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Code With Python

Part 2: Advanced Web Scraping Techniques – Mastering Dynamic Content, Authentication, and Large-Scale Data Extraction

Duration: ~60 minutes 😮

✅ Link: https://hackmd.io/@husseinsheikho/WS-2

#WebScraping #AdvancedScraping #Selenium #Scrapy #DataEngineering #Python #APIs #WebAutomation #DataCleaning #AntiScraping

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Code With Python

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

#EnterpriseScraping #DataEngineering #ScrapyCluster #MachineLearning #RealTimeData #Compliance #WebScraping #BigData #CloudScraping #DataMonetization

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Code With Python

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

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Code With Python

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

#SocialMediaScraping #MobileScraping #DarkWeb #FinancialData #MediaExtraction #AuthScraping #ScrapingSaaS #APIReverseEngineering #EthicalScraping #DataScience

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Code With Python

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

#AdvancedScraping #JavaScriptRendering #BrowserFingerprinting #DataPipelines #LegalCompliance #ScrapingOptimization #EnterpriseScraping #WebScraping #DataEngineering #TechInnovation

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Code With Python

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Code With Python

Transcribe Youtube Videos using Python

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Code With Python

Slugify module

A slug is a simplified version of a noscript or name where special characters are replaced with hyphens (-), and all letters are converted to lowercase. For example, the noscript "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

noscript = "Example post about creating slugs"
slug = slugify(noscript)
print(slug)  # output: example-post-about-creating-slugs

🔸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:

pip install python-slugify

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Code With Python

🐍 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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Code With Python

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 (noscript, 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

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:

🆘 ~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:

from html_to_markdown import convert_to_markdown

html = """
<!DOCTYPE html>
<html>
<head>
    <noscript>Sample Document</noscript>
    <meta name="denoscription" 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

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Code With Python

🐍📰 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/

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Code With Python

🐍📰 Python Mappings: A Comprehensive Guide

https://realpython.com/python-mappings/

#python

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Code With Python

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 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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Code With Python

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Code With Python

🐍

📰 Python String Formatting: Available Tools and Their Features

https://realpython.com/python-string-formatting/

#python

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Code With Python

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

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Code With Python

🐍📰 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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Stelvio v0.3.0 is here!

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.

Try it right now 💊

Documentation: https://docs.stelvio.dev

GitHub: https://github.com/michal-stlv/stelvio/

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Code With Python

Forwarded from Machine Learning with Python

This channels is for Programmers, Coders, Software Engineers.

0️⃣ Python
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2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages

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Code With Python

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:

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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Code With Python

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

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.'

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