Convert RGB to Hex using Python

 

from webcolors import name_to_hex

def color_name_to_code(color_name):

    try:

        color_code = name_to_hex(color_name)

        return color_code

    except ValueError:

        return None

        

colorname = input("Enter color name : ")

result_code = color_name_to_code(colorname)

print(result_code)  

Read More

PDF file protection using password in Python

 

from PyPDF2 import PdfReader, PdfWriter

import getpass

def protect_pdf(input_pdf, output_pdf):

    reader = PdfReader('clcoding.pdf')

    writer = PdfWriter()

    for page in reader.pages:

        writer.add_page(page)

    password = getpass.getpass("Enter a password : ")

    writer.encrypt(password)

    with open(output_pdf, "wb") as output_file:

        writer.write(output_file)

    print(f"The PDF has password.")

    

protect_pdf("clcoding.pdf", "protected_file.pdf")

Read More

Python OOPS Challenge | Day 12| What is the output of following Python code?

In this code snippet, one class is defined, which is named Tablet. The class definition begins with class Tablet: and includes a pass statement, which acts as a placeholder, meaning the class has no methods or attributes defined inside it for now.

After defining the class, two instances (or objects) of the Tablet class are created: tablet1 and tablet2. These are variables that hold instances of the Tablet class, not additional class definitions.

So, the answer is 1, as there is only one class (Tablet) defined in the code segment.

Read More

Rainbow Circle using Python

 

import turtle

t = turtle.Turtle()

t.speed(10)

colors = ['red', 'orange', 'yellow',

          'green', 'blue', 'indigo',

          'violet']

turtle.bgcolor('black')

for i in range(36):

    t.color(colors[i % 7])

    t.circle(100)

    t.right(10)

turtle.done()

Read More

Python OOPS Challenge | Day 11 | What is the output of following Python code?

In this code snippet, the output will be an Exception. Here’s why:

1. The TV class is defined with no attributes or methods (pass is used as a placeholder).

2. An instance of the TV class, obj, is created.

3. A new attribute price is assigned to obj with the value 200. This attribute is dynamically added to the instance obj but is not part of the TV class itself.

4. The code then attempts to print self.price. However, self is not defined in the current scope. In Python, self is a conventionally used parameter name that refers to the instance of the class within a method of the class. Since this code tries to access self outside of a class method, it will raise a NameError for the undefined variable self.

To fix this and print the price, you would need to use obj.price instead:

print(obj.price)

This would correctly print 200.

Read More

Python OOPS Challenge | Day 10 | What is the output of following Python code?

This code snippet demonstrates runtime polymorphism. Here’s why:

1. Polymorphism allows a method in a subclass to have the same name as a method in its superclass but behave differently. In this example, the printWeight() method is defined in both the PolarAnimal superclass and the Penguin subclass.

2. Method Overriding: The Penguin subclass overrides the printWeight() method of PolarAnimal. This means that if an object of Penguin is used to call printWeight(), it will execute the print("heavy") line instead of print("light") defined in the superclass.

3. Runtime Polymorphism (also known as dynamic polymorphism) happens at runtime, where the method to execute is determined based on the actual object type (i.e., whether it’s a Penguin or PolarAnimal instance) rather than at compile time.

Since printWeight() behaves differently in Penguin than in PolarAnimal, it demonstrates runtime polymorphism.

Read More

Python OOPS Challenge | Day 9 | What is the output of following Python code?

In this code snippet, we have two classes: OSDevice and SmartTV. The SmartTV class inherits from the OSDevice class.

Code Analysis

1. The OSDevice class has a method called printSize, which prints "medium".

2. The SmartTV class has its own printSize method that overrides the one from OSDevice and prints "large".

When we create an instance of SmartTV with obj = SmartTV() and call obj.printSize(), Python will look for the printSize method in the SmartTV class first. Since SmartTV has its own printSize method, it overrides the printSize method in OSDevice. Therefore, only "large" is printed.

Output

The output of this code will be:

large

So, the correct answer is the second option: large.

Read More

Python OOPS Challenge | Day 8 |What is the output of following Python code?

In this code snippet, we have two classes: Fruit and Apple. The Apple class inherits from the Fruit class.

Code Analysis

1. The Fruit class has an __init__ method (constructor) that prints '1'.

2. The Apple class also has its own __init__ method that overrides the one from Fruit and prints '2'.

When we create an instance of Apple with obj = Apple(), Python will look for the __init__ method in the Apple class first. Since Apple has its own __init__ method, it overrides the __init__ method of Fruit. Therefore, only '2' is printed.

Output

The output of this code will be:

2

So, the correct answer is the second option: 2.

Read More

Python OOPS Challenge | Day 7 |What is the output of following Python code?

