day 16 | What is the output of following Python code?

The code snippet is an example of Python multiple inheritance, and here’s the explanation of the output:

Code Analysis:

1. Classes:

Glasses: A basic class with no attributes or methods.

Shader: A class containing a method printShadeIndex(self) that prints the string "high".

Sunglasses: Inherits from both Glasses and Shader.

2. Object Creation:

An instance of the Sunglasses class is created: obj = Sunglasses().

Since Sunglasses inherits from Shader, it gains access to the printShadeIndex method from Shader.

3. Method Call:

obj.printShadeIndex() invokes the method from the Shader class.

This prints the string "high".

Multiple Inheritance in Action:

The method resolution order (MRO) ensures that Shader's printShadeIndex method is found and executed when called on the Sunglasses instance.

Output:

The output of the code snippet is:

high

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Day 13 : Python Program to Check whether a given year is a Leap Year

 

def is_leap_year(year):

    

    if (year % 4 == 0 and year % 100 != 0) or (year % 400 == 0):

        return True

    else:

        return False

year = int(input("Enter a year: "))

if is_leap_year(year):

    print(f"{year} is a leap year.")

else:

    print(f"{year} is not a leap year.")

Code Explanation:

1. Function Definition:

   
   def is_leap_year(year):

   • A function is_leap_year is defined, which takes one argument: year.

2. Leap Year Logic:

   if (year % 4 == 0 and year % 100 != 0) or (year % 400 == 0):

           return True
            else:
           return False

1. • Leap Year Rules:
     • A year is a leap year if:
       1. It is divisible by 4 and not divisible by 100.
       2. Or, it is divisible by 400.
   • Explanation of Conditions:
     • year % 4 == 0: The year is divisible by 4.
     • year % 100 != 0: The year is not divisible by 100 (to exclude years like 1900, 2100 which are divisible by 100 but not leap years).
     • year % 400 == 0: The year is divisible by 400 (e.g., 2000, 2400 which are leap years).
   • If either condition is true, the function returns True (indicating a leap year), otherwise False.

---

3. Input:

   year = int(input("Enter a year: "))
   
   • The program prompts the user to input a year, which is converted to an integer and stored in the variable year.

4. Check Leap Year:

   if is_leap_year(year):
   print(f"{year} is a leap year.")

           else:
           print(f"{year} is not a leap year.")

3. • The function is_leap_year is called with the input year.
   • Depending on whether the function returns True or False:
     • If True: The year is printed as a leap year.
     • If False: The year is printed as not a leap year.

    #source code --> clcoding.com 

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DATA SCIENCE AND PYTHON LOOPS: UNLOCKING THE SECRETS OF DATA SCIENCE: STEP-BY-STEP INSTRUCTIONS FOR ASPIRING DATA SCIENTISTS - 2 BOOKS IN 1

 

"Data Science Demystified: A Beginner's Guide to Mastering Data Analysis and Machine Learning for Career Success 

Unlocking the Secrets of Data Science: Step-by-Step Instructions for Aspiring Data Scientists".

Unlock the Secrets of Data Science

Discover the fundamentals of data analysis and machine learning in easy-to-understand language. From understanding data structures and algorithms to mastering statistical techniques and predictive modeling, this book covers it all. Step-by-step instructions and practical examples guide you through each concept, ensuring you develop a strong foundation in data science.

Master Data Analysis and Machine Learning

Gain hands-on experience with data analysis and machine learning techniques using popular tools and programming languages such as Python, R, and SQL. Learn how to collect, clean, and analyze data effectively, and build powerful machine learning models to extract insights and make data-driven decisions.

Prepare for Career Success

Whether you're aiming for a career as a data analyst, data engineer, data scientist, or machine learning engineer, this book equips you with the skills and knowledge needed to succeed in the field of data science. Learn how to build a professional portfolio, network with industry professionals, and navigate the job market with confidence.

Why Choose "Data Science Demystified?

Comprehensive coverage of data science fundamentals

Easy-to-follow explanations and practical examples

Hands-on experience with popular tools and programming languages

Insights from industry experts and real-world case studies

Practical tips for career development and job search strategies

"Python Mastery: A Beginner's Guide to Unlocking the Power of Loops for Seamless Coding - Building a Solid Foundation in Python Programming." This comprehensive book is meticulously crafted for beginners, providing an immersive and accessible journey into the world of Python programming.

Dive into the foundations of Python with a focus on mastering the art of loops, a fundamental concept crucial for seamless and efficient coding. Each chapter is carefully designed to guide beginners through essential programming principles, ensuring a solid understanding of Python's syntax and functionality.

Key Features:

1. Clear and Concise Introduction to Python: This book serves as your gateway to Python programming, introducing the language in a clear, beginner-friendly manner. Whether you are new to coding or transitioning from another language, the book caters to learners of all backgrounds.

2. Focused Exploration of Loops: Loops are the backbone of many programming tasks, and this book places a special emphasis on unraveling their power. Through detailed explanations and practical examples, readers gain mastery over both "for" and "while" loops, unlocking the ability to create efficient and elegant solutions to a variety of programming challenges.

3. Practical Examples and Hands-On Exercises: Learning by doing is at the heart of this guide. With a plethora of practical examples and hands-on exercises, readers get the chance to apply their newfound knowledge immediately. This interactive approach solidifies learning and boosts confidence in Python programming.

4. Building a Strong Python Foundation: Beyond loops, this book lays the groundwork for a strong Python foundation. Readers explore key concepts, including variables, data types, control flow, functions, and more. Each chapter builds upon the previous, ensuring a seamless progression in mastering Python.

