Python Coding Challange - Question with Answer (01310525)

 

Line-by-Line Explanation:

🔸 n = 2

• Initializes a variable n with value 2.

---

🔸 while n < 20:

• This starts a loop that runs as long as n is less than 20.

---

🔸 print(n)

• Prints the current value of n.

---

🔸 n *= 2

• Multiplies n by 2.
  (Same as n = n * 2)

---

🔸 if n > 15: break

• If n becomes greater than 15, the loop is stopped immediately with break.

---

 Step-by-Step Execution:

Step	Value of n	Printed?	After n *= 2	n > 15?	Break?
1	2	✅ 2	4	❌ No	No
2	4	✅ 4	8	❌ No	No
3	8	✅ 8	16	✅ Yes	✅ Break

 Final Output:

2  
4 
8

---

 Summary:

• This is a while loop with:

  • A multiplication step (n *= 2)

  • A condition to stop early (break if n > 15)

• The loop prints 2, 4, 8, then exits when n becomes 16

APPLICATION OF PYTHON FOR CYBERSECURITY 

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Python Coding challenge - Day 521| What is the output of the following Python Code?

 

Code Explanation:

Function Definition

def count_paths(r, c):

Defines a function named count_paths that takes two parameters:

r: number of rows

c: number of columns

Base Case

if r == 1 or c == 1:

    return 1

If there's only 1 row or 1 column, there's only one path — either all the way right or all the way down.

This stops the recursion.

Recursive Case

return count_paths(r - 1, c) + count_paths(r, c - 1)

You try both:

Moving down (reducing row by 1)

Moving right (reducing column by 1)

The total number of paths is the sum of the two possibilities.

Function Call

print(count_paths(4, 3))

Calls the function with a grid of size 4 x 3.

You are asked: "How many ways can you go from the top-left to the bottom-right corner using only right and down moves?"

Mathematical Equivalent

This problem is equivalent to:

Number of paths=( (r−1)(r+c−2) )=( 35)=10

Final Output

10

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Python Coding challenge - Day 520| What is the output of the following Python Code?

 

Code Explanation:

Function Definition

def catalan(n):

This defines a recursive function named catalan that computes the nth Catalan number.

Base Case: Return 1 for n = 0 or n = 1

\    if n <= 1:

        return 1

This handles the base cases of the recursion.

By definition, Catalan(0) = 1 and Catalan(1) = 1.

If n is 0 or 1, the function immediately returns 1 without further recursion.

Initialize Result

    res = 0

Initializes a variable res to accumulate the total sum.

This variable will store the result of the recursive sum for Catalan(n).

Recursive Loop Over All Possible Partitions

    for i in range(n):

This loop runs from i = 0 to i = n-1.

Recursive Calls for Subproblems

        res += catalan(i) * catalan(n - i - 1)

This is the heart of the recursion.

For each i, the function recursively calculates:

catalan(i) – representing the number of structures in the left subtree.

catalan(n - i - 1) – representing the number of structures in the right subtree.

The product of the two is added to res.

Return Final Computed Result

    return res

After the loop finishes, the total value of res contains Catalan(n).

This value is returned.

Function Call and Output

print(catalan(4))

This line calls the function with n = 4.

It prints the result of catalan(4).

Final Output

14

As calculated, Catalan(4) = 14.

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Step Function Grid using Python

 import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

import numpy as np

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

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

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

Z = np.heaviside(np.sin(X) * np.cos(Y), 0.5)

fig = plt.figure()

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

ax.plot_surface(X, Y, Z, cmap='viridis')

ax.set_title("Step Function Grid")

plt.show()

#source code --> clcoding.com

Code Explanation:

1. Importing Required Libraries

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

import numpy as np

matplotlib.pyplot is used for plotting.

mpl_toolkits.mplot3d.Axes3D enables 3D plotting support.

numpy is used for numerical operations, especially arrays and math functions.

2. Creating the Grid

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 points between -5 and 5 for both x and y.

np.meshgrid(x, y) creates 2D coordinate matrices from the 1D x and y arrays. X and Y are 2D arrays representing all combinations of x and y.

3. Defining the Step Function Surface

Z = np.heaviside(np.sin(X) * np.cos(Y), 0.5)

This defines the Z values (heights) of the surface.

np.sin(X) * np.cos(Y) creates a pattern of values based on sine and cosine waves.

np.heaviside(..., 0.5) converts those values into step-like (binary) outputs:

Returns 1 where the argument is positive,

0 where it's negative,

0.5 exactly at zero (by definition here).