Let's go through this code snippet step-by-step:

try:

    print("1")

    raise Exception("2")

    print("3")

except Exception as e:

    print(str(e))

    print("4")

Explanation:

1. try Block Execution:

print("1"): This line executes first and outputs 1.

raise Exception("2"): This line raises an exception with the message "2". Because an exception is raised, the code execution immediately jumps to the except block, and the line print("3") is never executed.

2. except Block Execution:

print(str(e)): This line executes next, printing the exception message "2".

print("4"): This line then executes, printing 4.

Final Output:

The output is:

1

2

4

Correct Answer:

The correct answer is 124.

Read More

Automating Excel with Python

 

Automating Excel with Python 

In Automating Excel with Python: Processing Spreadsheets with OpenPyXL you will learn how to use Python to create, edit or read Microsoft Excel documents using OpenPyXL.

Python is a versatile programming language. You can use Python to read, write and edit Microsoft Excel documents. There are several different Python packages you can use, but this book will focus on OpenPyXL.

The OpenPyXL package allows you to work with Excel files on Windows, Mac and Linux, even if Excel isn't installed.

In this book, you will learn about the following:

• Opening and Saving Workbooks

• Reading Cells and Sheets

• Creating a Spreadsheet (adding / deleting rows and sheets, merging cells, folding, freeze panes)

• Cell Styling (font, alignment, side, border, images)

• Conditional Formatting

• Charts

• Comments

• and more!

Python is a great language that you can use to enhance your daily work, whether you are an experienced developer or a beginner!

Automating Excel with Python

Read More

Python OOPS Challenge | Day 6 |What is the output of following Python code?

In this code, we define a class Rectangle with an initializer method __init__ and a method calcPerimeter to calculate the perimeter of the rectangle.

Code Breakdown:

1. __init__ method: This is the constructor of the class. When an object of the Rectangle class is created, it initializes the attributes a and b with the values provided as arguments.

def __init__(self, aVal, bVal):

    self.a = aVal

    self.b = bVal

Here, self.a and self.b are set to aVal and bVal, respectively.

2. calcPerimeter method: This method calculates the perimeter of the rectangle using the formula .

def calcPerimeter(self):

    return 2 * self.a + 2 * self.b

3. Creating an object and calling calcPerimeter:

obj = Rectangle(1, 5)

print(obj.calcPerimeter())

Here, we create an object obj of the Rectangle class with a = 1 and b = 5.

Then, calcPerimeter is called on obj, which calculates .

Output:

The output of this code is 12.

Read More

Retrieve System Information using Python

 

import wmi

c = wmi.WMI()

for os in c.Win32_OperatingSystem():

    

    print(f"OS Name: {os.Name}")

    print(f"Version: {os.Version}")

    print(f"Manufacturer: {os.Manufacturer}")

    print(f"Architecture: {os.OSArchitecture}")

Read More

Python OOPS Challenge! Day 4 | What is the output of following Python code?

In this code snippet, an attempt is made to create an instance of the class SpaceObject, which inherits from ABC (Abstract Base Class) from Python's abc module. Here’s the breakdown of the code:

Explanation

1. Abstract Base Class (ABC):

The class SpaceObject inherits from ABC, which is used to define abstract base classes.

However, SpaceObject does not contain any abstract methods (methods decorated with @abstractmethod), making it a concrete class, even though it inherits from ABC.

2. Object Creation:

Since there are no abstract methods in SpaceObject, it is possible to instantiate this class directly.

The code obj = SpaceObject() will execute without any issues.

3. Output:

After creating the object, print('Object Creation') is called, which will print Object Creation to the console.

Conclusion

The correct answer is:

Object Creation

Read More

Python OOPS Challenge! Day 5 | What is the output of following Python code?

The code snippet contains a class Tablet with a static method printModel. However, there’s an issue with how this static method is defined and used.

Explanation

1. Static Method Misuse:

printModel is decorated with @staticmethod, which means it should not accept any arguments except for optional ones. However, self is being used as a parameter, which is misleading.

Static methods do not have access to instance-specific data, so they cannot use self to access instance attributes like self.model.

2. Error Triggered:

When Tablet.printModel() is called, it tries to execute print(self.model).

Because printModel is a static method, self is not automatically passed, leading to a missing argument error.

This will raise a TypeError, saying something like "printModel() takes 0 positional arguments but 1 was given."