Kindle: DATA SCIENCE AND PYTHON LOOPS: UNLOCKING THE SECRETS OF DATA SCIENCE: STEP-BY-STEP INSTRUCTIONS FOR ASPIRING DATA SCIENTISTS - 2 BOOKS IN 1

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ChatGPT Prompts for Data Science: 625+ ChatGPT Done For You Prompts to Simplify, Solve, Succeed in Data Science

 

Are You Ready to Master Data Science with the Most Comprehensive and Practical Guide Available?

In today's data-driven world, staying ahead means mastering the tools and techniques that turn raw data into actionable insights. Whether you're a seasoned data scientist, an ambitious beginner, or a business leader hungry for clarity, "ChatGPT Prompts for Data Science" is your ultimate resource. This book is a game-changer—a 360-degree solution for all your data science challenges.

Why This Book is a Must-Have for Every Data Enthusiast:

Comprehensive Coverage: From foundational concepts to advanced techniques like machine learning, geospatial analysis, and natural language processing, this book covers it all.

Actionable Prompts: Packed with 500+ ready-to-use ChatGPT prompts tailored for real-world applications, this is your ultimate toolkit to solve problems quickly and effectively.

Expert Insights: Written by Jaideep Parashar, a researcher, entrepreneur, and keynote speaker with years of experience.

Universal Accessibility: Perfect for professionals, students, and leaders—no matter your level of expertise, this book has something for you.

What You’ll Learn:

Data Collection and Preparation: Clean, process, and organize data with ease.

Advanced Data Analysis: Dive into predictive analytics, machine learning, and more.

Data Visualization and Storytelling: Turn insights into compelling stories with actionable visuals.

Real-World Applications: Solve problems in industries like healthcare, retail, and logistics.

Future Trends: Stay ahead with insights into AI, edge computing, and ethical data science.

Who This Book is For:

Professionals: Accelerate workflows, enhance decision-making, and deliver results faster.

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What Makes This Book Special:

The last book on data science you’ll ever need—covering every major topic, tool, and challenge in the field.

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The book also help you with:

Data science tools

Artificial intelligence prompts

Machine learning guide

ChatGPT applications

Advanced analytics

Data visualization tips

Business intelligence techniques

Geospatial data analysis

Predictive modeling

Ethical AI and data privacy

This book is your opportunity to become a data science powerhouse. Don’t just stay ahead of the curve shape it. Get your copy now and start transforming data into meaningful action.

Hard Copy: ChatGPT Prompts for Data Science: 625+ ChatGPT Done For You Prompts to Simplify, Solve, Succeed in Data Science

Kindle:  ChatGPT Prompts for Data Science: 625+ ChatGPT Done For You Prompts to Simplify, Solve, Succeed in Data Science

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Spatial Data Science

 

Spatial Data Science

Spatial Data Science will show GIS scientists and practitioners how to add and use new analytical methods from data science in their existing GIS platforms. By explaining how the spatial domain can provide many of the building blocks, it's critical for transforming data into information, knowledge, and solutions.

"Spatial Data Science" is a specialized guide that delves into the intersection of spatial data and data science, focusing on analyzing, visualizing, and interpreting geospatial data. This book is tailored for professionals, researchers, and students who are interested in leveraging spatial data to solve real-world problems across various domains such as urban planning, environmental science, transportation, and business analytics.

Key Features of the Book

Comprehensive Introduction to Spatial Data

Covers fundamental concepts of spatial data, including coordinate systems, spatial relationships, and geographic data types (raster and vector).

Focus on Analytical Tools

Explores tools and libraries like:

Python: GeoPandas, Shapely, Folium, and Rasterio.

R: sf, sp, and tmap.

Demonstrates integration with GIS software such as QGIS and ArcGIS.

Real-World Applications

Case studies and projects focus on topics like mapping, geospatial machine learning, urban development analysis, and environmental modeling.

Visualization Techniques

Guides readers in creating compelling maps and interactive visualizations using tools like Matplotlib, Plotly, and Leaflet.

Advanced Topics

Covers spatial statistics, geostatistics, spatial interpolation, and network analysis, catering to advanced learners.

Who Should Read This Book?

Data Scientists and Analysts: Those looking to expand their expertise into spatial data applications.

GIS Professionals: Individuals interested in applying data science techniques to geospatial data.

Academics and Researchers: Useful for students and researchers in geography, environmental science, and related fields.

Urban Planners and Policymakers: Leverage spatial insights for decision-making and policy development.

Why It Stands Out

Interdisciplinary Approach: Combines spatial thinking with data science methodologies.

Practical Orientation: Emphasizes hands-on learning with examples and exercises.

Wide Applicability: Showcases how spatial data science impacts diverse fields, from disaster management to business intelligence.

This book is for those using or studying GIS and the computer scientists, engineers, statisticians, and information and library scientists leading the development and deployment of data science.

Hard Copy: Spatial Data Science

Kindle: Spatial Data Science

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Introduction to Data Analytics using Python for Beginners: Your First Steps in Data Analytics with Python

 

"Introduction to Data Analytics using Python for Beginners: Your First Steps in Data Analytics with Python" is a beginner-friendly guide designed to help readers take their initial steps into the exciting field of data analytics using Python. This book serves as a comprehensive introduction, offering an accessible learning experience for those with little to no prior knowledge of programming or data science.

In today’s data-driven world, the ability to analyze and interpret data is an essential skill across industries. From business and healthcare to education and social sciences, organizations increasingly rely on data analytics to inform decisions, optimize processes, and drive innovation. This growing demand has made proficiency in data analytics not just a valuable asset but a fundamental requirement for success.

"Introduction to Data Analytics using Python for Beginners" is designed for those embarking on their journey into the world of data analytics. Whether you’re a student, a professional looking to pivot your career, or simply someone eager to explore the capabilities of data analysis, this book serves as your comprehensive guide.

Python has emerged as one of the most popular programming languages in the data analytics landscape due to its simplicity, versatility, and powerful libraries. In this book, we will leverage Python’s rich ecosystem to demystify data analytics concepts and equip you with the practical skills needed to analyze real-world data.

