do you know difference between Data Analyst , Data Scientist and Data Engineer?

Data Analyst

A data analyst sits between business intelligence and data science. They provide vital information to business stakeholders.

Data Management in SQL (PostgreSQL)

Data Analysis in SQL (PostgreSQL)

Exploratory Analysis Theory

Statistical Experimentation Theory

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Data Scientist Associate 

A data scientist is a professional responsible for collecting, analyzing and interpreting extremely large amounts of data.

R / Python Programming

Data Manipulation in R/Python

1.1 Calculate metrics to effectively report characteristics of data and relationships between

features

● Calculate measures of center (e.g. mean, median, mode) for variables using R or Python.

● Calculate measures of spread (e.g. range, standard deviation, variance) for variables

using R or Python.

● Calculate skewness for variables using R or Python.

● Calculate missingness for variables and explain its influence on reporting characteristics

of data and relationships in R or Python.

● Calculate the correlation between variables using R or Python.

1.2 Create data visualizations in coding language to demonstrate the characteristics of data

● Create and customize bar charts using R or Python.

● Create and customize box plots using R or Python.

● Create and customize line graphs using R or Python.

● Create and customize histograms graph using R or Python.

1.3 Create data visualizations in coding language to represent the relationships between

features

● Create and customize scatterplots using R or Python.

● Create and customize heatmaps using R or Python.

● Create and customize pivot tables using R or Python.

1.4 Identify and reduce the impact of characteristics of data

● Identify when imputation methods should be used and implement them to reduce the

impact of missing data on analysis or modeling using R or Python.

● Describe when a transformation to a variable is required and implement corresponding

transformations using R or Python.

● Describe the differences between types of missingness and identify relevant approaches

to handling types of missingness.

● Identify and handle outliers using R or Python.

Statistical Fundamentals in R/Python

2.1 Perform standard data import, joining and aggregation tasks

● Import data from flat files into R or Python.

● Import data from databases into R or Python

● Aggregate numeric, categorical variables and dates by groups using R or Python.

● Combine multiple tables by rows or columns using R or Python.

● Filter data based on different criteria using R or Python.

2.2 Perform standard cleaning tasks to prepare data for analysis

● Match strings in a dataset with specific patterns using R or Python.

● Convert values between data types in R or Python.

● Clean categorical and text data by manipulating strings in R or Python.

● Clean date and time data in R or Python.

2.3 Assess data quality and perform validation tasks

● Identify and replace missing values using R or Python.

● Perform different types of data validation tasks (e.g. consistency, constraints, range

validation, uniqueness) using R or Python.

● Identify and validate data types in a data set using R or Python.

2.4 Collect data from non-standard formats by modifying existing code

● Adapt provided code to import data from an API using R or Python.

● Identify the structure of HTML and JSON data and parse them into a usable format for

data processing and analysis using R or Python

Importing & Cleaning in R/Python

3.1 Prepare data for modeling by implementing relevant transformations.

● Create new features from existing data (e.g. categories from continuous data, combining

variables with external data) using R or Python.

● Explain the importance of splitting data and split data for training, testing, and validation

using R or Python.

● Explain the importance of scaling data and implement scaling methods using R or Python.

● Transform categorical data for modeling using R or Python.

3.2 Implement standard modeling approaches for supervised learning problems.

● Identify regression problems and implement models using R or Python.

● Identify classification problems and implement models using R or Python.

3.3 Implement approaches for unsupervised learning problems.

● Identify clustering problems and implement approaches for them using R or Python.

● Explain dimensionality reduction techniques and implement the techniques using R or

Python.

3.4 Use suitable methods to assess the performance of a model.

● Select metrics to evaluate regression models and calculate the metrics using R or Python.

● Select metrics to evaluate classification models and calculate the metrics using R or

Python.

● Select metrics and visualizations to evaluate clustering models and implement them

using R or Python.

Machine Learning Fundamentals in R/Python

4.2 Demonstrates best practices in production code including version control, testing, and

package development.

● Describe the basic flow and structures of package development in R or Python.

● Explain how to document code in packages, or modules in R or Python.

● Explain the importance of the testing and write testing statements in R or Python.

● Explain the importance of version control and describe key concepts of versioning

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Data Engineer

A data engineer collects, stores, and pre-processes data for easy access and use within an organization. Associate certification is available.

Data Management in SQL (PostgreSQL)

Exploratory Analysis Theory

Free Certification : Data Science  

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Python Coding challenge - Day 175 | What is the output of the following Python Code?

 

Code:

x = [1, 2, 3]

y = [4, 5, 6]

z = [x, y]

print(z[0][1])

Solution and Explanation:

Let's break down the code step by step:

x = [1, 2, 3]: This line creates a list named x containing the elements 1, 2, and 3.

y = [4, 5, 6]: This line creates another list named y containing the elements 4, 5, and 6.

z = [x, y]: Here, a list named z is created, containing two lists: x and y. So, z becomes [[1, 2, 3], [4, 5, 6]].

print(z[0][1]): This line prints the element at index 1 of the first list in z. Since z[0] refers to [1, 2, 3] and z[0][1] refers to the element at index 1 of that list, the output will be 2.

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Automatically Generate Image CAPTCHAs with Python for Enhanced Security

 

Code:

from captcha.image import ImageCaptcha 

import random

# Specify the image size

image = ImageCaptcha(width=450, height=100)

# Generate random captcha text

def generate_random_captcha_text(length=6):

    characters = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789'

    captcha_text = ''.join(random.choice(characters) for _ in range(length))

    return captcha_text

# Get random captcha text

captcha_text = generate_random_captcha_text()

# Generate the image of the given text

data = image.generate(captcha_text)

# Write the image on the given file and save it

image.write(captcha_text, 'CAPTCHA1.png')

from PIL import Image

Image.open('CAPTCHA1.png')

#clcoding.com

Explanation: 

This code snippet demonstrates how to automatically generate an image CAPTCHA using Python. Here's a breakdown of each part:

from captcha.image import ImageCaptcha: This imports the ImageCaptcha class from the captcha.image module. This class allows you to create CAPTCHA images.

import random: This imports the random module, which is used to generate random characters for the CAPTCHA text.

image = ImageCaptcha(width=450, height=100): This initializes an instance of the ImageCaptcha class with the specified width and height for the CAPTCHA image.

generate_random_captcha_text(length=6): This is a function that generates random CAPTCHA text. It takes an optional parameter length, which specifies the length of the CAPTCHA text. By default, it generates a text of length 6.

captcha_text = generate_random_captcha_text(): This calls the generate_random_captcha_text function to generate random CAPTCHA text and assigns it to the variable captcha_text.

data = image.generate(captcha_text): This generates the CAPTCHA image using the generated text. It returns the image data.

image.write(captcha_text, 'CAPTCHA1.png'): This writes the generated CAPTCHA image to a file named "CAPTCHA1.png" with the text embedded in the image.

from PIL import Image: This imports the Image class from the Python Imaging Library (PIL) module, which is used to open and display the generated CAPTCHA image.