Output

The correct answer is:

Exception

Read More

Python Code for Periodic Table

 

import periodictable

Atomic_No = int(input("Enter Atomic No :"))

element = periodictable.elements[Atomic_No]

print('Name:', element.name)

print('Symbol:', element.symbol)

print('Atomic mass:', element.mass)

print('Density:', element.density)

#source code --> clcoding.com

Name: zinc

Symbol: Zn

Atomic mass: 65.409

Density: 7.133

Read More

Calculate Derivatives using Python

 

import sympy as sym

x = sym.Symbol('x')

func= input("Enter the function: ")   

sym.Derivative(func, x) 

sym.Derivative(func, x, evaluate=True)

Read More

Plotting a World Map with Country Borders

 

pip install geopandas

import geopandas as gpd

import matplotlib.pyplot as plt

world = gpd.read_file('ne_110m_admin_0_countries.shp')

world.plot(edgecolor='black')

plt.title("World Map with Country Borders")

plt.show()

Read More

Finally release your stress while Coding

 

About this item

[Good material] It is made of high-quality pearl foam material, it is not easy to age, durable and has a long service life.

[Big Enter Button]The Big Enter key is a button that is almost 6 times bigger than the real key. Dubbed as the "BIG ENTER", this is easy to use.

[Compatibility] All you need to do is to plug in the USB cable into your PC,laptop and it'll recognize as an "ENTER" key, it is compatibility with all operation systems such as windows,mac,linux etc .

[A pillow for your nap]The button itself is made out of soft sponge material so when you get tired, you can even use it as a pillow and take a nap on it.

[Release pressure]And when you're feeling stressed out, you can smash on it as hard as you can without fearing of breaking your keyboard. 

USA Buy: Finally release your stress while Coding

India Buy: Finally release your stress while Coding

Europe: Finally release your stress while Coding

Read More

Introduction to Networking (Free Courses)

 

What you'll learn

You will learn what a network is and why it is needed.

Describe the network components and provide the requirements for a networking solution.

Introduce the OSI model and the TCP/IP protocol suite and their role in networking.

Cover the basics of Ethernet technology and understand how data is forwarded in an Ethernet network.

There are 2 modules in this course

Welcome to the Introduction to Networking Course. 

In this course we will cover the basics of networking, introduce commonly used TCP/IP protocols and cover the fundamentals of an Ethernet network. 

In addition, you’ll be equipped with the basic knowledge to understand the main data center requirements and how they can be fulfilled.

Upon successful completion of the course's final exam, you will receive a digital completion certificate that confirms your understanding of Ethernet technology basics and data forwarding within an Ethernet network.

Join Free: Introduction to Networking (Free Courses)

Read More

Colorful QR Code using Python

 

import pyqrcode

from PIL import Image

link = input("Enter anything to generate QR : ")

qr_code = pyqrcode.create(link)

qr_code.png("QRCode.png", scale=5)

Image.open("QRCode.png")

Read More

Find director of a movie using Python

 

from imdb import IMDb

ia = IMDb()

movie_name = input("Enter the movie name: ")

movies = ia.search_movie(movie_name)

if movies:

    movie = movies[0]

    ia.update(movie)

    directors = movie.get('directors')

    if directors:

        print("Director(s):")

        for director in directors:

            print(director['name'])

    else:

        print("No director information found.")

else:

    print("Movie not found.")

Read More

Find your country on a Map using Python

 

import plotly.express as px

country = input("Enter the country name: ")

data = {

    'Country': [country],

    'Values': [100]  }

fig = px.choropleth(

    data,

    locations='Country',

    locationmode='country names',

    color='Values',

    color_continuous_scale='Inferno',

    title=f'Country Map Highlighting {country}')

fig.show()

#source code --> clcoding.com

Read More

Deep Learning with PyTorch : Image Segmentation

 

Deep Learning with PyTorch: Unveiling Image Segmentation

Mastering Image Segmentation Using PyTorch through Hands-on Learning

Introduction

Image segmentation is a critical task in computer vision that goes beyond classifying images. Instead of recognizing an object as a whole, segmentation involves identifying individual pixels belonging to each object, enabling applications in autonomous vehicles, medical imaging, and beyond. In hands-on project “Deep Learning with PyTorch: Image Segmentation,” learners explore the concepts and implementation of semantic segmentation using the power of PyTorch, a popular deep learning framework.

This blog takes you through the key highlights of the course and the insights you'll gain from participating in the project.

---

What is Image Segmentation?

At its core, image segmentation is about partitioning an image into multiple segments, where each pixel is assigned to a specific class or object. It generally comes in two primary types:

• Semantic segmentation: Assigns a label to every pixel, but objects of the same class (e.g., all cars) are treated identically.
• Instance segmentation: Differentiates individual objects, even if they belong to the same class.

Some real-world applications include:

• Autonomous vehicles: Identifying roads, pedestrians, and obstacles.
• Medical diagnosis: Locating tumors or abnormalities in MRI or X-ray images.
• Satellite imagery: Distinguishing between forests, cities, and water bodies.

---

Overview of the Course

This project provides a beginner-friendly introduction to building an image segmentation model using PyTorch. In just two hours, you’ll go through the entire process of preparing data, building the segmentation network, and training it for meaningful results.