We will start with the foundational concepts of data analytics, gradually building your knowledge and skills through hands-on examples and projects. Each chapter is designed to be approachable, with clear explanations and practical exercises that reinforce learning. By the end of this book, you will have a solid understanding of how to manipulate data, visualize insights, and derive meaningful conclusions.

This journey will not only enhance your technical skills but also encourage you to think critically about data. You will learn to ask the right questions, draw insights from data, and make data-driven decisions. As we navigate through various topics—such as data cleaning, exploratory data analysis, and machine learning—you will find that the process of data analysis is as much about understanding the data as it is about the tools you use.

I encourage you to dive into the exercises and projects with an open mind. Data analytics is a field where experimentation and curiosity are key. Embrace the challenges you encounter along the way, and remember that each obstacle is an opportunity for growth.

Key Features of the Book

Beginner-Focused Approach

The book assumes no prior experience and introduces concepts from the ground up.

It uses simple language and practical examples to explain Python programming and data analytics fundamentals.

Step-by-Step Guidance

Each topic is broken down into manageable steps, ensuring that readers can grasp one concept before moving on to the next.

Exercises and tutorials guide readers through hands-on tasks, helping to solidify their understanding.

Focus on Python Tools for Data Analytics

Covers essential Python libraries like:

Pandas for data manipulation.

NumPy for numerical computations.

Matplotlib and Seaborn for data visualization.

Introduces how to clean, analyze, and visualize datasets effectively.

Real-World Applications

Includes examples from everyday scenarios, such as sales analysis, customer trends, and performance evaluation.

The book bridges theoretical concepts with practical business use cases.

Project-Based Learning

Offers mini-projects that allow readers to apply what they’ve learned to realistic datasets.

Projects are designed to build confidence and problem-solving skills.

Who Should Read This Book?

Absolute Beginners: Those completely new to programming or data analytics.

Students: Ideal for learners in fields like business, social sciences, or engineering who want to explore data analysis.

Professionals: Individuals from non-technical backgrounds looking to transition into data-related roles.

Entrepreneurs and Small Business Owners: Learn to analyze business data for better decision-making.

Why It Stands Out

Practical and Approachable: The book simplifies complex topics, making it easy for beginners to follow along.

Focus on Essentials: Concentrates on the core skills needed to start working with data analytics right away.

Engaging Style: Uses relatable examples and a conversational tone to keep readers engaged.

Thank you for choosing this book as your guide. I am excited to embark on this journey with you, and I look forward to seeing the innovative insights you will uncover through data analytics.

Hard Copy: Introduction to Data Analytics using Python for Beginners: Your First Steps in Data Analytics with Python

Kindle: Introduction to Data Analytics using Python for Beginners: Your First Steps in Data Analytics with Python

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Introduction to Data Science for SMEs and Freelancers: How to Start Using Data to Make Money (DATA SCIENCE FOR EVERYONE Book 1)

 

Introduction to Data Science for SMEs and Freelancers: How to Start Leveraging Data to Make Money

Today, everyone seeks to harness data to boost profits, and small and medium-sized enterprises (SMEs) and freelancers cannot afford to be left behind. Although many believe that data science is reserved for large corporations, this book demonstrates that data science is within reach of any business, regardless of its size.

Introduction to Data Science for SMEs and Freelancers: How to Start Leveraging Data to Make Money is an accessible and straightforward guide designed to help you take your first steps in the world of data. In clear language, Rubén Maestre will show you how to harness the power of data, analyze it, and use it to make better decisions that propel your business forward.

What will you learn from this book?

What data science is and why it is essential for your business. Discover how data can help you identify patterns, optimize processes, and improve decision-making.

How to collect and manage your data. From transactions to customer interactions, you will learn to organize and evaluate the quality of your data.

Introduction to Python. Without needing to be a programmer, you will learn the basics of using this powerful language for data analysis with Pandas and NumPy.

Data cleaning and preparation. Discover techniques for cleaning and transforming data to enhance the quality of your analyses.

Exploratory data analysis and visualization. Learn how to create charts and use Matplotlib, Seaborn, and Plotly to visualize information.

Applying data science to business decision-making. Optimize inventories, enhance customer service, and make data-driven decisions.

Getting started with predictive models. Learn how to forecast trends and behaviors using tools like Scikit-Learn.

Why is this book different? 

Rubén Maestre, with experience in data science and digital marketing, has written this book specifically for SMEs and freelancers. It is not an overwhelming technical guide but rather a practical tool that democratizes access to data science. You will find real examples, straightforward explanations, and a hands-on approach to applying concepts from day one.

This book is only the first step. Rubén plans to delve into advanced topics in future books, such as visualizations, machine learning, and the use of artificial intelligence to improve processes.

Who should read this book? 

If you are a freelancer or a small business owner looking to optimize your business and make more informed decisions based on data, this book is for you. Even if you have no prior experience, Rubén will guide you step by step, making complex concepts easy to grasp.

About the Author Rubén Maestre is a professional passionate about technology, data, artificial intelligence, and digital marketing, with years of experience developing various digital projects to assist SMEs and freelancers. His goal is to democratize access to data science, showing that any business can harness the power of data to enhance its competitiveness.

Kindle: Introduction to Data Science for SMEs and Freelancers: How to Start Using Data to Make Money (DATA SCIENCE FOR EVERYONE Book 1)

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Learn Data Science Using Python: A Quick-Start Guide

 

"Learn Data Science Using Python: A Quick-Start Guide" is a practical introduction to the fundamentals of data science and Python programming. This book caters to beginners who want to delve into data analysis, visualization, and machine learning without a steep learning curve. 

