Hands-On APIs for AI and Data Science: Python Development with FastAPI

 Hands-On APIs for AI and Data Science: Python Development with FastAPI

As artificial intelligence (AI) and data science solutions become increasingly critical to modern businesses, the need for fast, scalable, and easy-to-deploy APIs has never been greater. APIs allow AI and data models to connect with real-world applications — from mobile apps to web dashboards — making them accessible to users and systems across the globe.

"Hands-On APIs for AI and Data Science: Python Development with FastAPI" serves as the ultimate guide for building production-ready APIs quickly and efficiently. By combining the power of Python, the speed of FastAPI, and the precision of AI models, this book equips developers, data scientists, and machine learning engineers to expose their models and data pipelines to the world in a robust and scalable way.

Why FastAPI?

In the world of web frameworks, FastAPI has emerged as a true game-changer. Built on top of Starlette for the web parts and Pydantic for the data validation parts, FastAPI offers:

Blazing Speed: One of the fastest frameworks thanks to asynchronous capabilities.

Automatic API Documentation: Generates interactive Swagger and ReDoc docs without extra code.

Type Hints and Validation: Deep integration with Python type hints, ensuring fewer bugs and better developer experience.

Easy Integration with AI/ML Pipelines: Built-in support for JSON serialization, async requests, background tasks, and more — essential for real-world AI serving.

"Hands-On APIs for AI and Data Science" teaches you not just how to use FastAPI, but how to optimize it specifically for data science and machine learning applications.

What This Book Covers

"Hands-On APIs for AI and Data Science" is structured to take you from basics to advanced deployment strategies. Here’s a breakdown of the key areas covered:

1. Fundamentals of APIs and FastAPI

The book starts with the core concepts behind APIs: what they are, why they matter, and how they serve as a bridge between users and AI models.

It introduces the basics of FastAPI, including setting up your environment, creating your first endpoints, understanding routing, and handling different types of requests (GET, POST, PUT, DELETE).

You’ll learn:

Setting up Python virtual environments

Building your first Hello World API

Sending and receiving JSON data

2. Data Validation and Serialization with Pydantic

One of FastAPI’s secret weapons is Pydantic, which ensures that the data coming into your API is exactly what you expect.

The book dives deep into using Pydantic models for input validation, output schemas, and error handling, ensuring your APIs are safe, predictable, and user-friendly.

Topics include:

Defining request and response models

Automatic data parsing and validation

Handling nested and complex data structures

3. Connecting AI and Data Science Models

This is where the book shines: showing how to take a trained ML model (like a scikit-learn, TensorFlow, or PyTorch model) and expose it through a FastAPI endpoint.

You will build endpoints where users can submit input data and receive predictions in real time.

Real-world examples include:

Serving a spam detection model

Deploying a computer vision image classifier

Predicting house prices from structured data

4. Handling Files, Images, and Large Data

Many data science applications involve uploading images, CSV files, or large datasets.

The book walks you through handling file uploads securely and efficiently, and teaches techniques like background tasks for long-running operations (like large file processing).

Learn how to:

Accept image uploads for prediction

Parse uploaded CSV files

Perform background processing for heavy workloads

5. Authentication, Authorization, and API Security

Security is a major concern when exposing models to the public.

The book covers best practices for authentication (e.g., OAuth2, API Keys, JWT tokens) and authorization to protect your APIs.

Topics include:

Implementing token-based authentication

Securing endpoints

User management basics

6. Building Real-Time APIs with WebSockets

For applications like real-time monitoring, chatbots, or dynamic dashboards, WebSockets are a powerful tool.

This book introduces you to building real-time, bidirectional communication channels in FastAPI, enhancing your AI applications.

7. Testing and Debugging APIs

A solid API is not only functional but also well-tested.

You’ll learn how to write automated tests for your endpoints using Python's pytest and FastAPI’s built-in testing utilities, ensuring reliability before deployment.

8. Deployment Strategies

Finally, you’ll explore how to move from local development to production.

The book guides you through deployment best practices, including setting up Uvicorn, Gunicorn, Docker containers, and even deploying on cloud platforms like AWS, Azure, and GCP.

Deployment topics include:

Running APIs with Uvicorn/Gunicorn

Dockerizing your FastAPI application

Using Nginx as a reverse proxy

Basic cloud deployment workflows

Who Should Read This Book?

• Data Scientists who want to expose models to end users or integrate predictions into applications.
• Machine Learning Engineers looking for scalable, production-ready deployment methods.
• Backend Developers who want to leverage Python for building AI-driven APIs.
• Researchers needing to share ML models easily with collaborators or stakeholders.
• Students and Enthusiasts eager to learn about modern API development and AI integration.
• No prior web development experience is strictly necessary — the book builds from beginner to intermediate concepts seamlessly.

Key Benefits After Reading This Book

• Build production-ready APIs in Python with modern best practices
• Seamlessly serve AI models as real-time web services
• Secure, test, and deploy your APIs with confidence
• Understand async programming, background tasks, and WebSockets
• Create scalable and efficient data science systems accessible to users and applications

Hard Copy : Hands-On APIs for AI and Data Science: Python Development with FastAPI

Kindle : Hands-On APIs for AI and Data Science: Python Development with FastAPI

Conclusion: Bring Your AI Models to Life

Building great AI models is only half the battle — deploying them for real-world use is where the real value lies.

"Hands-On APIs for AI and Data Science" offers a step-by-step guide to making your AI models accessible, secure, and scalable via FastAPI — one of the fastest-growing frameworks in the Python ecosystem.