What You'll Learn

1. PyTorch Basics:

   • Getting comfortable with PyTorch operations and tensors.
   • Understanding how neural networks are defined and trained.

2. Building a Segmentation Network:

   • Using U-Net, a well-known architecture for image segmentation tasks. U-Net is known for its ability to capture both global and local features, making it suitable for medical imaging and other pixel-based predictions.

3. Training and Evaluation:

   • Implementing the loss function to quantify segmentation errors.
   • Measuring accuracy using metrics like Intersection-over-Union (IoU).

4. Data Preparation:

   • Loading and preprocessing images and labels.
   • Working with image masks where each pixel’s value represents a class.

5. Visualizing Results:

   • Generating segmentation masks to compare predicted vs. actual outputs visually.

---

Why PyTorch for Image Segmentation?

PyTorch stands out for its flexibility, dynamic computation graphs, and strong support from the research community. For image segmentation, PyTorch offers several advantages:

• Customizability: Build and tweak models without extensive boilerplate code.
• Pre-trained Models: Access to pre-trained segmentation models via TorchVision.
• Rich Ecosystem: PyTorch integrates well with tools like TensorBoard for visualization and Hugging Face for additional resources.

---

Hands-on Approach to Learning

This project emphasizes practical, hands-on learning through a guided interface. You’ll build and train a model directly in your browser using cloud workspace—no need for separate installations! This project is especially helpful for those looking to:

• Get a quick introduction to PyTorch without diving into lengthy tutorials.
• Understand real-world workflows for image segmentation tasks.
• Explore how to prepare custom datasets for pixel-wise predictions.

---

Key Takeaways

By the end of this project, you will:

• Understand the concepts behind image segmentation and its applications.
• Know how to build a segmentation model from scratch using PyTorch.
• Be equipped with the knowledge to train and evaluate deep learning models for pixel-based tasks.

Whether you're a data science enthusiast, a student, or a professional exploring computer vision, this project provides a solid introduction to image segmentation and PyTorch fundamentals. With the knowledge gained, you can take on more advanced tasks like object detection, instance segmentation, and multi-class semantic segmentation.

---

Next Steps

Once you complete the project, consider:

• Exploring advanced architectures such as Mask R-CNN for instance segmentation.
• Working with larger datasets like COCO or Cityscapes.
• Building your own end-to-end computer vision applications using PyTorch.

“Deep Learning with PyTorch: Image Segmentation” serves as a launching pad into the fascinating world of computer vision. If you're ready to dive in, enroll now and start your journey toward mastering segmentation!

---

Final Thoughts

Image segmentation is not just a technical task—it’s an essential component in making AI systems understand the world at a granular level. This project will enable you to explore the magic of deep learning applied to computer vision, paving the way for both academic research and industry projects. With PyTorch in hand, the only limit is your imagination!

Join Free: Deep Learning with PyTorch : Image Segmentation

Read More

DeepLearning.AI TensorFlow Developer Professional Certificate

 

Master AI Development with TensorFlow: A Guide to Coursera's TensorFlow in Practice Professional Certificate 🧠🚀

Introduction

Machine learning is revolutionizing industries, and TensorFlow has become one of the most widely used frameworks for building intelligent systems. If you’re ready to dive deep into AI and enhance your machine learning skills, the TensorFlow in Practice Professional Certificate on Coursera is a great place to start. Whether you're a data enthusiast or aspiring ML engineer, this certificate equips you with the right skills to build, train, and deploy cutting-edge neural networks.

In this blog, we’ll take a closer look at what this certificate offers, why it matters, and how it can boost your career. 👇

---

📋 What is the TensorFlow in Practice Certificate?

This four-course series, developed by deeplearning.ai, focuses on mastering TensorFlow—a powerful open-source platform for building machine learning models. You’ll learn how to apply deep learning algorithms, work with large datasets, and design AI models that can handle real-world tasks like image recognition and natural language processing (NLP).

---

📚 What You’ll Learn

1. Introduction to TensorFlow for AI, ML, and DL

   • Start with the fundamentals of TensorFlow.
   • Learn to implement basic neural networks.
   • Explore computer vision concepts for image recognition.

2. Convolutional Neural Networks (CNNs) in TensorFlow

   • Understand how CNNs power applications like facial recognition and image classification.
   • Build CNNs for practical projects, including data from real-world images.

3. Natural Language Processing in TensorFlow

   • Explore Recurrent Neural Networks (RNNs) and LSTMs to handle sequential data.
   • Apply NLP techniques to sentiment analysis, text generation, and more.

4. Sequences, Time Series, and Prediction

   • Work with time-series data for forecasting and predictive models.
   • Build LSTM networks and other advanced models to capture temporal patterns.