Harness the capabilities of Python and gain the expertise need to master data science techniques. This step-by-step book guides you through using Python to achieve tasks related to data cleaning, statistics, and visualization.

You’ll start by reviewing the foundational aspects of the data science process. This includes an extensive overview of research points and practical applications, such as the insightful analysis of presidential elections. The journey continues by navigating through installation procedures and providing valuable insights into Python, data types, typecasting, and essential libraries like Pandas and NumPy. You’ll then delve into the captivating world of data visualization. Concepts such as scatter plots, histograms, and bubble charts come alive through detailed discussions and practical code examples, unraveling the complexities of creating compelling visualizations for enhanced data understanding.

Statistical analysis, linear models, and advanced data preprocessing techniques are also discussed before moving on to preparing data for analysis, including renaming variables, variable rearrangement, and conditional statements. Finally, you’ll be introduced to regression techniques, demystifying the intricacies of simple and multiple linear regression, as well as logistic regression.

What You’ll Learn

Understand installation procedures and valuable insights into Python, data types, typecasting

Examine the fundamental statistical analysis required in most data science and analytics reports

Clean the most common data set problems

Use linear progression for data prediction

What You Can Learn

Python Basics: Understand variables, data types, loops, and functions.

Data Manipulation: Learn to clean and process datasets using Pandas and NumPy.

Data Visualization: Create compelling charts and graphs to understand trends and patterns.

Machine Learning Basics: Implement algorithms like regression, classification, and clustering.

Real-World Problem Solving: Apply your skills to projects in areas like forecasting, recommendation systems, and more.

Who Should Read This Book?

Aspiring Data Scientists: Individuals seeking an accessible entry into the field of data science.

Professionals Transitioning Careers: Those looking to upskill or shift into data-focused roles.

Students and Researchers: Learners wanting to add data analysis and visualization to their skill set.

Why It Stands Out

The book’s balance of theory and practice makes it ideal for learning by doing. Its concise and well-structured format ensures that readers can quickly pick up skills without getting overwhelmed.

If you're looking to get started with Python for data science in a clear, concise, and engaging way, this book serves as an excellent resource.

Hard Copy: Learn Data Science Using Python: A Quick-Start Guide

Kindle: Learn Data Science Using Python: A Quick-Start Guide

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Python Coding challenge - Day 256 | What is the output of the following Python Code?

 

Explanation:

def greet(name="Guest"):

def: This keyword is used to define a function.

greet: This is the name of the function.

name="Guest": This is a parameter with a default value. If the caller of the function does not provide an argument, name will default to the string "Guest".

 return f"Hello, {name}!"

return: This keyword specifies the value the function will output when it is called.

f"Hello, {name}!": This is a formatted string (f-string) that inserts the value of the name variable into the string. For example, if name is "John", the output will be "Hello, John!".

print(greet())

greet(): The function is called without any arguments. Since no argument is provided, the default value "Guest" is used for name.

print(): This prints the result of the greet() function call, which in this case is "Hello, Guest!".

print(greet("John"))

greet("John"): The function is called with the argument "John". This value overrides the default value of "Guest".

print(): This prints the result of the greet("John") function call, which is "Hello, John!".

Output:

Hello, Guest!

Hello, John!

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Python Coding challenge - Day 257 | What is the output of the following Python Code?

 

Explanation:

def calculate(a, b=5, c=10):

def: This keyword is used to define a function.

calculate: This is the name of the function.

a: This is a required parameter. The caller must provide a value for a.

b=5: This is an optional parameter with a default value of 5. If no value is provided for b when calling the function, it will default to 5.

c=10: This is another optional parameter with a default value of 10. If no value is provided for c when calling the function, it will default to 10.

  return a + b + c

return: This specifies the value the function will output.

a + b + c: The function adds the values of a, b, and c together and returns the result.

print(calculate(3, c=7))

calculate(3, c=7):

The function is called with a=3 and c=7.

The argument for b is not provided, so it uses the default value of 5.

Inside the function:

a = 3

b = 5 (default value)

c = 7 (overrides the default value of 10).

print(): This prints the result of the calculate() function call, which is 3 + 5 + 7 = 15.

Output:

15

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Python Coding challenge - Day 259 | What is the output of the following Python Code?

 Explanation:

def divide(a, b):

def: This keyword is used to define a function.

divide: This is the name of the function.

a, b: These are parameters. The caller must provide two values for these parameters when calling the function.

  quotient = a // b

a // b: This performs integer division (also called floor division). It calculates how many whole times b fits into a and discards any remainder.

For example, if a=10 and b=3, then 10 // 3 equals 3 because 3 fits into 10 three whole times.

  remainder = a % b

a % b: This calculates the remainder of the division of a by b.

For example, if a=10 and b=3, then 10 % 3 equals 1 because when you divide 10 by 3, the remainder is 1.

  return quotient, remainder

return: This specifies the values the function will output.

quotient, remainder:

The function returns both values as a tuple.

For example, if a=10 and b=3, the function returns (3, 1).

result = divide(10, 3)

divide(10, 3):

The function is called with a=10 and b=3.

Inside the function:

quotient = 10 // 3 = 3

remainder = 10 % 3 = 1

The function returns (3, 1).

result:

The tuple (3, 1) is assigned to the variable result.

print(result)

print():

This prints the value of result, which is the tuple (3, 1).

 Final Output:

(3, 1)

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Python Coding challenge - Day 260 | What is the output of the following Python Code?

 

Explanation:

x = 5

A variable x is defined in the global scope and assigned the value 5.

def update_value():

A function named update_value is defined.

The function does not take any arguments.

  x = 10

Inside the function, a new variable x is defined locally (within the function's scope) and assigned the value 10.

This x is a local variable, distinct from the global x.

  print(x)

The function prints the value of the local variable x, which is 10.

update_value()

The update_value function is called.