Image.open('CAPTCHA1.png'): This opens the generated CAPTCHA image named "CAPTCHA1.png" using the PIL library.

Overall, this code generates a random CAPTCHA text, creates an image of the text using the ImageCaptcha class, saves the image to a file, and then displays the image using PIL.

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Free Top 3 Machine Learning Books 📚

 Advances in Financial Machine Learning

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations.

Listeners will learn how to structure big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis and explains scientifically sound solutions using math, supported by code and examples. Listeners become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.

Graph-Powered Machine Learning 

Upgrade your machine learning models with graph-based algorithms, the perfect structure for complex and interlinked data.

In Graph-Powered Machine Learning, you will learn:

The lifecycle of a machine learning project

Graphs in big data platforms

Data source modeling using graphs

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Graph algorithms

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Graph-Powered Machine Learning teaches to use graph-based algorithms and data organization strategies to develop superior machine learning applications. You’ll dive into the role of graphs in machine learning and big data platforms, and take an in-depth look at data source modeling, algorithm design, recommendations, and fraud detection. Explore end-to-end projects that illustrate architectures and help you optimize with best design practices.

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Identifying relationships is the foundation of machine learning. By recognizing and analyzing the connections in your data, graph-centric algorithms like K-nearest neighbor or PageRank radically improve the effectiveness of ML applications.

About the Audiobook

Graph-Powered Machine Learning teaches you how to exploit the natural relationships in structured and unstructured datasets using graph-oriented machine learning algorithms and tools. In this authoritative audiobook, you’ll master the architectures and design practices of graphs, and avoid common pitfalls. Author Alessandro Negro explores examples from real-world applications that connect GraphML concepts to real world tasks.

Machine Learning for Absolute Beginners: Python for Data Science, Book 3 

Featured by Tableau as the first of "7 Books About Machine Learning for Beginners."

Ready to spin up a virtual GPU instance and smash through petabytes of data? Want to add "Machine Learning" to your LinkedIn profile?

Well, hold on there.... Before you embark on your journey, there are some high-level theory and statistical principles to weave through first.

But rather than spend $30-$50 USD on a thick textbook, you may want to listen to this book first. As a clear and concise alternative, this book provides a high-level introduction to machine learning, free downloadable code exercises, and video demonstrations.

Machine Learning for Absolute Beginners Third Edition has been written and designed for absolute beginners. This means plain-English explanations and no coding experience required. Where core algorithms are introduced, clear explanations and visual examples are added to make it easy to follow along at home.

New Updated Edition

This new edition features extended chapters with quizzes, free supplementary online video tutorials for coding models in Python, and downloadable resources not included in the Second Edition.

Disclaimer: If you have passed the "beginner" stage in your study of machine learning and are ready to tackle coding and deep learning, you would be well served with a long-format textbook. If, however, you are yet to reach that Lion King moment - as a fully grown Simba looking over the Pride Lands of Africa - then this is the book to gently hoist you up and give a clear lay of the land.

In this step-by-step guide, you will learn:

How to download free datasets

What tools and machine learning libraries you need

Data scrubbing techniques, including one-hot encoding, binning and dealing with missing data

Preparing data for analysis, including k-fold Validation

Regression analysis to create trend lines

k-Means Clustering to find new relationships

The basics of Neural Networks

Bias/Variance to improve your machine learning model

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What is the output of following Python Code?

 

What is the output of following Python Code? 

s = 'clcoding'

print(s[1:6][1:3])

Solution and Explanation: 

Let's break down the expression step by step:

s = 'clcoding': This assigns the string 'clcoding' to the variable s.

s[1:6]: This slices the string from index 1 (inclusive) to index 6 (exclusive), resulting in 'lcod'.

s[1:6][1:3]: This further slices the result of the previous step 'lcod' from index 1 to index 3, resulting in 'co'.

So, the output of print(s[1:6][1:3]) will be 'co'.

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Python Coding challenge - Day 174 | What is the output of the following Python Code?

 

Code:

s = 'coder'

print(s[::0])

Solution and Explanation: 

n Python, the expression s[::0] raises a ValueError. This error occurs because the step value of the slice operation cannot be zero.

When using slice notation [start:stop:step], the step parameter specifies the increment between the elements. A step of zero doesn't make sense because it would mean "take every element" but without progressing through the sequence, which is undefined.

So, attempting to slice with a step of zero will result in an error. If you want to print the string 'coder', you can simply use print(s).

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4 Free books to master Data Analytics

 Storytelling with Data: A Data Visualization Guide for Business Professionals  

Don't simply show your data - tell a story with it!

Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory but made accessible through numerous real-world examples - ready for immediate application to your next graph or presentation.

Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to:

Understand the importance of context and audience

Determine the appropriate type of graph for your situation

Recognize and eliminate the clutter clouding your information

Direct your audience's attention to the most important parts of your data

Think like a designer and utilize concepts of design in data visualization

Leverage the power of storytelling to help your message resonate with your audience

Together, the lessons in this book will help you turn your data into high-impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data - Storytelling with Data will give you the skills and power to tell it!

Fundamentals of Data Analytics: Learn Essential Skills, Embrace the Future, and Catapult Your Career in the Data-Driven World—A Comprehensive Guide to Data Literacy for Beginners

Gain a competitive edge in today’s data-driven world and build a rich career as a data professional that drives business success and innovation…

Today, data is everywhere… and it has become the essential building block of this modern society.

And that’s why now is the perfect time to pursue a career in data.

But what does it take to become a competent data professional?