If you are serious about taking your machine learning, AI, or data science skills to the next level, this book is your roadmap to doing just that — with speed, clarity, and professional excellence.

Don’t just build models — build products that people can actually use.

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Python Coding challenge - Day 469| What is the output of the following Python Code?

 

Code Explanation:

1. Class Definition

class Circle:

A class named Circle is being defined.

This class will represent a geometric circle and contain methods to operate on it.

2. Constructor Method (__init__)

Edit

def __init__(self, radius): 

    self._radius = radius

The __init__ method is a constructor that is called when a new object of Circle is created.

It takes a radius argument and stores it in a private attribute _radius.

The underscore _ is a naming convention indicating that this attribute is intended for internal use.

3. Area Property Using @property Decorator

@property

def area(self): 

    return 3.14 * (self._radius ** 2)

The @property decorator makes the area() method behave like a read-only attribute.

This means you can access circle.area instead of calling it like circle.area().

The method returns the area of the circle using the formula:

Area=πr 2

 =3.14×(radius) 2

 

4. Creating an Instance of the Circle

print(Circle(5).area)

A new object of the Circle class is created with a radius of 5.

Then, the area property is accessed directly (not called like a function).

5. Final Output

78.5

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Python Coding challenge - Day 467| What is the output of the following Python Code?

Code Explanation:

1. Importing heapq Module

import heapq

The heapq module provides an implementation of the heap queue algorithm, also known as the priority queue algorithm.

A heap is a binary tree where the parent node is smaller (for a min-heap) or larger (for a max-heap) than its child nodes.

The heapq module in Python supports min-heaps by default.

2. Initializing a List

heap = [3, 1, 4, 5, 2]

Here, we define a list called heap that contains unsorted elements: [3, 1, 4, 5, 2].

This list is not yet in heap order (i.e., not arranged according to the heap property).

3. Applying heapify() to the List

heapq.heapify(heap)

The heapq.heapify() function transforms the list into a valid min-heap.

After calling this function, the smallest element will be at the root (the first element of the list).

The list heap will now be rearranged into the heap order. The smallest element (1) will be at the root, and the children nodes (2, 5, etc.) will satisfy the heap property.

The list after heapq.heapify() becomes:

[1, 2, 4, 5, 3]

Explanation:

1 is the smallest element, so it stays at the root.

The heap property is maintained (parent is smaller than its children).

4. Pushing a New Element into the Heap

heapq.heappush(heap, 6)

The heapq.heappush() function is used to push a new element (in this case, 6) into the heap while maintaining the heap property.

After inserting 6, the heap will rearrange itself to keep the smallest element at the root.

The list after heappush() becomes:

[1, 2, 4, 5, 3, 6]

The element 6 is added, and the heap property is still preserved.

5. Printing the Resulting Heap

print(heap)

Finally, the print() function displays the heap after performing the heap operations.

The printed output will be the heapified list with the new element pushed in, maintaining the heap property.

Output:

[1, 2, 4, 5, 3, 6]

 

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Generative AI: Prompt Engineering Basics

 

Generative AI: Prompt Engineering Basics – A Comprehensive Guide

The surge in generative AI technologies, especially large language models (LLMs) like ChatGPT, Claude, and Gemini, has revolutionized how humans interact with machines. At the heart of these interactions lies an essential skill: Prompt Engineering. Whether you're a developer, data scientist, content creator, or a business leader, understanding prompt engineering is key to unlocking the full potential of generative AI.

In this blog, we’ll walk through the course “Generative AI: Prompt Engineering Basics”, exploring what it covers, why it matters, and how you can apply its concepts effectively.

What is Prompt Engineering?

Prompt engineering is the art and science of crafting inputs—called prompts—to get desired, high-quality outputs from generative AI systems. It’s about asking the right question in the right way.

Generative models like GPT-4 are powerful but non-deterministic—they don’t “know” what you want unless you clearly guide them. That’s where prompt engineering steps in.

 About the Course

"Generative AI: Prompt Engineering Basics" is a beginner-friendly course designed to introduce learners to:

How generative models work (with a focus on LLMs)

How prompts influence model behavior

Best practices for crafting effective prompts

Different prompting techniques (zero-shot, few-shot, chain-of-thought, etc.)

Common pitfalls and how to avoid them

Course Outline & Key Concepts

1. Introduction to Generative AI

What is generative AI?

• History and evolution of large language models
• Use cases: content creation, code generation, design, education, customer support, etc.

2. Understanding Prompts

• Anatomy of a prompt
• Role of context, clarity, and specificity
• Output formats (text, code, tables, etc.)

3. Prompting Techniques

• Zero-shot prompting: Giving no examples and relying on the model’s general knowledge.
• Example: “Summarize this article in two sentences.”
• Few-shot prompting: Providing a few examples to guide the model’s output.
• Example: “Translate English to French. English: Cat → French: Chat…”
• Chain-of-thought prompting: Encouraging the model to reason step-by-step.
• Example: “Let’s think step by step…”

4. Iterative Prompting

• How to refine prompts based on results
• Evaluating outputs: fluency, relevance, accuracy
• Prompt-debugging: solving hallucinations or off-topic responses

5. Prompt Templates & Use Cases

• Templates for summarization, classification, Q&A, translation, etc.
• Real-world applications in:
• Marketing (ad copy generation)
• Education (tutoring bots)
• Coding (pair programming)
• Healthcare (clinical note summarization)

Why Prompt Engineering Matters

Productivity: Well-crafted prompts save time and reduce the need for post-editing.

Accuracy: The quality of your prompt directly impacts the accuracy of the AI’s output.