This creates a checkerboard-like grid of 0s and 1s.

 4. Creating the 3D Figure and Axes

fig = plt.figure()

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

plt.figure() initializes a new figure.

add_subplot(111, projection='3d') adds one 3D subplot to the figure.

 5. Plotting the Surface

ax.plot_surface(X, Y, Z, cmap='viridis')

plot_surface() creates a 3D surface plot.

X, Y, Z define the surface coordinates.

cmap='viridis' sets the color gradient for the surface.

 6. Adding Title and Displaying the Plot

ax.set_title("Step Function Grid")

plt.show()

set_title() adds a title to the plot.

plt.show() renders and displays the plot window.

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Fundamentals of Robust Machine Learning: Handling Outliers and Anomalies in Data Science

 

Navigating Uncertainty: A Deep Dive into Fundamentals of Robust Machine Learning: Handling Outliers and Anomalies in Data Science

In the world of machine learning, the quality of your data often determines the success of your models. Real-world datasets are rarely perfect — they frequently contain outliers, anomalies, and noise that can mislead algorithms, cause inaccurate predictions, or even break models entirely.

This is where robust machine learning comes in — a vital approach that builds models capable of performing well despite imperfections in data.

Fundamentals of Robust Machine Learning: Handling Outliers and Anomalies in Data Science is a comprehensive book that focuses on equipping readers with the knowledge and tools to handle such challenges head-on.

Why Robust Machine Learning Matters

Traditional machine learning models typically assume clean, well-behaved data. But data scientists often encounter:

Measurement errors

Faulty sensors

Fraudulent transactions

Rare but critical events

These outliers and anomalies can skew models, leading to poor generalization, false insights, or even costly errors.

This book emphasizes techniques that make ML models resilient — so they can identify, tolerate, and adapt to problematic data, resulting in more reliable and trustworthy systems.

Who Should Read This Book?

Data scientists and ML engineers working with messy or large-scale real-world data.

Researchers interested in the theory and practice of anomaly detection and outlier handling.

Practitioners building models for finance, healthcare, cybersecurity, manufacturing, and more — where robust predictions are critical.

Students and learners who want to understand a less commonly covered but crucial aspect of ML.

Core Concepts Covered in the Book

1. Understanding Outliers and Anomalies

• What defines an outlier versus an anomaly
• Types of anomalies: point, contextual, and collective
• Sources and causes of anomalies in data
• Impact on model training and evaluation

2. Statistical Foundations for Robustness

• Robust statistics concepts such as median, trimmed means, and M-estimators
• Influence functions and breakdown points
• Estimators that resist the effect of outliers
• Techniques for cleaning and preprocessing noisy data

3. Robust Machine Learning Algorithms

• Robust regression methods (e.g., RANSAC, Huber regression)
• Outlier-resistant clustering algorithms
• Ensemble methods designed for noisy data
• Deep learning techniques with robustness components

4. Anomaly Detection Techniques

• Supervised vs. unsupervised anomaly detection
• Density-based, distance-based, and reconstruction-based approaches
• Isolation Forests, One-Class SVMs, Autoencoders
• Evaluation metrics specific to anomaly detection

5. Practical Strategies and Case Studies

Real-world examples from finance (fraud detection), healthcare (disease outbreak), cybersecurity (intrusion detection)

• Data augmentation and synthetic anomaly generation
• Dealing with imbalanced data in anomaly detection
• Best practices for deploying robust ML models in production

Why This Book Stands Out

Bridges theory with practice through clear explanations and real-world case studies.

Offers a broad yet detailed overview of robustness in ML—covering statistical methods, classical ML, and deep learning.

Focus on interpretability and explainability of robust models.

Provides actionable strategies to make your ML pipeline more reliable.

Potential Drawbacks

Some advanced mathematical sections may require background knowledge in statistics and optimization.

The book is comprehensive; readers should be prepared for an in-depth study rather than a quick read.

Hands-on coding examples are limited — pairing with practical tutorials is recommended.