This book is your ultimate guide to understanding the fundamentals of data analytics, helping you unlock the expertise of efficiently solving real-world data-related problems.

Here is just a fraction of what you will discover:

A beginner-friendly 5-step framework to kickstart your journey into analyzing and processing data

How to get started with the fundamental concepts, theories, and models for accurately analyzing data

Everything you ever needed to know about data mining and machine learning principles

Why business run on a data-driven culture, and how you can leverage it using real-time business intelligence analytics

Strategies and techniques to build a problem-solving mindset that can overcome any complex and unique dataset

How to create compelling and dynamic visualizations that help generate insights and make data-driven decisions

The 4 pillars of a new digital world that will transform the landscape of analyzing data

And much more.

Believe it or not, you can be terrible in math or statistics and still pursue a career in data.

And this book is here to guide you throughout this journey, so that crunching data becomes second nature to you.

Ready to master the fundamentals and build a successful career in data analytics? Click the “Add to Cart” button right now.

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Data Analytics for Absolute Beginners: A Deconstructed Guide to Data Literacy: Python for Data Science, Book 2

Make better decisions with this easy deconstructed guide to data analytics.

Want to add data analytics to your skill stack? Having trouble finding where to start?

Cell-by-cell, bit-by-bit, this audiobook teaches you the vocabulary, tools, and basic algorithms to think like a data scientist.

Like putting together a complex Lego set, each section connects and adds individual blocks of knowledge to build your data literacy. This linear structure to unpacking data analytics takes you from zero to confidently analyzing and discussing data problems.

Who is this audiobook for? This audiobook is ideal for anyone interested in making sense of data analytics without the assumption that you understand data science terminology or advanced math. If you've tried to learn data analytics before and failed, this audiobook is for you.

Practical approach. This audiobook takes a hands-on approach to learning. This includes practical examples, visual examples, as well as two bonus coding exercises in Python, including free video content to walk you through both exercises. By the end of the audiobook, you will have the practical knowledge to tackle real data problems in your organization or daily life.

What you will learn:

How to recognize the common data types every data scientist needs to master

Where to store your data, including big data

New trends in data analytics, including what is alternative data and why not many people know about it

How to explain the distinction between data mining, machine learning, and analytics to your colleagues

When and how to use regression analysis, classification, clustering, association analysis, and natural language processing

How to make better business decisions using data visualization and business intelligence

Data Analytics, Data Visualization & Communicating Data: 3 books in 1: Learn the Processes of Data Analytics and Data Science, Create Engaging Data Visualizations, and Present Data Effectively

Harvard Business Review called data science “the sexiest job of the 21st century,” so it's no surprise that data science jobs have grown up to 20 times in the last three years. With demand outpacing supply, companies are willing to pay top dollar for talented data professionals. However, to stand out in one of these positions, having foundational knowledge of interpreting data is essential. You can be a spreadsheet guru, but without the ability to turn raw data into valuable insights, the data will render useless. That leads us to data analytics and visualization, the ability to examine data sets, draw meaningful conclusions and trends, and present those findings to the decision-maker effectively.

Mastering this skill will undoubtedly lead to better and faster business decisions. The three audiobooks in this series will cover the foundational knowledge of data analytics, data visualization, and presenting data, so you can master this essential skill in no time. This series includes:

Everything data analytics: a beginner's guide to data literacy and understanding the processes that turns data into insights.

Beginner's guide to data visualization: how to understand, design, and optimize over 40 different charts.

How to win with your data visualizations: the five part guide for junior analysts to create effective data visualizations and engaging data stories.

These three audiobooks cover an extensive amount of information, such as:

Overview of the data collection, management, and storage processes.

Fundamentals of cleaning data.

Essential machine learning algorithms required for analysis such as regression, clustering, classification, and more....

The fundamentals of data visualization.

An in-depth view of over 40 plus charts and when to use them.

A comprehensive data visualization design guide.

Walkthrough on how to present data effectively.

And so much more!

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Python Coding challenge - Day 173 | What is the output of the following Python Code?

 

Code: 

def fun(a, b):

    if a == 1:

        return b

    else:

        return fun(a - 1, a * b)

print(fun(4, 2))

Solution and Explanation: 

This code defines a recursive function fun(a, b) that takes two parameters, a and b. Let's break down how the function works:

Function Definition:

def fun(a, b):

Conditional Statement:

if a == 1:

    return b

else:

If the value of a is equal to 1, the function returns the value of b.

If a is not equal to 1, the function proceeds to the else block.

Recursion:

return fun(a - 1, a * b)

If a is not equal to 1, the function calls itself recursively with modified arguments:

a - 1 decrements the value of a by 1 in each recursive call.

a * b multiplies a with b and passes the result as the second argument.

This recursive call continues until a becomes 1.

Function Call:

print(fun(4, 2))

This line calls the fun() function with initial values of a = 4 and b = 2.

The result of the function call is printed.

Now let's see how the function operates with the given input fun(4, 2):

Initially, a = 4 and b = 2.

Since a is not equal to 1, the function enters the else block and makes a recursive call with a = 3 and b = 4 * 2 = 8.

Again, since a is not equal to 1, another recursive call is made with a = 2 and b = 3 * 8 = 24.

Once more, a recursive call is made with a = 1. Now, the base case is satisfied, and the function returns b, which is 24.

The final result of fun(4, 2) is 24, which is printed.

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Remove Image Background using Python

 

from rembg import remove

from PIL import Image

input_path = 'p22.jpg'

output_path = 'p22.png'

inp = Image.open(input_path)

output = remove(inp)

output.save(output_path)

#clcoding.com

Explanantion: 

This code snippet appears to be a Python script using the rembg library to remove the background from an image. Let me break it down for you:

from rembg import remove: This line imports the remove function from the rembg library. rembg is likely a library designed for background removal from images.

from PIL import Image: This line imports the Image module from the Python Imaging Library (PIL). PIL is used for opening, manipulating, and saving many different image file formats.

input_path = 'p22.jpg': This line sets the path to the input image file named "p22.jpg". This is the image from which we want to remove the background.

output_path = 'p22.png': This line sets the path for the output image file named "p22.png". The background-removed image will be saved with this filename and format (PNG).

inp = Image.open(input_path): This line opens the input image file using PIL's Image.open() function and assigns it to the variable inp. Now inp holds the image object.

output = remove(inp): This line calls the remove() function from the rembg library and passes the input image (inp) as an argument. This function is expected to perform the background removal operation on the input image.

output.save(output_path): This line saves the background-removed image (output) to the specified output path (output_path). It's saved in PNG format due to the extension provided in the output_path.