Innovation: Prompt engineering enables rapid prototyping of ideas and products.

Control: Provides a layer of control over AI outputs without needing to retrain models.

Tools & Platforms

The course often demonstrates concepts using tools like:

OpenAI's Playground

ChatGPT or Claude web apps

Google Colab for programmatic prompting with Python

Prompt libraries or tools like PromptLayer, LangChain, and Guidance

Who Should Take This Course?

Beginners with an interest in AI/ML

Developers and engineers building AI-powered tools

Content creators and marketers

Educators looking to integrate AI into teaching

Business leaders exploring generative AI solutions

Learning Outcomes

By the end of this course, learners will:

Understand the mechanics behind LLMs and prompts

Be able to craft clear, effective, and creative prompts

Use prompting to solve diverse real-world problems

Build prompt-driven workflows using popular AI tools

Join Free : Generative AI: Prompt Engineering Basics

Final Thoughts

Prompt engineering is more than a buzzword—it's a foundational skill in the age of generative AI. As these models become more embedded in our tools and platforms, knowing how to “speak their language” will be critical.

This course offers a clear, practical introduction to the field and sets the stage for deeper explorations into fine-tuning, API integrations, and autonomous agents.

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Python Coding challenge - Day 468| What is the output of the following Python Code?

 Code Explanation:

1. Importing the bisect Module

import bisect

The bisect module is used for maintaining a list in sorted order without having to sort it after each insertion.

It provides functions like bisect() and insort() which help in inserting elements into a sorted list at the correct position.

2. Initializing a Sorted List

sorted_list = [1, 2, 4, 5]

The list is already sorted in ascending order.

This is a requirement when using bisect.insort() — it assumes the list is sorted.

3. Using bisect.insort() to Insert an Element

bisect.insort(sorted_list, 3)

insort() inserts the element (3) into the list while keeping it sorted.

Internally, it uses binary search to find the correct index where 3 should go.

In this case, 3 is inserted between 2 and 4.

4. Printing the Updated List

print(sorted_list)

This prints the updated list after inserting 3.

Output:

[1, 2, 3, 4, 5]

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Python Coding challenge - Day 466| What is the output of the following Python Code?

 Code Explanation:

Original List:

data = [(1, 3), (3, 1), (2, 2)]

This is a list of 3 tuples. Each tuple has two integers.

Sorting Logic:

sorted(data, key=lambda x: x[1])

The sorted() function returns a new sorted list without changing the original.

The key argument tells Python how to compare the elements.

lambda x: x[1] means: for each tuple x, use the second element (x[1]) as the sorting key.

Sorting in Action:

Let's evaluate the second elements:

(1, 3) → 3

(3, 1) → 1

(2, 2) → 2

Now, sort based on these values: 1, 2, 3

So the sorted order becomes:

[(3, 1), (2, 2), (1, 3)]

Output:

print(sorted_data)

This prints:

[(3, 1), (2, 2), (1, 3)]

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Python Coding challenge - Day 465| What is the output of the following Python Code?

 

Code Explanation:

Import deque:

from collections import deque allows you to use the deque class.

Initialize deque:

dq = deque([1, 2, 3]) creates a deque containing [1, 2, 3].

Append to deque:

dq.append(4) adds the number 4 to the right end of the deque.

Now the deque becomes [1, 2, 3, 4].

Print deque:

print(dq) will output:

deque([1, 2, 3, 4])

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Python Coding challenge - Day 464| What is the output of the following Python Code?

 Code Explanation:

Importing the integration module:

import scipy.integrate as spi

You are importing the integrate module from SciPy and aliasing it as spi.

Defining the function to integrate:

def integrand(x):

    return x ** 2

This is the function 

f(x)=x 2

  which you want to integrate.

Calling quad to perform definite integration:

result, error = spi.quad(integrand, 0, 1)

spi.quad performs numerical integration.

It integrates integrand(x) from x = 0 to x = 1.

It returns:

result: The value of the integral.

error: An estimate of the absolute error.

Printing the result:

print(result)

Output:

0.33333333333333337

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Python Coding challenge - Day 463| What is the output of the following Python Code?

 

Code Explanation:

Importing defaultdict:

from collections import defaultdict

You import defaultdict from the collections module.

defaultdict is like a regular dictionary but provides a default value if the key has not been set yet.

Creating a defaultdict with int:

d = defaultdict(int)

int is the default factory function. It returns 0 when a new key is accessed.

So, d['missing_key'] will return 0 instead of raising a KeyError.

Updating the dictionary:

d['a'] += 1

d['a'] is not in the dictionary, so defaultdict uses int() to assign it 0.

Then 0 + 1 = 1 → d['a'] = 1

d['b'] += 2

Similarly, d['b'] = 0 + 2 = 2

Accessing a non-existent key:

print(d['a'], d['b'], d['c'])

d['a'] → 1

d['b'] → 2

d['c'] is not set, so it gets the default value of 0 (via int())

Final Output:

1 2 0

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3D Butterfly Wing (Mathematical Model) using Python

 

import matplotlib.pyplot as plt

import numpy as np

u=np.linspace(0,2*np.pi,100)

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

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

x=np.sin(u)*(1+0.5*np.cos(v))*np.cos(v)

y=np.cos(u)*(1+0.5*np.cos(v))*np.cos(v)

z=np.sin(v)+0.2*np.sin(3*u)

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

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

ax.plot_surface(x,y,z,cmap='coolwarm',edgecolor='k',alpha=0.9)

ax.set_title('3D Butterfly Wing')

ax.set_xlabel('X axis')

ax.set_ylabel('Y axis')

ax.set_zlabel('Z axis')

ax.set_box_aspect([1,1,0.5])

plt.tight_layout()

plt.show()

#source code --> clcoding.com 

Code Explanation:

1. Importing Libraries

import numpy as np

import matplotlib.pyplot as plt

numpy: Used for creating numerical arrays and trigonometric functions.

 matplotlib.pyplot: Used for creating the 3D plot.

