Learn Python Coding

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 🚗

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 ✅ https://t.me/Codeprogrammer

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/ 👉 https://t.me/DataScience4 🌟

🐍📰 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 🩷

🐍📰 Python String Formatting: Available Tools and Their Features https://realpython.com/python-string-formatting/ #python

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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 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 ⭐️

🐍 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/

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

Transcribe Youtube Videos using Python

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