The comments at the end (#clcoding.com) seem to indicate the source or reference of the code, possibly a website or a tutorial where this code was found.

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Python library to filter and censor offensive language

 

from better_profanity import profanity

text = "What the hell is going on?"

censored_text = profanity.censor(text)

print(censored_text)  

#clcoding.com

Explanation: 

The line from better_profanity import profanity imports the profanity module from the better_profanity library. This module provides functions and utilities for filtering and censoring offensive language in text.

Here's a breakdown of the subsequent code:

text = "What the hell is going on?": This line assigns a string containing a sentence to the variable text. The sentence includes the word "hell," which is considered offensive.

censored_text = profanity.censor(text): This line uses the censor function from the profanity module to censor any profanity in the text variable. The function replaces offensive words with asterisks (*) or other censor characters.

print(censored_text): This line prints the censored version of the text to the console. In this case, since "hell" is considered offensive, it will be replaced with asterisks, resulting in a censored version of the original sentence.

So, the output of this code will be a censored version of the original text, where the offensive word "hell" is replaced with asterisks.

from better_profanity import profanity

text = "I can't believe he said that!"

has_profanity = profanity.contains_profanity(text)

print(has_profanity) 

#clcoding.com

Explanation:

The line from better_profanity import profanity imports the profanity module from the better_profanity library. This module provides functions and utilities for filtering and detecting offensive language in text.

Here's a breakdown of the subsequent code:

text = "I can't believe he said that!": This line assigns a string containing a sentence to the variable text. The sentence does not contain any explicit profanity.

has_profanity = profanity.contains_profanity(text): This line calls the contains_profanity function from the profanity module and passes the text variable as an argument. This function checks whether the provided text contains any profanity.

print(has_profanity): This line prints the result of the profanity check to the console. If the text contains any profanity, the result will be True; otherwise, it will be False.

After executing this code, the result printed to the console will indicate whether the provided text contains any profanity. Since the text "I can't believe he said that!" does not contain explicit profanity, the output will be False.

from better_profanity import profanity

custom_censor_words = ["heck", "darn", "frick"]

censor_word = profanity.load_censor_words(custom_censor_words)

#clcoding.com

Explanation:

The line from better_profanity import profanity imports the profanity module from the better_profanity library. This module provides functions and utilities for filtering and censoring offensive language in text.

Here's an explanation of the subsequent code:

custom_censor_words = ["heck", "darn", "frick"]: This line creates a list named custom_censor_words containing custom words that you want to be treated as profanity and censored.

censor_word = profanity.load_censor_words(custom_censor_words): This line calls the load_censor_words function from the profanity module and passes the custom_censor_words list as an argument. This function loads the custom censor words into the profanity filter.

However, there's a small correction here: The load_censor_words function doesn't return anything (None), so assigning its result to censor_word doesn't serve any purpose. It's primarily used to load the custom censor words into the profanity filter.

After executing this code, the profanity filter will include the custom censor words provided in the custom_censor_words list. Any occurrence of these words in the text will be censored according to the filter's settings.

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Python Coding challenge - Day 172 | What is the output of the following Python Code?

 

Code:

def fun(x, y):

    if x == 0:

        return y

    else:

        return fun(x - 1, x * y)

print(fun(3, 5))

Solution and Explanation: 

Let's break down the provided Python function fun(x, y) and an example call print(fun(3, 5)):

def fun(x, y):

    if x == 0:

        return y

    else:

        return fun(x - 1, x * y)

print(fun(3, 5))

Function Definition:

The function fun(x, y) takes two parameters, x and y.

Base Case:

The function checks if x is equal to 0. If it is, the function returns y. This serves as the base case for the recursive function.

Recursive Case:

If x is not equal to 0, the function calls itself recursively with x - 1 and x * y.

Recursion:

The function keeps calling itself with a decremented x until x becomes 0, each time multiplying y by the current value of x.

Example Call:

print(fun(3, 5)) calls the fun function with x = 3 and y = 5.

The function first checks if x is 0. Since it's not, it enters the recursive case.

It calls fun(2, 3 * 5), then fun(1, 2 * (3 * 5)), and finally fun(0, 1 * (2 * (3 * 5))).

When x becomes 0, it returns the accumulated value of y, which is 2 * (3 * 5) = 30.

So, the output of print(fun(3, 5)) will be 30.

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Yellow Heart using Python ♥

 

Code : 

import turtle

t = turtle.Turtle()

t.shapesize(0.2, 0.2)

s = turtle.Screen()

s.bgcolor('black')

t.fillcolor("yellow")

t.begin_fill()

t.left(50)

t.forward(240)  

t.circle(90, 200)  

t.left(221)

t.circle(90, 200)  

t.forward(260)  

t.end_fill()

turtle.done()

#clcoding.com

Explanation: 

This code utilizes the Turtle module in Python to create a graphic using turtle graphics. Let's break down each part of the code:

import turtle: This line imports the Turtle module, which provides a drawing canvas and various methods for creating graphics using turtle graphics.

t = turtle.Turtle(): This creates a turtle object named t. The turtle object represents a turtle that can move around the screen and draw lines and shapes.

t.shapesize(0.2, 0.2): This line sets the size of the turtle shape. The parameters 0.2, 0.2 specify that the turtle's shape should be scaled to 20% of its default size in both the x and y directions.

s = turtle.Screen(): This creates a screen object named s. The screen object represents the window or canvas where the turtle will draw.

s.bgcolor('black'): This sets the background color of the screen to black.

t.fillcolor("yellow"): This sets the fill color of the turtle to yellow.

t.begin_fill(): This marks the beginning of a shape that will be filled with the fill color.

t.left(50): This turns the turtle left by 50 degrees.

t.forward(240): This moves the turtle forward by 240 units.

t.circle(90, 200): This draws a partial circle with a radius of 90 units and an extent of 200 degrees. This creates a curved shape.

t.left(221): This turns the turtle left by 221 degrees.

t.circle(90, 200): This draws another partial circle similar to the previous one.

t.forward(260): This moves the turtle forward by 260 units.

t.end_fill(): This marks the end of the shape to be filled and fills the shape with the specified fill color.

turtle.done(): This keeps the window open after the drawing is complete until the user closes it manually.

