Google Prompting Essentials

 

Google Prompting Essentials: Unlocking the Power of Conversational AI

Artificial intelligence has rapidly evolved from a futuristic concept to a practical tool that professionals, creators, and students use every day. At the heart of this revolution are powerful large language models (LLMs), like those developed by Google—such as PaLM 2, Gemini, and Bard. But there’s a catch: these AI tools are only as good as the instructions we give them. That’s where prompting comes in.

Prompting is the art and science of communicating with AI to get exactly what you want—and doing it well is the key to unlocking the full potential of these models. In this blog, we’ll dive deep into the Google Prompting Essentials, a set of best practices for crafting high-quality prompts that consistently produce useful, relevant, and creative outputs.

What Is Prompting?

In simple terms, prompting is the process of giving input to an AI model in the form of text—like a question, instruction, or request. You might prompt an AI to summarize an article, generate code, write a poem, or even simulate a conversation between historical figures.

However, not all prompts are created equal. Just as talking to a human requires clarity, context, and purpose, prompting AI requires the same level of intentionality. The better your prompt, the better the AI’s response.

Why Prompting Matters

With traditional software, you interact using buttons, forms, and menus. But with AI, your words are your interface. Prompting determines:

The relevance of the response

The tone and depth of information

The format of the output

The accuracy of facts and ideas

A vague prompt like “Tell me about marketing” may result in a generic, unfocused answer. A refined prompt like “Write a 200-word overview of digital marketing trends in 2024, using simple language for small business owners” will return a far more useful response.

Good prompting turns AI into a creative partner. Poor prompting makes it feel like a random content generator.

The Core Principles of Google Prompting Essentials

Google's approach to prompting is built on a few key principles. These can be used with Bard, Gemini, or any LLM built using Google's technology:

1.Be Clear, Specific, and Intentional

Always provide clear context and instructions. Include what you want, how you want it delivered, and sometimes even why. For example:

2. Use Role-Based Prompts

Assign the AI a role or identity to guide tone and style. This is called role prompting and it’s especially useful when you want the AI to adopt a professional or creative perspective.

3. Specify the Format

If you want a certain output format, say so explicitly. You can request lists, tables, bullet points, essays, JSON, and more.

4.  Provide Examples

AI learns from context. Providing an example of what you're looking for helps guide the model’s style and structure.

5.  Iterate and Refine

Prompting is an interactive process. If the AI response isn’t quite right, tweak your prompt and try again. You can:

Add or remove details

Clarify the role or tone

Break the task into smaller parts

The best results often come from multiple rounds of refinement.

Where You Can Use These Prompts

Google Prompting Essentials aren’t just for fun. These techniques can be used across a wide range of real-world applications:

Content creation (blog posts, emails, social media)

Education (tutoring, explaining concepts, summarizing material)

Productivity (meeting notes, project plans, reports)

Coding (generating scripts, explaining code, debugging)

Customer support (drafting FAQs, support messages)

Marketing (copywriting, branding ideas, competitor analysis)

The better you get at prompting, the more value you’ll extract from AI—across industries and job roles.

Advanced Prompting Concepts

Once you're comfortable with the basics, you can explore more advanced techniques, including:

Chain-of-thought prompting: Ask the AI to “think step by step.”

Few-shot prompting: Give 2–3 examples in your prompt to guide tone and format.

Multi-turn dialogue: Build context over a conversation, like a chat.

These advanced skills take your prompting game to the next level, especially when working with complex tasks or developing AI-powered tools.

Join Free : Google Prompting Essentials

Final Thoughts: Prompting Is a Power Skill

In the age of AI, prompting is more than a technical skill—it’s a 21st-century superpower. It’s how we collaborate with machines to write, code, learn, and innovate.

With Google Prompting Essentials, you have a practical set of tools to:

Communicate with clarity

Create with purpose

Collaborate with intelligence

So whether you're chatting with Bard, designing workflows in Vertex AI, or building apps with Google’s Gemini models, remember: how you ask determines what you get.

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3D Circular Arc Helicoid shape using Python

 

import matplotlib.pyplot as plt

import numpy as np

u=np.linspace(0,4*np.pi,100)

v=np.linspace(-2,2,50)

U,V=np.meshgrid(u,v)

a=0.5

X=V*np.cos(U)

Y=V*np.sin(U)

Z=a*U

fig=plt.figure(figsize=(6,6))

ax=fig.add_subplot(111,projection='3d')

ax.plot_surface(X,Y,Z,cmap='plasma',alpha=0.8,edgecolor='k')

ax.set_title('3D Circular Arc Pattern')

ax.set_xlabel('X axis')

ax.set_ylabel('Y axis')

ax.set_zlabel('Z axis')

ax.set_box_aspect([1,1,1])

plt.tight_layout()

plt.show()

#source code --> clcoding.com 

Code Explanation:

1. Imports and Setup

import numpy as np

import matplotlib.pyplot as plt

numpy is used for numerical operations and creating coordinate grids.

 matplotlib.pyplot is used to plot the 3D surface.