 2. Creating Parameter Grids

u = np.linspace(0, 2 * np.pi, 100)

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

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

u: Controls the angular direction (think of it like horizontal spread of the wing).

 v: Controls the vertical sweep or curvature of the wings.

 meshgrid: Creates a 2D grid to evaluate the parametric surface over.

 3. Defining the Parametric Equations

x = np.sin(u) * (1 + 0.5 * np.cos(v)) * np.cos(v)

y = np.cos(u) * (1 + 0.5 * np.cos(v)) * np.cos(v)

z = np.sin(v) + 0.2 * np.sin(3 * u)

These equations build a curved, sinusoidal surface that resembles butterfly wings:

 x and y: Give a spiraling shape using a modified polar coordinate system.

 The term (1 + 0.5 * cos(v)) * cos(v) gives depth and curvature.

 z: Controls the vertical deformation, making the wings appear "flapping" or "wavy."

 sin(v) gives the main vertical structure.

 0.2 * sin(3 * u) adds a ripple or flutter pattern, mimicking wing detail.

 4. Setting Up the 3D Plot

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

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

Creates a square figure with a 3D plotting environment.

 5. Plotting the Surface

ax.plot_surface(x, y, z, cmap='coolwarm', edgecolor='k', alpha=0.9)

plot_surface: Draws the 3D shape.

 cmap='coolwarm': Uses a smooth gradient of blue to red.

 edgecolor='k': Adds a black gridline for better surface structure visibility.

 alpha=0.9: Slight transparency for softness.

 6. Customizing the Axes

ax.set_title('3D Butterfly Wings (Mathematical Model)', fontsize=14)

ax.set_xlabel('X axis')

ax.set_ylabel('Y axis')

ax.set_zlabel('Z axis')

ax.set_box_aspect([1, 1, 0.5])

Adds title and axis labels.

 set_box_aspect([1, 1, 0.5]): Makes the Z-axis compressed to enhance the wing appearance.

7. Show the Plot

plt.tight_layout()

plt.show()

tight_layout(): Adjusts padding between plot elements.

 show(): Displays the 3D plot.

 

 

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Crack the Python Interview : 160+ Questions & Answers for Job Seekers (Crack the Interview Book 2)

 

Python has established itself as one of the most sought-after programming languages across industries — from web development to data science, automation to artificial intelligence. Whether you are a fresher or an experienced developer aiming for your next big role, technical interviews often pose a major challenge.

This is where the book "Crack the Python Interview: 160+ Questions & Answers for Job Seekers" (part of the Crack the Interview series) steps in. Designed specifically to prepare candidates for real-world Python interviews, this book offers an extensive collection of carefully selected questions and model answers.

Let’s explore this book in depth and understand how it can become a vital resource in your job preparation toolkit.

Objective of the Book

The primary goal of this book is to help Python job seekers get ready for technical interviews. It does this by:

Providing a broad range of Python interview questions, covering both fundamental and advanced topics.

Offering concise and practical answers that interviewers expect.

Helping readers understand core Python concepts deeply enough to handle variations of standard questions during interviews.

Rather than being a traditional Python learning book, it serves as a focused interview preparation guide — a “last-mile” tool to polish your knowledge and boost your confidence.

Structure and Organization

The book is logically divided into sections that mirror the kind of topics commonly covered in Python job interviews. Here's an overview of the key areas:

1. Python Basics

The book begins with questions about:

Python syntax and structure

Variables, data types, operators

Control flow (loops, conditionals)

Functions and scope

This section ensures the reader is grounded in the building blocks of Python — a crucial starting point for any role.

2. Object-Oriented Programming (OOP)

Covers essential topics such as:

Classes and objects

Inheritance and polymorphism

Encapsulation

Special methods like __init__, __str__, and operator overloading

OOP concepts are vital for technical interviews, especially for roles that emphasize software engineering principles.

3. Data Structures and Algorithms

Focuses on:

Lists, dictionaries, sets, tuples

Stack, queue, linked lists (Pythonic approaches)

Sorting and searching algorithms

Time and space complexity

Many interviews involve solving problems related to efficient data handling and manipulation, and this section prepares readers for such challenges.

4. Advanced Python Concepts

Delves into more sophisticated areas:

Generators and iterators

Decorators and context managers

Lambdas, map, filter, and reduce

Modules and packages

Memory management and garbage collection

Having a grasp of these topics often distinguishes candidates in technical interviews for mid to senior-level positions.

5. Error Handling

Discusses:

Try, except, else, finally blocks

Custom exception classes

Common pitfalls and error patterns

Effective error handling is often assessed in coding rounds and technical discussions.

6. Python Libraries and Frameworks

Briefly touches upon popular libraries such as:

pandas, numpy for data manipulation

flask, django for web development

Testing frameworks like unittest and pytest

While not in-depth tutorials, this exposure is crucial for real-world project discussions during interviews.

7. Coding Exercises and Logical Puzzles

Small Python programs

Logic puzzles using Python

Practical coding challenges that interviewers often use to test logical thinking and code efficiency

Unique Features of the Book

160+ Curated Questions: Carefully selected to cover not just rote knowledge but conceptual depth and practical application.

Concise, Interview-Ready Answers: Each answer is designed to be explained verbally in an interview scenario, striking a balance between brevity and completeness.