This code creates a graphic consisting of two curved shapes filled with yellow color on a black background.

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Colorful Galaxy using Python

 

Code : 

import turtle

t = turtle.Turtle()

#clcoding.com 

s = turtle.Screen()

colors=['orange', 'red', 'magenta', 'blue', 'magenta',

        'yellow', 'green', 'cyan', 'purple']

s.bgcolor('black')

t.pensize('2')

t.speed(0)

for x in range (360):

    t.pencolor(colors[x%6])

    t.width(x//100+1)

    t.forward(x) 

    t.right(59)

    turtle.hideturtle()    

#clcoding.com    

Explanation: 

let's break down the code step by step:

import turtle: This line imports the Turtle module, which provides a graphics environment for drawing shapes and patterns.

t = turtle.Turtle(): This creates a new Turtle object named t. The Turtle object represents a pen that can draw on the screen.

s = turtle.Screen(): This creates a new Screen object named s. The Screen object represents the window or canvas where the turtle will draw.

colors = ['orange', 'red', 'magenta', 'blue', 'magenta', 'yellow', 'green', 'cyan', 'purple']: This is a list of colors that will be used for drawing. Each color is represented by a string.

s.bgcolor('black'): This sets the background color of the screen to black.

t.pensize(2): This sets the width of the pen to 2 pixels.

t.speed(0): This sets the drawing speed of the turtle to the fastest speed (0 means fastest).

for x in range(360):: This starts a loop that will repeat 360 times. The loop will draw a spiral pattern.

t.pencolor(colors[x % 6]): This sets the color of the pen to one of the colors from the colors list. The % operator is used to cycle through the colors repeatedly as x increases.

t.width(x // 100 + 1): This sets the width of the pen based on the current value of x. As x increases, the pen width will gradually increase.

t.forward(x): This moves the turtle forward by a distance equal to the current value of x.

t.right(59): This rotates the turtle to the right by 59 degrees.

turtle.hideturtle(): This hides the turtle cursor from the screen.

#clcoding.com: This appears to be a comment indicating the source or reference of the code.

Overall, this code creates a colorful spiral pattern using the Turtle module in Python.

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Python Coding challenge - Day 171 | What is the output of the following Python Code?

 

Let's break down the code:

list1 = [0, 1, 2, 3]

list2 = list1[1::-1]

print(list2)

list1 = [0, 1, 2, 3]: This line initializes a list list1 with elements [0, 1, 2, 3].

list2 = list1[1::-1]: Here, list1[1::-1] is using list slicing to create a new list list2. Let's break down the slicing expression:

1: This is the start index of the slice. It starts from index 1, which is the second element in list1.

::-1: This specifies the step value for the slice. In this case, -1 means to step backward through the list.

So, list1[1::-1] starts from index 1 (the second element) and goes backward to the beginning of the list.

When slicing backward ([::-1]), it reverses the order of elements. So, list2 will contain elements from index 1 (inclusive) to the beginning of the list (inclusive), in reverse order.

print(list2): This line prints the contents of list2.

Now, let's evaluate list2 based on the slicing operation:

list1[1::-1] starts from index 1, which is 1, and includes the element at that index.

The step -1 means it goes backward.

So, it goes from index 1 (1) to the beginning of the list (0) in reverse order.

As a result, list2 will contain [1, 0].

Therefore, the output of print(list2) will be:

[1, 0]

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Fibonacci sequence in Python - 5 ways

 

import math

def fibonacci_closed_form(n):

    phi = (1 + math.sqrt(5)) / 2

    return round((phi**n - (-1/phi)**n) / math.sqrt(5))

# Define the number of Fibonacci numbers you want to print

num_terms = 5

# Print the Fibonacci sequence

for i in range(num_terms):

    print(fibonacci_closed_form(i))

#clcoding.com 

Explanation: 

The import math statement in Python allows you to access various mathematical functions and constants provided by the Python math module. These functions include trigonometric functions, logarithmic functions, and mathematical constants such as π (pi) and e.

In the given code snippet, import math is used to access the sqrt() function from the math module. This function calculates the square root of a number.

The fibonacci_closed_form() function uses the golden ratio (phi) and its inverse to compute the nth Fibonacci number using Binet's formula. Binet's formula is a closed-form expression that directly calculates the nth Fibonacci number without recursion or iteration.

Here's a breakdown of the fibonacci_closed_form() function:

It calculates the golden ratio (phi) using the formula (1 + math.sqrt(5)) / 2.

Then, it uses phi and its inverse (-1/phi) to compute the nth Fibonacci number using Binet's formula: 

​

Finally, it rounds the result using the round() function to get the nearest integer, which is the nth Fibonacci number.

The code then prints the Fibonacci sequence for a specified number of terms (num_terms) using a loop. Each iteration of the loop calls the fibonacci_closed_form() function to compute the nth Fibonacci number and prints the result.

def fibonacci_generator():

    a, b = 0, 1

    while True:

        yield a

        a, b = b, a + b

sequence = fibonacci_generator()

for _ in range(6):

    print(next(sequence))

#clcoding.com 

Explanantion: 

Let's break down the code:

def fibonacci_generator():

    a, b = 0, 1

    while True:

        yield a

        a, b = b, a + b

This defines a generator function fibonacci_generator(). Generators are special functions in Python that allow you to generate a sequence of values lazily, i.e., one at a time, rather than generating them all at once and storing them in memory.

Inside the function, two variables a and b are initialized to 0 and 1 respectively. These variables are used to generate the Fibonacci sequence.

The while True loop creates an infinite loop, which means the generator will continue generating Fibonacci numbers indefinitely.

Inside the loop, the yield keyword is used to yield (or produce) the current value of a, making this function a generator. yield pauses the function's execution and returns a value to the caller without exiting the function.

After yielding a, the values of a and b are updated for the next iteration of the loop. This simulates the Fibonacci sequence generation logic where each number is the sum of the two preceding numbers.

sequence = fibonacci_generator()

for _ in range(6):

    print(next(sequence))

Here, fibonacci_generator() is called to create a generator object named sequence. This generator object will produce Fibonacci numbers when iterated over.