 2. Creating the Grid

u = np.linspace(0, 4 * np.pi, 100)  # Angular parameter (rotation)

v = np.linspace(-2, 2, 50)          # Radial parameter (radius from center)

U, V = np.meshgrid(u, v)

u spans from 0 to 4π — this means the surface will make 2 full twists (since one full twist is 2π).

 v spans from -2 to 2 — gives the radius of the spiral arms.

 meshgrid(U, V) allows you to create a 2D grid of values to generate a surface.

 3. Parametric Equations of a Helicoid

a = 0.5  # Twist rate or height per unit angle

X = V * np.cos(U)

Y = V * np.sin(U)

Z = a * U

These are the parametric equations for a helicoid:

 X and Y describe a circle at every vertical level.

 Z increases with U, making it spiral upwards — that's the helical twist.

 The value a = 0.5 controls the pitch or height per rotation.

 4. Plotting the Surface

fig = plt.figure(figsize=(10, 8))

ax = fig.add_subplot(111, projection='3d')

Creates a 3D figure and axis.

ax.plot_surface(X, Y, Z, cmap='plasma', alpha=0.8, edgecolor='k')

plot_surface() draws the helicoid.

 cmap='plasma' gives it a vivid color gradient.

 alpha=0.8 adds a little transparency.

 edgecolor='k' adds a thin black edge for clarity.

 5. Titles and Labels

ax.set_title('3D Circular Arc (Helicoid Surface)')

ax.set_xlabel('X axis')

ax.set_ylabel('Y axis')

ax.set_zlabel('Z axis')

Labels the axes and gives the chart a title.

 6. Aspect Ratio and Display

ax.set_box_aspect([1,1,1])  # Ensures all axes are equally scaled

plt.tight_layout()

plt.show()

Ensures the plot isn't distorted and then renders it on screen.

 

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Python Coding Challange - Question with Answer (01240425)

 

Python code line by line:

import array as arr

• This imports Python's built-in array module and gives it the alias arr.

• The array module provides a space-efficient array data structure (like a list but with a fixed type).

nums = arr.array('b', [5, -2, 7, 0, 3])

• This creates an array named nums.

• 'b' means the array will store signed integers (1 byte each, ranging from -128 to 127).

• The array initially contains: [5, -2, 7, 0, 3].

nums.remove(-2)

• This removes the first occurrence of -2 from the array.

• Now the array becomes: [5, 7, 0, 3].

print(nums.index(3))

• This prints the index of the first occurrence of the value 3 in the array.

• Since 3 is now at index 3 (0-based index), it prints: 3.

Final Output:

3

100 Python Programs for Beginner with explanation
 https://pythonclcoding.gumroad.com/l/qijrws

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Google Data Analytics Professional Certificate

 

In a world increasingly shaped by data, the demand for professionals who can make sense of it has never been higher. Businesses, governments, and organizations across every sector are seeking individuals who can transform raw numbers into meaningful insights. If you're considering entering this exciting and fast-growing field, the Google Data Analytics Professional Certificate might be one of the smartest first steps you can take.

It is designed for complete beginners. You don’t need any prior experience in tech or mathematics to begin. It’s structured to guide you gently from the very foundations of data analysis all the way through to applying real-world data skills using industry-standard tools.

What Is the Google Data Analytics Certificate?

The Google Data Analytics Professional Certificate is part of Google’s “Grow with Google” initiative, aimed at helping people develop job-ready skills. The goal is to equip learners with all the foundational knowledge they need to secure an entry-level job in data analytics — without requiring a degree or prior background in the field.

The course is available through Coursera, and while you can audit it for free, completing the full certificate and accessing graded assignments requires a monthly subscription (usually around $39 USD). Most learners finish the program in 4–6 months, depending on how much time they dedicate each week.

This certificate is not just a training program. It’s a structured journey that helps learners not only develop technical skills but also build the mindset and communication habits required to succeed in professional analytical roles.

Course Structure: Learning in Layers

The certificate is composed of eight carefully curated courses, each building on the previous one. Rather than overwhelming students with complex statistics or programming right away, the program introduces concepts gradually — first focusing on understanding what data analytics is, why it matters, and how it’s applied in the real world.

You begin by exploring the broader world of data. What types of data exist? What is the role of a data analyst? What does a day in the life of an analyst look like? These are the kinds of foundational questions that get answered early on.

As you progress, the course guides you into more hands-on territory. You’ll learn how to collect, clean, organize, and analyze data — turning messy spreadsheets or datasets into polished, valuable insights. You’ll be introduced to widely used tools such as Google Sheets, SQL, Tableau, and R programming.

A particularly valuable feature of the certificate is the capstone project at the end. Here, you complete a full case study using real-world data, where you get to ask questions, clean and analyze your dataset, and present your findings — just like you would in a real job.

