Part 3: Enterprise Web Scraping – Building Scalable, Compliant, and Future-Proof Data Extraction Systems
Duration: ~60 minutes
Link A: https://hackmd.io/@husseinsheikho/WS-3A
Link B (Rest): https://hackmd.io/@husseinsheikho/WS-3B
Duration: ~60 minutes
Link A: https://hackmd.io/@husseinsheikho/WS-3A
Link B (Rest): https://hackmd.io/@husseinsheikho/WS-3B
#EnterpriseScraping #DataEngineering #ScrapyCluster #MachineLearning #RealTimeData #Compliance #WebScraping #BigData #CloudScraping #DataMonetization
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Part 4: Cutting-Edge Web Scraping – AI, Blockchain, Quantum Resistance, and the Future of Data Extraction
Duration: ~60 minutes
Link A: https://hackmd.io/@husseinsheikho/WS-4A
Link B: https://hackmd.io/@husseinsheikho/WS-4B
#AIWebScraping #BlockchainData #QuantumScraping #EthicalAI #FutureProof #SelfHealingScrapers #DataSovereignty #LLM #Web3 #Innovation
Duration: ~60 minutes
Link A: https://hackmd.io/@husseinsheikho/WS-4A
Link B: https://hackmd.io/@husseinsheikho/WS-4B
#AIWebScraping #BlockchainData #QuantumScraping #EthicalAI #FutureProof #SelfHealingScrapers #DataSovereignty #LLM #Web3 #Innovation
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Part 5: Specialized Web Scraping – Social Media, Mobile Apps, Dark Web, and Advanced Data Extraction
Duration: ~60 minutes
Link A: https://hackmd.io/@husseinsheikho/WS-5A
Link B: https://hackmd.io/@husseinsheikho/WS-5B
Duration: ~60 minutes
Link A: https://hackmd.io/@husseinsheikho/WS-5A
Link B: https://hackmd.io/@husseinsheikho/WS-5B
#SocialMediaScraping #MobileScraping #DarkWeb #FinancialData #MediaExtraction #AuthScraping #ScrapingSaaS #APIReverseEngineering #EthicalScraping #DataScience
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Part 6: Advanced Web Scraping Techniques – JavaScript Rendering, Fingerprinting, and Large-Scale Data Processing
Duration: ~60 minutes
Link A: https://hackmd.io/@husseinsheikho/WS-6A
Link B: https://hackmd.io/@husseinsheikho/WS-6B
Duration: ~60 minutes
Link A: https://hackmd.io/@husseinsheikho/WS-6A
Link B: https://hackmd.io/@husseinsheikho/WS-6B
#AdvancedScraping #JavaScriptRendering #BrowserFingerprinting #DataPipelines #LegalCompliance #ScrapingOptimization #EnterpriseScraping #WebScraping #DataEngineering #TechInnovation
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Slugify module
A slug is a simplified version of a title or name where special characters are replaced with hyphens (-), and all letters are converted to lowercase. For example, the title
A slug is a friendly and readable string format commonly used in URLs to identify a resource.
🔸 The string is converted to lowercase.
🔸 Special characters and spaces are removed and replaced with hyphens.
🔸 The result is short and easy to read.
Library installation:
👉 @DataScience4
A slug is a simplified version of a title or name where special characters are replaced with hyphens (-), and all letters are converted to lowercase. For example, the title
"How to create a slug in Python!" becomes "how-to-create-a-slug-in-python"A slug is a friendly and readable string format commonly used in URLs to identify a resource.
from slugify import slugify
title = "Example post about creating slugs"
slug = slugify(title)
print(slug) # output: example-post-about-creating-slugs
Library installation:
pip install python-slugify
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🐍 Python GUI Programming 📈
Does your Python program need a Graphical User Interface (GUI)? With this learning path you'll develop your Python GUI programming skills from scratch
#python #learnpython
Link: https://realpython.com/learning-paths/python-gui-programming/
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Does your Python program need a Graphical User Interface (GUI)? With this learning path you'll develop your Python GUI programming skills from scratch
#python #learnpython
Link: https://realpython.com/learning-paths/python-gui-programming/
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html-to-markdown
A modern, fully typed Python library for converting HTML to Markdown. This library is a completely rewritten fork of markdownify with a modernized codebase, strict type safety and support for Python 3.9+.