Then, a loop runs six times, each time calling next(sequence). This retrieves the next value from the generator sequence. The _ variable is used as a placeholder for the loop variable, as its value is not used inside the loop. The print() function then prints each Fibonacci number generated by the generator function.

So, the output of this code will be the first six Fibonacci numbers:

0

1

1

2

3

5

And the generator will continue to generate Fibonacci numbers if you keep calling next(sequence).

def fibonacci_memoization(n, memo={}):

    if n in memo:

        return memo[n]

    if n <= 1:

        return n

    else:

        memo[n] = fibonacci_memoization(n-1, memo) + fibonacci_memoization(n-2, memo)

        return memo[n]

# Define the number of Fibonacci numbers you want to print

num_terms = 6

# Print the Fibonacci sequence

for i in range(num_terms):

    print(fibonacci_memoization(i))

    

#clcoding.com 

Explanation: 

This code implements the Fibonacci sequence using memoization to optimize performance by avoiding redundant calculations. Let's break down how it works:

def fibonacci_memoization(n, memo={}):

    if n in memo:

        return memo[n]

    if n <= 1:

        return n

    else:

        memo[n] = fibonacci_memoization(n-1, memo) + fibonacci_memoization(n-2, memo)

        return memo[n]

The function fibonacci_memoization takes two parameters: n, which is the index of the Fibonacci number to compute, and memo, which is a dictionary used to store previously computed Fibonacci numbers to avoid redundant calculations.

It first checks if the Fibonacci number for index n is already in the memo dictionary. If it is, it returns the value directly from the memo, avoiding recalculation.

If the Fibonacci number for index n is not in the memo, it proceeds to compute it.

If n is 0 or 1, it returns n directly since the Fibonacci sequence starts with 0 and 1.

Otherwise, it recursively computes the (n-1)th and (n-2)th Fibonacci numbers using memoization, adds them together, stores the result in the memo dictionary, and returns the result.

This memoization technique ensures that each Fibonacci number is computed only once, significantly reducing the number of recursive calls needed to generate the sequence.

# Define the number of Fibonacci numbers you want to print

num_terms = 6

# Print the Fibonacci sequence

for i in range(num_terms):

    print(fibonacci_memoization(i))

Here, num_terms specifies the number of Fibonacci numbers to print.

The for loop iterates num_terms times, computing and printing each Fibonacci number using the fibonacci_memoization function.

When you run this code, it will print the first six Fibonacci numbers:

0

1

1

2

3

5

And it will continue generating Fibonacci numbers indefinitely if you continue calling fibonacci_memoization(i).

def fibonacci_recursive(n):

    if n <= 1:

        return n

    else:

        return fibonacci_recursive(n-1) + fibonacci_recursive(n-2)

# Define the number of Fibonacci numbers you want to print

num_terms = 5

# Print the Fibonacci sequence

for i in range(num_terms):

    print(fibonacci_recursive(i))

#clcoding.com 

Explanation: 

This code defines a recursive function fibonacci_recursive to generate the Fibonacci sequence. Let's break it down:

def fibonacci_recursive(n):

    if n <= 1:

        return n

    else:

        return fibonacci_recursive(n-1) + fibonacci_recursive(n-2)

The function fibonacci_recursive takes a single argument n, which represents the index of the Fibonacci number to compute.

If n is less than or equal to 1, meaning it's either 0 or 1, the function returns n itself. This is because the Fibonacci sequence starts with 0 and 1.

If n is greater than 1, the function recursively computes the (n-1)th and (n-2)th Fibonacci numbers by calling itself with n-1 and n-2, respectively, and then adds them together to get the nth Fibonacci number.

# Define the number of Fibonacci numbers you want to print

num_terms = 5

# Print the Fibonacci sequence

for i in range(num_terms):

    print(fibonacci_recursive(i))

Here, num_terms specifies the number of Fibonacci numbers to print.

The for loop iterates num_terms times, starting from 0 and ending at num_terms - 1. In each iteration, it computes and prints the Fibonacci number for the current index using the fibonacci_recursive function.

When you run this code, it will print the first five Fibonacci numbers:

0

1

1

2

3

And it will continue generating Fibonacci numbers indefinitely if you continue calling fibonacci_recursive(i). However, recursive Fibonacci calculation can become inefficient for large values of n due to redundant computations.

def fibonacci_iterative(n):

    fib_sequence = [0, 1]

    for i in range(2, n+1):

        fib_sequence.append(fib_sequence[-1] + fib_sequence[-2])

    return fib_sequence

sequence = fibonacci_iterative(5)

print(sequence)

#clcoding.com 

Explanation: 

This code defines a function fibonacci_iterative to generate the Fibonacci sequence using an iterative approach. Let's go through it step by step:

def fibonacci_iterative(n):

    fib_sequence = [0, 1]

    for i in range(2, n+1):

        fib_sequence.append(fib_sequence[-1] + fib_sequence[-2])

    return fib_sequence

The function fibonacci_iterative takes a single argument n, which represents the number of Fibonacci numbers to generate.

It initializes a list fib_sequence with the first two Fibonacci numbers, 0 and 1.

It then enters a loop starting from index 2 (since the first two Fibonacci numbers are already in the list) up to n. In each iteration, it calculates the next Fibonacci number by adding the last two numbers in the sequence (fib_sequence[-1] and fib_sequence[-2]) and appends it to the list.

After the loop completes, the function returns the entire list fib_sequence containing the generated Fibonacci sequence.

sequence = fibonacci_iterative(5)

print(sequence)

This code calls the fibonacci_iterative function with n = 5 to generate the first 5 Fibonacci numbers.

It then prints the generated sequence.

When you run this code, it will print the first 5 Fibonacci numbers:

[0, 1, 1, 2, 3, 5]

This iterative approach is efficient and straightforward for generating Fibonacci numbers, especially for small values of n.

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Python Coding challenge - Day 170 | What is the output of the following Python Code?