Hard Copy : Fundamentals of Robust Machine Learning: Handling Outliers and Anomalies in Data Science

Kindle : Fundamentals of Robust Machine Learning: Handling Outliers and Anomalies in Data Science

Final Thoughts

Fundamentals of Robust Machine Learning: Handling Outliers and Anomalies in Data Science is an indispensable resource for anyone who wants to build trustworthy, resilient machine learning systems. As data complexity and stakes increase, mastering robust techniques will differentiate good practitioners from great ones.

By understanding and implementing the principles and algorithms in this book, you’ll be equipped to tackle one of the biggest challenges in real-world data science: handling the unexpected.

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Mathematics of Machine Learning: Master linear algebra, calculus, and probability for machine learning

 

Deep Dive into Mathematics of Machine Learning: Master Linear Algebra, Calculus, and Probability for Machine Learning

Machine learning has revolutionized the way we solve problems—from recommendation systems and speech recognition to autonomous vehicles and medical diagnosis. But beneath every powerful algorithm lies a foundation built on solid mathematics.

If you want to move beyond “black-box” use of machine learning and truly understand how and why these algorithms work, Mathematics of Machine Learning: Master Linear Algebra, Calculus, and Probability for Machine Learning is a must-read book.

What This Book Is About

This book is carefully designed to equip readers with the three core mathematical tools essential to machine learning:

Linear Algebra — representing and manipulating data and model parameters.

Calculus — understanding optimization and learning processes.

Probability and Statistics — modeling uncertainty and making inferences.

Unlike many dry math textbooks, this book combines theory, intuition, and practical applications, making it accessible for learners who want to strengthen their mathematical foundation without getting lost in overly abstract concepts.

Who Should Read This Book?

Aspiring data scientists and machine learning engineers who want to build a strong math foundation.

Students preparing for advanced AI or ML coursework.

Practitioners who want to deepen their understanding beyond coding and libraries.

Self-learners aiming to read research papers or understand cutting-edge ML models.

The book assumes some basic familiarity with algebra but explains concepts step-by-step, making it suitable for beginners and intermediate learners alike.

Key Sections and What You Will Learn

1. Linear Algebra: The Backbone of ML Data and Models

• Understand vectors, matrices, and operations like multiplication and transposition.
• Learn about eigenvalues and eigenvectors, essential for dimensionality reduction techniques such as PCA.
• Explore matrix factorization methods like Singular Value Decomposition (SVD).
• See how these concepts map directly to ML algorithms like linear regression and neural networks.

Why this matters: Data in ML is often represented as matrices; knowing how to manipulate and transform this data mathematically is critical for building and optimizing models.

2. Calculus: The Engine of Learning and Optimization

• Grasp the fundamentals of derivatives and partial derivatives.
• Understand the chain rule, which underpins backpropagation in neural networks.
• Dive into gradient descent and optimization strategies for minimizing error.
• Learn about functions of multiple variables, essential for tuning complex models.

Why this matters: Calculus helps explain how models learn by adjusting parameters to minimize error, a key step in training ML systems.

3. Probability & Statistics: Reasoning Under Uncertainty

• Master basic probability concepts, conditional probability, and Bayes’ theorem.
• Explore probability distributions like Gaussian, Bernoulli, and Binomial.
• Understand expectation, variance, and their importance in measuring uncertainty.
• Learn how statistical inference and hypothesis testing apply to model validation.

Why this matters: Machine learning is inherently probabilistic because real-world data is noisy and uncertain. Statistical thinking helps create models that can handle this uncertainty effectively.

Strengths of the Book

• Clear explanations that blend rigor with intuition.
• Practical examples tying math concepts to actual ML tasks.
• Visual aids and diagrams to help understand abstract ideas.
• Exercises that reinforce learning.
• Bridges the gap between pure math and applied machine learning.

Areas for Improvement

The book focuses on essentials; those wanting deep theoretical proofs or advanced topics may need supplementary resources.

Coding examples are minimal; readers may want to pair it with practical programming tutorials.

Some sections move quickly; a basic math background helps.

Hard Copy : Mathematics of Machine Learning: Master linear algebra, calculus, and probability for machine learning

Kindle : Mathematics of Machine Learning: Master linear algebra, calculus, and probability for machine learning

Final Thoughts

Mathematics of Machine Learning: Master Linear Algebra, Calculus, and Probability for Machine Learning is an excellent resource for anyone serious about mastering the mathematics that power machine learning algorithms. Whether you want to improve your intuition, prepare for technical interviews, or read ML research papers with confidence, this book offers a comprehensive and accessible path.

