Python Programming Books
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Best Resource to learn Python Programming & DSA (Data Structure and Algorithms) 📚📝 For collaborations: @coderfun
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网络:📈 Telegram 频道 Python Programming Books 的分析概览
频道 Python Programming Books (@dsabooks) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 59 050 名订阅者，在 技术与应用 类别中位列第 2 178，并在 印度 地区排名第 5 799 位。
📊 受众指标与增长动态
自 невідомо 创建以来，项目保持高速增长，吸引了 59 050 名订阅者。
根据 25 八月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 373，过去 24 小时变化为 4，整体触达仍然可观。
- 认证状态： 未认证
- 互动率 (ER)： 平均受众互动率为 5.80%。内容发布后 24 小时内通常能获得 1.37% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 3 427 次浏览，首日通常累积 809 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 7。
- 主题关注点： 内容集中在 panda, learning, programming, api, dataset 等核心主题上。
📝 描述与内容策略
作者将该频道定位为表达主观观点的平台：
“Best Resource to learn Python Programming & DSA (Data Structure and Algorithms) 📚📝
For collaborations: @coderfun”
凭借高频更新（最新数据采集于 26 八月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为 技术与应用 类别中的关键影响点。
59 050
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频道帖子
Data Science Interview Questions with Answers
Q. Explain the data preprocessing steps in data analysis.
Ans. Data preprocessing transforms the data into a format that is more easily and effectively processed in data mining, machine learning and other data science tasks.
1. Data profiling.
2. Data cleansing.
3. Data reduction.
4. Data transformation.
5. Data enrichment.
6. Data validation.
Q. What Are the Three Stages of Building a Model in Machine Learning?
Ans. The three stages of building a machine learning model are:
Model Building: Choosing a suitable algorithm for the model and train it according to the requirement
Model Testing: Checking the accuracy of the model through the test data
Applying the Model: Making the required changes after testing and use the final model for real-time projects
Q. What are the subsets of SQL?
Ans. The following are the four significant subsets of the SQL:
Data definition language (DDL): It defines the data structure that consists of commands like CREATE, ALTER, DROP, etc.
Data manipulation language (DML): It is used to manipulate existing data in the database. The commands in this category are SELECT, UPDATE, INSERT, etc.
Data control language (DCL): It controls access to the data stored in the database. The commands in this category include GRANT and REVOKE.
Transaction Control Language (TCL): It is used to deal with the transaction operations in the database. The commands in this category are COMMIT, ROLLBACK, SET TRANSACTION, SAVEPOINT, etc.
Q. What is a Parameter in Tableau? Give an Example.
Ans. A parameter is a dynamic value that a customer could select, and you can use it to replace constant values in calculations, filters, and reference lines.
For example, when creating a filter to show the top 10 products based on total profit instead of the fixed value, you can update the filter to show the top 10, 20, or 30 products using a parameter.
Free Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
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| 3 | ✅ Graphic Design A-Z! 🎨✨
A: Alignment - Arranging elements in a straight line or in proper order, creating visual connection and organization.
B: Branding - The process of creating a unique identity for a business or product, encompassing its visual style, voice, and values.
C: Color Theory - The study of how colors interact with each other and how they affect human perception and emotions.
D: Design Principles - Fundamental rules that guide the creation of visually appealing and effective designs, such as balance, contrast, emphasis, and unity.
E: Elements of Design - The basic building blocks of visual communication, including line, shape, color, texture, space, and form.
F: Font - A specific typeface in a particular size and style, used to convey text and create visual hierarchy.
G: Grid Systems - A framework of horizontal and vertical lines used to structure and organize content on a page or layout.
H: Hierarchy - The arrangement of elements in a design to visually prioritize information and guide the viewer's eye.
I: Illustration - Hand-drawn or digitally created images used to enhance visual communication and storytelling.
J: JPEG (Joint Photographic Experts Group) - A common image file format used for photographs and complex graphics, known for its lossy compression.
K: Kerning - The adjustment of space between individual letters to improve readability and visual appeal.
L: Layout - The arrangement of visual elements on a page or screen to create a cohesive and effective design.
M: Mockup - A static, high-fidelity representation of a design used to visualize its appearance and functionality.
N: Negative Space (White Space) - The empty areas around and between design elements, used to create visual balance and improve readability.
O: Opacity - The degree to which an element is transparent, allowing underlying elements to show through.
P: Photoshop - A popular image editing software used for photo retouching, compositing, and creating graphics.
Q: Quality - The overall excellence or superiority of a design, reflecting its effectiveness, aesthetics, and technical execution.
R: Resolution - The number of pixels in an image, determining its level of detail and clarity.
S: Typography - The art and technique of arranging type to make written language legible, readable, and visually appealing.
T: Texture - The visual appearance or feel of a surface, adding depth and realism to designs.