Skills You’ll Learn Along the Way

Throughout the certificate, you will develop a toolkit that includes both technical and soft skills. On the technical side, you’ll get hands-on experience with data cleaning, analysis, visualization, and statistical thinking. You’ll also develop the ability to use spreadsheets, write basic SQL queries, and even perform data manipulation using the R programming language.

But technical skills alone are not enough. The course emphasizes the importance of asking the right questions, communicating with stakeholders, and creating compelling visual stories with data. These are the kinds of soft skills that make the difference between someone who can crunch numbers and someone who can drive business decisions.

One of the standout features of the program is the attention it gives to data ethics, privacy, and bias — helping learners understand how to responsibly handle data, especially in sensitive or high-impact areas.

 Real Tools, Real Practice

The certificate doesn’t teach you data in the abstract. Instead, it trains you in tools and workflows that are actively used in the job market today.

You’ll start with spreadsheets — still one of the most powerful and accessible tools in any analyst’s toolkit. Then you move on to SQL, the language used to query databases and extract information efficiently. You also get introduced to Tableau, one of the most popular data visualization tools in the world.

One particularly valuable and sometimes challenging module introduces you to R and RStudio, which are widely used for statistical computing and data visualization. Although this section may feel like a leap for non-programmers, it gives you a taste of how analysts use code to streamline and scale their work.

Who Should Take This Certificate?

This course is ideal for:

People new to tech or data who want a clear starting point

Career changers who want to pivot into analytics

Recent graduates without a technical degree

Professionals in non-technical roles who want to upskill

Whether you come from customer service, education, marketing, or retail, this certificate is designed to be accessible and practical, not overwhelming.

You don’t need to be a math wizard or have experience with programming. All that’s required is curiosity, a willingness to learn, and a commitment to completing the lessons.

What Comes After the Certificate?

Once you complete the certificate, you’ll have developed a strong foundation for entry-level jobs such as:

Data Analyst

Junior Analyst

Business Intelligence Analyst

Operations Analyst

Data Technician

In addition, Google provides access to a job board where graduates can connect with employers who value the certificate. These include major companies like Accenture, Deloitte, Verizon, and more. According to Google and Coursera, many learners report getting interviews or jobs within six months of completing the program.

What’s more, you’ll finish the course with a portfolio project — a case study you can show to employers as proof of your skills.

Join Free : Google Data Analytics Professional Certificate

Final Thoughts: Is It Worth It?

The Google Data Analytics Professional Certificate stands out not only because of the brand behind it, but because of the clarity, quality, and accessibility of its content. It doesn’t assume any prior knowledge, yet it doesn’t talk down to learners. Instead, it offers a structured, supportive pathway into the world of data — one that emphasizes practical skills and real-world application.

For a fraction of the cost of a traditional degree, and in a fraction of the time, you can come out with a highly respected credential, a solid portfolio piece, and job-ready skills in one of the most in-demand fields today.

If you're looking for a way to break into data analytics without quitting your job or going back to school, the Google Data Analytics Certificate might be one of the smartest investments you can make in your future.

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Python Coding challenge - Day 446| What is the output of the following Python Code?

Code Explanation:

Importing ThreadPoolExecutor

from concurrent.futures import ThreadPoolExecutor

This imports the ThreadPoolExecutor class from the concurrent.futures module.

ThreadPoolExecutor allows you to run functions in separate threads without managing threads manually.

Defining the Task

def task(x): return x * 2

This defines a simple function named task that takes one argument x and returns x * 2.

In this case, it's a placeholder for any CPU-light or I/O-bound task you'd want to run in a separate thread.

Using the Executor

with ThreadPoolExecutor() as ex:

This line creates a thread pool executor using a context manager (with block).

The context manager ensures that the thread pool is properly shut down when the block ends (no need to manually call shutdown()).

Submitting the Task

    f = ex.submit(task, 3)

This submits the function task with argument 3 to the executor.

ex.submit() returns a Future object (f in this case), which acts like a placeholder for the result of the task.

The task runs in a separate thread from the main program.

Getting the Result

    print(f.result())

f.result() waits for the thread to complete (if it hasn’t already), and then returns the result.

Since task(3) returns 6, this line prints:

6

Final Output

6

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Python Quiz on NumPy Array Shape

 

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Python Coding Challange - Question with Answer (01230425)

 

Let's break down the code step by step:

marks = 75

 This line assigns the value 75 to the variable marks. It represents a student's marks.

---

if marks >= 90:
   print("Excellent")

 This checks if the marks are 90 or more.

 Since 75 is not greater than or equal to 90, this condition is False, so "Excellent" will not be printed.

---

if 60 <= marks < 90:
   print("Good")

 This is a chained condition: it checks if the marks are between 60 and 89 (inclusive of 60, but less than 90).