Inside the function:

A local variable x is created and set to 10.

print(x) outputs 10.

print(x)

Outside the function, the global x is printed.

The global x has not been modified by the function because the local x inside the function is separate from the global x.

The value of the global x remains 5.

 Final Output:

 10

 5

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Python Coding challenge - Day 258 | What is the output of the following Python Code?

 Explanation:

def add_all(*args):

def: This keyword is used to define a function.

add_all: This is the name of the function.

*args:

The * before args allows the function to accept any number of positional arguments.

All the passed arguments are collected into a tuple named args.

  return sum(args)

sum(args):

The sum() function calculates the sum of all elements in an iterable, in this case, the args tuple.

For example, if args = (1, 2, 3, 4), sum(args) returns 10.

print(add_all(1, 2, 3, 4))

add_all(1, 2, 3, 4):

The function is called with four arguments: 1, 2, 3, and 4.

These arguments are collected into the tuple args = (1, 2, 3, 4).

The sum(args) function calculates 1 + 2 + 3 + 4 = 10, and the result 10 is returned.

print():

This prints the result of the add_all() function call, which is 10.

 Final Output:

10

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Functions : What will this code output?

 

def my_generator():

    for i in range(3):

        yield i

gen = my_generator()

print(next(gen))

print(next(gen))

Explanation:

1. my_generator():
   • This defines a generator function that yields values from 0 to 2 (range(3)).
2. gen = my_generator():
   • Creates a generator object gen.
3. print(next(gen)):
   • The first call to next(gen) will execute the generator until the first yield statement.
   • i = 0 is yielded and printed: 0.
print(next(gen)):
• The second call to next(gen) resumes execution from where it stopped.
• The next value i = 1 is yielded and printed: 1.

Output:

0

1

If you call next(gen) again, it will yield 2. A fourth call to next(gen) would raise a StopIteration exception because the generator is exhausted.

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Day 12 : Python Program to Check Armstrong number

 

def armstrong(number):

    num_str = str(number)

    return number == sum(int(digit) ** len(num_str) for digit in num_str)

num = int(input("Enter a number: "))

if armstrong(num):

    print(f"{num} is an Armstrong number.")

else:

    print(f"{num} is not an Armstrong number.")

    

Explanation:

Function Definition

def armstrong(number):

     num_str = str(number)

  # Convert the number to a string to access individual digits.

    return number == sum(int(digit) ** len(num_str) for digit in num_str)

str(number):

 Converts the input number into a string so that you can iterate over its digits (e.g., 153 becomes "153").

len(num_str):

 Counts the number of digits in the number (e.g., for "153", the length is 3).

for digit in num_str: 

Iterates over each digit in the string representation of the number.

int(digit) ** len(num_str):

 Converts the digit back to an integer and raises it to the power of the number of digits.

sum(...): Sums up all the powered values for the digits.

number == ...: Compares the sum of powered digits with the original number to check if they are equal. The function returns True if they match, meaning the number is an Armstrong number.

Input

num = int(input("Enter a number: "))

Prompts the user to input a number, which is converted to an integer using int().

Check and Output

if armstrong(num):

    print(f"{num} is an Armstrong number.")

else:

    print(f"{num} is not an Armstrong number.")

if armstrong(num):: Calls the armstrong function to check if the number is an Armstrong number.

Depending on the result:

If True, prints: <number> is an Armstrong number.

If False, prints: <number> is not an Armstrong number.

#source code --> clcoding.com 

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Day 11 : Python Program to calculate the power and exponent using recursion

 

def power(base, exp):

    if exp == 0:

        return 1

    return base * power(base, exp - 1)

base = int(input("Enter the base number: "))

exp = int(input("Enter the exponent: "))

print(power(base, exp))

This code calculates the power of a number (base) raised to an exponent (exp) using recursion. Let's break it down step-by-step:

Code Breakdown:

1. Function Definition:

   def power(base, exp):
   • A function power is defined with two parameters:
     • base: The base number.
     • exp: The exponent to which the base number will be raised.

2. Base Case:

   if exp == 0:  return 1

   • If the exponent exp is 0, the function returns 1 because any number raised to the power of 0 is 1.

3. Recursive Case:

   return base * power(base, exp - 1)
   • This is the key recursive step.
   • The function multiplies the base by the result of calling power(base, exp - 1).
   • It reduces the exponent by 1 each time, breaking the problem into smaller sub-problems, until exp equals 0 (base case).

   For example, if base = 2 and exp = 3, the recursion works like this:

   power(2, 3) = 2 * power(2, 2)
   power(2, 2) = 2 * power(2, 1)
   power(2, 1) = 2 * power(2, 0)
   power(2, 0) = 1 (base case)

   Then, the results are combined:

   power(2, 1) = 2 * 1 = 2
   power(2, 2) = 2 * 2 = 4
   power(2, 3) = 2 * 4 = 8

4. Taking Input:

   base = int(input("Enter the base number: "))exp = int(input("Enter the exponent: "))

   • The user is prompted to enter the base and exponent values, which are then converted to integers.

5. Calling the Function:

   print(power(base, exp))
   • The power function is called with the input values of base and exp, and the result is printed.