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Python Quiz on NumPy Array Filter

 

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Python Coding challenge - Day 516| What is the output of the following Python Code?

 

Code Explanation:

Function Definition

def generate_subsets(s, current=""):

Purpose: This function generates all subsets (power set) of a given string s.

s: The remaining string to process.

current: Keeps track of the subset being built so far. Initially, it's an empty string.

Base Case – When the Input String is Empty

    if not s:

        print(current)

        return

Check: If the string s is empty (i.e., all characters have been processed).

Action: Print the current subset formed in the current variable.

Return: End this branch of recursion.

Recursive Case – Include the First Character

    generate_subsets(s[1:], current + s[0])

Include s[0] (the first character of s) in the subset.

Move to the rest of the string s[1:], and add s[0] to current.

This represents the branch where we take the current character.

Recursive Case – Exclude the First Character

    generate_subsets(s[1:], current)

Exclude s[0] from the subset.

Move to the rest of the string s[1:] without adding anything to current.

This represents the branch where we skip the current character.

Example Call

generate_subsets("ab")

Starts generating subsets of "ab":

Include 'a', then include 'b' → 'ab'

Include 'a', then exclude 'b' → 'a'

Exclude 'a', then include 'b' → 'b'

Exclude 'a', then exclude 'b' → '' (empty set)

Final Output

ab

a

b

(empty line representing "")

This is the power set of "ab": ["ab", "a", "b", ""].

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Python Coding Challange - Question with Answer (01300525)

 

What is happening?

This is a recursive function, where the function sum() calls itself.

But it is missing a base case, which is essential in recursion to stop the loop.

---

 Step-by-step execution:

• sum(2)
  → returns 2 + sum(1)

• sum(1)
  → returns 1 + sum(0)

• sum(0)
  → returns 0 + sum(-1)

• sum(-1)
  → returns -1 + sum(-2)

• ... and so on, forever...

It keeps calling itself with smaller and smaller numbers and never stops.

---

❌ Problem:

There is no base case like:

if num == 0:
    return 0

So Python will eventually stop the program and raise this error:

RecursionError: maximum recursion depth exceeded

---

✅ How to fix it?

Add a base case to stop recursion:

def sum(num):
    if num == 0:
        return 0
    return num + sum(num - 1)
print(sum(2)) # Output: 3

---

 Summary:

Concept	Explanation
Recursion	Function calling itself
Base Case	Missing → causes infinite recursion
Error Raised	RecursionError
Fix	Add if num == 0: return 0

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Python Coding challenge - Day 519| What is the output of the following Python Code?

 

Code Explanation:

 1. Function Definition

def count_paths(r, c):

Defines a function named count_paths with two parameters:

r: number of rows

c: number of columns

2. Base Case: When One Dimension is 1

    if r == 1 or c == 1:

        return 1

If either the number of rows r or the number of columns c is 1:

There is only one path (straight line across the row or down the column).

This condition stops the recursion when we reach the edge of the grid.

3. Recursive Case: Sum of Two Choices

    return count_paths(r - 1, c) + count_paths(r, c - 1)

This line calculates the total number of paths by:

Moving down: reduces rows by 1 (count_paths(r - 1, c))

Moving right: reduces columns by 1 (count_paths(r, c - 1))

It adds the number of paths from both possibilities.

4. Function Call and Output

print(count_paths(3, 3))

This calls the function with a 3×3 grid and prints the result.

Step-by-Step Evaluation

Let's walk through the recursion for count_paths(3, 3):

count_paths(3, 3)

= count_paths(2, 3) + count_paths(3, 2)

count_paths(2, 3) = count_paths(1, 3) + count_paths(2, 2) = 1 + 2 = 3

count_paths(3, 2) = count_paths(2, 2) + count_paths(3, 1) = 2 + 1 = 3

So,

count_paths(3, 3) = 3 + 3 = 6

Final Output

6

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Python Coding challenge - Day 518| What is the output of the following Python Code?

Code Explanation:

1. Function Definition

def dec_to_bin(n):

This defines a function dec_to_bin that takes an integer n.

The function’s goal is to convert a decimal number (n) to its binary representation using recursion.

2. Base Case: When n is 0

    if n == 0:

        return ""

This is the base case for the recursive function.

When n becomes 0, return an empty string.

This stops the recursion and begins building the final binary string.