---

💼 Why Should You Take This Course?

This certification not only teaches TensorFlow, but also covers essential deep learning concepts that are in high demand today. Here are a few benefits:

• Hands-on Projects: Work with real datasets and practical AI scenarios.
• Career Boost: TensorFlow is widely used by Google, Uber, Twitter, and more—making this a valuable skill.
• Job-Ready Skills: Prepare for roles like Machine Learning Engineer or Data Scientist.

---

🕒 Time Commitment

• 4 Courses (About 1 month per course, working part-time)
• Completely Online: Learn at your own pace with flexible deadlines.

With a total commitment of approximately 4 months, this program is ideal for busy professionals and students alike.

---

🌟 Who is it For?

This certificate is for anyone interested in building AI systems using TensorFlow, including:

• Data Scientists and Machine Learning Engineers
• Software Developers expanding into AI
• AI enthusiasts looking to build real-world projects

Basic Python programming skills are recommended before starting. If you’re already familiar with neural networks, you’ll get a chance to deepen your understanding and apply your knowledge more effectively.

---

🎯 How to Enroll

Enrollment is open year-round on Coursera, and financial aid is available. Upon completing the program, you’ll earn a professional certificate from Coursera and deeplearning.ai—a valuable credential to showcase on LinkedIn.

🔗 Enroll Now: TensorFlow in Practice Certificat

---

✨ Final Thoughts

As AI and machine learning become increasingly essential across industries, mastering TensorFlow gives you a competitive edge. The TensorFlow in Practice Professional Certificate offers a perfect blend of theory and practice, empowering you to create AI solutions for real-world challenges.

Whether you’re an aspiring ML engineer or a developer looking to enhance your skills, this certificate will set you on the right track for success.

Join Free: DeepLearning.AI TensorFlow Developer Professional Certificate

---

Read More

Heatmap Plot in Python

 

import seaborn as sns

import matplotlib.pyplot as plt

import numpy as np

data = np.random.rand(10, 12)

sns.heatmap(data, cmap='Reds')

plt.show()

#source code -- clcoding.com

Read More

Python Coding challenge - Day 245 | What is the output of the following Python Code?

 

Let's break down the code step by step to explain what happens in the modify_list function and why the final result of print(my_list) is [1, 2, 3, 4].

def modify_list(lst, val):

    lst.append(val)

    lst = [100, 200, 300]

my_list = [1, 2, 3]

modify_list(my_list, 4)

print(my_list)

Step-by-step Explanation:

Function Definition: The function modify_list(lst, val) accepts two arguments:

lst: a list passed by reference (so modifications within the function affect the original list unless reassigned).

val: a value that will be appended to the list lst.

Initial State of my_list: Before calling the function, the list my_list is initialized with the values [1, 2, 3].

Calling the Function:

modify_list(my_list, 4)

We pass the list my_list and the value 4 as arguments to the function.

Inside the function, lst refers to the same list as my_list because lists are mutable and passed by reference.

First Line Inside the Function:

lst.append(val)

lst.append(4) adds the value 4 to the list.

Since lst refers to the same list as my_list, this operation modifies my_list as well.

At this point, my_list becomes [1, 2, 3, 4].

Reassignment of lst:

lst = [100, 200, 300]

This line creates a new list [100, 200, 300] and assigns it to the local variable lst.

However, this reassignment only affects the local variable lst inside the function. It does not modify the original list my_list.

After this line, lst refers to the new list [100, 200, 300], but my_list remains unchanged.

End of the Function: When the function finishes execution, lst (which is now [100, 200, 300]) is discarded because it was only a local variable.

my_list retains its modified state from earlier when the value 4 was appended.

Final Output:

print(my_list)

When we print my_list, it shows [1, 2, 3, 4] because the list was modified by lst.append(val) but not affected by the reassignment of lst.

Key Takeaways:

List Mutation: The append() method modifies the list in place, and since lists are mutable and passed by reference, my_list is modified by lst.append(val).

Local Reassignment: The line lst = [100, 200, 300] only reassigns lst within the function's scope. It does not affect my_list outside the function because the reassignment creates a new list that is local to the function.

Thus, the final output is [1, 2, 3, 4].