 Since 75 is in this range, the condition is True, so "Good" will be printed.

---

✅ Output:

Good

Let me know if you want a version with else or elif too!

Python for Backend Development 

https://pythonclcoding.gumroad.com/l/wnnyq

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Python Coding challenge - Day 445| What is the output of the following Python Code?

 

Code Explanation:

 1. Importing the bisect module

import bisect

This imports Python’s bisect module, which is used for working with sorted lists.

It provides support for:

Finding the insertion point for a new element while maintaining sort order.

Inserting the element in the correct place.

2. Creating a sorted list

lst = [1, 3, 4]

This is your initial sorted list.

It must be sorted in ascending order for the bisect functions to work correctly.

3. Inserting 2 in order

bisect.insort(lst, 2)

insort() inserts the element 2 into the correct position to maintain the sorted order.

It does binary search behind the scenes to find the right spot (efficient).

Resulting list becomes:

lst → [1, 2, 3, 4]

4. Printing the result

print(lst)

This prints the updated list after the insertion.

Output:

[1, 2, 3, 4]

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Python Coding challenge - Day 444| What is the output of the following Python Code?

 

Code Explanation:

import heapq

Purpose: This line imports Python’s built-in heapq module.

What it does: heapq provides an implementation of the heap queue algorithm, also known as a priority queue.

Note: Heaps in Python using heapq are min-heaps, meaning the smallest element is always at the root (index 0 of the list).

Initialize a list

h = [5, 8, 10]

Purpose: Create a regular list h containing three integers: 5, 8, and 10.

Note: At this point, h is just a plain list — not a heap yet.

Convert the list into a heap

heapq.heapify(h)

Purpose: Transforms the list h into a valid min-heap in-place.

Result: After heapifying, the smallest element moves to index 0.

For h = [5, 8, 10], it's already a valid min-heap, so the structure doesn't visibly change:

h → [5, 8, 10]

Push and Pop in one step

print(heapq.heappushpop(h, 3))

Purpose: Pushes the value 3 into the heap, then immediately pops and returns the smallest item from the heap.

What happens:

Push 3 → temporary heap is [3, 5, 10, 8]

Pop the smallest item → 3 is the smallest, so it's popped.

Final heap → [5, 8, 10] (same as before)

Return value: The popped value, which is 3, is printed.

Final Output:

3

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Iso Surface marching Cube using Python

 

import numpy as np

import matplotlib.pyplot as plt

from skimage import measure

n = 64

x, y, z = np.ogrid[-1:1:n*1j, -1:1:n*1j, -1:1:n*1j]

sphere = x**2 + y**2 + z**2

verts, faces, normals, values = measure.marching_cubes(sphere, level=0.5)

fig = plt.figure(figsize=(6, 6))

ax = fig.add_subplot(111, projection='3d')

mesh = ax.plot_trisurf(

    verts[:, 0], verts[:, 1], faces, verts[:, 2],

    cmap='Spectral', lw=1, alpha=0.9

)

ax.set_title('Iso-Surface Plot (Marching Cubes)')

ax.set_xlabel('X axis')

ax.set_ylabel('Y axis')

ax.set_zlabel('Z axis')

plt.tight_layout()

plt.show()

#source code --> clcoding.com 

Code Explanation:

Libraries Imported

import numpy as np

import matplotlib.pyplot as plt

from skimage import measure

numpy → for numerical calculations and creating 3D grid.

 matplotlib.pyplot → for 3D plotting.

 measure from scikit-image → contains marching_cubes for surface extraction.

Step 1: Create a Scalar Field (3D Grid of Values)

n = 64

x, y, z = np.ogrid[-1:1:n*1j, -1:1:n*1j, -1:1:n*1j]

sphere = x**2 + y**2 + z**2

np.ogrid creates 3D grid coordinates with n = 64 steps in each dimension.

 sphere = x² + y² + z² defines a scalar field that increases with distance from the center.

This effectively creates a spherical shape within the volume.

 Step 2: Extract Iso-Surface Using Marching Cubes

verts, faces, normals, values = measure.marching_cubes(sphere, level=0.5)

marching_cubes() extracts the surface where the scalar field equals 0.5.

 Returns:

 verts: the coordinates of the vertices that form the mesh.

 faces: the triangles that connect those vertices.

 normals: surface normals for shading (not used here).

 values: scalar field values at the vertices (not used here).

 Step 3: Plot the Mesh in 3D

fig = plt.figure(figsize=(10, 8))

ax = fig.add_subplot(111, projection='3d')

Sets up a 3D plot figure and axis.

 mesh = ax.plot_trisurf(

    verts[:, 0], verts[:, 1], faces, verts[:, 2],

    cmap='Spectral', lw=1, alpha=0.9

)

plot_trisurf() creates a triangular surface from the mesh.

 verts[:, 0], verts[:, 1], verts[:, 2] → the x, y, z coordinates of the vertices.

 faces → triangle indices.

 cmap='Spectral' → colorful colormap.

 alpha=0.9 → almost opaque surface.

