Learn Python Coding

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. 💊

🐍📰 Python Mappings: A Comprehensive Guide https://realpython.com/python-mappings/ #python 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 ⭐️

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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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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Transcribe Youtube Videos using Python

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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

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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

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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 Hashtags:

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Today we're going to start a lesson on web scraping

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

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