👉 @DataScience4

Python Cheat sheet

👉 @DataScience4

Another powerful open-source text-to-speech tool for Python has been found on GitHub — Abogen

🌟 link: https://github.com/denizsafak/abogen

It allows you to quickly convert ePub, PDF, or plain text files into high-quality audio with auto-generated synchronized subtitles.

Main features:

🔸Support for input files in ePub, PDF, and TXT formats
🔸Generation of natural, smooth speech based on the Kokoro-82M model
🔸Automatic creation of subtitles with time stamps
🔸Built-in voice mixer for customizing sound
🔸Support for multiple languages, including Chinese, English, Japanese, and more
🔸Processing multiple files through batch queue

👉 @DataScience4

📘 Ultimate Guide to Web Scraping with Python: Part 1 — Foundations, Tools, and Basic Techniques

Duration: ~60 minutes reading time | Comprehensive introduction to web scraping with Python

Start learn: https://hackmd.io/@husseinsheikho/WS1

https://hackmd.io/@husseinsheikho/WS1#WebScraping #Python #DataScience #WebCrawling #DataExtraction #WebMining #PythonProgramming #DataEngineering #60MinuteRead

✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk

📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

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

✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk

📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

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

✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk

📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

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

🔹Explains complex things in simple words
🔹Based on a real story with tasks throughout the plot
🔹Free start

Ready to begin? Click https://codeeasy.io/course/python-essentials 🌟

👉 @DataScience4

Transcribe Youtube Videos using Python

https://t.me/DataScience4 🔰

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

👉 @DataScience4

🐍 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 🏐

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 ⭐️

🐍📰 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 ⭐️

🐍📰 Python Mappings: A Comprehensive Guide

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

#python

https://t.me/DataScience4 ❤️

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. 💊