Features:
⭐️ Full HTML5 Support: Comprehensive support for all modern HTML5 elements including semantic, form, table, ruby, interactive, structural, SVG, and math elements
⭐️ Enhanced Table Support: Advanced handling of merged cells with rowspan/colspan support for better table representation
⭐️ Type Safety: Strict MyPy adherence with comprehensive type hints
Metadata Extraction: Automatic extraction of document metadata (title, meta tags) as comment headers
⭐️ Streaming Support: Memory-efficient processing for large documents with progress callbacks
⭐️ Highlight Support: Multiple styles for highlighted text (<mark> elements)
⭐️ Task List Support: Converts HTML checkboxes to GitHub-compatible task list syntax
nstallation
Optional lxml Parser
For improved performance, you can install with the optional lxml parser:
The lxml parser offers:
🆘 ~30% faster HTML parsing compared to the default html.parser
🆘 Better handling of malformed HTML
🆘 More robust parsing for complex documents
Quick Start
Convert HTML to Markdown with a single function call:
Working with BeautifulSoup:
If you need more control over HTML parsing, you can pass a pre-configured BeautifulSoup instance:
Github: https://github.com/Goldziher/html-to-markdown
https://t.me/DataScience4⭐️
A modern, fully typed Python library for converting HTML to Markdown. This library is a completely rewritten fork of markdownify with a modernized codebase, strict type safety and support for Python 3.9+.
Features:
Metadata Extraction: Automatic extraction of document metadata (title, meta tags) as comment headers
nstallation
pip install html-to-markdown
Optional lxml Parser
For improved performance, you can install with the optional lxml parser:
pip install html-to-markdown[lxml]
The lxml parser offers:
Quick Start
Convert HTML to Markdown with a single function call:
from html_to_markdown import convert_to_markdown
html = """
<!DOCTYPE html>
<html>
<head>
<title>Sample Document</title>
<meta name="description" content="A sample HTML document">
</head>
<body>
<article>
<h1>Welcome</h1>
<p>This is a <strong>sample</strong> with a <a href="https://example.com">link</a>.</p>
<p>Here's some <mark>highlighted text</mark> and a task list:</p>
<ul>
<li><input type="checkbox" checked> Completed task</li>
<li><input type="checkbox"> Pending task</li>
</ul>
</article>
</body>
</html>
"""
markdown = convert_to_markdown(html)
print(markdown)
Working with BeautifulSoup:
If you need more control over HTML parsing, you can pass a pre-configured BeautifulSoup instance:
from bs4 import BeautifulSoup
from html_to_markdown import convert_to_markdown
# Configure BeautifulSoup with your preferred parser
soup = BeautifulSoup(html, "lxml") # Note: lxml requires additional installation
markdown = convert_to_markdown(soup)
Github: https://github.com/Goldziher/html-to-markdown
https://t.me/DataScience4
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🐍📰 Python args and kwargs: Demystified
In this step-by-step tutorial, you'll learn how to use args and kwargs in Python to add more flexibility to your functions
#python
Link: https://realpython.com/python-kwargs-and-args/
https://t.me/DataScience4⭐️
In this step-by-step tutorial, you'll learn how to use args and kwargs in Python to add more flexibility to your functions
#python
Link: https://realpython.com/python-kwargs-and-args/
https://t.me/DataScience4
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🐍📰 Python Mappings: A Comprehensive Guide
https://realpython.com/python-mappings/
#python
https://t.me/DataScience4❤️
https://realpython.com/python-mappings/
#python
https://t.me/DataScience4
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Regular Expressions in Python
Regular expressions (regex) in #Python are used for searching, matching, and manipulating strings based on patterns. In Python, regular expressions are implemented in the
Main functions of the re module:
🔸
🔸
🔸
🔸
🔸
🔸
Usage examples:
Explanation of the example:
>
>
>
>
>
>
Additional pattern examples:
Regular expressions are a powerful tool for working with text and can be useful in a wide range of tasks, from simple input validation to complex text parsing.💊
Regular expressions (regex) in #Python are used for searching, matching, and manipulating strings based on patterns. In Python, regular expressions are implemented in the
re module.Main functions of the re module:
re.match(): Checks if the beginning of a string matches a given pattern.re.search(): Searches for a pattern in a string and returns the first matching object found.re.findall(): Finds all occurrences of a pattern in a string and returns them as a list.re.finditer(): Finds all occurrences of a pattern and returns them as an iterator.re.sub(): Replaces all occurrences of a pattern with a given string.re.split(): Splits a string by a given pattern.Usage examples:
import re
# Example string
text = "The rain in Spain falls mainly in the plain."
# 1. re.match()
match = re.match(r'The', text)
if match:
print("Match found:", match.group())
else:
print("No match found")
# 2. re.search()
search = re.search(r'rain', text)
if search:
print("Search found:", search.group())
else:
print("No search found")
# 3. re.findall()
findall = re.findall(r'in', text)
print("Findall results:", findall)
# 4. re.finditer()
finditer = re.finditer(r'in', text)
for match in finditer:
print("Finditer match:", match.group(), "at position", match.start())
# 5. re.sub()
substitute = re.sub(r'rain', 'snow', text)
print("Substitute result:", substitute)
# 6. re.split()
split = re.split(r'\s', text)
print("Split result:", split)
Explanation of the example:
>
re.match(r'The', text): Checks if the string text starts with "The".>
re.search(r'rain', text): Searches for the first occurrence of "rain" in the string text.>
re.findall(r'in', text): Finds all occurrences of "in" in the string text.>
re.finditer(r'in', text): Returns an iterator that iterates over all occurrences of "in" in the string text.>
re.sub(r'rain', 'snow', text): Replaces all occurrences of "rain" with "snow" in the string text.>
re.split(r'\s', text): Splits the string text by spaces (whitespace characters).Additional pattern examples:
\d: Any digit.\D: Any character except a digit.\w: Any letter, digit, or underscore.\W: Any character except a letter, digit, or underscore.\s: Any whitespace character.\S: Any non-whitespace character..: Any character except a newline.^: Start of the string.$: End of the string.*: 0 or more repetitions.+: 1 or more repetitions.?: 0 or 1 repetition.{n}: Exactly n repetitions.{n,}: n or more repetitions.{n,m}: Between n and m repetitions.
