Forwarded from Code With Python
Python Cheat sheet
#python #oop #interview #coding #programming #datastructures
https://t.iss.one/DataScience4
#python #oop #interview #coding #programming #datastructures
https://t.iss.one/DataScience4
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📚 "Python Project Setup: A Quick Guide"
In this article, you'll learn how to create and set up a new Django project. Follow along with our quiz to reinforce your understanding of creating a virtual environment, installing Django, and setting up dependencies. You'll also see how isolating dependencies helps others reproduce the setup.
Read more about creating a Django project: How to Set Up a Django Project
In this article, you'll learn how to create and set up a new Django project. Follow along with our quiz to reinforce your understanding of creating a virtual environment, installing Django, and setting up dependencies. You'll also see how isolating dependencies helps others reproduce the setup.
Read more about creating a Django project: How to Set Up a Django Project
Realpython
How to Set Up a Django Project – Real Python
In this course, you'll learn the necessary steps you'll need to take to set up a new Django project. You'll learn the basic setup for any new Django project that needs to happen before programming the specific functionality of your project.
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"🚀 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.
tags: #interview
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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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