Read More

Density plot using Python

 import seaborn as sns

import matplotlib.pyplot as plt

import numpy as np

data = np.random.normal(size=1000)

sns.kdeplot(data, fill=True, color="blue")

plt.title("Density Plot")

plt.xlabel("Value")

plt.ylabel("Density")

plt.show()

#source code --> clcoding.com

Read More

Map chart using Python

 

import plotly.express as px

data = {

    'Country': ['United States', 'Canada',

                'Brazil', 'Russia', 'India'],

    'Values': [100, 50, 80, 90, 70]

}

fig = px.choropleth(

    data,

    locations='Country',      

    locationmode='country names',

    color='Values',           

    color_continuous_scale='Blues',

    title='Choropleth Map of Values by Country')

fig.show()

Read More

Gauge charts using Python

 

import plotly.graph_objects as go

fig = go.Figure(go.Indicator(

    mode="gauge+number",

    value=65,

    title={'text': "Speed"},

    gauge={'axis': {'range': [0, 100]},

           'bar': {'color': "darkblue"},

           'steps': [{'range': [0, 50], 'color': "lightgray"},

                     {'range': [50, 100], 'color': "gray"}],

           'threshold': {'line': {'color': "red", 'width': 4},

                         'thickness': 0.75, 'value': 80}}))

fig.show()

#source code --> clcoding.com

Read More

Waterfall Chart using Python

 

import plotly.graph_objects as go

fig = go.Figure(go.Waterfall(

    name = "20", orientation = "v",

    measure = ["relative", "relative", "total", "relative",

               "relative", "total"],

    x = ["Sales", "Consulting", "Net revenue", "Purchases",

         "Other expenses", "Profit before tax"],

    textposition = "outside",

    text = ["+60", "+80", "", "-40", "-20", "Total"],

    y = [60, 80, 0, -40, -20, 0],

    connector = {"line":{"color":"rgb(63, 63, 63)"}},

))

fig.update_layout(

        title = "Profit and loss statement 2024",

        showlegend = True

)

fig.show()

#source code --> clcoding.com

Read More

Pareto Chart using Python

 

import pandas as pd

import matplotlib.pyplot as plt

data = {'Category': ['A', 'B', 'C', 'D', 'E'],

        'Frequency': [50, 30, 15, 5, 2]}

df = pd.DataFrame(data)

df = df.sort_values('Frequency', ascending=False)

df['Cumulative %'] = df['Frequency'].cumsum() / df['Frequency'].sum() * 100

fig, ax1 = plt.subplots()

ax1.bar(df['Category'], df['Frequency'], color='C4')

ax1.set_ylabel('Frequency')

ax2 = ax1.twinx()

ax2.plot(df['Category'], df['Cumulative %'], 'C1D')

ax2.set_ylabel('Cumulative %')

plt.title('Pareto Chart')

plt.show()

#source code --> clcoding.com

Read More

Python programming workbook for IoT Development with Raspberry pi and MQTT: Hands-on Projects and exercises for building smart devices and IoT ... programming and code mastery books)

 

Ready to turn your Raspberry Pi into a smart device powerhouse?

This Python workbook is your ticket to building incredible IoT applications using MQTT, the communication protocol behind the Internet of Things. It's packed with hands-on projects that take you from beginner to builder, one step at a time.

What's inside?

• Learn by doing: Forget boring theory – we dive right into building smart home systems, environmental monitors, and more.
• Master MQTT: Understand this essential protocol, the backbone of IoT communication.
• Python skills made easy: Develop your coding confidence and create powerful IoT devices.
• Problem-solving: Get past common hurdles like complex coding, connectivity issues, data management, and security concerns.

Who's it for?

Whether you're a hobbyist tinkering in your garage, a student eager to learn, or an aspiring IoT developer, this workbook is your guide.

It's time to unleash the power of the Internet of Things.

Hard Copy : Python programming workbook for IoT Development with Raspberry pi and MQTT: Hands-on Projects and exercises for building smart devices and IoT ... programming and code mastery books)

Read More

30 Essential Topics Every Python Programmer Should Know: From Fundamentals to Advanced Techniques: A Comprehensive Guide to Elevate Your Python Skills

 

Unlock the Full Power of Python Programming with "30 Essential Topics Every Python Programmer Should Know"

Are you ready to elevate your Python skills and become a more proficient and confident programmer? Whether you're just starting out or already have some experience, this comprehensive guide will transform the way you code with Python. "30 Essential Topics Every Python Programmer Should Know" is your definitive resource for mastering the most critical concepts in Python, from foundational principles to advanced techniques.

What You’ll Discover Inside:

• Core Concepts Explained Clearly: Dive deep into Python’s syntax, data structures, and control flow. Learn how to write clean, efficient, and Pythonic code that adheres to best practices.
• Mastering Object-Oriented Programming: Understand the power of classes, inheritance, and polymorphism, and learn how to design and implement robust object-oriented systems.
• Exploring Functional Programming: Discover the elegance and efficiency of functional programming in Python. Get to grips with lambda functions, map, filter, and reduce, and see how to apply these techniques to real-world problems.
• Error Handling and Debugging: Develop your ability to write resilient code with comprehensive error handling strategies. Learn how to debug like a pro and ensure your applications run smoothly.
• Working with Data: Delve into Python’s powerful libraries for data manipulation and analysis. From handling CSV files to using Pandas for complex data operations, this book will arm you with the skills needed to tackle data-driven projects.
• Concurrency and Asynch: Unlock the potential of concurrent and Asynchronous programming in Python. Learn how to write programs that efficiently use multiple threads or processes, speeding up your applications and making them more responsive.
• Best Practices and Advanced Techniques: Refine your skills with a focus on best practices in Python coding. Learn about testing, version control, and deployment strategies that will help you maintain and scale your projects.