 Step 4: Labeling and Display

 ax.set_title('Iso-Surface Plot (Marching Cubes)')

ax.set_xlabel('X axis')

ax.set_ylabel('Y axis')

ax.set_zlabel('Z axis')

plt.tight_layout()

plt.show()

Adds axis labels and a title.

 plt.tight_layout() adjusts padding.

 plt.show() renders the final 3D plot.

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Python Coding Challange - Question with Answer (01220425)

 

Explanation:

• lambda a, b: a + b + 1 is an anonymous function that takes two inputs, a and b.

• It returns the result of a + b + 1.

Now, let's call this function with a = 7 and b = 2:

7 + 2 + 1 = 10

So, print(sum_nums(7, 2)) will output:

10

Why the +1?

It’s just part of the function's definition — maybe to always add 1 as a bonus or offset.

Let me know if you want to see how this compares to a regular function definition too.

Application of Python Libraries for Civil Engineering

https://pythonclcoding.gumroad.com/l/feegvl

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3D Volume Rendering Pattern using Python

 

import numpy as np

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

data = np.random.rand(50, 50, 50)

fig = plt.figure(figsize=(10, 8))

ax = fig.add_subplot(111, projection='3d')

x, y, z = np.indices(data.shape)

threshold = 0.5

ax.scatter(x[data > threshold], y[data > threshold], z[data > threshold],

           c=data[data > threshold], cmap='inferno', marker='o')

ax.set_title("3D Volume Rendering")

ax.set_xlabel("X axis")

ax.set_ylabel("Y axis")

ax.set_zlabel("Z axis")

plt.show()

#source code --> clcoding.com 

Code Explanation:

1. Importing Libraries:

 import numpy as np

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

numpy: A powerful library used for numerical computations in Python, particularly for creating and manipulating arrays.

 matplotlib.pyplot: A plotting library for creating static, animated, and interactive visualizations in Python.

 mpl_toolkits.mplot3d: A module within matplotlib that provides tools for creating 3D plots.

 2. Generating Random 3D Data:

 data = np.random.rand(50, 50, 50)

data is a 3D numpy array of shape (50, 50, 50), filled with random floating-point numbers between 0 and 1.

 This represents a 3D volume where each value corresponds to a voxel (volume element), and each voxel has a random intensity value.

 3. Creating a 3D Plot:

fig = plt.figure(figsize=(10, 8))

ax = fig.add_subplot(111, projection='3d')

A figure object is created with a size of 10 by 8 inches.

 The add_subplot(111, projection='3d') creates a 3D subplot (ax) for visualizing the data in 3D space.

 4. Generating Indices for the Volume:

x, y, z = np.indices(data.shape)

np.indices(data.shape) generates 3D arrays for the x, y, and z indices corresponding to the shape of the data array, i.e., (50, 50, 50). This creates 3D grids for each dimension (x, y, and z).

 So, x, y, and z are 3D arrays, where each element represents the index position in the respective dimension.

 5. Thresholding:

threshold = 0.5

This is the threshold value. Any voxel (point) in the volume with a value greater than 0.5 will be considered for rendering.

 6. Scatter Plot of Voxel Points:

ax.scatter(x[data > threshold], y[data > threshold], z[data > threshold], c=data[data > threshold], cmap='inferno', marker='o')

ax.scatter: This creates a scatter plot of the 3D data points.

 x[data > threshold]: Selects the x-coordinates of the voxels where the value in data is greater than the threshold (0.5).

 y[data > threshold]: Selects the y-coordinates of the voxels where the value in data is greater than the threshold.

 z[data > threshold]: Selects the z-coordinates of the voxels where the value in data is greater than the threshold.

 The condition data > threshold creates a boolean array where True corresponds to values greater than the threshold (0.5), and False corresponds to values below the threshold. Only the voxels where this condition is True are included in the scatter plot.

 c=data[data > threshold]: Specifies the colors of the points based on the values in the data array that are above the threshold. These values are mapped to colors using the specified colormap ('inferno').

 cmap='inferno': This applies a colormap to the scatter plot. The 'inferno' colormap is a perceptually uniform colormap that ranges from dark purple to yellow.

 marker='o': This sets the marker for the scatter plot points to be circular ('o').

 7. Setting Plot Labels and Title:

ax.set_title("3D Volume Rendering")

ax.set_xlabel("X axis")

ax.set_ylabel("Y axis")

ax.set_zlabel("Z axis")

set_title: Sets the title of the plot as "3D Volume Rendering".

 set_xlabel, set_ylabel, and set_zlabel: Set labels for the X, Y, and Z axes, respectively.

 8. Displaying the Plot:

plt.show()

This line displays the 3D plot to the screen.