3. Recursive Case: Divide and Conquer

    return dec_to_bin(n // 2) + str(n % 2)

This line performs the core recursive logic:

n // 2: Divides the number by 2 (integer division), moving toward the base case.

Recursive call: Converts the quotient into binary.

n % 2: Gets the remainder, which is either 0 or 1—this is the current binary digit.

Combines: Appends the current binary digit to the end of the binary string returned from the recursive call.

4. Function Call and Output

print(dec_to_bin(10))

Let’s walk through what happens step-by-step for dec_to_bin(10):

Step-by-step breakdown:

dec_to_bin(10) 

= dec_to_bin(5) + "0"

= (dec_to_bin(2) + "1") + "0"

= ((dec_to_bin(1) + "0") + "1") + "0"

= (((dec_to_bin(0) + "1") + "0") + "1") + "0"

= ((("" + "1") + "0") + "1") + "0"

= ("1" + "0") + "1" + "0"

= "10" + "1" + "0"

= "1010"

Final Output

1010

The binary representation of decimal 10 is 1010.

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Python Coding challenge - Day 517| What is the output of the following Python Code?

 

Code Explanation:

Function Definition

def is_sorted(arr):

Purpose: Checks if a list arr is sorted in non-decreasing order (each element is less than or equal to the next).

Base Case – List of Length 0 or 1

    if len(arr) <= 1:

        return True

Why: A list with 0 or 1 element is trivially sorted.

Action: Return True.

Recursive Case – Compare First Two Elements

    return arr[0] <= arr[1] and is_sorted(arr[1:])

Step 1: Check if the first element is less than or equal to the second: arr[0] <= arr[1].

Step 2: Recursively check if the rest of the list arr[1:] is sorted.

Logic: The entire list is sorted only if:

The first pair is in order

The remaining sublist is also sorted

Example Call

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

List: [1, 2, 3, 4, 5]

Each pair of elements is in order.

Recursive calls proceed:

[2, 3, 4, 5] → [3, 4, 5] → [4, 5] → [5] → returns True

Final Output

True

The list [1, 2, 3, 4, 5] is sorted, so the function returns True.

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Python Coding challenge - Day 515| What is the output of the following Python Code?

 

Code Explanation:

Step 1: Base Case

if m == 1 or n == 1:

    return 1

If either m == 1 or n == 1, there's only one way to reach the destination: go straight down or straight right, respectively.

So, return 1 in this case.

Step 2: Recursive Case

return count_paths(m - 1, n) + count_paths(m, n - 1)

If you're not at the edge of the grid, the total paths are the sum of:

All paths by going down (reduce m by 1),

All paths by going right (reduce n by 1).

Step 3: Trace count_paths(3, 3)

Let’s calculate count_paths(3, 3) recursively:

count_paths(3, 3)

= count_paths(2, 3) + count_paths(3, 2)

= (count_paths(1, 3) + count_paths(2, 2)) + (count_paths(2, 2) + count_paths(3, 1))

= (1 + (count_paths(1, 2) + count_paths(2, 1))) + ((count_paths(1, 2) + count_paths(2, 1)) + 1)

= (1 + (1 + 1)) + ((1 + 1) + 1)

= (1 + 2) + (2 + 1)

= 3 + 3 = 6

Final Answer:

print(count_paths(3, 3))  # Output: 6

There are 6 unique paths in a 3×3 grid from top-left to bottom-right moving only right or down.

Final Output:

6

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Python Coding Challange - Question with Answer (01290525)

 

Understanding the Loops:

1. for i in range(3):

This is the outer loop.

It runs i = 0, 1, and 2.

2. for j in range(2):

This is the inner loop, and it runs inside the outer loop.

It runs j = 0 and 1 for each value of i.

 Iteration Breakdown:

Let's see what happens in each iteration:

i j i == j? i * j Printed?

0 0 ✅ Yes 0 × 0 = 0 ✅ Yes

0 1 ❌ No – ❌ No

1 0 ❌ No – ❌ No

1 1 ✅ Yes 1 × 1 = 1 ✅ Yes

2 0 ❌ No – ❌ No

2 1 ❌ No – ❌ No

 Output:

0

1

 Explanation Summary:

The code runs a nested loop.

It checks: "Are i and j equal?"

If yes, it prints the product i * j.

Only when i == j (i.e., both are 0 or both are 1), the condition is true.