Each chapter in this book is designed to build upon the previous one, ensuring a smooth learning curve that will guide you from Python basics to more complex, professional-level programming. The clear explanations and practical examples will not only help you understand each topic but also give you the confidence to apply what you’ve learned in your own projects.

Why This Book?

Python is one of the most versatile and widely-used programming languages in the world. Whether you’re developing web applications, automating tasks, analyzing data, or venturing into machine learning, having a strong grasp of these 30 essential topics will set you apart as a Python programmer.

"30 Essential Topics Every Python Programmer Should Know" isn’t just a book—it’s your pathway to Python mastery. By the end of this book, you’ll not only be familiar with Python's most important concepts but also have the practical skills to apply them in real-world scenarios.

Who Should Read This Book?

• Aspiring developers looking to build a solid foundation in Python.
• Intermediate programmers wanting to enhance their Python knowledge and tackle more complex challenges.
• Experienced coders seeking a comprehensive reference guide to brush up on key topics.
• Professionals in fields such as data science, web development, and automation who use Python in their day-to-day work.

Embark on your journey to becoming a Python expert today. With "30 Essential Topics Every Python Programmer Should Know," you’re one step closer to mastering the language that powers everything from simple scripts to cutting-edge technologies. Click buy and let’s dive in!

Hard Copy : 30 Essential Topics Every Python Programmer Should Know: From Fundamentals to Advanced Techniques: A Comprehensive Guide to Elevate Your Python Skills

Read More

Python Natural Language Processing Cookbook: Over 60 recipes for building powerful NLP solutions using Python and LLM libraries

 

Updated to include three new chapters on transformers, natural language understanding (NLU) with explainable AI, and dabbling with popular LLMs from Hugging Face and OpenAI

Key Features

• Leverage ready-to-use recipes with the latest LLMs, including Mistral, Llama, and OpenAI models
• Use LLM-powered agents for custom tasks and real-world interactions
• Gain practical, in-depth knowledge of transformers and their role in implementing various NLP tasks with open-source and advanced LLMs
• Purchase of the print or Kindle book includes a free PDF eBook

Book Description

Harness the power of Natural Language Processing to overcome real-world text analysis challenges with this recipe-based roadmap written by two seasoned NLP experts with vast experience transforming various industries with their NLP prowess.

You’ll be able to make the most of the latest NLP advancements, including large language models (LLMs), and leverage their capabilities through Hugging Face transformers. Through a series of hands-on recipes, you’ll master essential techniques such as extracting entities and visualizing text data. The authors will expertly guide you through building pipelines for sentiment analysis, topic modeling, and question-answering using popular libraries like spaCy, Gensim, and NLTK. You’ll also learn to implement RAG pipelines to draw out precise answers from a text corpus using LLMs.

This second edition expands your skillset with new chapters on cutting-edge LLMs like GPT-4, Natural Language Understanding (NLU), and Explainable AI (XAI)—fostering trust and transparency in your NLP models.

By the end of this book, you'll be equipped with the skills to apply advanced text processing techniques, use pre-trained transformer models, build custom NLP pipelines to extract valuable insights from text data to drive informed decision-making.

What you will learn

• Understand fundamental NLP concepts along with their applications using examples in Python
• Classify text quickly and accurately with rule-based and supervised methods
• Train NER models and perform sentiment analysis to identify entities and emotions in text
• Explore topic modeling and text visualization to reveal themes and relationships within text
• Leverage Hugging Face and OpenAI LLMs to perform advanced NLP tasks
• Use question-answering techniques to handle both open and closed domains
• Apply XAI techniques to better understand your model predictions

Who this book is for

This updated edition of the Python Natural Language Processing Cookbook is for data scientists, machine learning engineers, and developers with a background in Python. Whether you’re looking to learn NLP techniques, extract valuable insights from textual data, or create foundational applications, this book will equip you with basic to intermediate skills. No prior NLP knowledge is necessary to get started. All you need is familiarity with basic programming principles. For seasoned developers, the updated sections offer the latest on transformers, explainable AI, and Generative AI with LLMs.