Coverage of Edge Cases: Highlights tricky aspects and common mistakes — for example, Python's mutable default arguments or the intricacies of object mutability.

Quick Revision Format: Designed to enable quick revisits before interviews or coding assessments.

Bridges Knowledge Gaps: Helps candidates identify weaker areas that might not surface until faced with real interview questions.

Strengths of the Book

Focused on Interview Success: It doesn’t waste time on lengthy explanations — perfect for candidates who already know Python but need sharp revision.

Comprehensive Range: Covers everything from Python 101 to advanced-level topics, making it useful for both entry-level and experienced developers.

Practical Perspective: The book emphasizes how to answer interview questions, not just what the answer is.

Accessible Language: Clear and simple explanations without unnecessary jargon.

Useful for Different Roles: Whether you're applying for a developer, automation engineer, backend engineer, or data analyst role, the book touches on the Python essentials relevant to each.

Who Should Use This Book?

This book is ideal for:

Job seekers preparing for Python-based interviews.

Students looking to succeed in campus placements.

Working professionals aiming to switch to Python-heavy roles.

Developers needing a structured revision tool before technical tests or whiteboard interviews.

It’s especially useful for people who have learned Python theoretically but need help connecting their knowledge to interview questions.

Kindle : Crack the Python Interview : 160+ Questions & Answers for Job Seekers (Crack the Interview Book 2)

Hard Copy : Crack the Python Interview : 160+ Questions & Answers for Job Seekers (Crack the Interview Book 2)

Final Thoughts

"Crack the Python Interview: 160+ Questions & Answers for Job Seekers" is a well-crafted, efficient, and highly practical guide for anyone serious about succeeding in Python interviews. By concentrating on likely interview questions, explaining them in a concise and understandable way, and highlighting important nuances, the book provides readers with a serious advantage in a competitive job market.

It’s not a textbook — it’s a strategic companion for technical interview preparation. For candidates looking to move quickly from theory to job offer, this book can serve as the perfect final-stage resource.

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Python Coding Challange - Question with Answer (01030525)

 

Explanation:

1. import array as arr
   This imports Python's built-in array module and gives it the nickname arr.

2. e = arr.array('i', [7, 14, 21, 28])
   This creates an array named e with integer type 'i'.
   The array contains 4 elements:
   e = [7, 14, 21, 28]

3. e[:3]
   This is slicing the array. It selects the first 3 elements (index 0 to 2):
   [7, 14, 21]

4. sum(e[:3])
   This calculates the sum of the sliced array:
   7 + 14 + 21 = 42

5. print(...)
   The output will be:
   42

---

 Final Output:

42

PYTHON FOR MEDICAL SCIENCE
https://pythonclcoding.gumroad.com/l/luqzrg

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3D Plasma Wave Simulation using Python

 

import matplotlib.pyplot as plt

import numpy as np

from mpl_toolkits.mplot3d import Axes3D

x=np.linspace(-5,5,30)

y=np.linspace(-5,5,30)

z=np.linspace(-5,5,30)

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

wave=np.sin(2*np.pi*X*10)*np.cos(2*np.pi*Y/10)*np.sin(2*np.pi*Z/10)

threshold=0.5

mask=np.abs(wave)>threshold

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

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

sc=ax.scatter(X[mask],Y[mask],Z[mask],c=wave[mask],cmap='plasma',s=15,alpha=0.8)

ax.set_title('3D Plasma Wave Simulation')

ax.set_xlabel('X axis')

ax.set_ylabel('Y axis')

ax.set_zlabel('Z axis')

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

fig.colorbar(sc,shrink=0.6,label='Wave Amplitude')

plt.tight_layout()

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 generating arrays.

 matplotlib.pyplot: For plotting.

 mpl_toolkits.mplot3d: Enables 3D plotting with Axes3D.

 2. Define 3D Coordinate Grids

x = np.linspace(-5, 5, 30)

y = np.linspace(-5, 5, 30)

z = np.linspace(-5, 5, 30)

Creates evenly spaced values from -5 to 5 along each axis (30 points).

 These serve as the spatial coordinates in the 3D space.

 3. Create 3D Meshgrid

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

np.meshgrid converts the 1D arrays into 3D coordinate grids.

 Each point in the 3D volume now has corresponding (X, Y, Z) coordinates.

 4. Define the Plasma Wave Function

wave = np.sin(2 * np.pi * X / 10) * np.cos(2 * np.pi * Y / 10) * np.sin(2 * np.pi * Z / 10)

A mathematical expression to simulate a 3D plasma wave.

 Combines sine and cosine functions to simulate oscillating wave patterns in space.

 5. Apply Wave Threshold Mask

threshold = 0.5

mask = np.abs(wave) > threshold

Sets a cutoff (threshold) to visualize only strong wave amplitudes.

 mask is a boolean array selecting only points where wave amplitude exceeds 0.5.

 6. Set Up the 3D Plot

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

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

Initializes a figure with a 3D subplot.

 7. Scatter Plot the Wave Points

sc = ax.scatter(X[mask], Y[mask], Z[mask], c=wave[mask], cmap='plasma', s=15, alpha=0.8)

Plots only the points that passed the threshold mask.

c=wave[mask]: Colors each point based on wave amplitude.

cmap='plasma': Uses a vibrant colormap.

 s=15: Sets the point size.

 alpha=0.8: Semi-transparent points for better 3D depth effect.