#source code --> clcoding.com

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9 Python function-based quiz questions

1. Basic Function Syntax

What will be the output of the following code?

def greet(name="Guest"):
 return f"Hello, {name}!"
print(greet())
print(greet("John"))

a. Hello, Guest!, Hello, John!
b. Hello, John!, Hello, Guest!
c. Hello, Guest!, Hello, Guest!
d. Error

---

2. Positional and Keyword Arguments

What does the following function call print?

def calculate(a, b=5, c=10):
    return a + b + cprint(calculate(3, c=7))

a. 15
b. 20
c. 25
d. Error

---

3. Function with Variable Arguments

What will be the output of this code?

def add_all(*args):
  return sum(args)print(add_all(1, 2, 3, 4))

a. 10
b. [1, 2, 3, 4]
c. Error
d. 1, 2, 3, 4

---

4. Returning Multiple Values

What will print(result) output?

def divide(a, b):
    quotient = a // b
    remainder = a % b
    return quotient, remainder

result = divide(10, 3)
print(result)

a. 10, 3
b. (3, 1)
c. 3.1
d. Error

---

5. Scope of Variables

What will the following code print?

x = 5

def update_value():
    x = 10
    print(x)

update_value()
print(x)

a. 10, 5
b. 10, 10
c. 5, 5
d. Error

---

6. Default and Non-Default Arguments

Why does this code throw an error?

def example(a=1, b): return a + b

a. b is not assigned a default value
b. Default arguments must come after non-default arguments
c. Both a and b must have default values
d. No error

---

7. Lambda Functions

What will the following code print?

double = lambda x: x * 2print(double(4))

a. 2
b. 4
c. 8
d. Error

---

8. Nested Functions

What will the following code output?

def outer_function(x):
    def inner_function(y):
        return y + 1
    return inner_function(x) + 1
print(outer_function(5))

a. 6
b. 7
c. 8
d. Error

---

9. Anonymous Functions with map()

What is the result of the following code?

numbers = [1, 2, 3, 4]
result = list(map(lambda x: x ** 2, numbers))print(result)

a. [1, 4, 9, 16]
b. [2, 4, 6, 8]
c. None
d. Error

---

1. Basic Function Syntax

Answer: a. Hello, Guest!, Hello, John!

Explanation:

• Default value Guest is used when no argument is passed.
• Passing "John" overrides the default value.

---

2. Positional and Keyword Arguments

Answer: b. 20

Explanation:

• a = 3, b = 5 (default), c = 7 (overrides the default value of 10).
• Result: 3 + 5 + 7 = 20.

---

3. Function with Variable Arguments

Answer: a. 10

Explanation:

• *args collects all arguments into a tuple.
• sum(args) calculates the sum: 1 + 2 + 3 + 4 = 10.

---

4. Returning Multiple Values

Answer: b. 

(3, 1)

Explanation:

• The function returns a tuple (quotient, remainder).
• 10 // 3 = 3 (quotient), 10 % 3 = 1 (remainder).

---

5. Scope of Variables

Answer: a. 10, 5

Explanation:

• x = 10 inside the function is local and does not affect the global x.
• Outside the function, x = 5.

---

6. Default and Non-Default Arguments

Answer: b. Default arguments must come after non-default arguments

Explanation:

• In Python, arguments with default values (like a=1) must appear after those without defaults (like b).

---

7. Lambda Functions

Answer: c. 8

Explanation:

• The lambda function doubles the input: 4 * 2 = 8.

---

8. Nested Functions

Answer: b. 7

Explanation:

• inner_function(5) returns 5 + 1 = 6.
• Adding 1 in outer_function: 6 + 1 = 7.

---

9. Anonymous Functions with map()

Answer: a. [1, 4, 9, 16]

Explanation:

• The lambda function squares each number in the list:
  [1^2, 2^2, 3^2, 4^2] = [1, 4, 9, 16].

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Code to Send Laptop Notifications using Python

 

from plyer import notification

# Send notification

notification.notify(

    title="Reminder",

    message="Take a break and stretch!",

    app_name="Python Notifier",

    timeout=10  

)

#source code --> clcoding.com

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Python OOPS Challenge | Day 15 | What is the output of following Python code?

The code snippet in the image is invalid and will raise an exception. Here's why:

Explanation:

1. Class TV Definition:

class TV:

    pass

A class TV is defined, but it has no attributes or methods.

2. Object Creation:

obj = TV()

An object obj is created from the TV class.

3. Dynamic Attribute Assignment:

obj.price = 200

A new attribute price is dynamically added to the obj instance, and its value is set to 200.

4. Invalid Access of self:

print(self.price)

The variable self is used outside of a method in the class, which is invalid.

In Python, self is a convention used as the first parameter of instance methods to refer to the calling instance. It cannot be used directly outside a method context.

What Happens:

When the Python interpreter reaches the print(self.price) statement, it will raise a NameError because self is not defined in the global scope.

Corrected Code (if you want to print the price):

To fix the code, the price attribute can be printed using the instance obj instead of self:

class TV:

    pass

obj = TV()

obj.price = 200

print(obj.price) # Outputs: 200

In this corrected version, obj.price correctly accesses the price attribute of the obj instance.

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10-Question quiz on Python Data Types

 

1.Which of the following is a mutable data type in Python?

Options:

a) List

b) Tuple

c) String

d) All of the above

2. What is the data type of True and False in Python?

Options:

a) Integer

b) Boolean

c) String

d) Float

3. Which data type allows duplicate values?

Options:

a) Set

b) Dictionary

c) List

d) None of the above

4. Which Python data type is used to store key-value pairs?

Options:

a) List

b) Tuple

c) Dictionary

d) Set

Intermediate Questions

5. What does the type() function do in Python?

Options:

a) Checks the length of a variable

b) Returns the data type of a variable

c) Converts a variable to another type

d) Prints the variable's value

6. Which of the following Python data types is ordered and immutable?

Options:

a) List

b) Tuple

c) Set

d) Dictionary

7. What is the default data type of a number with a decimal point in Python?

Options:

a) Integer

b) Float

c) Complex

d) Boolean

Advanced Questions

8. What is the main difference between a list and a tuple in Python?

Options:

a) Lists are ordered, tuples are not

b) Tuples are immutable, lists are mutable

c) Lists are faster than tuples

d) There is no difference

9. Which of the following data types does not allow duplicate values?

Options:

a) List

b) Tuple

c) Set

d) Dictionary

10.What data type will the expression 5 > 3 return?

