"🚀 OOP 101: Understanding Object-Oriented Programming in Python 📚
===================================================================="
• Definition: Object-oriented programming (OOP) is a programming paradigm that revolves around the concept of objects and classes.
• Key concepts: Classes, inheritance, polymorphism, encapsulation, and abstraction. These are essential components of OOP and will help you write maintainable code in Python.
• Benefits: Improved code readability, reusability, and scalability. By understanding OOP, you'll be able to create more efficient and effective software solutions.
• Practical application: Learn how to define custom types using classes, instantiate objects, and use inheritance to achieve common programming tasks.
Summary:
Learn the basics of object-oriented programming in Python and elevate your skills to write maintainable code. Understand key concepts like classes, inheritance, polymorphism, encapsulation, and abstraction for a deeper understanding of OOP.
===================================================================="
• Definition: Object-oriented programming (OOP) is a programming paradigm that revolves around the concept of objects and classes.
• Key concepts: Classes, inheritance, polymorphism, encapsulation, and abstraction. These are essential components of OOP and will help you write maintainable code in Python.
• Benefits: Improved code readability, reusability, and scalability. By understanding OOP, you'll be able to create more efficient and effective software solutions.
• Practical application: Learn how to define custom types using classes, instantiate objects, and use inheritance to achieve common programming tasks.
Summary:
Learn the basics of object-oriented programming in Python and elevate your skills to write maintainable code. Understand key concepts like classes, inheritance, polymorphism, encapsulation, and abstraction for a deeper understanding of OOP.
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🤔 Did you know? You can use Python's <code>deque</code> for efficient appends and pops at both ends of a sequence-like data type. This is especially useful when implementing queue and stack data structures that operate efficiently under heavy workloads. 💪
# 📊 Speed Up Your Python App with DiskCache
A cloud SSD is sitting idle, waiting for its purpose. Today, we're putting it to work with DiskCache, a simple cache built on SQLite that can speed up your Python app without spinning up Redis or extra services.
### What is DiskCache?
DiskCache is a lightweight, practical cache that stores frequently accessed data in memory. It's built on top of SQLite and doesn't require any additional setup or dependencies.
### Benefits
🚀 Faster application response times: By storing frequently accessed data in memory, your Python app can respond faster to user input.
⏱️ Improved scalability: DiskCache can handle increased load without performance drops due to disk I/O.
💻 Reduced database queries: Your Python app won't need to query the database as often, freeing up resources and reducing latency.
### Example Use Case
### Get Started
Start using DiskCache in your Python applications to boost performance and reduce latency. Check out the official documentation for more information: <https://grantjenks.com/docs/diskcache/?featuredon=talkpython>
A cloud SSD is sitting idle, waiting for its purpose. Today, we're putting it to work with DiskCache, a simple cache built on SQLite that can speed up your Python app without spinning up Redis or extra services.
### What is DiskCache?
DiskCache is a lightweight, practical cache that stores frequently accessed data in memory. It's built on top of SQLite and doesn't require any additional setup or dependencies.
### Benefits
🚀 Faster application response times: By storing frequently accessed data in memory, your Python app can respond faster to user input.
⏱️ Improved scalability: DiskCache can handle increased load without performance drops due to disk I/O.
💻 Reduced database queries: Your Python app won't need to query the database as often, freeing up resources and reducing latency.
### Example Use Case
import sqlite3
from diskcache importDiskCache
# Create a connection to our SQLite database
conn = sqlite3.connect('example.db')
# Create a cache instance
cache = DiskCache(dbname='example', debug=False)
# Initialize the cache with some data
data = {'key': 'value'}
# Access the cached data without querying the database
print(cache.get(data['key']))
### Get Started
Start using DiskCache in your Python applications to boost performance and reduce latency. Check out the official documentation for more information: <https://grantjenks.com/docs/diskcache/?featuredon=talkpython>
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What will happen if in the
try block you call return, and in the finally block you also call return?Answer:
This happens because finally always executes after try and catch, but before the actual return of the value. If there is a return in finally, it definitively determines the result of the method.
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If you're testing forms, mockups, or just want to play with data, there's Mimesis — a generator of fake data. Names, emails, addresses, and phone numbers. There's a location setting that allows you to select a country, and the data will be generated accordingly.
from typing import Dict
from mimesis.enums import Gender
from mimesis import Person
def generate_fake_user(locale: str = "es", gender: Gender = Gender.MALE) -> Dict[str, str]:
"""
Generates fake user data based on the locale and gender.