Table of Contents

1. Learning NLP Basics
2. Playing with Grammar
3. Representing Text – Capturing Semantics
4. Classifying Texts
5. Getting Started with Information Extraction
6. Topic Modeling
7. Visualizing Text Data
8. Transformers and Their Applications
9. Natural Language Understanding
10. Generative AI and Large Language Models

Hard Copy : Python Natural Language Processing Cookbook: Over 60 recipes for building powerful NLP solutions using Python and LLM libraries

Read More

A New Keeper's Guide to Ball Pythons (from a woman who never in a million years intended to keep snakes)

 

If you're new to ball pythons or reptile care in general, this book offers a friendly, easy-to-follow guide to get you started. It covers everything from acquiring a ball python and setting up their habitat to daily care, feeding, handling, health issues, and safety tips. The book emphasizes practical advice grounded in scientific knowledge and offers flexible strategies to help you progress in caring for your new pet.

"A New Keeper's Guide to Ball Pythons" can be read from start to finish or used as a quick reference for specific concerns and stages of care. Drawing from the author's personal experience with her own four ball pythons, along with insights from her dedicated Facebook admin team of over 25 reptile experts, and collaborations with breeders, veterinarians, and animal behavior specialists, this guide is designed to support new keepers every step of the way. The author's Facebook group, New Ball Python Keepers, which started in 2019, has grown to around 50,000 members, reflecting the broad, real-world expertise shared within these pages.

Hard Copy : A New Keeper's Guide to Ball Pythons (from a woman who never in a million years intended to keep snakes)

Read More

Python For Data Analysis: Unlocking The Power Of Data Analysis With Python Programming And Hands-On Projects (complete python programming handbooks)

 

Are you ready to unlock the power of data analysis and harness Python’s potential to turn raw data into valuable insights? Python Programming for Data Analysis: Unlocking the Power of Data Analysis with Python Programming and Hands-On Projects is your comprehensive guide to mastering data analysis techniques and tools using Python.

Whether you're a beginner eager to dive into the world of data or a professional looking to enhance your skills, this hands-on guide will equip you with everything you need to analyze, visualize, and interpret data like never before.

Why this book is essential for data enthusiasts:

•  Learn how to use Python programming to handle, clean, and analyze large datasets with ease. From basic techniques to advanced methods, this book covers everything you need to know to excel in data analysis.
•  Apply your learning with real-world projects that provide a practical understanding of data analysis in action. You'll work through examples in finance, healthcare, marketing, and more to deepen your skills.
•  Discover the power of Python libraries like Pandas, NumPy, Matplotlib, and Seaborn to transform raw data into meaningful insights. Learn how to manipulate data efficiently, perform statistical analysis, and visualize results beautifully.
•  Understand how to create stunning visualizations that communicate your findings effectively. Learn best practices for visualizing data in a way that tells compelling stories and drives decisions.
•  Gain experience in applying Python to solve real-world data challenges, whether it's analyzing sales trends, predicting customer behavior, or optimizing business processes through data-driven insights.
•  Whether you're just starting out or refining your data skills, this book provides a clear, step-by-step approach to understanding the principles of data analysis and how to apply them in Python.

By the end of Python Programming for Data Analysis, you’ll have the confidence and capability to tackle any data analysis challenge, backed by a solid foundation in Python programming. This is your gateway to becoming a data-driven problem solver in any field.

Unlock the potential of your data—click the "Buy Now" button and start your journey into Python-powered data analysis today.

Hard Copy : Python For Data Analysis: Unlocking The Power Of Data Analysis With Python Programming And Hands-On Projects (complete python programming handbooks)

Read More

Python Scripting for ArcGIS Pro

 

Unlock the power of Python in ArcGIS® Pro with this definitive, easy-to-follow guide designed for users with limited programming or scripting experience.

Get started learning to write Python scripts to automate tasks in ArcGIS Pro with Python Scripting for ArcGIS Pro. This book begins with the fundamentals of Python programming and then dives into how to write useful Python scripts that work with spatial data in ArcGIS Pro. You’ll learn how to use geoprocessing tools; describe, create, and update data; and execute specialized tasks. With step-by-step instructions, practical examples, and insightful guidance, you’ll be able to write scripts that will automate and improve your ArcGIS Pro workflows.

This third edition has been revised for ArcGIS Pro 3.2 and Python 3.9.18 and includes updated images; a fully updated chapter 2; and expanded chapters 4, 8, 9, and 10.

The key topics you will learn include:

• Python fundamentals
• Setting up a Python editor
• Automating geoprocessing tasks using ArcPy™
• Exploring and manipulating spatial and tabular data
• Working with geometries using cursors
• Working with rasters and map algebra
• Map scripting
• Debugging and error handling

Helpful points to remember, key terms, and review questions are included at the end of each chapter to reinforce your understanding of Python. Corresponding data and tutorials are available online.

Hard Copy : Python Scripting for ArcGIS Pro

Read More