 

Code:

def fibonacci_tail_recursive(n, a=0, b=1):

    if n == 0:

        return a

    elif n == 1:

        return b

    else:

        return fibonacci_tail_recursive(n - 1, b, a + b)

print(fibonacci_tail_recursive(7))  

Solution and Explanation:

Let's break down the code step by step:

def fibonacci_tail_recursive(n, a=0, b=1):

This line defines a function called fibonacci_tail_recursive that takes three parameters: n, a, and b. n represents the term of the Fibonacci sequence to compute, while a and b represent the current and next terms of the sequence respectively. By default, a is set to 0 and b is set to 1.

    if n == 0:

        return a

    elif n == 1:

        return b

Here, the function checks if the input n is 0 or 1. If n is 0, it returns the current term a (which is 0). If n is 1, it returns the next term b (which is 1). These base cases terminate the recursion.

    else:

        return fibonacci_tail_recursive(n - 1, b, a + b)

If n is greater than 1, the function makes a recursive call to itself with the parameters (n - 1, b, a + b). This means it calculates the next term by adding the current term a to the next term b, and it decrements n by 1 to move closer to the base cases. This process continues until n reaches 0 or 1, at which point the function returns a or b respectively.

print(fibonacci_tail_recursive(7))

Finally, the function is called with n = 7, which computes the 7th term of the Fibonacci sequence using tail recursion. The result is printed to the console.

This code demonstrates a tail-recursive implementation of the Fibonacci sequence, where the recursive call is the last operation performed by the function. However, it's worth noting that Python's interpreter does not perform tail call optimization by default, so this tail-recursive implementation doesn't gain any performance benefits over a non-tail-recursive version in Python.

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Happy Eid Al-Fitr using Python

 

Code :

import pyfiglet

from termcolor import colored

import random

def eid_al_fitr_wishes():

    colors = ['red', 'green', 'yellow', 'blue', 'magenta', 'cyan', 'white']

    ascii_art = pyfiglet.figlet_format("Eid Mubarak!", font="slant")

    print(colored(ascii_art, color=random.choice(colors)))    

# Call the function to display Eid-al-Fitr wishes

eid_al_fitr_wishes()

#clcoding.com

Explanation: 

Let me break down the code step by step:

import pyfiglet: This line imports the pyfiglet module, which is used for creating ASCII art text. In this script, the figlet_format() function from pyfiglet module is used to generate ASCII art for the text "Eid Mubarak!" with the specified font style ("slant").

from termcolor import colored: This line imports the colored() function from the termcolor module. The colored() function is used to add color to text printed in the terminal. It takes the text and color as arguments and returns the colored text.

import random: This line imports the random module, which provides functions for generating random numbers. It is used in this script to randomly select a color from the list of colors defined later.

def eid_al_fitr_wishes():: This line defines a function named eid_al_fitr_wishes(). This function encapsulates the logic for printing Eid-al-Fitr wishes with ASCII art and colored text.

colors = ['red', 'green', 'yellow', 'blue', 'magenta', 'cyan', 'white']: This line defines a list named colors containing various color names.

ascii_art = pyfiglet.figlet_format("Eid Mubarak!", font="slant"): This line uses the figlet_format() function from the pyfiglet module to generate ASCII art for the text "Eid Mubarak!" with the specified font style ("slant"). The resulting ASCII art is stored in the variable ascii_art.

print(colored(ascii_art, color=random.choice(colors))): This line prints the ASCII art generated earlier with a randomly chosen color from the colors list. The colored() function applies the selected color to the ASCII art before printing it.

eid_al_fitr_wishes(): This line calls the eid_al_fitr_wishes() function, executing the code within it and printing the Eid-al-Fitr wishes with colored ASCII art.

That's a breakdown of the code provided. If you have any further questions or need clarification on any part, feel free to ask!

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IP Address Infromation using Python

 

Code : 

import os

import urllib.request as urllib2

import json

# Use a while loop to continuously prompt the user for the target IP address

while True:

    # Prompt the user to input the target IP address

    ip = input("What is your target IP: ")

    

    # Construct the URL for the ip-api.com JSON API

    url = "http://ip-api.com/json/"

    

    # Send a request to the ip-api.com API to retrieve the geolocation information

    response = urllib2.urlopen(url + ip)

    

    # Read the response data

    data = response.read()

    

    # Parse the JSON response data into a Python dictionary

    values = json.loads(data)

    

    # Print the geolocation information for the target IP address

    print("IP: " + values["query"])

    print("City: " + values["city"])

    print("ISP: " + values["isp"])

    print("Country: " + values["country"])

    print("Region: " + values["region"])

    print("Timezone: " + values["timezone"])

    

    # Exit the loop after printing the information once

    break

#clcoding.com 

Explanation: 

This code snippet is a Python script that continuously prompts the user to input a target IP address and then retrieves geolocation information for that IP address using the ip-api.com JSON API. Let's break down each part of the code:

import os: This imports the os module, which provides a way to interact with the operating system. However, it's not used in this particular snippet.

import urllib.request as urllib2: This imports the urllib.request module under the alias urllib2. This module provides functionality for making HTTP requests, which is used to fetch data from the ip-api.com API.

import json: This imports the json module, which provides functions for encoding and decoding JSON data.

The code then enters a while True loop, which means it will continue to run indefinitely until explicitly stopped.

Inside the loop, the user is prompted to input a target IP address using the input() function. The IP address is stored in the variable ip.

The URL for the ip-api.com JSON API is constructed as http://ip-api.com/json/.

A request is made to the ip-api.com API using urllib2.urlopen(), passing in the constructed URL along with the target IP address.

The response data is read using the read() method of the response object.

The JSON response data is parsed into a Python dictionary using json.loads(), storing the result in the variable values.

The geolocation information extracted from the JSON response is printed to the console, including the IP address, city, ISP, country, region, and timezone.

Finally, the loop breaks after printing the information once, effectively ending the program.

This script allows users to input a target IP address and quickly obtain geolocation information about it using the ip-api.com API.

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Plant Leaf using Python

 

import numpy as np

import matplotlib.pyplot as plt

t = np.linspace(0, 39*np.pi/2, 1000)

x = t * np.cos(t)**3

y = 9*t * np.sqrt(np.abs(np.cos(t))) + t * np.sin(0.2*t) * np.cos(4*t)

plt.plot(x, y, c="green")

plt.show()

#clcoding.com

Explanation: 

This code snippet is written in Python and utilizes two popular libraries for numerical computing and visualization: NumPy and Matplotlib.