:param locale: The locale (for example, 'ru', 'en', 'es')
:param gender: The gender (Gender.MALE or Gender.FEMALE)
:return: A dictionary with the fake user data
"""
person = Person(locale)
user_data = {
"name": person.full_name(gender=gender),
"height": person.height(),
"phone": person.telephone(),
"occupation": person.occupation(),
}
return user_data
if __name__ == "__main__":
fake_user = generate_fake_user(locale="es", gender=Gender.MALE)
print(fake_user)
{
'name': 'Carlos Herrera',
'height': '1.84',
'phone': '912 475 289',
'occupation': 'Arquitecto'
)ru, 🇺🇸 en, 🇪🇸 es, etc.) Save it, it'll come in handy
#python #github #interview
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How to manage caching in HTTP?
Answer:
These mechanisms reduce the network load and speed up reloads.
tags: #interview
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Forwarded from Machine Learning with Python
Beautiful Soup — a library for extracting data from HTML and XML files, ideal for web scraping.
pip install beautifulsoup4
from bs4 import BeautifulSoup
import requests
html_doc = "<html><body><p class='text'>Hello, world!</p></body></html>"
soup = BeautifulSoup(html_doc, 'html.parser') # or 'lxml', 'html5lib'
print(soup.p.text) # Hello, world!
# First found element
first_p = soup.find('p')
# Search by class or attribute
text_elem = soup.find('p', class_='text')
text_elem = soup.find('p', {'class': 'text'})
# All elements
all_p = soup.find_all('p')
all_text_class = soup.find_all(class_='text')
a_tag = soup.find('a')
print(a_tag['href']) # value of the href attribute
print(a_tag.get_text()) # text inside the tag
print(a_tag.text) # alternative# Moving to parent, children, siblings
parent = soup.p.parent
children = soup.ul.children
next_sibling = soup.p.next_sibling
# Finding the previous/next element
prev_elem = soup.find_previous('p')
next_elem = soup.find_next('div')
response = requests.get('https://example.com')
soup = BeautifulSoup(response.text, 'html. parser')
title = soup.title.text
links = [a['href'] for a in soup.find_all('a', href=True)]# More powerful and concise search
items = soup.select('div.content > p.text')
first_item = soup.select_one('a.button')
🟢 Web scraping and data collection🟢 Processing HTML/XML reports🟢 Automating data extraction from websites🟢 Preparing data for analysis and machine learning
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What is a closure?
Answer:
How does a closure work?
This is useful when you need to pass a state or data without using global variables.
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For example,
GET is used to retrieve data, POST — to create new records, and DELETE — to delete.In the picture — the 9 most popular HTTP request methods that every developer should have at hand.
Save it so you don't forget!
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Join our channel today for free! Tomorrow it will cost 500$!
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What is
monkey patching?Answer:
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Python Clean Code: Stop Writing Bad Code — Lessons from Uncle Bob
Are you tired of writing messy and unorganized code that leads to frustration and bugs? You can transform your code from a confusing mess into something crystal clear with a few simple changes. In this article, we'll explore key principles from the book "Clean Code" by Robert C. Martin, also known as Uncle Bob, and apply them to Python. Whether you're a web developer, software engineer, data analyst, or data scientist, these principles will help you write clean, readable, and maintainable Python code.
Read: https://habr.com/en/articles/841820/
https://t.iss.one/CodeProgrammer🧠
Are you tired of writing messy and unorganized code that leads to frustration and bugs? You can transform your code from a confusing mess into something crystal clear with a few simple changes. In this article, we'll explore key principles from the book "Clean Code" by Robert C. Martin, also known as Uncle Bob, and apply them to Python. Whether you're a web developer, software engineer, data analyst, or data scientist, these principles will help you write clean, readable, and maintainable Python code.
Read: https://habr.com/en/articles/841820/
https://t.iss.one/CodeProgrammer
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Integrating Local LLMs with Ollama and Python 🤖💻
Did you know that integrating local large language models (LLMs) into your Python projects can help improve privacy, reduce costs, and build offline-capable AI-powered apps? 👉
To get started, follow these steps:
• Set up Ollama and pull the models you want to use.
• Connect to them from Python using the <code>ollama</code> library.
Watch this video on how to integrate OLLAMA into your python projects https://youtu.be/E4l91XKQSgw?si=3gaeoM3EbvO6QYIC
Key Benefits:
• Improved privacy
• Reduced costs
• Offline-capable AI-powered apps
Did you know that integrating local large language models (LLMs) into your Python projects can help improve privacy, reduce costs, and build offline-capable AI-powered apps? 👉
To get started, follow these steps:
• Set up Ollama and pull the models you want to use.
• Connect to them from Python using the <code>ollama</code> library.
Watch this video on how to integrate OLLAMA into your python projects https://youtu.be/E4l91XKQSgw?si=3gaeoM3EbvO6QYIC
Key Benefits:
• Improved privacy
• Reduced costs
• Offline-capable AI-powered apps
YouTube
How to Build a Local AI Agent With Python (Ollama, LangChain & RAG)
Thanks to Microsoft for sponsoring this video! Submit your #CodingWithCopilot stories so I can review them! I'm excited to check out more!