U: UI (User Interface) - The visual elements of a design that allow users to interact with a software application or website.
V: Vector Graphics - Images created using mathematical equations, allowing them to be scaled without loss of quality.
W: Wireframe - A basic, low-fidelity representation of a website or application layout, focusing on structure and functionality.
X: X-Height - The height of lowercase letters in a typeface, excluding ascenders and descenders.
Y: Year-over-Year (YoY) - Comparing design trends and styles from one year to the next to identify emerging patterns.
Z: Z-Pattern Layout - A design technique that guides the viewer's eye along a "Z" shape, commonly used in web design to highlight key information.
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| 4 | ✅ Python Scenario-Based Interview Question – List Comprehension 🐍💻
Scenario:
You are given a list of numbers:
numbers = [1, 2, 3, 4, 5, 6]
Question:
Write Python code to create a new list that contains:
1. Only the even numbers from the original list.
2. Each even number multiplied by 2.
Expected Output:
Answer:
even_doubled = [num * 2 for num in numbers if num % 2 == 0]
print(even_doubled)
Explanation:
⦁ The list comprehension iterates over each num in numbers.
⦁ The if num % 2 == 0 condition filters to only even numbers (remainder 0 when divided by 2).
⦁ For those, num * 2 doubles them, building the new list concisely—way cleaner than a for loop with append!
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| 7 | 🐍 Python Beginner Roadmap 🚀
📂 Start Here
• 📂 What is Python Why Use It?
• 📂 Install Python Setup IDE (VS Code / PyCharm)
📂 Python Basics
• 📂 Variables Data Types (int, float, string, boolean)
• 📂 Input Output Functions
• 📂 Operators (Arithmetic, Comparison, Logical)
📂 Control Flow
• 📂 Conditional Statements (if, elif, else)
• 📂 Loops (for loop, while loop)
• 📂 Break, Continue, Pass
📂 Data Structures
• 📂 Lists List Operations
• 📂 Tuples Sets
• 📂 Dictionaries (Key–Value Data)
📂 Functions Modules
• 📂 Creating Functions Parameters
• 📂 Lambda Functions
• 📂 Importing Modules Using Libraries
📂 File Handling
• 📂 Reading Files (TXT, CSV)
• 📂 Writing Appending Data
• 📂 Working with JSON
📂 Object-Oriented Programming
• 📂 Classes Objects
• 📂 Inheritance Polymorphism
• 📂 Encapsulation
📂 Python for Data
• 📂 NumPy Basics
• 📂 Pandas for Data Analysis
• 📂 Data Cleaning Transformation
📂 Data Visualization
• 📂 Matplotlib Basics
• 📂 Seaborn Charts
• 📂 Visualizing Data Insights
📂 Practice Projects
• 📌 To-Do List App
• 📌 Web Scraper with Python
• 📌 Data Analysis Project with Pandas
📂 Move to Next Level
• 📂 Automation with Python
• 📂 APIs Web Development (Flask / Django)
• 📂 Machine Learning with Scikit-Learn
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| 8 |  Python Projects For Hacking | 4 538 |
| 9 |  🔰 Print or Logging? Know the difference.
print() is quick and simple — perfect for short-term debugging.
But when your project grows, logging is what keeps things under control.
It adds structure, severity levels, and persistent records.
Use print() for now. Use logging for when it matters. | 5 490 |
| 10 | 🚀 NumPy for Beginners – Part 1 🐍📊
Learn the Fundamentals of Numerical Computing with Python
Now that you've completed Python Basics, it's time to learn NumPy—the foundation of data analysis, machine learning, and scientific computing.
In this part, you'll learn:
✅ What is NumPy?
✅ Why Use NumPy?
✅ Creating Arrays
✅ Array Properties
✅ Indexing & Slicing
✅ Basic Operations
🧠 1. What is NumPy?
NumPy Numerical Python is a powerful Python library used for working with numbers and arrays.
It is:
✔ Fast
✔ Memory Efficient
✔ Easy to Use
✔ Widely Used in Data Science and AI
❓ 2. Why Use NumPy?
Python lists work well, but NumPy arrays are much faster for mathematical operations.
NumPy is used for:
📊 Data Analysis
🤖 Machine Learning
📈 Data Visualization
🔬 Scientific Computing
📦 3. Install NumPy
Install NumPy using pip:
pip install numpy
Import the library:
import numpy as np
🔢 4. Creating a NumPy Array
Example:
import numpy as np
numbers = np.array([10, 20, 30, 40])
print(numbers)
📌 Output:
[10 20 30 40]
📏 5. Check Array Properties
Example:
import numpy as np
numbers = np.array([10, 20, 30, 40])
print(numbers.ndim)
print(numbers.size)
print(numbers.shape)
📌 Output:
1
4
(4,)
Meaning:
✔ ndim → Number of dimensions
✔ size → Total number of elements
✔ shape → Structure of the array
🎯 6. Access Array Elements
Example:
import numpy as np
numbers = np.array([10, 20, 30, 40])
print(numbers[0])
print(numbers[2])
📌 Output:
10
30
✂ 7. Array Slicing
Extract part of an array.