 8. Customize the Plot

ax.set_title('3D Plasma Wave Simulation')

ax.set_xlabel('X Axis')

ax.set_ylabel('Y Axis')

ax.set_zlabel('Z Axis')

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

Adds title and axis labels.

 set_box_aspect([1, 1, 1]): Ensures equal aspect ratio for proper 3D geometry.

 9. Add Colorbar and Show Plot

fig.colorbar(sc, shrink=0.6, label='Wave Amplitude')

plt.tight_layout()

plt.show()

Adds a color bar indicating amplitude values.

 tight_layout() adjusts spacing to prevent clipping.

 show() displays the final visualization.

 

 

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Python Coding challenge - Day 462| What is the output of the following Python Code?

 

Code Explanation:

1. Importing heapq module

import heapq

Purpose:This line imports the heapq module, which provides an implementation of the heap queue algorithm (also known as the priority queue algorithm). The heapq module provides functions to maintain a heap data structure in Python.

2. Initializing the list heap

heap = [1, 3, 2, 4, 5]

Purpose:

This line initializes a list heap containing five elements: [1, 3, 2, 4, 5].

Although it is a simple list, it will be transformed into a heap using the heapq.heapify() function.

3. Converting the list into a heap

heapq.heapify(heap)

Purpose:

The heapq.heapify() function transforms the list into a min-heap in-place. A min-heap is a binary tree where each parent node is less than or equal to its children. In Python, a min-heap is implemented as a list, and the smallest element is always at the root (index 0).

After applying heapify(), the list heap will be rearranged to satisfy the heap property.

The resulting heap will look like this: [1, 3, 2, 4, 5].

Notice that the original list was already almost a min-heap. However, the heapify() step ensures that the heap property is enforced, so the root element is always the smallest.

4. Popping the smallest element from the heap

popped = heapq.heappop(heap)

Purpose:

The heapq.heappop() function removes and returns the smallest element from the heap. After this operation, the heap will reorganize itself to maintain the heap property.

Since the heap is [1, 3, 2, 4, 5], the smallest element is 1. Therefore, heappop() will remove 1 and return it, and the heap will be reorganized to ensure the heap property is maintained.

The resulting heap will look like this: [2, 3, 5, 4] after popping 1.

5. Printing the popped element

print(popped)

Purpose:

This line prints the element that was popped from the heap. As explained, the smallest element 1 is removed from the heap, so 1 will be printed.

Final Explanation:

The program first transforms the list [1, 3, 2, 4, 5] into a min-heap using heapq.heapify(), and then pops the smallest element (1) using heapq.heappop(). The popped value is stored in popped, and the value 1 is printed.

Output:

1

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Python Coding challenge - Day 461| What is the output of the following Python Code?

 Code Explanation:

1. Initializing the string s

s = "abcdef"

Purpose:

This line initializes the string s with the value "abcdef". This string contains six characters: 'a', 'b', 'c', 'd', 'e', and 'f'.

2. Initializing the variable total

total = 0

Purpose:

This line initializes the variable total to 0. The total variable will be used to accumulate the sum of the ASCII values of specific characters from the string s (those with even indices).

3. The for loop with range()

for i in range(0, len(s), 2):

Purpose:

This line initiates a for loop that iterates through the indices of the string s.

range(0, len(s), 2) generates a sequence of numbers starting from 0, going up to len(s) (which is 6), with a step of 2. This means it will only include the indices 0, 2, and 4, which corresponds to the characters 'a', 'c', and 'e' in the string.

In summary, the loop will run 3 times, with i taking the values 0, 2, and 4.

4. Adding the ASCII values to total

total += ord(s[i])

Purpose:

Inside the loop, for each value of i, this line:

s[i] retrieves the character at the index i of the string s.

ord(s[i]) gets the ASCII (Unicode) value of that character.

The result of ord(s[i]) is then added to the total variable.

So, this line accumulates the sum of the ASCII values of the characters at indices 0, 2, and 4.

For i = 0, s[0] = 'a', ord('a') = 97, so total becomes 97.

For i = 2, s[2] = 'c', ord('c') = 99, so total becomes 97 + 99 = 196.

For i = 4, s[4] = 'e', ord('e') = 101, so total becomes 196 + 101 = 297.

5. Printing the final total

print(total)

Purpose:

After the loop completes, this line prints the final value of total, which contains the sum of the ASCII values of the characters 'a', 'c', and 'e'.

The final value of total is 297, as explained in the previous step.

Final Explanation:

The program sums up the ASCII values of every second character in the string "abcdef". It processes the characters at indices 0, 2, and 4 (i.e., 'a', 'c', 'e'), and adds their ASCII values:

ord('a') = 97

ord('c') = 99

ord('e') = 101

Thus, the total sum is 97 + 99 + 101 = 297, which is then printed.

Output:

297

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NEW BOOK LAUNCH: Python for Medical Science by CL Coding

Bridge the gap between healthcare and technology with this all-in-one practical guide designed for students, healthcare professionals, and researchers!
Whether you're new to coding or exploring how Python can be applied in medical science, this book walks you through from the basics to real-world applications.

🔬 What You’ll Learn – Week by Week:

✅ Week 1: Python Basics — Set up your Python environment and learn variables, data types, and basic health calculations like BMI & BSA
✅ Week 2: Control Structures & Functions — Write logic for BP and heart rate analysis, use loops to monitor vitals
✅ Week 3: Data Structures — Store and manage patient records, health logs, and build simple console-based medical systems
✅ Week 4: File Handling & Real Medical Data — Read/write to CSV & TXT, work with WHO/CDC datasets, clean and analyze medical data
✅ Week 5: Visualization — Use matplotlib and seaborn to visualize epidemic curves, BP trends, and drug dosage graphs
✅ Week 6: Medical Image Processing — Load and enhance DICOM, MRI, and X-ray images with OpenCV and pydicom
✅ Week 7: Biostatistics — Perform statistical analysis on blood pressure, sugar levels, and cholesterol, and learn how to visualize diagnostic accuracy
✅ Week 8: Mini Projects — Build real applications like a Patient Health Tracker, Heart Disease Predictor, and a Medical Image Annotator

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Healthcare data analysts

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Anyone eager to integrate coding with clinical knowledge

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Grab your copy of Python for Medical Science and start building the future of healthcare with code!

