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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Want to learn Python quickly and from scratch? Then here’s what you need — CodeEasy: Python Essentials

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Code With Python

Transcribe Youtube Videos using Python

https://t.me/DataScience4

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Code With Python

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 "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

🔸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/

https://t.me/DataScience4

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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 (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

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>
    <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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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/

https://t.me/DataScience4

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Code With Python

🐍📰 Python Mappings: A Comprehensive Guide

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

#python

https://t.me/DataScience4

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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

https://t.me/DataScience4

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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

https://t.me/DataScienceQ 🧠

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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/

https://t.me/DataScience4 🩷

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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
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages

✅

https://t.me/addlist/8_rRW2scgfRhOTc0

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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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@DataScience4

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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.'

https://t.me/DataScienceN

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Code With Python

Get a weather forecast without API and complex settings in Python

We use the wttr.in: https://github.com/chubin/wttr.in

service — a simple and powerful tool that shows the weather right in the console.

To work with the HTTP request, you only need one library - requests. Installing it is very easy:

pip install requests

Here is the minimal and clear code to get the forecast:

import requests

city = input("Enter the city name: ")
url = f"https://wttr.in/{city}"

try:
    response = requests.get(url)
    print(response.text)
except Exception:
    print("Oops! Something went wrong. Please try again later.")

Just enter the desired city and get a detailed forecast with temperature, precipitation

Try it yourself 😏

https://t.me/DataScience4

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Code With Python

Django REST Framework and Vue versus Django and HTMX

https://testdriven.io/blog/drf-vue-vs-django-htmx/

Learn how the development process varies between working with Django REST Framework and Vue versus #Django and #HTMX.

https://t.me/DataScience4

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Code With Python

Nested Loops in Python

Nested loops in Python allow you to place one loop inside another, enabling you to perform repeated actions over multiple sequences. Understanding nested loops helps you write more efficient code, manage complex data structures, and avoid common pitfalls such as poor readability and performance issues.

Learn: https://realpython.com/nested-loops-python/

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