Python for LINUX System Administration 

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

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Enneper Surface Pattern using Python

 

import matplotlib.pyplot as plt

import numpy as np

from mpl_toolkits.mplot3d import Axes3D

u=np.linspace(-2,2,100)

v=np.linspace(-2,2,100)

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

X=U-(U**3)/3+U*V**2

Y=V-(V**3)/3+V*U**2

Z=U**2-V**2

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

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

ax.plot_surface(X,Y,Z,cmap='viridis',edgecolor='none',alpha=0.9)

ax.set_title('Enneper Surface')

ax.set_xlabel('X')

ax.set_ylabel('Y')

ax.set_zlabel('Z')

plt.show()

#source code --> clcoding.com 

Code Explanation:

1. Import Libraries

import numpy as np

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

NumPy: For numerical operations and creating arrays.

Matplotlib.pyplot: For plotting graphs.

Axes3D: Enables 3D plotting capabilities.

 2. Define Parameter Grid

u = np.linspace(-2, 2, 100)

v = np.linspace(-2, 2, 100)

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

Creates two arrays u and v with 100 points each from -2 to 2.

np.meshgrid creates coordinate matrices U and V for all combinations of u and v. This forms a grid over which the surface is calculated.

 3. Calculate Coordinates Using Parametric Equations

X = U - (U**3)/3 + U*V**2

Y = V - (V**3)/3 + V*U**2

Z = U**2 - V**2

Computes the 3D coordinates of the Enneper surface using its parametric formulas:

 4. Create Figure and 3D Axis

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

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

Creates a new figure with size 10x8 inches.

 Adds a 3D subplot to the figure for 3D plotting.

 5. Plot the Surface

ax.plot_surface(X, Y, Z, cmap='viridis', edgecolor='none', alpha=0.9)

Plots a 3D surface using the (X, Y, Z) coordinates.

 Uses the 'viridis' colormap for coloring the surface.

 Removes edge lines for a smooth look.

 Sets surface transparency to 90% opacity for better visual depth.

 6. Set Titles and Axis Labels

ax.set_title('Enneper Surface')

ax.set_xlabel('X')

ax.set_ylabel('Y')

ax.set_zlabel('Z')

Adds a title and labels to the X, Y, and Z axes.

 7. Display the Plot

plt.show()

Shows the final 3D plot window displaying the Enneper surface.

 

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Rossler Attractor Pattern using Python

 

import matplotlib.pyplot as plt

import numpy as np

from scipy.integrate import solve_ivp

def rossler(t,state,a=0.2,b=0.2,c=5.7):

    x,y,z=state

    dx=-y-z

    dy=x+a*y

    dz=b+z*(x-c)

    return[dx,dy,dz]

initial_state=[0.0,1.0,0.0]

t_span=(0,100)

t_eval=np.linspace(t_span[0],t_span[1],10000)

solution=solve_ivp(rossler,t_span,initial_state,t_eval=t_eval,rtol=1e-8)

x=solution.y[0]

y=solution.y[1]

z=solution.y[2]

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

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

ax.plot(x,y,z,lw=0.5,color='purple')

ax.set_title('Rossler Attractor')

ax.set_xlabel('X')

ax.set_ylabel('Y')

ax.set_zlabel('Z')

plt.show()

#source code --> clcoding.com 

Code Explanation:

1. Import Required Libraries

import numpy as np

import matplotlib.pyplot as plt

from scipy.integrate import solve_ivp

numpy for numerical operations.

matplotlib.pyplot for plotting.

solve_ivp from SciPy to numerically solve differential equations.

2. Define the Rössler System Differential Equations

def rossler(t, state, a=0.2, b=0.2, c=5.7):

    x, y, z = state

    dx = -y - z

    dy = x + a * y

    dz = b + z * (x - c)

    return [dx, dy, dz]

Function rossler returns derivatives [dx/dt, dy/dt, dz/dt] for given state (x, y, z).

Parameters a, b, c control system behavior.

The Rössler system is a famous chaotic system.

3. Set Initial Conditions

initial_state = [0.0, 1.0, 0.0]

Starting point of the system in 3D space (x=0, y=1, z=0).

4. Define Time Interval and Evaluation Points

t_span = (0, 100)

t_eval = np.linspace(t_span[0], t_span[1], 10000)

t_span is time range from 0 to 100.

t_eval defines 10,000 evenly spaced points where the solution is calculated.