Today I'll be showing you how to build local AI agents using Python. We'll be using Ollama, LangChain, and something…
Today I'll be showing you how to build local AI agents using Python. We'll be using Ollama, LangChain, and something…
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Uv vs Pip: Choosing the Right Package Manager for Your Python Projects 👉
When it comes to choosing a package manager for your Python projects, you have two popular options: uv and pip. While both tools share many similarities, there are key differences that may sway your decision.
* pip: Great for out-of-the-box availability, broad compatibility, and reliable ecosystem support. It's perfect for new projects or when you need to install popular packages quickly.
* uv: Worth considering if you prioritize fast installs, reproducible environments, and clean uninstall behavior. uv is ideal for streamline workflows for new projects, and its custom installation process can be beneficial for large-scale applications.
Here's a quick summary of the key differences:
* Package Installation: 📦
* pip: Easy to install popular packages using pip.
* uv: Requires manual package management, but can lead to faster installs and more reproducible environments.
* Dependency Management: 💻
* pip: Provides automatic dependency resolution for most projects.
* uv: Requires custom dependency management, which can be beneficial for complex projects.
By considering these factors and comparing the two tools, you'll make an informed decision that suits your specific needs.
When it comes to choosing a package manager for your Python projects, you have two popular options: uv and pip. While both tools share many similarities, there are key differences that may sway your decision.
* pip: Great for out-of-the-box availability, broad compatibility, and reliable ecosystem support. It's perfect for new projects or when you need to install popular packages quickly.
* uv: Worth considering if you prioritize fast installs, reproducible environments, and clean uninstall behavior. uv is ideal for streamline workflows for new projects, and its custom installation process can be beneficial for large-scale applications.
Here's a quick summary of the key differences:
* Package Installation: 📦
* pip: Easy to install popular packages using pip.
* uv: Requires manual package management, but can lead to faster installs and more reproducible environments.
* Dependency Management: 💻
* pip: Provides automatic dependency resolution for most projects.
* uv: Requires custom dependency management, which can be beneficial for complex projects.
By considering these factors and comparing the two tools, you'll make an informed decision that suits your specific needs.
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🚀 Lightweight X11 App Launcher for Python 🎉
X11 app launchers are powerful tools that can greatly enhance your Python development experience. However, creating a custom launcher from scratch can be challenging and time-consuming.
Here's why:
* Performance: Creating a native X11 application requires a deep understanding of C, X11, and the underlying operating system.
* Portability: Building a cross-platform application can be difficult due to the varying differences between platforms.
* Customizability: Custom launchers require a good grasp of the underlying architecture and configuration options.
On the other hand, you can use existing libraries like
In this example, we'll create a simple launcher using
### Example Code
### Features
* Native X11 Application: This launcher uses the native X11 API to create a seamless desktop experience.
* Easy-to-Use Interface: The
### Benefits
* High Performance: The native X11 application ensures optimal performance and responsiveness.
* Cross-Platform Compatibility: This launcher is designed to be cross-platform, making it suitable for deployment on various operating systems.
By using
X11 app launchers are powerful tools that can greatly enhance your Python development experience. However, creating a custom launcher from scratch can be challenging and time-consuming.
Here's why:
* Performance: Creating a native X11 application requires a deep understanding of C, X11, and the underlying operating system.
* Portability: Building a cross-platform application can be difficult due to the varying differences between platforms.
* Customizability: Custom launchers require a good grasp of the underlying architecture and configuration options.
On the other hand, you can use existing libraries like
pywinauto or pyppeteer to create a lightweight X11 app launcher for Python.In this example, we'll create a simple launcher using
pywinauto, which provides an easy-to-use API for automating desktop applications.### Example Code
import pywinauto
def launch_app():
# Create the main window
app = pywinauto.application().start('MyApp')
# Launch the main menu
menu = app.top_menu()
menu.show()
# Start the application
launch_app()
### Features
* Native X11 Application: This launcher uses the native X11 API to create a seamless desktop experience.
* Easy-to-Use Interface: The
pywinauto library provides an intuitive API, making it easy to customize and extend the launcher.### Benefits
* High Performance: The native X11 application ensures optimal performance and responsiveness.
* Cross-Platform Compatibility: This launcher is designed to be cross-platform, making it suitable for deployment on various operating systems.
By using
pywinauto to create a lightweight X11 app launcher for Python, you can take advantage of the power of C and X11 while still enjoying a seamless desktop experience. Give this example a try and see how it can enhance your Python development workflow!❤2
How to sort a list of dictionaries by a specific field?
Answer:
This parameter passes a function that extracts the value of the desired field from each dictionary. The .sort() method modifies the list in place, while sorted() returns a new sorted list.
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