 

 

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3D Conical Surface using Python

 

import numpy as np

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

r_max=5

h=10

num_points=100

r=np.linspace(0,r_max,num_points)

theta=np.linspace(0,2*np.pi,num_points)

R,Theta=np.meshgrid(r,theta)

X=R*np.cos(Theta)

Y=R*np.sin(Theta)

Z=h*(1-R/r_max)

fig=plt.figure(figsize=(6,6))

ax=fig.add_subplot(111,projection='3d')

ax.plot_surface(X,Y,Z,cmap='inferno',edgecolor='k',alpha=0.7)

ax.set_title('3D conical Pattern')

ax.set_xlabel('X Axis')

ax.set_ylabel('Y Axis')

ax.set_zlabel('Z Axis')

plt.show()

#source code --> clcoding.com 

Code Explanation:

1. Importing Libraries

import numpy as np

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

numpy: This library is used for numerical computations and working with arrays. In this code, it is used to create arrays for the radial distance (r) and the angle (theta), as well as to handle the parametric equations for the cone's surface.

 matplotlib.pyplot: This is a plotting library that is used for creating static, animated, and interactive visualizations in Python. It’s commonly used for generating 2D plots and charts. Here, it is used to create a 3D plot of the cone surface.

 mpl_toolkits.mplot3d.Axes3D: This is a toolkit for creating 3D plots in matplotlib. It provides the functionality for 3D plotting (e.g., plot_surface) and visualization in 3D space.

 2. Defining Parameters for the Cone

r_max = 5    

h = 10      

num_points = 100 

r_max: This defines the maximum radius of the cone's base. The cone will have a maximum radius of 5 units at the base.

 h: This represents the height of the cone, which is set to 10 units. It defines how tall the cone will be.

 num_points: This is the number of points used to discretize the radial distance (r) and angular coordinates (theta). More points give higher resolution in the plot. Here, it is set to 100.

 3. Creating the Radial and Angular Coordinates

r = np.linspace(0, r_max, num_points)

theta = np.linspace(0, 2 * np.pi, num_points) 

R, Theta = np.meshgrid(r, theta)

r = np.linspace(0, r_max, num_points): This creates a 1D array of num_points equally spaced values between 0 and r_max (which is 5). These represent the radial distances from the center of the cone.

 theta = np.linspace(0, 2 * np.pi, num_points): This generates num_points equally spaced values between 0 and

2π (360 degrees), representing the angle around the center of the cone.

 R, Theta = np.meshgrid(r, theta): This generates two 2D arrays (R and Theta) from the 1D arrays r and theta. The R array represents the radial distances for every value of theta, and the Theta array represents the angular values for every value of r. These 2D arrays are the grid of coordinates used to define the surface of the cone.

 4. Parametric Equations for the Cone Surface

X = R * np.cos(Theta)

Y = R * np.sin(Theta)

Z = h * (1 - R / r_max)

X = R * np.cos(Theta): Using polar-to-Cartesian conversion, the X coordinate is calculated by multiplying the radial distance R by the cosine of the angle Theta.

 Y = R * np.sin(Theta): Similarly, the Y coordinate is calculated by multiplying the radial distance R by the sine of the angle Theta.

 Z = h * (1 - R / r_max): The Z coordinate represents the height of each point on the cone's surface. At the apex of the cone (where R = 0), the height Z is equal to h (the cone's height). As R increases toward r_max, the height decreases linearly. This gives the cone its tapered shape.

 5. Creating the 3D Plot

fig = plt.figure(figsize=(10, 8))

ax = fig.add_subplot(111, projection='3d')

fig = plt.figure(figsize=(10, 8)): This creates a new figure for the plot with a specified size of 10x8 inches.

 ax = fig.add_subplot(111, projection='3d'): This adds a 3D subplot to the figure, meaning the plot will be created in 3D space. The 111 argument specifies a 1x1 grid and places the subplot in the first position.

 6. Plotting the Cone Surface

ax.plot_surface(X, Y, Z, cmap='inferno', edgecolor='k', alpha=0.7)

ax.plot_surface(X, Y, Z, cmap='inferno', edgecolor='k', alpha=0.7): This function plots the surface of the cone using the X, Y, and Z coordinates calculated earlier. The parameters used are:

 cmap='inferno': This specifies the color map to use for the surface. The 'inferno' colormap gives the surface a warm color gradient (yellow to red to dark).

 edgecolor='k': This sets the color of the edges of the surface to black ('k' stands for black).

 alpha=0.7: This sets the transparency of the surface to 70%. The surface will be partially transparent, which allows for better visualization of underlying structures if needed.

 7. Customizing the Plot

ax.set_title('3D Conical Surface')

ax.set_xlabel('X axis')

ax.set_ylabel('Y axis')

ax.set_zlabel('Z axis')

ax.set_title('3D Conical Surface'): This sets the title of the plot to "3D Conical Surface".

 ax.set_xlabel('X axis'): This labels the X-axis as "X axis".

 ax.set_ylabel('Y axis'): This labels the Y-axis as "Y axis".

 ax.set_zlabel('Z axis'): This labels the Z-axis as "Z axis".