Example:
import numpy as np
numbers = np.array([10, 20, 30, 40, 50])
print(numbers[1:4])
📌 Output:
[20 30 40]
➕ 8. Basic Array Operations
Example:
import numpy as np
numbers = np.array([10, 20, 30])
print(numbers + 5)
print(numbers * 2)
📌 Output:
[15 25 35]
[20 40 60]
NumPy performs operations on every element at once.
🛠 Practice Exercises
✅ Create an array of 10 numbers
✅ Print the first and last element
✅ Slice the middle three elements
✅ Multiply every element by 3
✅ Add 100 to every element
🔥 Common Beginner Mistakes
❌ Forgetting to import NumPy
❌ Mixing Python lists and NumPy arrays
❌ Using incorrect indexes
❌ Confusing shape with size
💡 Pro Tip
Master these concepts before moving to advanced topics like:
✔ 2D Arrays
✔ Array Reshaping
✔ Mathematical Functions
✔ Filtering & Boolean Indexing
A strong understanding of NumPy makes learning Pandas and Machine Learning much easier.
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| 11 | Top Github Repos You Should Know About 😱
1/ What the f*ck JavaScript?
https://github.com/denysdovhan/wtfjs
2/ Frontend Developer Interview-Questions
https://github.com/h5bp/Front-end-Developer-Interview-Questions
3/ React Interview Questions & Answers
https://github.com/sudheerj/reactjs-interview-questions
4/ Awesome Algorithms
https://github.com/tayllan/awesome-algorithms
5/ Every Programmer Should Know
https://github.com/mtdvio/every-programmer-should-know
6/ DSA Bootcamp Java
https://github.com/kunal-kushwaha/DSA-Bootcamp-Java
7/ Awesome Cheatsheets:
https://github.com/LeCoupa/awesome-cheatsheets
8/ Developer Roadmap
https://github.com/kamranahmedse/developer-roadmap
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| 12 | 5 Must-Know Python Concepts for AI Engineers
1. 🔥 Tensors & Autograd
Stop writing backprop by hand. requires_grad=True tracks every operation → .backward() applies the chain rule automatically.
import torch
x = torch.tensor(2.0)
y = torch.tensor(5.0)
w = torch.tensor(0.5, requires_grad=True)
b = torch.tensor(0.1, requires_grad=True)
pred = w * x + b
loss = (pred - y) ** 2
loss.backward()
print(w.grad.item(), b.grad.item())
✅ Exact gradients, zero math errors.
2. ⚙️ The __call__ Method
Why model(x) works, not model.forward(x). call runs hooks before forward.
class LinearLayer:
def __init__(self, w, b):
self.w, self.b = w, b
self._hooks = []
def __call__(self, x):
for hook in self._hooks:
hook(x)
return self.forward(x)
def forward(self, x):
return x * self.w + self.b
⚠️ Always call model(x) — .forward() skips hooks → silent bugs.
3. 💾 Pickle vs ONNX
pickle = Python-locked + code execution risk 🚨. ONNX = static, language-agnostic graph.
import torch
model.eval()
dummy_input = torch.randn(1, 10)
torch.onnx.export(
model, dummy_input, "model.onnx",
export_params=True,
opset_version=15,
input_names=["input"],
output_names=["output"],
dynamic_axes={"input": {0: "batch_size"}}
)
✅ Portable, fast, decoupled from training code.
4. 🧱 Abstract Base Classes
@abstractmethod forces subclasses to implement methods. Miss one → fails at startup, not mid-request.
from abc import ABC, abstractmethod
class ModelInterface(ABC):
@abstractmethod
def predict(self, x: list) -> list: ...
@abstractmethod
def get_metadata(self) -> dict: ...
✅ Fail fast, fail safe.
5. 🔐 Env Variables & Secrets
Never hardcode keys. Store in .env, gitignore it, load with python-dotenv.
import os
from dotenv import load_dotenv
load_dotenv()
api_key = os.getenv("OPENAI_API_KEY")
if not api_key:
raise ValueError("OPENAI_API_KEY is not set!")
✅ Same code locally + Docker/Lambda. Zero leaks.