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Python Quiz on NumPy Splitting Array

 

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Python Quiz on NumPy Array Search

 

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Python Coding Challange - Question with Answer (01020525)

Step-by-step Breakdown

nums = [100, 200, 300, 400]

A list of 4 integers is defined.

range(len(nums)-1, -1, -2)

This expands to:

range(3, -1, -2)

• len(nums) - 1 = 4 - 1 = 3 → Start at index 3 (last element)

• -1 is the stop (not inclusive), so it goes until index 0

• -2 is the step → go backwards by 2 steps each time

Iteration over range(3, -1, -2):

• i = 3 → nums[3] = 400

• i = 1 → nums[1] = 200

Loop stops before reaching -1.

---

Output

400
200

---

 Summary

• The code iterates in reverse, skipping every second element.

• It accesses and prints nums[3], then nums[1].

Application of Python Libraries for Civil Engineering

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

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Statistics with Python. 100 solved exercises for Data Analysis (Your Data Teacher Books Book 1)

 

Statistics with Python – 100 Solved Exercises for Data Analysis

In the evolving world of data analysis, one skill remains timeless and fundamental: statistics. No matter how advanced your machine learning models or data pipelines are, a core understanding of statistics empowers you to make sound, interpretable decisions with data. One book that takes a unique approach to this subject is "Statistics with Python. 100 Solved Exercises for Data Analysis" by Your Data Teacher.

Unlike dense academic textbooks or broad theoretical overviews, this book positions itself as a hands-on guide, ideal for readers who want to build statistical intuition by applying concepts directly in Python.

Purpose and Audience

The book is tailored for:

Beginners in data science or analytics

Students studying statistics who want practical coding experience

Python programmers wanting to develop statistical understanding

Professionals seeking to upgrade from Excel or business intelligence tools to code-based analysis

Its objective is clear: make statistical thinking accessible and actionable through practical Python exercises.

It does not attempt to be a comprehensive treatise on statistics. Instead, it serves as a practice workbook, offering 100 problems with structured solutions that demonstrate how to use Python’s statistical and data-handling libraries effectively.

Book Structure and Flow

The book is logically structured and progresses from foundational to more applied topics. Here's a breakdown of its main sections:

1. Descriptive Statistics

This section lays the groundwork by focusing on measures that summarize data. Readers are introduced to core metrics like central tendency, variability, and data distribution characteristics. The solutions show how to compute and interpret these metrics using Python’s numerical libraries.

2. Probability and Distributions

This portion delves into the probabilistic foundations of data analysis. It covers probability distributions — both discrete and continuous — and explains concepts such as randomness, density functions, and the shape and behavior of data under various theoretical models.

3. Inferential Statistics

Here, the focus shifts from describing data to making judgments and predictions. Readers learn how to estimate population parameters, conduct hypothesis testing, and interpret significance levels. The book uses real-world logic to introduce tests such as t-tests and chi-square tests, helping readers understand when and why these tools are applied.

4. Correlation and Regression

This section is dedicated to exploring relationships between variables. By walking through correlation coefficients and linear regression modeling, it helps readers grasp the difference between correlation and causation and learn how to model simple predictive relationships.

5. Practical Data Analysis and Interpretation

Toward the end of the book, the exercises become more integrated and context-driven. This final section simulates the kind of challenges data analysts face in real projects — synthesizing techniques, interpreting results in business or research contexts, and visualizing insights.

 Teaching Approach

The strength of this book lies in its pedagogical approach:

Problem-Solution Format: Each exercise starts with a clear problem statement, followed by a step-by-step walkthrough of the solution. This scaffolding allows readers to understand both how and why a method works.

Progressive Complexity: Exercises are arranged to build on previous concepts. This makes the book suitable for sequential study, ensuring a solid foundation before moving to complex analysis.

Interpretation Over Memorization: While computation is central, the book repeatedly emphasizes understanding the meaning of results, not just the mechanics of calculation.

Library Familiarity: Readers gain experience using key Python libraries such as pandas, numpy, scipy, and visualization tools like matplotlib and seaborn. This also prepares them for working with real data in more complex environments.

Strengths of the Book

Practical Focus: Rather than overwhelming readers with abstract concepts, the book shows how statistics are used in actual data analysis workflows.

Compact and Accessible: The writing is concise and approachable. It's free of unnecessary jargon, making it friendly for self-learners and non-technical professionals.

Real Python Usage: Solutions are grounded in actual Python code, reinforcing programming skills while teaching statistics. It’s a dual-purpose resource that strengthens both areas.

Excellent for Reinforcement: The sheer volume of exercises makes this a powerful tool for practice. It's ideal for students preparing for exams or interviews where applied statistics are tested

Use Cases and Practical Value

This book is a great resource for:

Building confidence in applying statistical techniques

Practicing Python coding in a data analysis context

Preparing for technical interviews or data science bootcamps

Creating a structured self-study plan

Enhancing an academic course with additional problem-solving

It’s especially valuable for those who have taken an online course in statistics or Python and now want to solidify their skills through application.