 8. Displaying the Plot

plt.show()

plt.show(): This function displays the plot. It renders the figure with the cone surface and allows you to view the result.

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Python Coding challenge - Day 443| What is the output of the following Python Code?

Code Explanation:

Line 1:

from itertools import groupby

This imports the groupby function from Python’s built-in itertools module.

groupby is used to group consecutive elements in an iterable (like a list) that are the same.

Line 2:

nums = [1, 1]

This creates a list called nums containing two elements: [1, 1].

Both elements are the same and are next to each other — this is important for groupby.

Line 3:

groups = [(k, list(g)) for k, g in groupby(nums)]

This line uses list comprehension to group the items. Let's break it into parts:

groupby(nums):

Looks at the list from left to right.

Groups consecutive elements that are the same.

In this case, 1 and 1 are next to each other, so they’re grouped into one group.

For each group:

k is the value being grouped (in this case, 1)

g is a generator (iterator) over the group of matching values

 list(g):

Converts the group iterator g into an actual list, so we can see the contents.

So the comprehension becomes:

groups = [(1, [1, 1])]

Line 4:

print(groups)

This prints the final result.

Final Output:

[(1, [1, 1])]

 

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Python Coding challenge - Day 442| What is the output of the following Python Code?

 

Code Explanation:

Line 1

import torch

This imports the PyTorch library.

PyTorch is a powerful library for tensor computations and automatic differentiation, often used in deep learning.

Line 2

x = torch.tensor(2.0, requires_grad=True)

Creates a tensor x with the value 2.0.

requires_grad=True tells PyTorch:

“Please keep track of all operations involving this tensor.”

So later, we can calculate gradients (i.e., derivatives) with respect to x.

Line 3

y = x**3 + 2 * x + 1

Defines a function y in terms of x:

Since x has requires_grad=True, PyTorch builds a computation graph behind the scenes.

Every operation (**3, *2, +1) is tracked so we can differentiate y later.

 Line 4

y.backward()

This tells PyTorch to compute the derivative of y with respect to x.

Since y is a scalar (a single value), calling .backward() automatically computes:

Line 5

print(x.grad)

Prints the computed gradient of y with respect to x.

Final Output:

tensor(14.)

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Fundamentals of Digital Image and Video Processing

 

Fundamentals of Digital Image and Video Processing

In today's visually-driven world, digital images and videos are everywhere—from the selfies we snap to the blockbuster films we stream. But have you ever wondered what goes on behind the scenes to make it all work? That’s where Digital Image and Video Processing (DIP & DVP) comes in.

Let’s take a deep dive into the fascinating world of how digital content is captured, processed, and enhanced.

What Is Digital Image Processing?

Digital Image Processing refers to the use of computer algorithms to perform image processing on digital images. Unlike analog image processing, this technique allows for a much wider range of algorithms to be applied, which can avoid problems such as noise and distortion.

Key Stages:

Image Acquisition: Capturing the image using a digital camera or scanner.

Preprocessing: Cleaning up the image (e.g., removing noise).

Segmentation: Dividing the image into regions or objects.

Representation & Description: Translating visual information into a format that can be analyzed.

Recognition & Interpretation: Understanding the content, like detecting faces or reading text.

What Is Video Processing?

Digital Video Processing is similar to image processing but works with sequences of images—also known as video frames—at a certain frame rate (e.g., 30fps or 60fps).

Additional Complexity:

Temporal Dimension: Unlike images, video processing must handle motion and time.

Compression: Video data is huge, so codecs like H.264 or HEVC are used to reduce file size.

Synchronization: Audio and video must remain perfectly timed.

Core Concepts in Image & Video Processing

1. Pixels & Resolution

Pixel: The smallest unit of a digital image.

Resolution: The number of pixels in an image (e.g., 1920x1080 = Full HD).

2. Color Models

Grayscale: Shades of gray (0–255).

RGB: Red, Green, Blue.

YCbCr: Common in video compression; separates brightness from color.

3. Transforms

Fourier Transform: Converts spatial data into frequency data.

DCT (Discrete Cosine Transform): Used in JPEG compression.

4. Filtering

Spatial Filters: Modify pixel values using neighborhood data (e.g., blurring, sharpening).

Frequency Filters: Remove noise or enhance features based on frequency.

5. Compression Techniques

Lossy: Sacrifices some quality for size (JPEG, MPEG).

Lossless: Preserves original quality (PNG, Huffman coding).

Applications

Gaming and Augmented Reality

Real-time processing enables AR games like Pokémon GO and motion capture in VR.

Mobile Photography

AI-based enhancement, face detection, and beauty filters are driven by image processing.

Medical Imaging

Used in MRI, CT scans, and X-rays to enhance image clarity and assist in diagnosis.