Here's a breakdown of what each part of the code does:

import numpy as np: This line imports the NumPy library and allows you to use its functionalities in your code. NumPy is a powerful library for numerical computations in Python, providing support for arrays, matrices, and mathematical functions.

import matplotlib.pyplot as plt: This line imports the pyplot module from the Matplotlib library, which is used for creating static, animated, and interactive visualizations in Python. It's typically imported with the alias plt for convenience.

t = np.linspace(0, 39*np.pi/2, 1000): This line generates an array t of 1000 equally spaced values ranging from 0 to 

39

�

2

2

39π

​

 . np.linspace() is a NumPy function that generates linearly spaced numbers.

x = t * np.cos(t)**3: This line computes the x-coordinates of the points on the plot. It utilizes NumPy's array operations to compute the expression 

�

×

cos

⁡

(

�

)

3

t×cos(t) 

3

 .

y = 9*t * np.sqrt(np.abs(np.cos(t))) + t * np.sin(0.2*t) * np.cos(4*t): This line computes the y-coordinates of the points on the plot. It's a more complex expression involving trigonometric functions and mathematical operations.

plt.plot(x, y, c="green"): This line plots the x and y coordinates as a line plot. The c="green" argument specifies the color of the line as green.

plt.show(): This line displays the plot on the screen. It's necessary to call this function to actually see the plot.

Overall, this code generates a plot of a parametric curve defined by the equations for x and y. The resulting plot will depict the curve in green.

import numpy as np

import matplotlib.pyplot as plt

# Define the parameter t

t = np.linspace(0, 39*np.pi/2, 1000)

# Define the equations for x and y

x = t * np.cos(t)**3

y = 9*t * np.sqrt(np.abs(np.cos(t))) + t * np.sin(0.2*t) * np.cos(4*t)

# Define the segment indices and corresponding colors

segments = [

    (0, 200, 'orange'),  # Green segment

    (200, 600, 'magenta'),   # Red segment

    (600, 1000, 'red')  # Orange segment

]

# Plot each segment separately with the corresponding color

for start, end, color in segments:

    plt.plot(x[start:end], y[start:end], c=color)

plt.show()

#clcoding.com 

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Playing a YouTube Video using Python

 

import pywhatkit

# Using Exception Handling to avoid unprecedented errors

try:

    # Ask the user to input the song name

    song = input("Enter Song Name: ")

    # Play a YouTube video corresponding to the search term entered by the user

    pywhatkit.playonyt(song)    

    # Print a success message if the video is played successfully

    print("Successfully Played!") 

except:

    # Handle exceptions and print an error message if any unexpected error occurs

    print("An Unexpected Error!")

Explanation:

Importing the Module: import pywhatkit imports the pywhatkit module, which provides various functionalities, including playing YouTube videos.

Exception Handling (try-except): The code is wrapped in a try block, which allows Python to attempt the code within it. If an error occurs during the execution of the code inside the try block, Python will stop executing that block and jump to the except block.

User Input: Inside the try block, the input() function prompts the user to enter the name of the song they want to play on YouTube. The entered song name is stored in the variable song.

Playing YouTube Video: The pywhatkit.playonyt() function is called with the song variable as an argument. This function opens a web browser and plays the YouTube video corresponding to the search term entered by the user.

Success Message: If the video is played successfully without any errors, the code inside the try block will execute completely, and the success message "Successfully Played!" will be printed.

Exception Handling (except): If any unexpected error occurs during the execution of the code inside the try block, Python will jump to the except block and execute the code within it. In this case, it simply prints the error message "An Unexpected Error!". This ensures that the program does not crash abruptly if an error occurs during video playback.

Overall, this code allows users to input the name of a song, and it plays the corresponding YouTube video while handling any unexpected errors that may occur during execution.

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Sending a WhatsApp Message using Python

 

# importing the module

import pywhatkit

# using Exception Handling to avoid unprecedented errors

try:

# sending message to receiver using pywhatkit

    pywhatkit.sendwhatmsg("+919767292502","Hello Python",21, 23)

    print("Successfully Sent!")

except:

# handling exception and printing error message

    print("An Unexpected Error!")

Here's what each part does:

Importing the Module: import pywhatkit imports the pywhatkit module, which provides various functionalities, including sending WhatsApp messages.

Exception Handling (try-except): The code is wrapped in a try block, which allows Python to attempt the code within it. If an error occurs during the execution of the code inside the try block, Python will stop executing that block and jump to the except block.

Sending WhatsApp Message: Inside the try block, the pywhatkit.sendwhatmsg() function is called to send a WhatsApp message. It takes four arguments: the recipient's phone number (including the country code), the message content, the hour of the day (in 24-hour format) at which the message will be sent, and the minute of the hour.

Success Message: If the message is sent successfully without any errors, the code inside the try block will execute completely, and the success message "Successfully Sent!" will be printed.

Exception Handling (except): If any unexpected error occurs during the execution of the code inside the try block, Python will jump to the except block and execute the code within it. In this case, it simply prints the error message "An Unexpected Error!". This ensures that the program does not crash abruptly if an error occurs during message sending.

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Python Coding challenge - Day 169 | What is the output of the following Python Code?

 

Code: 

def sum(num):

    if num == 1:

        return 1

    return num + sum(num-1)

print(sum(4))

Solution and Explanantion: 

This is a recursive Python function named sum. It calculates the sum of integers from 1 to a given number (num). Let's break it down:

def sum(num)::

This line defines a function named sum that takes one argument, num.

if num == 1::

This is a base case for the recursion. It checks if the input num is equal to 1.

return 1:

If num is indeed 1, it returns 1. This is the base case of the recursion where the sum of integers from 1 to 1 is 1.

return num + sum(num-1):

If num is not equal to 1, it returns the sum of num and the result of calling sum recursively with num-1. This line adds the current value of num to the sum of all integers from 1 to num-1.

print(sum(4)):

This line calls the sum function with the argument 4, meaning it will calculate the sum of integers from 1 to 4.

Let's trace how this works with sum(4):

sum(4) calls sum(3)

sum(3) calls sum(2)

sum(2) calls sum(1)

sum(1) returns 1 (base case)

sum(2) returns 2 + 1 = 3

sum(3) returns 3 + 3 = 6

sum(4) returns 4 + 6 = 10

So, print(sum(4)) will output 10.

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