Kindle : Statistics with Python. 100 solved exercises for Data Analysis (Your Data Teacher Books Book 1)

Hard Copy : Statistics with Python. 100 solved exercises for Data Analysis (Your Data Teacher Books Book 1)

Final Thoughts

"Statistics with Python. 100 Solved Exercises for Data Analysis" is a focused, hands-on guide that hits a sweet spot for learners who are tired of passive theory and want to do statistics. Its clear explanations and practical Python implementations make it an ideal companion for aspiring data analysts and self-taught programmers.

If your goal is to become statistically fluent while coding in Python, this book provides the daily practice and reinforcement you need. It won’t replace a full statistics curriculum, but it makes an excellent bridge between learning concepts and applying them to data problems.

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Python Coding challenge - Day 460| What is the output of the following Python Code?

 

Code Explanation:

1. Importing the NumPy Library

import numpy as np

Explanation: This line imports the NumPy library, which is used for numerical computing in Python. The alias np is commonly used to refer to NumPy, allowing us to call NumPy functions with this shorthand.

2. Defining the First Vector

vector1 = np.array([1, 2])

Explanation: Here, we define vector1 as a NumPy array with two elements: [1, 2].

Why?: Using np.array converts the list [1, 2] into a NumPy array, which is the appropriate format for performing vector operations like dot product.

3. Defining the Second Vector

vector2 = np.array([3, 4])

Explanation: This line defines vector2 as a NumPy array with two elements: [3, 4].

Why?: Similarly to vector1, vector2 is a NumPy array, enabling vector operations to be performed efficiently.

4. Calculating the Dot Product

dot_product = np.dot(vector1, vector2)

Explanation: This line calculates the dot product of the two vectors, vector1 and vector2, using the np.dot() function.

Why?: The np.dot() function computes the dot product of two arrays (vectors). The result is a scalar (single number), which is the sum of the products of corresponding elements from each vector.

5. Applying the Dot Product Formula to the Vectors

For vector1 = [1, 2] and vector2 = [3, 4]:

vector1⋅vector2=1×3+2×4

Explanation:

The first element of vector1 is 1, and the first element of vector2 is 3. The product is: 

1×3=3.

The second element of vector1 is 2, and the second element of vector2 is 4. The product is: 

2×4=8.

6. Summing the Products

dot product

dot product=3+8=11

Explanation: The results of the multiplications (3 and 8) are summed together to get the final dot product value: 

3+8=11.

7. Storing the Result in dot_product

Explanation: The result of the dot product calculation (which is 11) is stored in the variable dot_product.

Why?: We store the result so that it can be used later in the program or printed to the console.

8. Final Output

If you print the result:

print(dot_product)

Output:

11

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Python Coding challenge - Day 459| What is the output of the following Python Code?

 

Code Explanation:

Importing Required Modules

from functools import reduce

import timeit

reduce is imported from functools — it applies a function cumulatively to a list.

timeit is used to measure execution time.

Statement to Time

stmt = 'reduce(lambda x, y: x * y, [1, 2, 3, 4])'

This is a string of code that:

Uses reduce to multiply all elements of the list [1, 2, 3, 4]

That is:

(((1 * 2) * 3) * 4) = 24

Since this is a string, timeit will treat it as code to execute.

3. Calling timeit

print(timeit.timeit(stmt, number=1000))

This runs the stmt 1,000 times and prints the total time taken in seconds.

Final Output:

D: Error

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Python Quiz on NumPy Array Join

 

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Python Coding Challange - Question with Answer (01010525)

 

Line-by-line Explanation

a = 5

This initializes the variable a with the value 5.

---

while a < 20:

This starts a while loop that continues as long as a is less than 20.

---

print(a)

This prints the current value of a before it's updated.

---

a = a * 2 - 1

This is the key logic:

• Multiply a by 2.

• Subtract 1 from the result.

• Store the new value back in a.

So the formula is:

new a = (old a × 2) - 1

---

What Happens in Each Iteration?

Iteration	Value of a before print	Printed	New value of a (a * 2 - 1)
1	5	5	(5×2)-1 = 9
2	9	9	(9×2)-1 = 17
3	17	17	(17×2)-1 = 33 → loop ends

Why the loop ends?

Because after the 3rd iteration, a becomes 33, which is not less than 20, so the loop condition a < 20 fails.

---

✅ Final Output:

5
9
17

Application of Python in Audio and Video Processing
https://pythonclcoding.gumroad.com/l/vrhkx 

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Python Coding challenge - Day 458| What is the output of the following Python Code?

 

Code Explanation:

Importing Required Module

import timeit

This imports the timeit module, which is specifically used to measure the execution time of small bits of Python code with high accuracy.

Defining the Test Function

def test_function():

    return sum(range(100))

Defines a simple function that computes the sum of integers from 0 to 99.

This is the function whose execution time we want to benchmark.

Preparing the Statement to Time

stmt = "test_function()"

This is the string of code that timeit will execute.

It must be a string, not a direct function call.

Setting Up the Environment

setup = globals()

globals() provides the global namespace so timeit can access test_function.

Without this, timeit would not know what test_function is and would raise a NameError.

Measuring Execution Time

print(timeit.timeit(stmt, globals=setup, number=1000))

timeit.timeit(...) runs the stmt 1,000 times (number=1000) and returns the total time taken in seconds.

print(...) then outputs this time.

Output

The printed result will be a float (e.g., 0.0054321), representing how many seconds it took to run test_function() 1,000 times.

This value gives you a rough sense of the performance of the function.

Final Output:

A: 0.0025

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