Regular expressions are a powerful tool for working with text and can be useful in a wide range of tasks, from simple input validation to complex text parsing.
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https://realpython.com/python-string-formatting/
#python
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Master Python Interviews with These 150 Essential Questions.pdf
360.5 KB
Master Python Interviews with These 150 Essential Questions
Preparing for a Python-based role in data science, analytics, software development, or AI?
You need more than just coding skills — you need clarity on concepts, frameworks, and best practices.
This document contains 150 most commonly asked Python interview questions with clear, concise answers covering:
-Core Python – data types, control flow, OOP, memory management, iterators, decorators, and more
-Data Science Libraries – NumPy, Pandas, Matplotlib, Seaborn
-Frameworks – Flask, Django, Pyramid
-Data Handling – CSV reading, DataFrames, joins, merges, file handling
-Advanced Topics – GIL, multithreading, pickling, deep vs. shallow copy, generators
-Coding Challenges – from Fibonacci to palindrome checkers, sorting algorithms, and data structure problems
https://t.me/DataScienceQ 🧠
Preparing for a Python-based role in data science, analytics, software development, or AI?
You need more than just coding skills — you need clarity on concepts, frameworks, and best practices.
This document contains 150 most commonly asked Python interview questions with clear, concise answers covering:
-Core Python – data types, control flow, OOP, memory management, iterators, decorators, and more
-Data Science Libraries – NumPy, Pandas, Matplotlib, Seaborn
-Frameworks – Flask, Django, Pyramid
-Data Handling – CSV reading, DataFrames, joins, merges, file handling
-Advanced Topics – GIL, multithreading, pickling, deep vs. shallow copy, generators
-Coding Challenges – from Fibonacci to palindrome checkers, sorting algorithms, and data structure problems
https://t.me/DataScienceQ 🧠
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🐍📰 Skip Ahead in Loops With Python's Continue Keyword
Learn how #Python's continue statement works, when to use it, common mistakes to avoid, and what happens under the hood in CPython byte code
https://realpython.com/python-continue/
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Learn how #Python's continue statement works, when to use it, common mistakes to avoid, and what happens under the hood in CPython byte code
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Stelvio v0.3.0 is here!
The easiest way to deploy a Python application on AWS.
Only Python.
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✓ CLI with no prior setup
✓ Environment support
Watch how I deploy an API from an empty folder — in less than 60 seconds.
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Documentation: https://docs.stelvio.dev
GitHub: https://github.com/michal-stlv/stelvio/
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The easiest way to deploy a Python application on AWS.
Only Python.
No YAML. No JSON. No clicking around in the AWS Console.
✓ CLI with no prior setup
✓ Environment support
Watch how I deploy an API from an empty folder — in less than 60 seconds.
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Documentation: https://docs.stelvio.dev
GitHub: https://github.com/michal-stlv/stelvio/
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Clean code advice for Python:
Do not add redundant context.
Avoid adding unnecessary data to variable names, especially when working with classes.
Example:
This is bad:
This is good:
👉 @DataScience4
Do not add redundant context.
Avoid adding unnecessary data to variable names, especially when working with classes.
Example:
This is bad:
class Person:
def __init__(self, person_first_name, person_last_name, person_age):
self.person_first_name = person_first_name
self.person_last_name = person_last_name
self.person_age = person_age
This is good:
class Person:
def __init__(self, first_name, last_name, age):
self.first_name = first_name
self.last_name = last_name
self.age = age
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python-docx: Create and Modify Word Documents #python
python-docx is a Python library for reading, creating, and updating Microsoft Word 2007+ (.docx) files.
Installation
Example
https://t.me/DataScienceN🚗
python-docx is a Python library for reading, creating, and updating Microsoft Word 2007+ (.docx) files.
Installation
pip install python-docx
Example
from docx import Document
document = Document()
document.add_paragraph("It was a dark and stormy night.")
<docx.text.paragraph.Paragraph object at 0x10f19e760>
document.save("dark-and-stormy.docx")
document = Document("dark-and-stormy.docx")
document.paragraphs[0].text
'It was a dark and stormy night.'
https://t.me/DataScienceN
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