Surveillance and Security

Motion detection, facial recognition, and license plate readers rely on video processing.

Remote Sensing

Satellites capture and process large-scale images for environmental monitoring and mapping.

Tools and Libraries

OpenCV (C++/Python): Industry standard for real-time computer vision.

MATLAB: Widely used in academic and research settings.

FFmpeg: Powerful tool for video encoding, decoding, and conversion.

TensorFlow/Keras: For applying deep learning to image and video tasks.

Emerging Trends

AI & Deep Learning

CNNs (Convolutional Neural Networks) have revolutionized image classification, object detection, and style transfer.

Real-Time Video Analytics

Used in autonomous vehicles, live sports analysis, and intelligent surveillance.

3D and Multiview Processing

Used in AR/VR and 3D modeling to process depth and spatial cues from multiple cameras.

Learning Resources

Books: “Digital Image Processing” by Gonzalez & Woods is a classic.

Courses: Check out Coursera’s course by Northwestern University on "Digital Image and Video Processing".

Online Communities: Stack Overflow, Reddit’s r/computervision, GitHub repositories.

Join Free : Fundamentals of Digital Image and Video Processing

Final Thoughts

Digital image and video processing isn’t just a technical niche—it’s the backbone of many modern innovations. From enhancing your Instagram selfies to powering self-driving cars, the applications are vast and growing. Whether you're a student, a hobbyist, or a developer, diving into this field can open doors to endless creativity and impact.

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3D Topographical Pattern using Python

 

import matplotlib.pyplot as plt

import numpy as np

from mpl_toolkits.mplot3d import Axes3D

x=np.linspace(-5,5,100)

y=np.linspace(-5,5,100)

x,y=np.meshgrid(x,y)

z=np.sin(np.sqrt(x**2+y**2))

fig=plt.figure(figsize=(6,6))

ax=fig.add_subplot(111,projection='3d')

surface=ax.plot_surface(x,y,z,cmap='terrain',edgecolor='none')

fig.colorbar(surface)

ax.set_xlabel('X (Longitude)')

ax.set_ylabel('Y(Latitude)')

ax.set_zlabel('Z(Elevation)')

ax.set_title('3D Topographical pattern')

plt.show()

#source code --> clcoding.com 

Code Explanation:

1. Importing Libraries

import numpy as np

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

numpy (np): Used for numerical operations like creating arrays and applying mathematical functions.

 matplotlib.pyplot (plt): Used for plotting graphs and figures.

 mpl_toolkits.mplot3d: Adds support for 3D plotting using Axes3D.

 2. Creating Grid Data

x = np.linspace(-5, 5, 100)

y = np.linspace(-5, 5, 100)

x, y = np.meshgrid(x, y)

np.linspace(-5, 5, 100): Creates 100 evenly spaced points from -5 to 5 for both x and y axes.

 np.meshgrid(x, y): Creates a 2D grid of all combinations of x and y — needed for surface plotting.

 3. Defining the Elevation (Z-values)

z = np.sin(np.sqrt(x**2 + y**2))

This creates elevation data using a sine wave based on the distance from the origin (0,0). It looks like ripples or waves radiating from the center.

 Formula breakdown:

x**2 + y**2: Computes the square of the distance.

np.sqrt(...): Takes the square root → gives radial distance.

np.sin(...): Applies the sine function to generate oscillations.

 4. Creating the Plot Figure

fig = plt.figure(figsize=(10, 7))

Initializes a new figure window.

figsize=(10, 7): Sets the width and height of the figure in inches.

 5. Adding a 3D Subplot

ax = fig.add_subplot(111, projection='3d')

Adds a 3D plotting area to the figure.

111 = 1 row, 1 column, 1st subplot.

projection='3d': Tells matplotlib to enable 3D plotting for this subplot.

 6. Plotting the 3D Surface

surface = ax.plot_surface(x, y, z, cmap='terrain', edgecolor='none')

Creates the 3D surface from x, y, and z data.

cmap='terrain': Applies a terrain color map to simulate real-world elevation colors (greens, browns, etc.).

edgecolor='none': Removes grid lines for a cleaner look.

 7. Adding a Color Bar

fig.colorbar(surface)

Adds a color bar beside the plot.

Shows how colors map to elevation values (Z-axis).

 8. Labeling the Axes

ax.set_xlabel('X (Longitude)')

ax.set_ylabel('Y (Latitude)')

ax.set_zlabel('Z (Elevation)')

Labels each axis for clarity:

 X: "Longitude"

Y: "Latitude"

Z: "Elevation"

 9. Setting the Plot Title

ax.set_title('3D Topographical Plot')

Adds a descriptive title to the top of the plot.

 10. Displaying the Plot

plt.show()

Renders the plot in a window or notebook.

Allows for interactive rotation, zoom, and pan if run in a GUI or Jupyter Notebook.

 

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