Options:

a) Integer

b) Boolean

c) String

d) None

Basic Questions

1. Which of the following is a mutable data type in Python?
   Answer: a) List

2. What is the data type of True and False in Python?
   Answer: b) Boolean

3. Which data type allows duplicate values?
   Answer: c) List

4. Which Python data type is used to store key-value pairs?
   Answer: c) Dictionary

---

Intermediate Questions

5. What does the type() function do in Python?
   Answer: b) Returns the data type of a variable

6. Which of the following Python data types is ordered and immutable?
   Answer: b) Tuple

7. What is the default data type of a number with a decimal point in Python?
   Answer: b) Float

---

Advanced Questions

8. What is the main difference between a list and a tuple in Python?
   Answer: b) Tuples are immutable, lists are mutable

9. Which of the following data types does not allow duplicate values?
   Answer: c) Set

10. What data type will the expression 5 > 3 return?
    Answer: b) Boolean

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Combined operators in Python

 

What does the following Python code return?

a = 9

b = 7

a *= 2

b += a // 3

a %= 4

print(a, b)

Answer: Let's break down the code step by step:

a = 9

b = 7

Here, a is assigned the value 9, and b is assigned the value 7.

Step 1: a *= 2

This is a combined multiplication assignment operator (*=). It multiplies a by 2 and then assigns the result back to a.

a = a * 2
• a = 9 * 2 = 18 Now, a = 18.

Step 2: b += a // 3

This is a combined addition assignment operator (+=). It adds the result of a // 3 to b and assigns the result back to b.

• a // 3 performs integer division of a by 3. Since a = 18, we calculate 18 // 3 = 6.
• Now, b += 6, which means b = b + 6 = 7 + 6 = 13. Now, b = 13.

Step 3: a %= 4

This is a combined modulus assignment operator (%=). It calculates the remainder when a is divided by 4 and assigns the result back to a.

a = a % 4
• a = 18 % 4 = 2 (since the remainder when dividing 18 by 4 is 2). Now, a = 2.

Final Output:

After all the operations:

a = 2

b = 13

So, the code will print:  2 13

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Hands-on Foundations for Data Science and Machine Learning with Google Cloud Labs Specialization

 

The Hands-On Foundations for Data Science and Machine Learning Specialization on Coursera, offered by Google Cloud, is designed to equip learners with practical skills in data science and machine learning. Through real-world projects and interactive labs, learners gain hands-on experience working with Google Cloud tools, Python, and SQL. This program is ideal for those seeking to master data analysis, machine learning basics, and cloud technologies, providing a strong foundation for roles in data science, machine learning engineering, and data analysis.

The Hands-On Foundations for Data Science and Machine Learning Specialization on Coursera, offered by Google Cloud, provides a practical approach to mastering data science and machine learning. This program is designed for learners who want to acquire technical expertise and apply it through real-world labs powered by Google Cloud.

What You’ll Learn

Data Science Fundamentals

Understand the foundational concepts of data science and machine learning.

Work with tools like BigQuery and Jupyter Notebooks.

Hands-On Learning with Google Cloud Labs

Practice on real-world datasets with guided labs.

Learn to preprocess and analyze data using Python and SQL.

Machine Learning Basics

Build and evaluate machine learning models.

Explore TensorFlow and AutoML tools.

Big Data Tools

Learn to manage and query large datasets efficiently.

Understand how to utilize cloud-based solutions like Google BigQuery.

Why Choose This Specialization?

Real-World Skills: Unlike purely theoretical courses, this specialization integrates labs that mimic actual workplace tasks.

Cloud Integration: The use of Google Cloud tools prepares learners for industry-standard workflows.

Flexibility: The self-paced structure allows learners to study alongside work or other commitments.

Career Impact

This specialization is perfect for:

Aspiring data scientists and machine learning engineers.

Professionals looking to enhance their data-handling skills with cloud technologies.

Students aiming to gain hands-on experience with industry-leading tools.

Future Enhancements through this Specialization

Completing the Hands-On Foundations for Data Science and Machine Learning Specialization equips you with industry-relevant skills to leverage cloud tools and machine learning frameworks. This can open doors to advanced opportunities such as:

Specialization in AI and Machine Learning: Build on your foundational knowledge to develop deep expertise in neural networks and AI technologies.

Cloud Data Engineering: Transition into roles managing large-scale cloud-based data solutions.

Advanced Certifications: Pursue advanced Google Cloud certifications to validate your expertise.

Join Free: Hands-on Foundations for Data Science and Machine Learning with Google Cloud Labs Specialization

Conclusion:

The Hands-On Foundations for Data Science and Machine Learning Specialization bridges the gap between theory and practice, offering learners the chance to work on real-world projects with the latest tools. Whether you’re starting in data science or looking to expand your skills, this program is a powerful way to accelerate your learning journey.

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Expressway to Data Science: Python Programming Specialization

 

The Python Programming for Data Science Specialization on Coursera, offered by the University of Colorado Boulder, is tailored for beginners eager to harness Python for data-driven insights. It combines foundational programming skills with specialized training in essential data science tools and techniques.

The Python Programming for Data Science Specialization on Coursera by the University of Colorado Boulder is an ideal starting point for beginners. It covers Python basics, including variables, functions, loops, and essential data science libraries like Pandas, Numpy, and Matplotlib. The program features hands-on projects to teach data manipulation, exploratory analysis, and visualization. With self-paced learning, it equips learners with practical skills for roles in data analytics and science.

Dive into Data Science with Python: A Comprehensive Specialization

The Python Programming for Data Science Specialization on Coursera, offered by the University of Colorado Boulder, is tailored for beginners eager to harness Python for data-driven insights. It combines foundational programming skills with specialized training in essential data science tools and techniques.