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| 13 |  🔰 Download Instagram profile picture using Python | 5 293 |
| 14 | ✅ Machine Learning Roadmap: Step-by-Step Guide to Master ML 🤖📊
Whether you’re aiming to be a data scientist, ML engineer, or AI specialist — this roadmap has you covered 👇
📍 1. Math Foundations
⦁ Linear Algebra (vectors, matrices)
⦁ Probability & Statistics basics
⦁ Calculus essentials (derivatives, gradients)
📍 2. Programming & Tools
⦁ Python basics & libraries (NumPy, Pandas)
⦁ Jupyter notebooks for experimentation
📍 3. Data Preprocessing
⦁ Data cleaning & transformation
⦁ Handling missing data & outliers
⦁ Feature engineering & scaling
📍 4. Supervised Learning
⦁ Regression (Linear, Logistic)
⦁ Classification algorithms (KNN, SVM, Decision Trees)
⦁ Model evaluation (accuracy, precision, recall)
📍 5. Unsupervised Learning
⦁ Clustering (K-Means, Hierarchical)
⦁ Dimensionality reduction (PCA, t-SNE)
📍 6. Neural Networks & Deep Learning
⦁ Basics of neural networks
⦁ Frameworks: TensorFlow, PyTorch
⦁ CNNs for images, RNNs for sequences
📍 7. Model Optimization
⦁ Hyperparameter tuning
⦁ Cross-validation & regularization
⦁ Avoiding overfitting & underfitting
📍 8. Natural Language Processing (NLP)
⦁ Text preprocessing
⦁ Common models: Bag-of-Words, Word Embeddings
⦁ Transformers & GPT models basics
📍 9. Deployment & Production
⦁ Model serialization (Pickle, ONNX)
⦁ API creation with Flask or FastAPI
⦁ Monitoring & updating models in production
📍 10. Ethics & Bias
⦁ Understand data bias & fairness
⦁ Responsible AI practices
📍 11. Real Projects & Practice
⦁ Kaggle competitions
⦁ Build projects: Image classifiers, Chatbots, Recommendation systems
📍 12. Apply for ML Roles
⦁ Prepare resume with projects & results
⦁ Practice technical interviews & coding challenges
⦁ Learn business use cases of ML
💡 Pro Tip: Combine ML skills with SQL and cloud platforms like AWS or GCP for career advantage.
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| 17 | ![Top 10 Python One Liners! 1️⃣ Reverse a string: reversed_string = "Hello World"[::-1] 2️⃣ Check if a number is even: is_even](data:image/jpeg;base64,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){kind=small} Top 10 Python One Liners!
1️⃣ Reverse a string:
reversed_string = "Hello World"[::-1]
2️⃣ Check if a number is even:
is_even = lambda x: x % 2 == 0
3️⃣ Find the factorial of a number:
factorial = lambda x: 1 if x == 0 else x * factorial(x - 1)
4️⃣ Read a file and print its contents:
[print(line.strip()) for line in open('file.txt')]
5️⃣ Create a list of squares:
squares = [x**2 for x in range(10)]
6️⃣ Flatten a list of lists:
flat_list = [item for sublist in [[1, 2], [3, 4], [5, 6]] for item in sublist]
7️⃣ Find the length of a list:
length = len([1, 2, 3, 4])
8️⃣ Create a dictionary from two lists:
keys = ['a', 'b', 'c']; values = [1, 2, 3]; dictionary = dict(zip(keys, values))
9️⃣ Generate a list of random numbers:
import random; random_numbers = [random.randint(0, 100) for _ in range(10)]
🔟 Check if a string is a palindrome:
is_palindrome = lambda s: s == s[::-1]
Mastering these one-liners can significantly improve your coding efficiency and make your code more concise.
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| 19 | Important Topics You Should Know to Learn Python 👇
Lists, Strings, Tuples, Dictionaries, Sets – Learn the core data structures in Python.
Boolean, Arithmetic, and Comparison Operators – Understand how Python evaluates conditions.
Operations on Data Structures – Append, delete, insert, reverse, sort, and manipulate collections efficiently.
Reading and Extracting Data – Learn how to access, modify, and extract values from lists and dictionaries.
Conditions and Loops – Master if, elif, else, for, while, break, and continue statements.
Range and Enumerate – Efficiently loop through sequences with indexing.
Functions – Create functions with and without parameters, and understand *args and **kwargs.
Classes & Object-Oriented Programming – Work with init methods, global/local variables, and concepts like inheritance and encapsulation.
File Handling – Read, write, and manipulate files in Python.
Free Resources to learn Python👇👇
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👉 Python Essentials
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👉 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝗳𝗶𝗰 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗣𝘆𝘁𝗵𝗼𝗻
https://freecodecamp.org/learn/scientific-computing-with-python/
👉 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝘄𝗶𝘁𝗵 𝗣𝘆𝘁𝗵𝗼𝗻
https://freecodecamp.org/learn/data-analysis-with-python/
👉 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗣𝘆𝘁𝗵𝗼𝗻
https://freecodecamp.org/learn/machine-learning-with-python/
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