5. Numerically Solve the ODE System

solution = solve_ivp(rossler, t_span, initial_state, t_eval=t_eval, rtol=1e-8)

Uses solve_ivp to integrate the Rössler system over time.

High accuracy is requested with rtol=1e-8.

6. Extract Solutions

x = solution.y[0]

y = solution.y[1]

z = solution.y[2]

Extract arrays of x, y, z values from the solution at each time point. 

7. Create 3D Plot

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

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

Creates a new figure with size 10x7 inches.

Adds a 3D subplot for plotting. 

8. Plot the Rössler Trajectory

ax.plot(x, y, z, lw=0.5, color='purple')

Plots the 3D curve traced by (x, y, z) over time.

 Line width is thin (0.5), color purple.

9. Set Plot Titles and Axis Labels

ax.set_title('Rössler Attractor')

ax.set_xlabel('X')

ax.set_ylabel('Y')

ax.set_zlabel('Z')

Sets the plot title and labels for the x, y, and z axes.

 10. Show the Plot

plt.show()

Displays the interactive 3D plot window with the attractor curve.

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Python Coding challenge - Day 514| What is the output of the following Python Code?

 

Code Explanation:

Function: reduce_to_one(n)

This function implements a process related to the Collatz conjecture — it reduces a number n down to 1 by following these rules recursively, and it counts the number of steps taken.

1. Base Case: Check if n is already 1

if n == 1:

    return 0

What it does:

If the input number n is already 1, the function returns 0 because no more steps are needed to reduce it further.

This is the stopping condition for the recursion.

2. If n is Even

if n % 2 == 0:

    return 1 + reduce_to_one(n // 2)

What it does:

If n is even (i.e., divisible by 2), it divides n by 2 and makes a recursive call on this smaller number.

It adds 1 to count this step (the division by 2).

The function keeps going until it reaches 1.

3. If n is Odd

else:

    return 1 + reduce_to_one(3 * n + 1)

What it does:

If n is odd, it applies the formula 3 * n + 1 and recursively calls itself with this new value.

Again, adds 1 to count this step.

4. Example Call and Output

print(reduce_to_one(3))

How it works step-by-step for n = 3:

Step Current n Action Next n Steps so far

1 3 (odd) 3 * 3 + 1 = 10 10 1

2 10 (even) 10 // 2 = 5 5 2

3 5 (odd) 3 * 5 + 1 = 16 16 3

4 16 (even) 16 // 2 = 8 8 4

5 8 (even) 8 // 2 = 4 4 5

6 4 (even) 4 // 2 = 2 2 6

7 2 (even) 2 // 2 = 1 1 7

8 1 Stop - 0 (base case)

Total steps taken: 7

The function returns 7.

Output:

7

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Python Coding challenge - Day 513| What is the output of the following Python Code?

 

Code Explanation:

Function Definition

def tricky(a=[], b=0):

Defines a function tricky with:

a: A list with a default value of [] (mutable object).

b: An integer with a default value 0 (immutable object).

Key Point: The default list [] is created only once when the function is defined, not each time it's called. So a will retain its state between calls unless explicitly passed a new list.

Append b to a

    a.append(b)

Adds the current value of b to the list a.

On first call, a is empty: [], and b is 0, so after this: a = [0].

Increment b

    b += 1

Increases the value of b by 1.

Now b = 1.

Return Tuple

    return a, b

Returns a tuple: the updated list a and the incremented value of b.

First Call

print(tricky())

a is the default list [], b = 0.

a.append(0) → a = [0]

b += 1 → b = 1

Returns: ([0], 1)

Output:

([0], 1)

Second Call

print(tricky())

Here's the tricky part:

a is still [0] from the first call (because the default list is reused).

b = 0 again (default integer is recreated every time).

a.append(0) → a = [0, 0]

b += 1 → b = 1

Returns: ([0, 0], 1)

Output:

([0, 0], 1)

Final Output

([0], 1)

([0, 0], 1)

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Python Coding challenge - Day 512| What is the output of the following Python Code?

 Code Example:

Function Definition

def memo(val, cache={}):

def memo(val, cache={}):

Defines a function named memo that takes two parameters:

val: A value to be processed.

cache: A dictionary used to store previously computed results.

Note: Using a mutable default argument (cache={}) means the same dictionary persists across function calls. This is key to how memoization works here.

Check if Value is Cached

    if val in cache:

        return f"Cached: {cache[val]}"

Checks if val is already a key in the cache dictionary:

If it is, return a string showing the cached result.