Completing the Python Programming for Data Science Specialization can open doors to future enhancements in your career. With foundational skills in Python and data science tools, learners can explore advanced certifications or specializations in fields such as machine learning, artificial intelligence, and big data analytics. These skills are essential for roles like data scientist, machine learning engineer, or business analyst. The hands-on projects in this program also prepare you to solve real-world challenges, making you a valuable asset in data-driven industries.

What you'll learn

• Fundamentals of Python Programming
• Data Manipulation Packages such as Numpy and Pandas
• Data Visualization Packages such as Matplotlib and Seaborn

This specialization introduces Python’s versatile capabilities, focusing on:

Core Python Programming: Variables, loops, functions, and data structures.

Data Science Libraries: Master libraries like Pandas, Numpy, Matplotlib, and Seaborn for data analysis and visualization.

Exploratory Data Analysis (EDA): Learn how to clean, manipulate, and interpret datasets effectively.

Hands-On Learning

The program emphasizes real-world applications, offering projects where learners work with datasets to create visualizations and derive actionable insights.

Benefits and Career Impact

Whether you’re a student, a professional, or a career changer, this specialization helps you:

Build a strong foundation in Python and data analysis.

Prepare for roles like data analyst or junior data scientist.

Obtain a Coursera certificate to showcase your skills.

Why Choose This Course?

Beginner-friendly and self-paced.

Taught by university experts with practical, industry-aligned lessons.

Gain skills applicable across industries, from finance to healthcare and beyond.

Join Free: Expressway to Data Science: Python Programming Specialization

Conclusion:

The Python Programming for Data Science Specialization is an excellent pathway for beginners to master Python and apply it to real-world data science challenges. With its focus on essential libraries, hands-on projects, and foundational skills, this program prepares learners for a successful career in data analytics or data science. Its self-paced structure makes it accessible for students, professionals, and career changers alike.

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Day 10 : Python Program to find sum of first N Natural Numbers

 

n = int(input("Enter a number: "))

sum_of_numbers = n * (n + 1) // 2

print(f"The sum of the first {n} natural numbers is: {sum_of_numbers}")

Code Explanation

Input:

n = int(input("Enter a number: "))

input(): Prompts the user to enter a number.

int(): Converts the entered value (a string) into an integer.

𝑛

n represents the count of natural numbers to sum.

Sum Calculation

sum_of_numbers = n * (n + 1) // 2

This implements the mathematical formula for the sum of the first 

𝑛 natural numbers

2: Divides the product by 2 using integer division (ensures the result is an integer).

Output

print(f"The sum of the first {n} natural numbers is: {sum_of_numbers}")

f"The sum of the first {n} natural numbers is: {sum_of_numbers}":

Formats the string to display 

𝑛

n and the calculated sum.

#source code --> clcoding.com 

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DeepLearning.AI Data Engineering Professional Certificate

 The Data Engineering Professional Certificate from DeepLearning.AI on Coursera is designed for anyone looking to break into the data engineering field. This program covers essential topics like data pipelines, SQL, Python, and cloud technologies. By completing the course, you'll gain practical experience working with large datasets and cloud-based infrastructure. The certificate is perfect for beginners and includes hands-on projects to solidify your learning.

key points for the Data Engineering Professional Certificate:

Advanced Data Integration: Learn how to integrate complex data sources for efficient decision-making.

Data Security & Compliance: Understand best practices for data security, privacy, and compliance in engineering environments.

Collaboration Skills: Develop skills to work with data scientists and business analysts in cross-functional teams.

Industry-Relevant Experience: Build a portfolio with hands-on projects to demonstrate your skills to potential employers.

What you'll learn

• Develop a mental model for the field of data engineering as a whole, including the data engineering lifecycle and its undercurrents.
• Learn a framework for approaching any data engineering project you work on so you can effectively create business value with data.
• Build your skill in the five stages of the data engineering lifecycle; including generating, ingesting, storing, transforming, and serving data.
• Learn the principles of good data architecture and apply them to build data systems on the AWS cloud.

Who should take this course:

The Data Engineering Professional Certificate is suitable for:

Beginners: Those with basic programming skills who want to learn data engineering from the ground up.

Aspiring Data Engineers: Individuals who aim to develop expertise in creating and managing data pipelines and cloud technologies.

Current Data Professionals: Data analysts, data scientists, or software engineers looking to deepen their knowledge in database management, cloud services, and data architecture.

Career Changers: Those transitioning into tech and data roles with no prior experience in data engineering.

Future Enhancements through the Data Engineering Professional Certificate:

Upon completing the course, you can advance your career by gaining proficiency in scalable data solutions and cloud technologies, making you eligible for roles like cloud architect, data architect, or machine learning engineer. With a deep understanding of data pipelines, security, and data integration techniques, you'll be prepared to work with the latest tools and tackle increasingly complex data problems, improving your potential for career advancement and providing the skill set required for evolving tech roles.

Join Free: DeepLearning.AI Data Engineering Professional Certificate

Conclusion:

The Data Engineering Professional Certificate equips learners with crucial skills to build and manage data systems, empowering them to pursue careers in data engineering. By mastering cloud technologies, data pipelines, and databases, you'll be well-prepared to solve complex data challenges and advance your career in tech. This certificate offers a comprehensive pathway to becoming a highly skilled data engineer, capable of supporting the data infrastructure needs of modern organizations.

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Image Mirroring with Python

 

from PIL import Image

Original_Image = 'pushpa.png'

Image.open(Original_Image) 

img = Image.open(Original_Image)  

Mirror_Image = img.transpose(Image.FLIP_LEFT_RIGHT)

Mirrored_Image = 'pushpa_mirror.png'

Mirror_Image.save(Mirrored_Image)

Image.open(Mirrored_Image) 

#source code --> clcoding.com

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