Example: If val is 2 and it's already in cache, return something like "Cached: 4".

Compute and Store New Value

    cache[val] = val * 2

If val is not in the cache:

Compute val * 2.

Store it in the dictionary with val as the key.

For val = 2, it stores 2: 4.

Return Computed Result

    return f"Computed: {cache[val]}"

After storing the new value in the cache, return a string like "Computed: 4" indicating a fresh computation.

Function Calls

print(memo(2))

print(memo(2))

First call: memo(2)

2 is not in cache → computes 2 * 2 = 4, stores it → returns "Computed: 4".

Second call: memo(2)

2 is now in cache → returns "Cached: 4".

Output

Computed: 4

Cached: 4

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Python Coding Challange - Question with Answer (01280525)

 

What does it do?

Outer Loop:

for i in range(1, 3):

• range(1, 3) generates numbers: 1, 2

• So, the loop runs 2 times, with i = 1 then i = 2

---

Inner Loop:

for j in range(2):

• range(2) generates numbers: 0, 1

• So, for each i, this inner loop runs twice, with j = 0, then j = 1

---

Inside Both Loops:

print(i + j)

• It prints the sum of i and j for each combination

---

 Iteration Breakdown:

i	j	i + j	Output
1	0	1	✅
1	1	2	✅
2	0	2	✅
2	1	3	✅

 Final Output:

1  
2 
2
3

---

 Summary:

• This is a nested loop.

• The outer loop controls i = 1, 2

• The inner loop controls j = 0, 1

• The program prints the sum of i + j for each combination.

APPLICATION OF PYTHON IN FINANCE

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

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3D Spiral Staircase Pattern using Python

 import numpy as np

import matplotlib.pyplot as plt

fig = plt.figure()

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

num_steps = 50         

height_per_step = 0.2  

radius = 2             

theta = np.linspace(0, 4 * np.pi, num_steps)  

x = radius * np.cos(theta)

y = radius * np.sin(theta)

z = height_per_step * np.arange(num_steps)

ax.scatter(x, y, z, c='brown', s=100, label='Steps')

ax.plot([0,0], [0,0], [0, z[-1]+1], color='grey', linestyle='--', linewidth=2, label='Central Pole')

ax.set_xlabel('X')

ax.set_ylabel('Y')

ax.set_zlabel('Z (height)')

ax.legend()

plt.show()

#source code --> clcoding.com 

Code Explanation:

1. Import Libraries

import numpy as np

import matplotlib.pyplot as plt

numpy for numerical operations and array handling.

matplotlib.pyplot for plotting graphs.

 2. Create Figure and 3D Axes

fig = plt.figure()

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

Creates a new plotting figure.

Adds a 3D subplot to the figure (one plot, with 3D projection).

 3. Define Parameters for Staircase

num_steps = 50         # Number of steps in the staircase

height_per_step = 0.2  # Height increase for each step

radius = 2             # Radius of spiral path

Sets how many steps.

How much each step rises vertically.

How far each step is from the center.

 4. Calculate Step Angles

theta = np.linspace(0, 4 * np.pi, num_steps)

Creates num_steps angles evenly spaced from 0 to

4π (two full rotations).

 5. Calculate Step Positions (x, y, z)

x = radius * np.cos(theta)

y = radius * np.sin(theta)

z = height_per_step * np.arange(num_steps)

Computes x, y coordinates on a circle of given radius (using cosine and sine).

 z increases linearly with step number, creating height.

 6. Plot Steps as Points

ax.scatter(x, y, z, c='brown', s=100, label='Steps')

Plots each step as a brown dot sized 100.

Labels them for the legend.

 7. Plot Central Pole

ax.plot([0,0], [0,0], [0, z[-1]+1], color='grey', linestyle='--', linewidth=2, label='Central Pole')

Draws a vertical dashed line at the center representing the staircase pole.

Goes from height 0 to just above the last step.

 8. Set Axis Labels

ax.set_xlabel('X')

ax.set_ylabel('Y')

ax.set_zlabel('Z (height)')

Labels each axis to indicate directions.

 9. Add Legend

ax.legend()

Adds a legend explaining plotted elements (steps and pole).

 10. Display the Plot

plt.show()

Shows the final 3D spiral staircase plot.

 

 

 

 

 

 

 

 

 

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